afiaka87
/
pyglide
The predecessor to DALLE-2, GLIDE (filtered) with faster PRK/PLMS sampling.
Prediction
afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0Input
- prompt
- a fox made from paper. paper fox.
- side_x
- 64
- side_y
- 64
- batch_size
- "1"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 150
{ "prompt": "a fox made from paper. paper fox.", "side_x": 64, "side_y": 64, "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", { input: { prompt: "a fox made from paper. paper fox.", side_x: 64, side_y: 64, batch_size: "1", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", input={ "prompt": "a fox made from paper. paper fox.", "side_x": 64, "side_y": 64, "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", "input": { "prompt": "a fox made from paper. paper fox.", "side_x": 64, "side_y": 64, "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T01:52:09.398359Z", "created_at": "2022-01-31T01:51:27.610273Z", "data_removed": false, "error": null, "id": "nffemv7i3fg4teta3h7l2wu36i", "input": { "prompt": "a fox made from paper. paper fox.", "side_x": 64, "side_y": 64, "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:24, 5.96it/s]\n 1%|▏ | 2/150 [00:00<00:27, 5.38it/s]\n 2%|▏ | 3/150 [00:00<00:27, 5.30it/s]\n 3%|▎ | 4/150 [00:00<00:27, 5.33it/s]\n 3%|▎ | 5/150 [00:00<00:26, 5.38it/s]\n 4%|▍ | 6/150 [00:01<00:26, 5.39it/s]\n 5%|▍ | 7/150 [00:01<00:26, 5.36it/s]\n 5%|▌ | 8/150 [00:01<00:26, 5.32it/s]\n 6%|▌ | 9/150 [00:01<00:26, 5.39it/s]\n 7%|▋ | 10/150 [00:01<00:26, 5.38it/s]\n 7%|▋ | 11/150 [00:02<00:25, 5.39it/s]\n 8%|▊ | 12/150 [00:02<00:25, 5.39it/s]\n 9%|▊ | 13/150 [00:02<00:26, 5.25it/s]\n 9%|▉ | 14/150 [00:02<00:25, 5.31it/s]\n 10%|█ | 15/150 [00:02<00:25, 5.34it/s]\n 11%|█ | 16/150 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17/27 [00:03<00:01, 5.61it/s]\n 67%|██████▋ | 18/27 [00:03<00:01, 5.61it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.61it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 5.62it/s]\n 78%|███████▊ | 21/27 [00:03<00:01, 5.64it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 5.62it/s]\n 85%|████████▌ | 23/27 [00:04<00:00, 5.60it/s]\n 89%|████████▉ | 24/27 [00:04<00:00, 5.59it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.61it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.61it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.61it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.60it/s]", "metrics": { "predict_time": 41.483572, "total_time": 41.788086 }, "output": [ { "file": "https://replicate.delivery/mgxm/855c42c8-bb51-4e22-8d9e-64274d413d98/upsample_predictions.png" } ], "started_at": "2022-01-31T01:51:27.914787Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nffemv7i3fg4teta3h7l2wu36i", "cancel": "https://api.replicate.com/v1/predictions/nffemv7i3fg4teta3h7l2wu36i/cancel" }, "version": "b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:24, 5.96it/s] 1%|▏ | 2/150 [00:00<00:27, 5.38it/s] 2%|▏ | 3/150 [00:00<00:27, 5.30it/s] 3%|▎ | 4/150 [00:00<00:27, 5.33it/s] 3%|▎ | 5/150 [00:00<00:26, 5.38it/s] 4%|▍ | 6/150 [00:01<00:26, 5.39it/s] 5%|▍ | 7/150 [00:01<00:26, 5.36it/s] 5%|▌ | 8/150 [00:01<00:26, 5.32it/s] 6%|▌ | 9/150 [00:01<00:26, 5.39it/s] 7%|▋ | 10/150 [00:01<00:26, 5.38it/s] 7%|▋ | 11/150 [00:02<00:25, 5.39it/s] 8%|▊ | 12/150 [00:02<00:25, 5.39it/s] 9%|▊ | 13/150 [00:02<00:26, 5.25it/s] 9%|▉ | 14/150 [00:02<00:25, 5.31it/s] 10%|█ | 15/150 [00:02<00:25, 5.34it/s] 11%|█ | 16/150 [00:02<00:24, 5.37it/s] 11%|█▏ | 17/150 [00:03<00:24, 5.36it/s] 12%|█▏ | 18/150 [00:03<00:24, 5.33it/s] 13%|█▎ | 19/150 [00:03<00:24, 5.30it/s] 13%|█▎ | 20/150 [00:03<00:24, 5.38it/s] 14%|█▍ | 21/150 [00:03<00:23, 5.38it/s] 15%|█▍ | 22/150 [00:04<00:23, 5.44it/s] 15%|█▌ | 23/150 [00:04<00:23, 5.44it/s] 16%|█▌ | 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- seed
- 0
- prompt
- latte art of an octopus. tentacled octopus drawn in the mocha latte.
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "1"
- guidance_scale
- 7
- timestep_respacing
- 100
{ "seed": 0, "prompt": "latte art of an octopus. tentacled octopus drawn in the mocha latte.", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "latte art of an octopus. tentacled octopus drawn in the mocha latte.", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "1", guidance_scale: 7, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "latte art of an octopus. tentacled octopus drawn in the mocha latte.", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "latte art of an octopus. tentacled octopus drawn in the mocha latte.", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T07:34:51.855890Z", "created_at": "2022-01-31T07:33:00.118750Z", "data_removed": false, "error": null, "id": "eo2dvcjzbrb3to2pqlbvweg6ae", "input": { "seed": 0, "prompt": "latte art of an octopus. tentacled octopus drawn in the mocha latte.", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:59, 1.65it/s]\n 2%|▏ | 2/100 [00:00<00:36, 2.69it/s]\n 3%|▎ | 3/100 [00:01<00:37, 2.56it/s]\n 4%|▍ | 4/100 [00:01<00:38, 2.48it/s]\n 5%|▌ | 5/100 [00:02<00:38, 2.45it/s]\n 6%|▌ | 6/100 [00:02<00:38, 2.42it/s]\n 7%|▋ | 7/100 [00:02<00:38, 2.42it/s]\n 8%|▊ | 8/100 [00:03<00:38, 2.41it/s]\n 9%|▉ | 9/100 [00:03<00:38, 2.39it/s]\n 10%|█ | 10/100 [00:04<00:37, 2.39it/s]\n 11%|█ | 11/100 [00:04<00:37, 2.39it/s]\n 12%|█▏ | 12/100 [00:05<00:36, 2.39it/s]\n 13%|█▎ | 13/100 [00:05<00:36, 2.40it/s]\n 14%|█▍ | 14/100 [00:05<00:36, 2.38it/s]\n 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2.07it/s]\n 85%|████████▌ | 23/27 [00:11<00:01, 2.07it/s]\n 89%|████████▉ | 24/27 [00:11<00:01, 2.07it/s]\n 93%|█████████▎| 25/27 [00:12<00:00, 2.07it/s]\n 96%|█████████▋| 26/27 [00:12<00:00, 2.07it/s]\n100%|██████████| 27/27 [00:12<00:00, 2.07it/s]\n100%|██████████| 27/27 [00:12<00:00, 2.08it/s]", "metrics": { "predict_time": 64.173082, "total_time": 111.73714 }, "output": [ { "file": "https://replicate.delivery/mgxm/f5e295e3-fbba-4392-8774-bbc8f8cd3221/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/d8687b95-81bd-4eb2-a3f7-746908d5ddd6/upsample_predictions.png" } ], "started_at": "2022-01-31T07:33:47.682808Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/eo2dvcjzbrb3to2pqlbvweg6ae", "cancel": "https://api.replicate.com/v1/predictions/eo2dvcjzbrb3to2pqlbvweg6ae/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:59, 1.65it/s] 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2.08it/s] 63%|██████▎ | 17/27 [00:08<00:04, 2.08it/s] 67%|██████▋ | 18/27 [00:08<00:04, 2.08it/s] 70%|███████ | 19/27 [00:09<00:03, 2.08it/s] 74%|███████▍ | 20/27 [00:09<00:03, 2.07it/s] 78%|███████▊ | 21/27 [00:10<00:02, 2.07it/s] 81%|████████▏ | 22/27 [00:10<00:02, 2.07it/s] 85%|████████▌ | 23/27 [00:11<00:01, 2.07it/s] 89%|████████▉ | 24/27 [00:11<00:01, 2.07it/s] 93%|█████████▎| 25/27 [00:12<00:00, 2.07it/s] 96%|█████████▋| 26/27 [00:12<00:00, 2.07it/s] 100%|██████████| 27/27 [00:12<00:00, 2.07it/s] 100%|██████████| 27/27 [00:12<00:00, 2.08it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- seed
- 0
- prompt
- a goat attacking the camera
- side_x
- "64"
- side_y
- "64"
- batch_size
- "4"
- upsample_temp
- "1"
- guidance_scale
- 5
- timestep_respacing
- 50
{ "seed": 0, "prompt": "a goat attacking the camera", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 5, "timestep_respacing": "50" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "a goat attacking the camera", side_x: "64", side_y: "64", batch_size: "4", upsample_temp: "1", guidance_scale: 5, timestep_respacing: "50" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "a goat attacking the camera", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 5, "timestep_respacing": "50" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "a goat attacking the camera", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 5, "timestep_respacing": "50" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T08:20:20.373810Z", "created_at": "2022-01-31T08:19:24.929644Z", "data_removed": false, "error": null, "id": "ec5al43frjepbpnsg7dq3isg5y", "input": { "seed": 0, "prompt": "a goat attacking the camera", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 5, "timestep_respacing": "50" }, "logs": "\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 2.93it/s]\n 4%|▍ | 2/50 [00:00<00:22, 2.11it/s]\n 6%|▌ | 3/50 [00:01<00:24, 1.90it/s]\n 8%|▊ | 4/50 [00:02<00:24, 1.84it/s]\n 10%|█ | 5/50 [00:02<00:25, 1.79it/s]\n 12%|█▏ | 6/50 [00:03<00:24, 1.77it/s]\n 14%|█▍ | 7/50 [00:03<00:24, 1.75it/s]\n 16%|█▌ | 8/50 [00:04<00:24, 1.74it/s]\n 18%|█▊ | 9/50 [00:04<00:23, 1.73it/s]\n 20%|██ | 10/50 [00:05<00:23, 1.73it/s]\n 22%|██▏ | 11/50 [00:06<00:22, 1.72it/s]\n 24%|██▍ | 12/50 [00:06<00:22, 1.72it/s]\n 26%|██▌ | 13/50 [00:07<00:21, 1.71it/s]\n 28%|██▊ | 14/50 [00:07<00:21, 1.71it/s]\n 30%|███ | 15/50 [00:08<00:20, 1.71it/s]\n 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1.56it/s]\n 33%|███▎ | 9/27 [00:05<00:11, 1.55it/s]\n 37%|███▋ | 10/27 [00:06<00:10, 1.55it/s]\n 41%|████ | 11/27 [00:07<00:10, 1.55it/s]\n 44%|████▍ | 12/27 [00:07<00:09, 1.55it/s]\n 48%|████▊ | 13/27 [00:08<00:09, 1.55it/s]\n 52%|█████▏ | 14/27 [00:09<00:08, 1.55it/s]\n 56%|█████▌ | 15/27 [00:09<00:07, 1.55it/s]\n 59%|█████▉ | 16/27 [00:10<00:07, 1.55it/s]\n 63%|██████▎ | 17/27 [00:10<00:06, 1.55it/s]\n 67%|██████▋ | 18/27 [00:11<00:05, 1.55it/s]\n 70%|███████ | 19/27 [00:12<00:05, 1.55it/s]\n 74%|███████▍ | 20/27 [00:12<00:04, 1.55it/s]\n 78%|███████▊ | 21/27 [00:13<00:03, 1.55it/s]\n 81%|████████▏ | 22/27 [00:14<00:03, 1.55it/s]\n 85%|████████▌ | 23/27 [00:14<00:02, 1.55it/s]\n 89%|████████▉ | 24/27 [00:15<00:01, 1.55it/s]\n 93%|█████████▎| 25/27 [00:16<00:01, 1.55it/s]\n 96%|█████████▋| 26/27 [00:16<00:00, 1.55it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.55it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.55it/s]", "metrics": { "predict_time": 55.287072, "total_time": 55.444166 }, "output": [ { "file": "https://replicate.delivery/mgxm/15a747f9-c5d5-4420-8113-0d9c6964b36f/upsample_predictions.png" } ], "started_at": "2022-01-31T08:19:25.086738Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ec5al43frjepbpnsg7dq3isg5y", "cancel": "https://api.replicate.com/v1/predictions/ec5al43frjepbpnsg7dq3isg5y/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 2.93it/s] 4%|▍ | 2/50 [00:00<00:22, 2.11it/s] 6%|▌ | 3/50 [00:01<00:24, 1.90it/s] 8%|▊ | 4/50 [00:02<00:24, 1.84it/s] 10%|█ | 5/50 [00:02<00:25, 1.79it/s] 12%|█▏ | 6/50 [00:03<00:24, 1.77it/s] 14%|█▍ | 7/50 [00:03<00:24, 1.75it/s] 16%|█▌ | 8/50 [00:04<00:24, 1.74it/s] 18%|█▊ | 9/50 [00:04<00:23, 1.73it/s] 20%|██ | 10/50 [00:05<00:23, 1.73it/s] 22%|██▏ | 11/50 [00:06<00:22, 1.72it/s] 24%|██▍ | 12/50 [00:06<00:22, 1.72it/s] 26%|██▌ | 13/50 [00:07<00:21, 1.71it/s] 28%|██▊ | 14/50 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- a plastic bag of the 🍏
- side_x
- "64"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "1"
- guidance_scale
- 5
- timestep_respacing
- 50
{ "prompt": "a plastic bag of the 🍏", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 5, "timestep_respacing": "50" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a plastic bag of the 🍏", side_x: "64", side_y: "64", batch_size: "1", upsample_temp: "1", guidance_scale: 5, timestep_respacing: "50" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a plastic bag of the 🍏", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 5, "timestep_respacing": "50" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a plastic bag of the 🍏", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 5, "timestep_respacing": "50" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T11:29:54.300163Z", "created_at": "2022-01-31T11:29:11.165868Z", "data_removed": false, "error": null, "id": "xdm6rpd3kvhbjo5evkvywjqklm", "input": { "prompt": "a plastic bag of the 🍏", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 5, "timestep_respacing": "50" }, "logs": "\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.99it/s]\n 4%|▍ | 2/50 [00:00<00:08, 5.61it/s]\n 6%|▌ | 3/50 [00:00<00:08, 5.56it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.58it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.54it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.48it/s]\n 14%|█▍ | 7/50 [00:01<00:07, 5.50it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.50it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.57it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.54it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.53it/s]\n 24%|██▍ | 12/50 [00:02<00:06, 5.56it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.50it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.56it/s]\n 30%|███ | 15/50 [00:02<00:06, 5.58it/s]\n 32%|███▏ | 16/50 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33%|███▎ | 9/27 [00:01<00:03, 5.91it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 5.89it/s]\n 41%|████ | 11/27 [00:01<00:02, 5.92it/s]\n 44%|████▍ | 12/27 [00:02<00:02, 5.97it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 5.96it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 5.93it/s]\n 56%|█████▌ | 15/27 [00:02<00:02, 5.97it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 5.95it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 5.96it/s]\n 67%|██████▋ | 18/27 [00:03<00:01, 5.96it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.94it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 5.92it/s]\n 78%|███████▊ | 21/27 [00:03<00:01, 5.86it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 5.89it/s]\n 85%|████████▌ | 23/27 [00:03<00:00, 5.91it/s]\n 89%|████████▉ | 24/27 [00:04<00:00, 5.95it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.96it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.94it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.91it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.91it/s]", "metrics": { "predict_time": 21.051365, "total_time": 43.134295 }, "output": [ { "file": "https://replicate.delivery/mgxm/dd05c0f3-bbff-4c17-ac53-42be796ef63f/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/cb6b7234-6bfa-48f4-abcc-fb63881e7b3f/upsample_predictions.png" } ], "started_at": "2022-01-31T11:29:33.248798Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xdm6rpd3kvhbjo5evkvywjqklm", "cancel": "https://api.replicate.com/v1/predictions/xdm6rpd3kvhbjo5evkvywjqklm/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:12, 3.99it/s] 4%|▍ | 2/50 [00:00<00:08, 5.61it/s] 6%|▌ | 3/50 [00:00<00:08, 5.56it/s] 8%|▊ | 4/50 [00:00<00:08, 5.58it/s] 10%|█ | 5/50 [00:00<00:08, 5.54it/s] 12%|█▏ | 6/50 [00:01<00:08, 5.48it/s] 14%|█▍ | 7/50 [00:01<00:07, 5.50it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.50it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.57it/s] 20%|██ | 10/50 [00:01<00:07, 5.54it/s] 22%|██▏ | 11/50 [00:02<00:07, 5.53it/s] 24%|██▍ | 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDoeeylebjkjhcrde7kjq6q4x4tmStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a photo of new york city taken from aerial satellite view
- side_x
- "64"
- side_y
- "64"
- batch_size
- "4"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 25
{ "prompt": "a photo of new york city taken from aerial satellite view", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "25" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a photo of new york city taken from aerial satellite view", side_x: "64", side_y: "64", batch_size: "4", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "25" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a photo of new york city taken from aerial satellite view", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "25" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a photo of new york city taken from aerial satellite view", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "25" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T13:57:03.948897Z", "created_at": "2022-01-31T13:56:24.409180Z", "data_removed": false, "error": null, "id": "oeeylebjkjhcrde7kjq6q4x4tm", "input": { "prompt": "a photo of new york city taken from aerial satellite view", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "25" }, "logs": "\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:07, 3.03it/s]\n 8%|▊ | 2/25 [00:00<00:10, 2.19it/s]\n 12%|█▏ | 3/25 [00:01<00:11, 1.99it/s]\n 16%|█▌ | 4/25 [00:01<00:10, 1.92it/s]\n 20%|██ | 5/25 [00:02<00:10, 1.87it/s]\n 24%|██▍ | 6/25 [00:03<00:10, 1.84it/s]\n 28%|██▊ | 7/25 [00:03<00:09, 1.83it/s]\n 32%|███▏ | 8/25 [00:04<00:09, 1.82it/s]\n 36%|███▌ | 9/25 [00:04<00:08, 1.81it/s]\n 40%|████ | 10/25 [00:05<00:08, 1.80it/s]\n 44%|████▍ | 11/25 [00:05<00:07, 1.80it/s]\n 48%|████▊ | 12/25 [00:06<00:07, 1.80it/s]\n 52%|█████▏ | 13/25 [00:07<00:06, 1.79it/s]\n 56%|█████▌ | 14/25 [00:07<00:06, 1.79it/s]\n 60%|██████ | 15/25 [00:08<00:05, 1.79it/s]\n 64%|██████▍ | 16/25 [00:08<00:05, 1.79it/s]\n 68%|██████▊ | 17/25 [00:09<00:04, 1.79it/s]\n 72%|███████▏ | 18/25 [00:09<00:03, 1.79it/s]\n 76%|███████▌ | 19/25 [00:10<00:03, 1.79it/s]\n 80%|████████ | 20/25 [00:10<00:02, 1.78it/s]\n 84%|████████▍ | 21/25 [00:11<00:02, 1.78it/s]\n 88%|████████▊ | 22/25 [00:12<00:01, 1.78it/s]\n 92%|█████████▏| 23/25 [00:12<00:01, 1.78it/s]\n 96%|█████████▌| 24/25 [00:13<00:00, 1.78it/s]\n100%|██████████| 25/25 [00:13<00:00, 1.78it/s]\n100%|██████████| 25/25 [00:13<00:00, 1.82it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:15, 1.64it/s]\n 7%|▋ | 2/27 [00:01<00:15, 1.62it/s]\n 11%|█ | 3/27 [00:01<00:14, 1.63it/s]\n 15%|█▍ | 4/27 [00:02<00:14, 1.63it/s]\n 19%|█▊ | 5/27 [00:03<00:13, 1.63it/s]\n 22%|██▏ | 6/27 [00:03<00:12, 1.62it/s]\n 26%|██▌ | 7/27 [00:04<00:12, 1.63it/s]\n 30%|██▉ | 8/27 [00:04<00:11, 1.62it/s]\n 33%|███▎ | 9/27 [00:05<00:11, 1.62it/s]\n 37%|███▋ | 10/27 [00:06<00:10, 1.62it/s]\n 41%|████ | 11/27 [00:06<00:09, 1.62it/s]\n 44%|████▍ | 12/27 [00:07<00:09, 1.62it/s]\n 48%|████▊ | 13/27 [00:08<00:08, 1.62it/s]\n 52%|█████▏ | 14/27 [00:08<00:08, 1.62it/s]\n 56%|█████▌ | 15/27 [00:09<00:07, 1.62it/s]\n 59%|█████▉ | 16/27 [00:09<00:06, 1.62it/s]\n 63%|██████▎ | 17/27 [00:10<00:06, 1.62it/s]\n 67%|██████▋ | 18/27 [00:11<00:05, 1.62it/s]\n 70%|███████ | 19/27 [00:11<00:04, 1.62it/s]\n 74%|███████▍ | 20/27 [00:12<00:04, 1.61it/s]\n 78%|███████▊ | 21/27 [00:12<00:03, 1.62it/s]\n 81%|████████▏ | 22/27 [00:13<00:03, 1.61it/s]\n 85%|████████▌ | 23/27 [00:14<00:02, 1.62it/s]\n 89%|████████▉ | 24/27 [00:14<00:01, 1.61it/s]\n 93%|█████████▎| 25/27 [00:15<00:01, 1.62it/s]\n 96%|█████████▋| 26/27 [00:16<00:00, 1.61it/s]\n100%|██████████| 27/27 [00:16<00:00, 1.61it/s]\n100%|██████████| 27/27 [00:16<00:00, 1.62it/s]", "metrics": { "predict_time": 39.333154, "total_time": 39.539717 }, "output": [ { "file": "https://replicate.delivery/mgxm/420e1f16-c5c2-4936-9e40-eba9c22472c8/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/97150a57-069d-4cfe-9e7a-4d69ff23f3b8/upsample_predictions.png" } ], "started_at": "2022-01-31T13:56:24.615743Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/oeeylebjkjhcrde7kjq6q4x4tm", "cancel": "https://api.replicate.com/v1/predictions/oeeylebjkjhcrde7kjq6q4x4tm/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:07, 3.03it/s] 8%|▊ | 2/25 [00:00<00:10, 2.19it/s] 12%|█▏ | 3/25 [00:01<00:11, 1.99it/s] 16%|█▌ | 4/25 [00:01<00:10, 1.92it/s] 20%|██ | 5/25 [00:02<00:10, 1.87it/s] 24%|██▍ | 6/25 [00:03<00:10, 1.84it/s] 28%|██▊ | 7/25 [00:03<00:09, 1.83it/s] 32%|███▏ | 8/25 [00:04<00:09, 1.82it/s] 36%|███▌ | 9/25 [00:04<00:08, 1.81it/s] 40%|████ | 10/25 [00:05<00:08, 1.80it/s] 44%|████▍ | 11/25 [00:05<00:07, 1.80it/s] 48%|████▊ | 12/25 [00:06<00:07, 1.80it/s] 52%|█████▏ | 13/25 [00:07<00:06, 1.79it/s] 56%|█████▌ | 14/25 [00:07<00:06, 1.79it/s] 60%|██████ | 15/25 [00:08<00:05, 1.79it/s] 64%|██████▍ | 16/25 [00:08<00:05, 1.79it/s] 68%|██████▊ | 17/25 [00:09<00:04, 1.79it/s] 72%|███████▏ | 18/25 [00:09<00:03, 1.79it/s] 76%|███████▌ | 19/25 [00:10<00:03, 1.79it/s] 80%|████████ | 20/25 [00:10<00:02, 1.78it/s] 84%|████████▍ | 21/25 [00:11<00:02, 1.78it/s] 88%|████████▊ | 22/25 [00:12<00:01, 1.78it/s] 92%|█████████▏| 23/25 [00:12<00:01, 1.78it/s] 96%|█████████▌| 24/25 [00:13<00:00, 1.78it/s] 100%|██████████| 25/25 [00:13<00:00, 1.78it/s] 100%|██████████| 25/25 [00:13<00:00, 1.82it/s] 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:15, 1.64it/s] 7%|▋ | 2/27 [00:01<00:15, 1.62it/s] 11%|█ | 3/27 [00:01<00:14, 1.63it/s] 15%|█▍ | 4/27 [00:02<00:14, 1.63it/s] 19%|█▊ | 5/27 [00:03<00:13, 1.63it/s] 22%|██▏ | 6/27 [00:03<00:12, 1.62it/s] 26%|██▌ | 7/27 [00:04<00:12, 1.63it/s] 30%|██▉ | 8/27 [00:04<00:11, 1.62it/s] 33%|███▎ | 9/27 [00:05<00:11, 1.62it/s] 37%|███▋ | 10/27 [00:06<00:10, 1.62it/s] 41%|████ | 11/27 [00:06<00:09, 1.62it/s] 44%|████▍ | 12/27 [00:07<00:09, 1.62it/s] 48%|████▊ | 13/27 [00:08<00:08, 1.62it/s] 52%|█████▏ | 14/27 [00:08<00:08, 1.62it/s] 56%|█████▌ | 15/27 [00:09<00:07, 1.62it/s] 59%|█████▉ | 16/27 [00:09<00:06, 1.62it/s] 63%|██████▎ | 17/27 [00:10<00:06, 1.62it/s] 67%|██████▋ | 18/27 [00:11<00:05, 1.62it/s] 70%|███████ | 19/27 [00:11<00:04, 1.62it/s] 74%|███████▍ | 20/27 [00:12<00:04, 1.61it/s] 78%|███████▊ | 21/27 [00:12<00:03, 1.62it/s] 81%|████████▏ | 22/27 [00:13<00:03, 1.61it/s] 85%|████████▌ | 23/27 [00:14<00:02, 1.62it/s] 89%|████████▉ | 24/27 [00:14<00:01, 1.61it/s] 93%|█████████▎| 25/27 [00:15<00:01, 1.62it/s] 96%|█████████▋| 26/27 [00:16<00:00, 1.61it/s] 100%|██████████| 27/27 [00:16<00:00, 1.61it/s] 100%|██████████| 27/27 [00:16<00:00, 1.62it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDe4vhuiykkjgjne7fop5qukfjsiStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- a photo of atlantis taken from aerial satellite view
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 25
{ "seed": 0, "prompt": "a photo of atlantis taken from aerial satellite view", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "25" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "a photo of atlantis taken from aerial satellite view", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "25" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "a photo of atlantis taken from aerial satellite view", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "25" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "a photo of atlantis taken from aerial satellite view", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "25" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T13:58:57.814491Z", "created_at": "2022-01-31T13:57:55.307236Z", "data_removed": false, "error": null, "id": "e4vhuiykkjgjne7fop5qukfjsi", "input": { "seed": 0, "prompt": "a photo of atlantis taken from aerial satellite view", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "25" }, "logs": "\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:06, 3.50it/s]\n 8%|▊ | 2/25 [00:00<00:08, 2.68it/s]\n 12%|█▏ | 3/25 [00:01<00:08, 2.48it/s]\n 16%|█▌ | 4/25 [00:01<00:08, 2.39it/s]\n 20%|██ | 5/25 [00:02<00:08, 2.35it/s]\n 24%|██▍ | 6/25 [00:02<00:08, 2.32it/s]\n 28%|██▊ | 7/25 [00:02<00:07, 2.31it/s]\n 32%|███▏ | 8/25 [00:03<00:07, 2.30it/s]\n 36%|███▌ | 9/25 [00:03<00:06, 2.29it/s]\n 40%|████ | 10/25 [00:04<00:06, 2.29it/s]\n 44%|████▍ | 11/25 [00:04<00:06, 2.28it/s]\n 48%|████▊ | 12/25 [00:05<00:05, 2.28it/s]\n 52%|█████▏ | 13/25 [00:05<00:05, 2.28it/s]\n 56%|█████▌ | 14/25 [00:05<00:04, 2.27it/s]\n 60%|██████ | 15/25 [00:06<00:04, 2.27it/s]\n 64%|██████▍ | 16/25 [00:06<00:03, 2.27it/s]\n 68%|██████▊ | 17/25 [00:07<00:03, 2.27it/s]\n 72%|███████▏ | 18/25 [00:07<00:03, 2.27it/s]\n 76%|███████▌ | 19/25 [00:08<00:02, 2.27it/s]\n 80%|████████ | 20/25 [00:08<00:02, 2.27it/s]\n 84%|████████▍ | 21/25 [00:09<00:01, 2.27it/s]\n 88%|████████▊ | 22/25 [00:09<00:01, 2.27it/s]\n 92%|█████████▏| 23/25 [00:09<00:00, 2.27it/s]\n 96%|█████████▌| 24/25 [00:10<00:00, 2.27it/s]\n100%|██████████| 25/25 [00:10<00:00, 2.26it/s]\n100%|██████████| 25/25 [00:10<00:00, 2.30it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:12, 2.08it/s]\n 7%|▋ | 2/27 [00:00<00:12, 2.05it/s]\n 11%|█ | 3/27 [00:01<00:11, 2.06it/s]\n 15%|█▍ | 4/27 [00:01<00:11, 2.07it/s]\n 19%|█▊ | 5/27 [00:02<00:10, 2.07it/s]\n 22%|██▏ | 6/27 [00:02<00:10, 2.07it/s]\n 26%|██▌ | 7/27 [00:03<00:09, 2.07it/s]\n 30%|██▉ | 8/27 [00:03<00:09, 2.06it/s]\n 33%|███▎ | 9/27 [00:04<00:08, 2.06it/s]\n 37%|███▋ | 10/27 [00:04<00:08, 2.06it/s]\n 41%|████ | 11/27 [00:05<00:07, 2.06it/s]\n 44%|████▍ | 12/27 [00:05<00:07, 2.06it/s]\n 48%|████▊ | 13/27 [00:06<00:06, 2.06it/s]\n 52%|█████▏ | 14/27 [00:06<00:06, 2.06it/s]\n 56%|█████▌ | 15/27 [00:07<00:05, 2.06it/s]\n 59%|█████▉ | 16/27 [00:07<00:05, 2.06it/s]\n 63%|██████▎ | 17/27 [00:08<00:04, 2.06it/s]\n 67%|██████▋ | 18/27 [00:08<00:04, 2.06it/s]\n 70%|███████ | 19/27 [00:09<00:03, 2.06it/s]\n 74%|███████▍ | 20/27 [00:09<00:03, 2.06it/s]\n 78%|███████▊ | 21/27 [00:10<00:02, 2.06it/s]\n 81%|████████▏ | 22/27 [00:10<00:02, 2.06it/s]\n 85%|████████▌ | 23/27 [00:11<00:01, 2.06it/s]\n 89%|████████▉ | 24/27 [00:11<00:01, 2.06it/s]\n 93%|█████████▎| 25/27 [00:12<00:00, 2.06it/s]\n 96%|█████████▋| 26/27 [00:12<00:00, 2.06it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.06it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.06it/s]", "metrics": { "predict_time": 31.97517, "total_time": 62.507255 }, "output": [ { "file": "https://replicate.delivery/mgxm/3d9caf0c-8288-404c-8bc8-25d1f5d49492/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/e6cf8ee3-f61d-4f52-892a-20e2129feb43/upsample_predictions.png" } ], "started_at": "2022-01-31T13:58:25.839321Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/e4vhuiykkjgjne7fop5qukfjsi", "cancel": "https://api.replicate.com/v1/predictions/e4vhuiykkjgjne7fop5qukfjsi/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:06, 3.50it/s] 8%|▊ | 2/25 [00:00<00:08, 2.68it/s] 12%|█▏ | 3/25 [00:01<00:08, 2.48it/s] 16%|█▌ | 4/25 [00:01<00:08, 2.39it/s] 20%|██ | 5/25 [00:02<00:08, 2.35it/s] 24%|██▍ | 6/25 [00:02<00:08, 2.32it/s] 28%|██▊ | 7/25 [00:02<00:07, 2.31it/s] 32%|███▏ | 8/25 [00:03<00:07, 2.30it/s] 36%|███▌ | 9/25 [00:03<00:06, 2.29it/s] 40%|████ | 10/25 [00:04<00:06, 2.29it/s] 44%|████▍ | 11/25 [00:04<00:06, 2.28it/s] 48%|████▊ | 12/25 [00:05<00:05, 2.28it/s] 52%|█████▏ | 13/25 [00:05<00:05, 2.28it/s] 56%|█████▌ | 14/25 [00:05<00:04, 2.27it/s] 60%|██████ | 15/25 [00:06<00:04, 2.27it/s] 64%|██████▍ | 16/25 [00:06<00:03, 2.27it/s] 68%|██████▊ | 17/25 [00:07<00:03, 2.27it/s] 72%|███████▏ | 18/25 [00:07<00:03, 2.27it/s] 76%|███████▌ | 19/25 [00:08<00:02, 2.27it/s] 80%|████████ | 20/25 [00:08<00:02, 2.27it/s] 84%|████████▍ | 21/25 [00:09<00:01, 2.27it/s] 88%|████████▊ | 22/25 [00:09<00:01, 2.27it/s] 92%|█████████▏| 23/25 [00:09<00:00, 2.27it/s] 96%|█████████▌| 24/25 [00:10<00:00, 2.27it/s] 100%|██████████| 25/25 [00:10<00:00, 2.26it/s] 100%|██████████| 25/25 [00:10<00:00, 2.30it/s] 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:12, 2.08it/s] 7%|▋ | 2/27 [00:00<00:12, 2.05it/s] 11%|█ | 3/27 [00:01<00:11, 2.06it/s] 15%|█▍ | 4/27 [00:01<00:11, 2.07it/s] 19%|█▊ | 5/27 [00:02<00:10, 2.07it/s] 22%|██▏ | 6/27 [00:02<00:10, 2.07it/s] 26%|██▌ | 7/27 [00:03<00:09, 2.07it/s] 30%|██▉ | 8/27 [00:03<00:09, 2.06it/s] 33%|███▎ | 9/27 [00:04<00:08, 2.06it/s] 37%|███▋ | 10/27 [00:04<00:08, 2.06it/s] 41%|████ | 11/27 [00:05<00:07, 2.06it/s] 44%|████▍ | 12/27 [00:05<00:07, 2.06it/s] 48%|████▊ | 13/27 [00:06<00:06, 2.06it/s] 52%|█████▏ | 14/27 [00:06<00:06, 2.06it/s] 56%|█████▌ | 15/27 [00:07<00:05, 2.06it/s] 59%|█████▉ | 16/27 [00:07<00:05, 2.06it/s] 63%|██████▎ | 17/27 [00:08<00:04, 2.06it/s] 67%|██████▋ | 18/27 [00:08<00:04, 2.06it/s] 70%|███████ | 19/27 [00:09<00:03, 2.06it/s] 74%|███████▍ | 20/27 [00:09<00:03, 2.06it/s] 78%|███████▊ | 21/27 [00:10<00:02, 2.06it/s] 81%|████████▏ | 22/27 [00:10<00:02, 2.06it/s] 85%|████████▌ | 23/27 [00:11<00:01, 2.06it/s] 89%|████████▉ | 24/27 [00:11<00:01, 2.06it/s] 93%|█████████▎| 25/27 [00:12<00:00, 2.06it/s] 96%|█████████▋| 26/27 [00:12<00:00, 2.06it/s] 100%|██████████| 27/27 [00:13<00:00, 2.06it/s] 100%|██████████| 27/27 [00:13<00:00, 2.06it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDdaiz3jtiovbk5ag74v2yfys6eeStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a dog made from crystal quartz
- side_x
- "64"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "0.996"
- guidance_scale
- 10
- timestep_respacing
- 100
{ "prompt": "a dog made from crystal quartz", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 10, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a dog made from crystal quartz", side_x: "64", side_y: "64", batch_size: "1", upsample_temp: "0.996", guidance_scale: 10, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a dog made from crystal quartz", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 10, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a dog made from crystal quartz", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 10, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T14:09:04.167512Z", "created_at": "2022-01-31T14:08:34.239357Z", "data_removed": false, "error": null, "id": "daiz3jtiovbk5ag74v2yfys6ee", "input": { "prompt": "a dog made from crystal quartz", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 10, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:16, 6.05it/s]\n 2%|▏ | 2/100 [00:00<00:18, 5.32it/s]\n 3%|▎ | 3/100 [00:00<00:17, 5.47it/s]\n 4%|▍ | 4/100 [00:00<00:17, 5.44it/s]\n 5%|▌ | 5/100 [00:00<00:17, 5.41it/s]\n 6%|▌ | 6/100 [00:01<00:16, 5.67it/s]\n 7%|▋ | 7/100 [00:01<00:16, 5.66it/s]\n 8%|▊ | 8/100 [00:01<00:16, 5.67it/s]\n 9%|▉ | 9/100 [00:01<00:16, 5.69it/s]\n 10%|█ | 10/100 [00:01<00:15, 5.70it/s]\n 11%|█ | 11/100 [00:01<00:15, 5.67it/s]\n 12%|█▏ | 12/100 [00:02<00:15, 5.71it/s]\n 13%|█▎ | 13/100 [00:02<00:15, 5.71it/s]\n 14%|█▍ | 14/100 [00:02<00:15, 5.69it/s]\n 15%|█▌ | 15/100 [00:02<00:14, 5.68it/s]\n 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4%|▎ | 1/27 [00:00<00:04, 5.97it/s]\n 7%|▋ | 2/27 [00:00<00:04, 6.00it/s]\n 11%|█ | 3/27 [00:00<00:03, 6.01it/s]\n 15%|█▍ | 4/27 [00:00<00:03, 6.00it/s]\n 19%|█▊ | 5/27 [00:00<00:03, 6.01it/s]\n 22%|██▏ | 6/27 [00:00<00:03, 6.00it/s]\n 26%|██▌ | 7/27 [00:01<00:03, 6.01it/s]\n 30%|██▉ | 8/27 [00:01<00:03, 6.02it/s]\n 33%|███▎ | 9/27 [00:01<00:02, 6.04it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 6.04it/s]\n 41%|████ | 11/27 [00:01<00:02, 6.04it/s]\n 44%|████▍ | 12/27 [00:01<00:02, 6.02it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 5.99it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 5.99it/s]\n 56%|█████▌ | 15/27 [00:02<00:01, 6.01it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 5.99it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 6.00it/s]\n 67%|██████▋ | 18/27 [00:02<00:01, 5.99it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.99it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 6.00it/s]\n 78%|███████▊ | 21/27 [00:03<00:00, 6.00it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 5.98it/s]\n 85%|████████▌ | 23/27 [00:03<00:00, 6.01it/s]\n 89%|████████▉ | 24/27 [00:03<00:00, 6.00it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 6.02it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 6.03it/s]\n100%|██████████| 27/27 [00:04<00:00, 6.02it/s]\n100%|██████████| 27/27 [00:04<00:00, 6.01it/s]", "metrics": { "predict_time": 29.700459, "total_time": 29.928155 }, "output": [ { "file": "https://replicate.delivery/mgxm/da05f53c-5d8a-40f7-9a50-7f0e751343bf/upsample_predictions.png" } ], "started_at": "2022-01-31T14:08:34.467053Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/daiz3jtiovbk5ag74v2yfys6ee", "cancel": "https://api.replicate.com/v1/predictions/daiz3jtiovbk5ag74v2yfys6ee/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:16, 6.05it/s] 2%|▏ | 2/100 [00:00<00:18, 5.32it/s] 3%|▎ | 3/100 [00:00<00:17, 5.47it/s] 4%|▍ | 4/100 [00:00<00:17, 5.44it/s] 5%|▌ | 5/100 [00:00<00:17, 5.41it/s] 6%|▌ | 6/100 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[00:03<00:01, 6.00it/s] 78%|███████▊ | 21/27 [00:03<00:00, 6.00it/s] 81%|████████▏ | 22/27 [00:03<00:00, 5.98it/s] 85%|████████▌ | 23/27 [00:03<00:00, 6.01it/s] 89%|████████▉ | 24/27 [00:03<00:00, 6.00it/s] 93%|█████████▎| 25/27 [00:04<00:00, 6.02it/s] 96%|█████████▋| 26/27 [00:04<00:00, 6.03it/s] 100%|██████████| 27/27 [00:04<00:00, 6.02it/s] 100%|██████████| 27/27 [00:04<00:00, 6.01it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- a canadian goose made from painted crystal quartz. crystal-goose is staring at the camera.
- side_x
- "64"
- side_y
- "96"
- batch_size
- "3"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "prompt": "a canadian goose made from painted crystal quartz. crystal-goose is staring at the camera.", "side_x": "64", "side_y": "96", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a canadian goose made from painted crystal quartz. crystal-goose is staring at the camera.", side_x: "64", side_y: "96", batch_size: "3", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a canadian goose made from painted crystal quartz. crystal-goose is staring at the camera.", "side_x": "64", "side_y": "96", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a canadian goose made from painted crystal quartz. crystal-goose is staring at the camera.", "side_x": "64", "side_y": "96", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T14:12:29.205436Z", "created_at": "2022-01-31T14:11:32.693338Z", "data_removed": false, "error": null, "id": "qiwnbgklhzakzjttntjm2zcvhe", "input": { "prompt": "a canadian goose made from painted crystal quartz. crystal-goose is staring at the camera.", "side_x": "64", "side_y": "96", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:34, 2.88it/s]\n 2%|▏ | 2/100 [00:00<00:29, 3.29it/s]\n 3%|▎ | 3/100 [00:00<00:28, 3.44it/s]\n 4%|▍ | 4/100 [00:01<00:27, 3.51it/s]\n 5%|▌ | 5/100 [00:01<00:26, 3.55it/s]\n 6%|▌ | 6/100 [00:01<00:26, 3.57it/s]\n 7%|▋ | 7/100 [00:01<00:25, 3.60it/s]\n 8%|▊ | 8/100 [00:02<00:25, 3.60it/s]\n 9%|▉ | 9/100 [00:02<00:25, 3.60it/s]\n 10%|█ | 10/100 [00:02<00:24, 3.62it/s]\n 11%|█ | 11/100 [00:03<00:24, 3.61it/s]\n 12%|█▏ | 12/100 [00:03<00:24, 3.60it/s]\n 13%|█▎ | 13/100 [00:03<00:24, 3.62it/s]\n 14%|█▍ | 14/100 [00:03<00:23, 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100/100 [00:27<00:00, 3.59it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:18, 1.38it/s]\n 7%|▋ | 2/27 [00:01<00:18, 1.37it/s]\n 11%|█ | 3/27 [00:02<00:17, 1.37it/s]\n 15%|█▍ | 4/27 [00:02<00:16, 1.37it/s]\n 19%|█▊ | 5/27 [00:03<00:16, 1.37it/s]\n 22%|██▏ | 6/27 [00:04<00:15, 1.37it/s]\n 26%|██▌ | 7/27 [00:05<00:14, 1.37it/s]\n 30%|██▉ | 8/27 [00:05<00:13, 1.37it/s]\n 33%|███▎ | 9/27 [00:06<00:13, 1.37it/s]\n 37%|███▋ | 10/27 [00:07<00:12, 1.37it/s]\n 41%|████ | 11/27 [00:08<00:11, 1.37it/s]\n 44%|████▍ | 12/27 [00:08<00:10, 1.37it/s]\n 48%|████▊ | 13/27 [00:09<00:10, 1.37it/s]\n 52%|█████▏ | 14/27 [00:10<00:09, 1.37it/s]\n 56%|█████▌ | 15/27 [00:10<00:08, 1.36it/s]\n 59%|█████▉ | 16/27 [00:11<00:08, 1.37it/s]\n 63%|██████▎ | 17/27 [00:12<00:07, 1.36it/s]\n 67%|██████▋ | 18/27 [00:13<00:06, 1.36it/s]\n 70%|███████ | 19/27 [00:13<00:05, 1.36it/s]\n 74%|███████▍ | 20/27 [00:14<00:05, 1.36it/s]\n 78%|███████▊ | 21/27 [00:15<00:04, 1.36it/s]\n 81%|████████▏ | 22/27 [00:16<00:03, 1.36it/s]\n 85%|████████▌ | 23/27 [00:16<00:02, 1.36it/s]\n 89%|████████▉ | 24/27 [00:17<00:02, 1.36it/s]\n 93%|█████████▎| 25/27 [00:18<00:01, 1.36it/s]\n 96%|█████████▋| 26/27 [00:19<00:00, 1.36it/s]\n100%|██████████| 27/27 [00:19<00:00, 1.36it/s]\n100%|██████████| 27/27 [00:19<00:00, 1.37it/s]", "metrics": { "predict_time": 56.280019, "total_time": 56.512098 }, "output": [ { "file": "https://replicate.delivery/mgxm/bfbdd33c-fd18-4687-8e22-02834e512c87/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/379f222c-1ba6-4d70-b340-8c004608bbf8/upsample_predictions.png" } ], "started_at": "2022-01-31T14:11:32.925417Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qiwnbgklhzakzjttntjm2zcvhe", "cancel": "https://api.replicate.com/v1/predictions/qiwnbgklhzakzjttntjm2zcvhe/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:34, 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- an image of a ham sandwich rendered in isometric minecraft
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "prompt": "an image of a ham sandwich rendered in isometric minecraft", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "an image of a ham sandwich rendered in isometric minecraft", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "an image of a ham sandwich rendered in isometric minecraft", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "an image of a ham sandwich rendered in isometric minecraft", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T14:20:28.709901Z", "created_at": "2022-01-31T14:19:24.093339Z", "data_removed": false, "error": null, "id": "3bpr6ypgozarpnn2ke7taign4e", "input": { "prompt": "an image of a ham sandwich rendered in isometric minecraft", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:27, 3.60it/s]\n 2%|▏ | 2/100 [00:00<00:35, 2.79it/s]\n 3%|▎ | 3/100 [00:01<00:37, 2.58it/s]\n 4%|▍ | 4/100 [00:01<00:38, 2.49it/s]\n 5%|▌ | 5/100 [00:01<00:38, 2.45it/s]\n 6%|▌ | 6/100 [00:02<00:38, 2.42it/s]\n 7%|▋ | 7/100 [00:02<00:38, 2.40it/s]\n 8%|▊ | 8/100 [00:03<00:38, 2.39it/s]\n 9%|▉ | 9/100 [00:03<00:38, 2.38it/s]\n 10%|█ | 10/100 [00:04<00:37, 2.38it/s]\n 11%|█ | 11/100 [00:04<00:37, 2.37it/s]\n 12%|█▏ | 12/100 [00:04<00:37, 2.37it/s]\n 13%|█▎ | 13/100 [00:05<00:36, 2.37it/s]\n 14%|█▍ | 14/100 [00:05<00:36, 2.37it/s]\n 15%|█▌ | 15/100 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85%|████████▌ | 23/27 [00:11<00:01, 2.09it/s]\n 89%|████████▉ | 24/27 [00:11<00:01, 2.09it/s]\n 93%|█████████▎| 25/27 [00:11<00:00, 2.09it/s]\n 96%|█████████▋| 26/27 [00:12<00:00, 2.08it/s]\n100%|██████████| 27/27 [00:12<00:00, 2.09it/s]\n100%|██████████| 27/27 [00:12<00:00, 2.09it/s]", "metrics": { "predict_time": 64.425688, "total_time": 64.616562 }, "output": [ { "file": "https://replicate.delivery/mgxm/0c6e62e2-5954-4f1d-9986-f16626488ce0/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/7999f98c-a64a-4303-958e-5be43d3d24fe/upsample_predictions.png" } ], "started_at": "2022-01-31T14:19:24.284213Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3bpr6ypgozarpnn2ke7taign4e", "cancel": "https://api.replicate.com/v1/predictions/3bpr6ypgozarpnn2ke7taign4e/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:27, 3.60it/s] 2%|▏ | 2/100 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63%|██████▎ | 17/27 [00:08<00:04, 2.09it/s] 67%|██████▋ | 18/27 [00:08<00:04, 2.09it/s] 70%|███████ | 19/27 [00:09<00:03, 2.09it/s] 74%|███████▍ | 20/27 [00:09<00:03, 2.09it/s] 78%|███████▊ | 21/27 [00:10<00:02, 2.09it/s] 81%|████████▏ | 22/27 [00:10<00:02, 2.09it/s] 85%|████████▌ | 23/27 [00:11<00:01, 2.09it/s] 89%|████████▉ | 24/27 [00:11<00:01, 2.09it/s] 93%|█████████▎| 25/27 [00:11<00:00, 2.09it/s] 96%|█████████▋| 26/27 [00:12<00:00, 2.08it/s] 100%|██████████| 27/27 [00:12<00:00, 2.09it/s] 100%|██████████| 27/27 [00:12<00:00, 2.09it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDdg7ssvdseramxhqogccmt5n47uStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a 3d model of a pepperoni pizza rendered in isometric minecraft
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "prompt": "a 3d model of a pepperoni pizza rendered in isometric minecraft", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a 3d model of a pepperoni pizza rendered in isometric minecraft", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a 3d model of a pepperoni pizza rendered in isometric minecraft", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a 3d model of a pepperoni pizza rendered in isometric minecraft", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T14:21:51.231101Z", "created_at": "2022-01-31T14:20:46.592223Z", "data_removed": false, "error": null, "id": "dg7ssvdseramxhqogccmt5n47u", "input": { "prompt": "a 3d model of a pepperoni pizza rendered in isometric minecraft", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:25, 3.94it/s]\n 2%|▏ | 2/100 [00:00<00:34, 2.84it/s]\n 3%|▎ | 3/100 [00:01<00:37, 2.59it/s]\n 4%|▍ | 4/100 [00:01<00:38, 2.48it/s]\n 5%|▌ | 5/100 [00:01<00:39, 2.44it/s]\n 6%|▌ | 6/100 [00:02<00:39, 2.39it/s]\n 7%|▋ | 7/100 [00:02<00:39, 2.38it/s]\n 8%|▊ | 8/100 [00:03<00:38, 2.36it/s]\n 9%|▉ | 9/100 [00:03<00:38, 2.36it/s]\n 10%|█ | 10/100 [00:04<00:38, 2.35it/s]\n 11%|█ | 11/100 [00:04<00:37, 2.34it/s]\n 12%|█▏ | 12/100 [00:04<00:37, 2.34it/s]\n 13%|█▎ | 13/100 [00:05<00:37, 2.34it/s]\n 14%|█▍ | 14/100 [00:05<00:36, 2.33it/s]\n 15%|█▌ | 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85%|████████▌ | 23/27 [00:11<00:01, 2.06it/s]\n 89%|████████▉ | 24/27 [00:11<00:01, 2.06it/s]\n 93%|█████████▎| 25/27 [00:12<00:00, 2.06it/s]\n 96%|█████████▋| 26/27 [00:12<00:00, 2.06it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.07it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.06it/s]", "metrics": { "predict_time": 64.44059, "total_time": 64.638878 }, "output": [ { "file": "https://replicate.delivery/mgxm/f9cbc7ba-fb4d-4fd9-a92c-d651f979e062/upsample_predictions.png" } ], "started_at": "2022-01-31T14:20:46.790511Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dg7ssvdseramxhqogccmt5n47u", "cancel": "https://api.replicate.com/v1/predictions/dg7ssvdseramxhqogccmt5n47u/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:25, 3.94it/s] 2%|▏ | 2/100 [00:00<00:34, 2.84it/s] 3%|▎ | 3/100 [00:01<00:37, 2.59it/s] 4%|▍ | 4/100 [00:01<00:38, 2.48it/s] 5%|▌ | 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[00:09<00:03, 2.06it/s] 74%|███████▍ | 20/27 [00:09<00:03, 2.06it/s] 78%|███████▊ | 21/27 [00:10<00:02, 2.06it/s] 81%|████████▏ | 22/27 [00:10<00:02, 2.06it/s] 85%|████████▌ | 23/27 [00:11<00:01, 2.06it/s] 89%|████████▉ | 24/27 [00:11<00:01, 2.06it/s] 93%|█████████▎| 25/27 [00:12<00:00, 2.06it/s] 96%|█████████▋| 26/27 [00:12<00:00, 2.06it/s] 100%|██████████| 27/27 [00:13<00:00, 2.07it/s] 100%|██████████| 27/27 [00:13<00:00, 2.06it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDyksh3fg67fh3fhfkgsnj3p6zoyStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a 3d model of a sandwich.
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.996"
- guidance_scale
- 6
- timestep_respacing
- 150
{ "prompt": "a 3d model of a sandwich.", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 6, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a 3d model of a sandwich.", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "0.996", guidance_scale: 6, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a 3d model of a sandwich.", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 6, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a 3d model of a sandwich.", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 6, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T14:27:17.032928Z", "created_at": "2022-01-31T14:25:49.345270Z", "data_removed": false, "error": null, "id": "yksh3fg67fh3fhfkgsnj3p6zoy", "input": { "prompt": "a 3d model of a sandwich.", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 6, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:37, 3.94it/s]\n 1%|▏ | 2/150 [00:00<00:52, 2.80it/s]\n 2%|▏ | 3/150 [00:01<00:57, 2.55it/s]\n 3%|▎ | 4/150 [00:01<00:59, 2.45it/s]\n 3%|▎ | 5/150 [00:01<01:00, 2.40it/s]\n 4%|▍ | 6/150 [00:02<01:01, 2.36it/s]\n 5%|▍ | 7/150 [00:02<01:01, 2.34it/s]\n 5%|▌ | 8/150 [00:03<01:01, 2.33it/s]\n 6%|▌ | 9/150 [00:03<01:00, 2.32it/s]\n 7%|▋ | 10/150 [00:04<01:00, 2.31it/s]\n 7%|▋ | 11/150 [00:04<01:00, 2.31it/s]\n 8%|▊ | 12/150 [00:05<00:59, 2.31it/s]\n 9%|▊ | 13/150 [00:05<00:59, 2.30it/s]\n 9%|▉ | 14/150 [00:05<00:59, 2.30it/s]\n 10%|█ | 15/150 [00:06<00:58, 2.30it/s]\n 11%|█ | 16/150 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146/150 [01:04<00:01, 2.25it/s]\n 98%|█████████▊| 147/150 [01:04<00:01, 2.25it/s]\n 99%|█████████▊| 148/150 [01:05<00:00, 2.25it/s]\n 99%|█████████▉| 149/150 [01:05<00:00, 2.24it/s]\n100%|██████████| 150/150 [01:06<00:00, 2.25it/s]\n100%|██████████| 150/150 [01:06<00:00, 2.26it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:12, 2.09it/s]\n 7%|▋ | 2/27 [00:00<00:12, 2.04it/s]\n 11%|█ | 3/27 [00:01<00:11, 2.06it/s]\n 15%|█▍ | 4/27 [00:01<00:11, 2.06it/s]\n 19%|█▊ | 5/27 [00:02<00:10, 2.06it/s]\n 22%|██▏ | 6/27 [00:02<00:10, 2.07it/s]\n 26%|██▌ | 7/27 [00:03<00:09, 2.07it/s]\n 30%|██▉ | 8/27 [00:03<00:09, 2.07it/s]\n 33%|███▎ | 9/27 [00:04<00:08, 2.07it/s]\n 37%|███▋ | 10/27 [00:04<00:08, 2.07it/s]\n 41%|████ | 11/27 [00:05<00:07, 2.07it/s]\n 44%|████▍ | 12/27 [00:05<00:07, 2.07it/s]\n 48%|████▊ | 13/27 [00:06<00:06, 2.07it/s]\n 52%|█████▏ | 14/27 [00:06<00:06, 2.07it/s]\n 56%|█████▌ | 15/27 [00:07<00:05, 2.07it/s]\n 59%|█████▉ | 16/27 [00:07<00:05, 2.07it/s]\n 63%|██████▎ | 17/27 [00:08<00:04, 2.07it/s]\n 67%|██████▋ | 18/27 [00:08<00:04, 2.07it/s]\n 70%|███████ | 19/27 [00:09<00:03, 2.07it/s]\n 74%|███████▍ | 20/27 [00:09<00:03, 2.07it/s]\n 78%|███████▊ | 21/27 [00:10<00:02, 2.07it/s]\n 81%|████████▏ | 22/27 [00:10<00:02, 2.07it/s]\n 85%|████████▌ | 23/27 [00:11<00:01, 2.06it/s]\n 89%|████████▉ | 24/27 [00:11<00:01, 2.06it/s]\n 93%|█████████▎| 25/27 [00:12<00:00, 2.07it/s]\n 96%|█████████▋| 26/27 [00:12<00:00, 2.07it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.07it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.07it/s]", "metrics": { "predict_time": 87.413457, "total_time": 87.687658 }, "output": [ { "file": "https://replicate.delivery/mgxm/f76f6b1a-9daa-410d-ac50-c647e9eaf4d2/upsample_predictions.png" } ], "started_at": "2022-01-31T14:25:49.619471Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yksh3fg67fh3fhfkgsnj3p6zoy", "cancel": "https://api.replicate.com/v1/predictions/yksh3fg67fh3fhfkgsnj3p6zoy/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:37, 3.94it/s] 1%|▏ | 2/150 [00:00<00:52, 2.80it/s] 2%|▏ | 3/150 [00:01<00:57, 2.55it/s] 3%|▎ | 4/150 [00:01<00:59, 2.45it/s] 3%|▎ | 5/150 [00:01<01:00, 2.40it/s] 4%|▍ | 6/150 [00:02<01:01, 2.36it/s] 5%|▍ | 7/150 [00:02<01:01, 2.34it/s] 5%|▌ | 8/150 [00:03<01:01, 2.33it/s] 6%|▌ | 9/150 [00:03<01:00, 2.32it/s] 7%|▋ | 10/150 [00:04<01:00, 2.31it/s] 7%|▋ | 11/150 [00:04<01:00, 2.31it/s] 8%|▊ | 12/150 [00:05<00:59, 2.31it/s] 9%|▊ | 13/150 [00:05<00:59, 2.30it/s] 9%|▉ | 14/150 [00:05<00:59, 2.30it/s] 10%|█ | 15/150 [00:06<00:58, 2.30it/s] 11%|█ | 16/150 [00:06<00:58, 2.29it/s] 11%|█▏ | 17/150 [00:07<00:58, 2.29it/s] 12%|█▏ | 18/150 [00:07<00:57, 2.29it/s] 13%|█▎ | 19/150 [00:08<00:57, 2.29it/s] 13%|█▎ | 20/150 [00:08<00:57, 2.28it/s] 14%|█▍ | 21/150 [00:08<00:56, 2.28it/s] 15%|█▍ | 22/150 [00:09<00:56, 2.28it/s] 15%|█▌ | 23/150 [00:09<00:55, 2.28it/s] 16%|█▌ | 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- 💰 💵 a trippy lsd acid art 💰 💵
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.995"
- guidance_scale
- 6
- timestep_respacing
- 150
{ "prompt": "💰 💵 a trippy lsd acid art 💰 💵", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "💰 💵 a trippy lsd acid art 💰 💵", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "0.995", guidance_scale: 6, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "💰 💵 a trippy lsd acid art 💰 💵", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "💰 💵 a trippy lsd acid art 💰 💵", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T14:36:55.253663Z", "created_at": "2022-01-31T14:35:28.688485Z", "data_removed": false, "error": null, "id": "y5mfzxlnb5hsbfbzssb3dei4ii", "input": { "prompt": "💰 💵 a trippy lsd acid art 💰 💵", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:39, 3.74it/s]\n 1%|▏ | 2/150 [00:00<00:52, 2.82it/s]\n 2%|▏ | 3/150 [00:01<00:56, 2.61it/s]\n 3%|▎ | 4/150 [00:01<00:58, 2.50it/s]\n 3%|▎ | 5/150 [00:01<00:59, 2.45it/s]\n 4%|▍ | 6/150 [00:02<00:59, 2.43it/s]\n 5%|▍ | 7/150 [00:02<00:59, 2.42it/s]\n 5%|▌ | 8/150 [00:03<00:59, 2.40it/s]\n 6%|▌ | 9/150 [00:03<00:58, 2.39it/s]\n 7%|▋ | 10/150 [00:04<00:58, 2.39it/s]\n 7%|▋ | 11/150 [00:04<00:58, 2.38it/s]\n 8%|▊ | 12/150 [00:04<00:57, 2.38it/s]\n 9%|▊ | 13/150 [00:05<00:57, 2.38it/s]\n 9%|▉ | 14/150 [00:05<00:57, 2.37it/s]\n 10%|█ | 15/150 [00:06<00:57, 2.37it/s]\n 11%|█ | 16/150 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146/150 [01:02<00:01, 2.29it/s]\n 98%|█████████▊| 147/150 [01:03<00:01, 2.29it/s]\n 99%|█████████▊| 148/150 [01:03<00:00, 2.29it/s]\n 99%|█████████▉| 149/150 [01:03<00:00, 2.29it/s]\n100%|██████████| 150/150 [01:04<00:00, 2.29it/s]\n100%|██████████| 150/150 [01:04<00:00, 2.33it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:12, 2.08it/s]\n 7%|▋ | 2/27 [00:00<00:12, 2.05it/s]\n 11%|█ | 3/27 [00:01<00:11, 2.07it/s]\n 15%|█▍ | 4/27 [00:01<00:11, 2.08it/s]\n 19%|█▊ | 5/27 [00:02<00:10, 2.08it/s]\n 22%|██▏ | 6/27 [00:02<00:10, 2.08it/s]\n 26%|██▌ | 7/27 [00:03<00:09, 2.08it/s]\n 30%|██▉ | 8/27 [00:03<00:09, 2.08it/s]\n 33%|███▎ | 9/27 [00:04<00:08, 2.08it/s]\n 37%|███▋ | 10/27 [00:04<00:08, 2.08it/s]\n 41%|████ | 11/27 [00:05<00:07, 2.07it/s]\n 44%|████▍ | 12/27 [00:05<00:07, 2.07it/s]\n 48%|████▊ | 13/27 [00:06<00:06, 2.07it/s]\n 52%|█████▏ | 14/27 [00:06<00:06, 2.07it/s]\n 56%|█████▌ | 15/27 [00:07<00:05, 2.08it/s]\n 59%|█████▉ | 16/27 [00:07<00:05, 2.08it/s]\n 63%|██████▎ | 17/27 [00:08<00:04, 2.07it/s]\n 67%|██████▋ | 18/27 [00:08<00:04, 2.07it/s]\n 70%|███████ | 19/27 [00:09<00:03, 2.07it/s]\n 74%|███████▍ | 20/27 [00:09<00:03, 2.07it/s]\n 78%|███████▊ | 21/27 [00:10<00:02, 2.08it/s]\n 81%|████████▏ | 22/27 [00:10<00:02, 2.08it/s]\n 85%|████████▌ | 23/27 [00:11<00:01, 2.08it/s]\n 89%|████████▉ | 24/27 [00:11<00:01, 2.07it/s]\n 93%|█████████▎| 25/27 [00:12<00:00, 2.07it/s]\n 96%|█████████▋| 26/27 [00:12<00:00, 2.08it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.07it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.07it/s]", "metrics": { "predict_time": 86.394186, "total_time": 86.565178 }, "output": [ { "file": "https://replicate.delivery/mgxm/c99037b6-1664-4bd4-9102-d1f3fddc0c55/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/3083fa84-f66e-452e-a4e7-7683fac3e46b/upsample_predictions.png" } ], "started_at": "2022-01-31T14:35:28.859477Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/y5mfzxlnb5hsbfbzssb3dei4ii", "cancel": "https://api.replicate.com/v1/predictions/y5mfzxlnb5hsbfbzssb3dei4ii/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:39, 3.74it/s] 1%|▏ | 2/150 [00:00<00:52, 2.82it/s] 2%|▏ | 3/150 [00:01<00:56, 2.61it/s] 3%|▎ | 4/150 [00:01<00:58, 2.50it/s] 3%|▎ | 5/150 [00:01<00:59, 2.45it/s] 4%|▍ | 6/150 [00:02<00:59, 2.43it/s] 5%|▍ | 7/150 [00:02<00:59, 2.42it/s] 5%|▌ | 8/150 [00:03<00:59, 2.40it/s] 6%|▌ | 9/150 [00:03<00:58, 2.39it/s] 7%|▋ | 10/150 [00:04<00:58, 2.39it/s] 7%|▋ | 11/150 [00:04<00:58, 2.38it/s] 8%|▊ | 12/150 [00:04<00:57, 2.38it/s] 9%|▊ | 13/150 [00:05<00:57, 2.38it/s] 9%|▉ | 14/150 [00:05<00:57, 2.37it/s] 10%|█ | 15/150 [00:06<00:57, 2.37it/s] 11%|█ | 16/150 [00:06<00:56, 2.37it/s] 11%|█▏ | 17/150 [00:07<00:56, 2.37it/s] 12%|█▏ | 18/150 [00:07<00:55, 2.37it/s] 13%|█▎ | 19/150 [00:07<00:55, 2.37it/s] 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[00:56<00:07, 2.29it/s] 89%|████████▉ | 134/150 [00:57<00:07, 2.29it/s] 90%|█████████ | 135/150 [00:57<00:06, 2.29it/s] 91%|█████████ | 136/150 [00:58<00:06, 2.28it/s] 91%|█████████▏| 137/150 [00:58<00:05, 2.29it/s] 92%|█████████▏| 138/150 [00:59<00:05, 2.29it/s] 93%|█████████▎| 139/150 [00:59<00:04, 2.29it/s] 93%|█████████▎| 140/150 [01:00<00:04, 2.29it/s] 94%|█████████▍| 141/150 [01:00<00:03, 2.29it/s] 95%|█████████▍| 142/150 [01:00<00:03, 2.29it/s] 95%|█████████▌| 143/150 [01:01<00:03, 2.29it/s] 96%|█████████▌| 144/150 [01:01<00:02, 2.29it/s] 97%|█████████▋| 145/150 [01:02<00:02, 2.29it/s] 97%|█████████▋| 146/150 [01:02<00:01, 2.29it/s] 98%|█████████▊| 147/150 [01:03<00:01, 2.29it/s] 99%|█████████▊| 148/150 [01:03<00:00, 2.29it/s] 99%|█████████▉| 149/150 [01:03<00:00, 2.29it/s] 100%|██████████| 150/150 [01:04<00:00, 2.29it/s] 100%|██████████| 150/150 [01:04<00:00, 2.33it/s] 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:12, 2.08it/s] 7%|▋ | 2/27 [00:00<00:12, 2.05it/s] 11%|█ | 3/27 [00:01<00:11, 2.07it/s] 15%|█▍ | 4/27 [00:01<00:11, 2.08it/s] 19%|█▊ | 5/27 [00:02<00:10, 2.08it/s] 22%|██▏ | 6/27 [00:02<00:10, 2.08it/s] 26%|██▌ | 7/27 [00:03<00:09, 2.08it/s] 30%|██▉ | 8/27 [00:03<00:09, 2.08it/s] 33%|███▎ | 9/27 [00:04<00:08, 2.08it/s] 37%|███▋ | 10/27 [00:04<00:08, 2.08it/s] 41%|████ | 11/27 [00:05<00:07, 2.07it/s] 44%|████▍ | 12/27 [00:05<00:07, 2.07it/s] 48%|████▊ | 13/27 [00:06<00:06, 2.07it/s] 52%|█████▏ | 14/27 [00:06<00:06, 2.07it/s] 56%|█████▌ | 15/27 [00:07<00:05, 2.08it/s] 59%|█████▉ | 16/27 [00:07<00:05, 2.08it/s] 63%|██████▎ | 17/27 [00:08<00:04, 2.07it/s] 67%|██████▋ | 18/27 [00:08<00:04, 2.07it/s] 70%|███████ | 19/27 [00:09<00:03, 2.07it/s] 74%|███████▍ | 20/27 [00:09<00:03, 2.07it/s] 78%|███████▊ | 21/27 [00:10<00:02, 2.08it/s] 81%|████████▏ | 22/27 [00:10<00:02, 2.08it/s] 85%|████████▌ | 23/27 [00:11<00:01, 2.08it/s] 89%|████████▉ | 24/27 [00:11<00:01, 2.07it/s] 93%|█████████▎| 25/27 [00:12<00:00, 2.07it/s] 96%|█████████▋| 26/27 [00:12<00:00, 2.08it/s] 100%|██████████| 27/27 [00:13<00:00, 2.07it/s] 100%|██████████| 27/27 [00:13<00:00, 2.07it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- -]💰 💵 a trippy lsd acid art 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.995"
- guidance_scale
- 6
- timestep_respacing
- 150
{ "prompt": "-]💰 💵 a trippy lsd acid art 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "-]💰 💵 a trippy lsd acid art 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "0.995", guidance_scale: 6, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "-]💰 💵 a trippy lsd acid art 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "-]💰 💵 a trippy lsd acid art 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T14:39:43.227260Z", "created_at": "2022-01-31T14:38:16.638829Z", "data_removed": false, "error": null, "id": "ntenge4ou5ewfn4faoqkkni2h4", "input": { "prompt": "-]💰 💵 a trippy lsd acid art 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:42, 3.47it/s]\n 1%|▏ | 2/150 [00:00<00:53, 2.75it/s]\n 2%|▏ | 3/150 [00:01<00:57, 2.57it/s]\n 3%|▎ | 4/150 [00:01<00:58, 2.49it/s]\n 3%|▎ | 5/150 [00:01<00:59, 2.45it/s]\n 4%|▍ | 6/150 [00:02<00:59, 2.42it/s]\n 5%|▍ | 7/150 [00:02<00:59, 2.41it/s]\n 5%|▌ | 8/150 [00:03<00:59, 2.40it/s]\n 6%|▌ | 9/150 [00:03<00:59, 2.38it/s]\n 7%|▋ | 10/150 [00:04<00:58, 2.38it/s]\n 7%|▋ | 11/150 [00:04<00:58, 2.38it/s]\n 8%|▊ | 12/150 [00:04<00:58, 2.38it/s]\n 9%|▊ | 13/150 [00:05<00:57, 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[00:06<00:06, 2.06it/s]\n 56%|█████▌ | 15/27 [00:07<00:05, 2.06it/s]\n 59%|█████▉ | 16/27 [00:07<00:05, 2.06it/s]\n 63%|██████▎ | 17/27 [00:08<00:04, 2.06it/s]\n 67%|██████▋ | 18/27 [00:08<00:04, 2.06it/s]\n 70%|███████ | 19/27 [00:09<00:03, 2.06it/s]\n 74%|███████▍ | 20/27 [00:09<00:03, 2.06it/s]\n 78%|███████▊ | 21/27 [00:10<00:02, 2.06it/s]\n 81%|████████▏ | 22/27 [00:10<00:02, 2.06it/s]\n 85%|████████▌ | 23/27 [00:11<00:01, 2.06it/s]\n 89%|████████▉ | 24/27 [00:11<00:01, 2.06it/s]\n 93%|█████████▎| 25/27 [00:12<00:00, 2.06it/s]\n 96%|█████████▋| 26/27 [00:12<00:00, 2.06it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.06it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.06it/s]", "metrics": { "predict_time": 86.407877, "total_time": 86.588431 }, "output": [ { "file": "https://replicate.delivery/mgxm/15c9ad8d-2978-4f6e-9937-7e8e42a2ab49/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/a45338c8-ff71-4241-bd4a-6123f1c96626/upsample_predictions.png" } ], "started_at": "2022-01-31T14:38:16.819383Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ntenge4ou5ewfn4faoqkkni2h4", "cancel": "https://api.replicate.com/v1/predictions/ntenge4ou5ewfn4faoqkkni2h4/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:42, 3.47it/s] 1%|▏ | 2/150 [00:00<00:53, 2.75it/s] 2%|▏ | 3/150 [00:01<00:57, 2.57it/s] 3%|▎ | 4/150 [00:01<00:58, 2.49it/s] 3%|▎ | 5/150 [00:01<00:59, 2.45it/s] 4%|▍ | 6/150 [00:02<00:59, 2.42it/s] 5%|▍ | 7/150 [00:02<00:59, 2.41it/s] 5%|▌ | 8/150 [00:03<00:59, 2.40it/s] 6%|▌ | 9/150 [00:03<00:59, 2.38it/s] 7%|▋ | 10/150 [00:04<00:58, 2.38it/s] 7%|▋ | 11/150 [00:04<00:58, 2.38it/s] 8%|▊ | 12/150 [00:04<00:58, 2.38it/s] 9%|▊ | 13/150 [00:05<00:57, 2.37it/s] 9%|▉ | 14/150 [00:05<00:57, 2.37it/s] 10%|█ | 15/150 [00:06<00:57, 2.37it/s] 11%|█ | 16/150 [00:06<00:56, 2.37it/s] 11%|█▏ | 17/150 [00:07<00:56, 2.37it/s] 12%|█▏ | 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93%|█████████▎| 25/27 [00:12<00:00, 2.06it/s] 96%|█████████▋| 26/27 [00:12<00:00, 2.06it/s] 100%|██████████| 27/27 [00:13<00:00, 2.06it/s] 100%|██████████| 27/27 [00:13<00:00, 2.06it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- a vector art illustration of psychedelic energy flowing throughout the trippy forest 💰 💵 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.995"
- guidance_scale
- 6
- timestep_respacing
- 150
{ "prompt": "a vector art illustration of psychedelic energy flowing throughout the trippy forest 💰 💵 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a vector art illustration of psychedelic energy flowing throughout the trippy forest 💰 💵 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "0.995", guidance_scale: 6, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a vector art illustration of psychedelic energy flowing throughout the trippy forest 💰 💵 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a vector art illustration of psychedelic energy flowing throughout the trippy forest 💰 💵 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T14:42:17.344493Z", "created_at": "2022-01-31T14:40:50.525099Z", "data_removed": false, "error": null, "id": "me37xj4fejfdnnrxvak7vrsmky", "input": { "prompt": "a vector art illustration of psychedelic energy flowing throughout the trippy forest 💰 💵 💰 💵- [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 - [ ] 💰 💵 - [ ] 🖼 🌌 ", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.995", "guidance_scale": 6, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:37, 3.94it/s]\n 1%|▏ | 2/150 [00:00<00:52, 2.84it/s]\n 2%|▏ | 3/150 [00:01<00:56, 2.61it/s]\n 3%|▎ | 4/150 [00:01<00:58, 2.50it/s]\n 3%|▎ | 5/150 [00:01<00:58, 2.46it/s]\n 4%|▍ | 6/150 [00:02<00:59, 2.43it/s]\n 5%|▍ | 7/150 [00:02<00:59, 2.41it/s]\n 5%|▌ | 8/150 [00:03<00:59, 2.39it/s]\n 6%|▌ | 9/150 [00:03<00:59, 2.38it/s]\n 7%|▋ | 10/150 [00:04<00:58, 2.37it/s]\n 7%|▋ | 11/150 [00:04<00:58, 2.37it/s]\n 8%|▊ | 12/150 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[00:06<00:06, 2.07it/s]\n 52%|█████▏ | 14/27 [00:06<00:06, 2.07it/s]\n 56%|█████▌ | 15/27 [00:07<00:05, 2.07it/s]\n 59%|█████▉ | 16/27 [00:07<00:05, 2.07it/s]\n 63%|██████▎ | 17/27 [00:08<00:04, 2.07it/s]\n 67%|██████▋ | 18/27 [00:08<00:04, 2.07it/s]\n 70%|███████ | 19/27 [00:09<00:03, 2.07it/s]\n 74%|███████▍ | 20/27 [00:09<00:03, 2.07it/s]\n 78%|███████▊ | 21/27 [00:10<00:02, 2.07it/s]\n 81%|████████▏ | 22/27 [00:10<00:02, 2.06it/s]\n 85%|████████▌ | 23/27 [00:11<00:01, 2.06it/s]\n 89%|████████▉ | 24/27 [00:11<00:01, 2.07it/s]\n 93%|█████████▎| 25/27 [00:12<00:00, 2.07it/s]\n 96%|█████████▋| 26/27 [00:12<00:00, 2.07it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.07it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.07it/s]", "metrics": { "predict_time": 86.611208, "total_time": 86.819394 }, "output": [ { "file": "https://replicate.delivery/mgxm/b917c9a6-8ab3-4195-b549-857b5d9d1849/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/e8f72cfd-4c17-4555-bf58-6ed8b7327b86/upsample_predictions.png" } ], "started_at": "2022-01-31T14:40:50.733285Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/me37xj4fejfdnnrxvak7vrsmky", "cancel": "https://api.replicate.com/v1/predictions/me37xj4fejfdnnrxvak7vrsmky/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:37, 3.94it/s] 1%|▏ | 2/150 [00:00<00:52, 2.84it/s] 2%|▏ | 3/150 [00:01<00:56, 2.61it/s] 3%|▎ | 4/150 [00:01<00:58, 2.50it/s] 3%|▎ | 5/150 [00:01<00:58, 2.46it/s] 4%|▍ | 6/150 [00:02<00:59, 2.43it/s] 5%|▍ | 7/150 [00:02<00:59, 2.41it/s] 5%|▌ | 8/150 [00:03<00:59, 2.39it/s] 6%|▌ | 9/150 [00:03<00:59, 2.38it/s] 7%|▋ | 10/150 [00:04<00:58, 2.37it/s] 7%|▋ | 11/150 [00:04<00:58, 2.37it/s] 8%|▊ | 12/150 [00:04<00:58, 2.37it/s] 9%|▊ | 13/150 [00:05<00:57, 2.36it/s] 9%|▉ | 14/150 [00:05<00:57, 2.36it/s] 10%|█ | 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2.06it/s] 85%|████████▌ | 23/27 [00:11<00:01, 2.06it/s] 89%|████████▉ | 24/27 [00:11<00:01, 2.07it/s] 93%|█████████▎| 25/27 [00:12<00:00, 2.07it/s] 96%|█████████▋| 26/27 [00:12<00:00, 2.07it/s] 100%|██████████| 27/27 [00:13<00:00, 2.07it/s] 100%|██████████| 27/27 [00:13<00:00, 2.07it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDate2yy4bxngznh2fzphhvyldy4StatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a detailed painting in trippy lsd acid colors resembling the cycles of life and infinity
- side_x
- "112"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "prompt": "a detailed painting in trippy lsd acid colors resembling the cycles of life and infinity", "side_x": "112", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a detailed painting in trippy lsd acid colors resembling the cycles of life and infinity", side_x: "112", side_y: "64", batch_size: "3", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a detailed painting in trippy lsd acid colors resembling the cycles of life and infinity", "side_x": "112", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a detailed painting in trippy lsd acid colors resembling the cycles of life and infinity", "side_x": "112", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-01T21:47:47.603670Z", "created_at": "2022-02-01T21:46:38.545409Z", "data_removed": false, "error": null, "id": "ate2yy4bxngznh2fzphhvyldy4", "input": { "prompt": "a detailed painting in trippy lsd acid colors resembling the cycles of life and infinity", "side_x": "112", "side_y": "64", "batch_size": "3", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:40, 2.43it/s]\n 2%|▏ | 2/100 [00:00<00:36, 2.70it/s]\n 3%|▎ | 3/100 [00:01<00:34, 2.81it/s]\n 4%|▍ | 4/100 [00:01<00:33, 2.83it/s]\n 5%|▌ | 5/100 [00:01<00:33, 2.86it/s]\n 6%|▌ | 6/100 [00:02<00:32, 2.88it/s]\n 7%|▋ | 7/100 [00:02<00:32, 2.88it/s]\n 8%|▊ | 8/100 [00:02<00:31, 2.89it/s]\n 9%|▉ | 9/100 [00:03<00:31, 2.89it/s]\n 10%|█ | 10/100 [00:03<00:31, 2.89it/s]\n 11%|█ | 11/100 [00:03<00:30, 2.89it/s]\n 12%|█▏ | 12/100 [00:04<00:30, 2.90it/s]\n 13%|█▎ | 13/100 [00:04<00:30, 2.90it/s]\n 14%|█▍ | 14/100 [00:04<00:29, 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[00:19<00:04, 1.12it/s]\n 85%|████████▌ | 23/27 [00:20<00:03, 1.12it/s]\n 89%|████████▉ | 24/27 [00:21<00:02, 1.13it/s]\n 93%|█████████▎| 25/27 [00:22<00:01, 1.13it/s]\n 96%|█████████▋| 26/27 [00:23<00:00, 1.13it/s]\n100%|██████████| 27/27 [00:24<00:00, 1.13it/s]\n100%|██████████| 27/27 [00:24<00:00, 1.11it/s]", "metrics": { "predict_time": 68.786209, "total_time": 69.058261 }, "output": [ { "file": "https://replicate.delivery/mgxm/eb264c94-ea60-4ed4-854d-c17c74db1db0/upsample_predictions.png" } ], "started_at": "2022-02-01T21:46:38.817461Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ate2yy4bxngznh2fzphhvyldy4", "cancel": "https://api.replicate.com/v1/predictions/ate2yy4bxngznh2fzphhvyldy4/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:40, 2.43it/s] 2%|▏ | 2/100 [00:00<00:36, 2.70it/s] 3%|▎ | 3/100 [00:01<00:34, 2.81it/s] 4%|▍ | 4/100 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- seed
- 0
- prompt
- an image of the ozarks in arkansas
- side_x
- "128"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "1"
- guidance_scale
- 3
- timestep_respacing
- 100
{ "seed": 0, "prompt": "an image of the ozarks in arkansas", "side_x": "128", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 3, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "an image of the ozarks in arkansas", side_x: "128", side_y: "64", batch_size: "1", upsample_temp: "1", guidance_scale: 3, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "an image of the ozarks in arkansas", "side_x": "128", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 3, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "an image of the ozarks in arkansas", "side_x": "128", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 3, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-01T21:57:45.912681Z", "created_at": "2022-02-01T21:57:11.945528Z", "data_removed": false, "error": null, "id": "wcuq3i37dnc7hnxmmbte7yle5q", "input": { "seed": 0, "prompt": "an image of the ozarks in arkansas", "side_x": "128", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 3, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:23, 4.29it/s]\n 2%|▏ | 2/100 [00:00<00:18, 5.29it/s]\n 3%|▎ | 3/100 [00:00<00:16, 5.72it/s]\n 4%|▍ | 4/100 [00:00<00:16, 5.96it/s]\n 5%|▌ | 5/100 [00:00<00:15, 6.10it/s]\n 6%|▌ | 6/100 [00:01<00:15, 6.19it/s]\n 7%|▋ | 7/100 [00:01<00:14, 6.25it/s]\n 8%|▊ | 8/100 [00:01<00:14, 6.29it/s]\n 9%|▉ | 9/100 [00:01<00:14, 6.32it/s]\n 10%|█ | 10/100 [00:01<00:14, 6.33it/s]\n 11%|█ | 11/100 [00:01<00:14, 6.35it/s]\n 12%|█▏ | 12/100 [00:01<00:13, 6.37it/s]\n 13%|█▎ | 13/100 [00:02<00:13, 6.39it/s]\n 14%|█▍ | 14/100 [00:02<00:13, 6.38it/s]\n 15%|█▌ | 15/100 [00:02<00:13, 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| 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:08, 3.11it/s]\n 7%|▋ | 2/27 [00:00<00:08, 3.12it/s]\n 11%|█ | 3/27 [00:00<00:07, 3.05it/s]\n 15%|█▍ | 4/27 [00:01<00:07, 3.08it/s]\n 19%|█▊ | 5/27 [00:01<00:07, 3.10it/s]\n 22%|██▏ | 6/27 [00:01<00:06, 3.09it/s]\n 26%|██▌ | 7/27 [00:02<00:06, 3.09it/s]\n 30%|██▉ | 8/27 [00:02<00:06, 3.09it/s]\n 33%|███▎ | 9/27 [00:02<00:05, 3.09it/s]\n 37%|███▋ | 10/27 [00:03<00:05, 3.08it/s]\n 41%|████ | 11/27 [00:03<00:05, 3.08it/s]\n 44%|████▍ | 12/27 [00:03<00:04, 3.08it/s]\n 48%|████▊ | 13/27 [00:04<00:04, 3.07it/s]\n 52%|█████▏ | 14/27 [00:04<00:04, 3.07it/s]\n 56%|█████▌ | 15/27 [00:04<00:03, 3.07it/s]\n 59%|█████▉ | 16/27 [00:05<00:03, 3.07it/s]\n 63%|██████▎ | 17/27 [00:05<00:03, 3.08it/s]\n 67%|██████▋ | 18/27 [00:05<00:02, 3.08it/s]\n 70%|███████ | 19/27 [00:06<00:02, 3.08it/s]\n 74%|███████▍ | 20/27 [00:06<00:02, 3.07it/s]\n 78%|███████▊ | 21/27 [00:06<00:01, 3.08it/s]\n 81%|████████▏ | 22/27 [00:07<00:01, 3.07it/s]\n 85%|████████▌ | 23/27 [00:07<00:01, 3.07it/s]\n 89%|████████▉ | 24/27 [00:07<00:00, 3.06it/s]\n 93%|█████████▎| 25/27 [00:08<00:00, 3.05it/s]\n 96%|█████████▋| 26/27 [00:08<00:00, 3.06it/s]\n100%|██████████| 27/27 [00:08<00:00, 3.06it/s]\n100%|██████████| 27/27 [00:08<00:00, 3.07it/s]", "metrics": { "predict_time": 33.788972, "total_time": 33.967153 }, "output": [ { "file": "https://replicate.delivery/mgxm/1e850a15-dd40-4f58-86ea-54e000e0b55b/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/dd11296f-733a-4c76-8334-bf7ad925ad08/upsample_predictions.png" } ], "started_at": "2022-02-01T21:57:12.123709Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wcuq3i37dnc7hnxmmbte7yle5q", "cancel": "https://api.replicate.com/v1/predictions/wcuq3i37dnc7hnxmmbte7yle5q/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:23, 4.29it/s] 2%|▏ | 2/100 [00:00<00:18, 5.29it/s] 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3.08it/s] 67%|██████▋ | 18/27 [00:05<00:02, 3.08it/s] 70%|███████ | 19/27 [00:06<00:02, 3.08it/s] 74%|███████▍ | 20/27 [00:06<00:02, 3.07it/s] 78%|███████▊ | 21/27 [00:06<00:01, 3.08it/s] 81%|████████▏ | 22/27 [00:07<00:01, 3.07it/s] 85%|████████▌ | 23/27 [00:07<00:01, 3.07it/s] 89%|████████▉ | 24/27 [00:07<00:00, 3.06it/s] 93%|█████████▎| 25/27 [00:08<00:00, 3.05it/s] 96%|█████████▋| 26/27 [00:08<00:00, 3.06it/s] 100%|██████████| 27/27 [00:08<00:00, 3.06it/s] 100%|██████████| 27/27 [00:08<00:00, 3.07it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDg7p3earlffajta7qr3jkfjdxciStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- college town
- side_x
- "64"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "1"
- guidance_scale
- 3
- timestep_respacing
- 100
{ "seed": 0, "prompt": "college town", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 3, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "college town", side_x: "64", side_y: "64", batch_size: "1", upsample_temp: "1", guidance_scale: 3, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "college town", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 3, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "college town", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 3, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-01T22:04:36.724072Z", "created_at": "2022-02-01T22:03:53.452131Z", "data_removed": false, "error": null, "id": "g7p3earlffajta7qr3jkfjdxci", "input": { "seed": 0, "prompt": "college town", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 3, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:16, 5.98it/s]\n 2%|▏ | 2/100 [00:00<00:18, 5.16it/s]\n 3%|▎ | 3/100 [00:00<00:18, 5.26it/s]\n 4%|▍ | 4/100 [00:00<00:18, 5.27it/s]\n 5%|▌ | 5/100 [00:00<00:18, 5.23it/s]\n 6%|▌ | 6/100 [00:01<00:17, 5.32it/s]\n 7%|▋ | 7/100 [00:01<00:17, 5.30it/s]\n 8%|▊ | 8/100 [00:01<00:17, 5.32it/s]\n 9%|▉ | 9/100 [00:01<00:17, 5.27it/s]\n 10%|█ | 10/100 [00:01<00:16, 5.32it/s]\n 11%|█ | 11/100 [00:02<00:16, 5.34it/s]\n 12%|█▏ | 12/100 [00:02<00:16, 5.26it/s]\n 13%|█▎ | 13/100 [00:02<00:16, 5.18it/s]\n 14%|█▍ | 14/100 [00:02<00:15, 5.39it/s]\n 15%|█▌ | 15/100 [00:02<00:15, 5.36it/s]\n 16%|█▌ | 16/100 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89%|████████▉ | 24/27 [00:04<00:00, 5.73it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.72it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.71it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.72it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.72it/s]", "metrics": { "predict_time": 31.633179, "total_time": 43.271941 }, "output": [ { "file": "https://replicate.delivery/mgxm/a04dd0e5-d44e-4aac-87a3-c8b2aadd13ca/upsample_predictions.png" } ], "started_at": "2022-02-01T22:04:05.090893Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/g7p3earlffajta7qr3jkfjdxci", "cancel": "https://api.replicate.com/v1/predictions/g7p3earlffajta7qr3jkfjdxci/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:16, 5.98it/s] 2%|▏ | 2/100 [00:00<00:18, 5.16it/s] 3%|▎ | 3/100 [00:00<00:18, 5.26it/s] 4%|▍ | 4/100 [00:00<00:18, 5.27it/s] 5%|▌ | 5/100 [00:00<00:18, 5.23it/s] 6%|▌ | 6/100 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[00:03<00:01, 5.71it/s] 78%|███████▊ | 21/27 [00:03<00:01, 5.70it/s] 81%|████████▏ | 22/27 [00:03<00:00, 5.72it/s] 85%|████████▌ | 23/27 [00:04<00:00, 5.72it/s] 89%|████████▉ | 24/27 [00:04<00:00, 5.73it/s] 93%|█████████▎| 25/27 [00:04<00:00, 5.72it/s] 96%|█████████▋| 26/27 [00:04<00:00, 5.71it/s] 100%|██████████| 27/27 [00:04<00:00, 5.72it/s] 100%|██████████| 27/27 [00:04<00:00, 5.72it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aID56pietfkxvhkzdn42i7eyjiq4aStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 55
- prompt
- battleship
- side_x
- "64"
- side_y
- "64"
- batch_size
- "4"
- upsample_temp
- "1"
- guidance_scale
- 2
- timestep_respacing
- 150
{ "seed": 55, "prompt": "battleship", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 2, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 55, prompt: "battleship", side_x: "64", side_y: "64", batch_size: "4", upsample_temp: "1", guidance_scale: 2, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 55, "prompt": "battleship", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 2, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 55, "prompt": "battleship", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 2, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-01T22:20:13.859910Z", "created_at": "2022-02-01T22:18:14.993521Z", "data_removed": false, "error": null, "id": "56pietfkxvhkzdn42i7eyjiq4a", "input": { "seed": 55, "prompt": "battleship", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 2, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:48, 3.09it/s]\n 1%|▏ | 2/150 [00:00<01:09, 2.12it/s]\n 2%|▏ | 3/150 [00:01<01:17, 1.90it/s]\n 3%|▎ | 4/150 [00:02<01:20, 1.82it/s]\n 3%|▎ | 5/150 [00:02<01:22, 1.76it/s]\n 4%|▍ | 6/150 [00:03<01:22, 1.74it/s]\n 5%|▍ | 7/150 [00:03<01:23, 1.72it/s]\n 5%|▌ | 8/150 [00:04<01:23, 1.71it/s]\n 6%|▌ | 9/150 [00:05<01:22, 1.70it/s]\n 7%|▋ | 10/150 [00:05<01:22, 1.70it/s]\n 7%|▋ | 11/150 [00:06<01:22, 1.69it/s]\n 8%|▊ | 12/150 [00:06<01:21, 1.69it/s]\n 9%|▊ | 13/150 [00:07<01:21, 1.68it/s]\n 9%|▉ | 14/150 [00:08<01:20, 1.68it/s]\n 10%|█ | 15/150 [00:08<01:20, 1.68it/s]\n 11%|█ | 16/150 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17/27 [00:11<00:06, 1.52it/s]\n 67%|██████▋ | 18/27 [00:11<00:05, 1.52it/s]\n 70%|███████ | 19/27 [00:12<00:05, 1.52it/s]\n 74%|███████▍ | 20/27 [00:13<00:04, 1.52it/s]\n 78%|███████▊ | 21/27 [00:13<00:03, 1.52it/s]\n 81%|████████▏ | 22/27 [00:14<00:03, 1.51it/s]\n 85%|████████▌ | 23/27 [00:15<00:02, 1.51it/s]\n 89%|████████▉ | 24/27 [00:15<00:01, 1.51it/s]\n 93%|█████████▎| 25/27 [00:16<00:01, 1.51it/s]\n 96%|█████████▋| 26/27 [00:17<00:00, 1.51it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.51it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.52it/s]", "metrics": { "predict_time": 118.679716, "total_time": 118.866389 }, "output": [ { "file": "https://replicate.delivery/mgxm/fc2eac58-3066-4e84-bef5-2932c223c4b0/upsample_predictions.png" } ], "started_at": "2022-02-01T22:18:15.180194Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/56pietfkxvhkzdn42i7eyjiq4a", "cancel": "https://api.replicate.com/v1/predictions/56pietfkxvhkzdn42i7eyjiq4a/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:48, 3.09it/s] 1%|▏ | 2/150 [00:00<01:09, 2.12it/s] 2%|▏ | 3/150 [00:01<01:17, 1.90it/s] 3%|▎ | 4/150 [00:02<01:20, 1.82it/s] 3%|▎ | 5/150 [00:02<01:22, 1.76it/s] 4%|▍ | 6/150 [00:03<01:22, 1.74it/s] 5%|▍ | 7/150 [00:03<01:23, 1.72it/s] 5%|▌ | 8/150 [00:04<01:23, 1.71it/s] 6%|▌ | 9/150 [00:05<01:22, 1.70it/s] 7%|▋ | 10/150 [00:05<01:22, 1.70it/s] 7%|▋ | 11/150 [00:06<01:22, 1.69it/s] 8%|▊ | 12/150 [00:06<01:21, 1.69it/s] 9%|▊ | 13/150 [00:07<01:21, 1.68it/s] 9%|▉ | 14/150 [00:08<01:20, 1.68it/s] 10%|█ | 15/150 [00:08<01:20, 1.68it/s] 11%|█ | 16/150 [00:09<01:19, 1.68it/s] 11%|█▏ | 17/150 [00:09<01:19, 1.67it/s] 12%|█▏ | 18/150 [00:10<01:19, 1.67it/s] 13%|█▎ | 19/150 [00:11<01:18, 1.67it/s] 13%|█▎ | 20/150 [00:11<01:17, 1.67it/s] 14%|█▍ | 21/150 [00:12<01:17, 1.66it/s] 15%|█▍ | 22/150 [00:12<01:17, 1.66it/s] 15%|█▌ | 23/150 [00:13<01:16, 1.66it/s] 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDnhgypnaxbbfgjmp5hi2khageeaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 55
- prompt
- battleship made out of minecraft
- side_x
- "64"
- side_y
- "64"
- batch_size
- "4"
- upsample_temp
- "1"
- guidance_scale
- 2
- timestep_respacing
- 150
{ "seed": 55, "prompt": "battleship made out of minecraft", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 2, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 55, prompt: "battleship made out of minecraft", side_x: "64", side_y: "64", batch_size: "4", upsample_temp: "1", guidance_scale: 2, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 55, "prompt": "battleship made out of minecraft", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 2, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 55, "prompt": "battleship made out of minecraft", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 2, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-01T22:22:32.040861Z", "created_at": "2022-02-01T22:20:31.464354Z", "data_removed": false, "error": null, "id": "nhgypnaxbbfgjmp5hi2khageea", "input": { "seed": 55, "prompt": "battleship made out of minecraft", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 2, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:50, 2.95it/s]\n 1%|▏ | 2/150 [00:00<01:11, 2.07it/s]\n 2%|▏ | 3/150 [00:01<01:19, 1.85it/s]\n 3%|▎ | 4/150 [00:02<01:22, 1.78it/s]\n 3%|▎ | 5/150 [00:02<01:23, 1.73it/s]\n 4%|▍ | 6/150 [00:03<01:24, 1.71it/s]\n 5%|▍ | 7/150 [00:03<01:24, 1.69it/s]\n 5%|▌ | 8/150 [00:04<01:24, 1.67it/s]\n 6%|▌ | 9/150 [00:05<01:24, 1.67it/s]\n 7%|▋ | 10/150 [00:05<01:24, 1.66it/s]\n 7%|▋ | 11/150 [00:06<01:23, 1.66it/s]\n 8%|▊ | 12/150 [00:06<01:23, 1.65it/s]\n 9%|▊ | 13/150 [00:07<01:23, 1.65it/s]\n 9%|▉ | 14/150 [00:08<01:22, 1.65it/s]\n 10%|█ | 15/150 [00:08<01:22, 1.64it/s]\n 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1.50it/s]\n 63%|██████▎ | 17/27 [00:11<00:06, 1.50it/s]\n 67%|██████▋ | 18/27 [00:11<00:05, 1.50it/s]\n 70%|███████ | 19/27 [00:12<00:05, 1.50it/s]\n 74%|███████▍ | 20/27 [00:13<00:04, 1.50it/s]\n 78%|███████▊ | 21/27 [00:13<00:04, 1.50it/s]\n 81%|████████▏ | 22/27 [00:14<00:03, 1.50it/s]\n 85%|████████▌ | 23/27 [00:15<00:02, 1.50it/s]\n 89%|████████▉ | 24/27 [00:15<00:02, 1.50it/s]\n 93%|█████████▎| 25/27 [00:16<00:01, 1.50it/s]\n 96%|█████████▋| 26/27 [00:17<00:00, 1.50it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.50it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.50it/s]", "metrics": { "predict_time": 120.367494, "total_time": 120.576507 }, "output": [ { "file": "https://replicate.delivery/mgxm/eb85dcd0-d9c2-4271-896c-9f409766e22f/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/4d770138-126f-4b61-b1df-fad38feec440/upsample_predictions.png" } ], "started_at": "2022-02-01T22:20:31.673367Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nhgypnaxbbfgjmp5hi2khageea", "cancel": "https://api.replicate.com/v1/predictions/nhgypnaxbbfgjmp5hi2khageea/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:50, 2.95it/s] 1%|▏ | 2/150 [00:00<01:11, 2.07it/s] 2%|▏ | 3/150 [00:01<01:19, 1.85it/s] 3%|▎ | 4/150 [00:02<01:22, 1.78it/s] 3%|▎ | 5/150 [00:02<01:23, 1.73it/s] 4%|▍ | 6/150 [00:03<01:24, 1.71it/s] 5%|▍ | 7/150 [00:03<01:24, 1.69it/s] 5%|▌ | 8/150 [00:04<01:24, 1.67it/s] 6%|▌ | 9/150 [00:05<01:24, 1.67it/s] 7%|▋ | 10/150 [00:05<01:24, 1.66it/s] 7%|▋ | 11/150 [00:06<01:23, 1.66it/s] 8%|▊ | 12/150 [00:06<01:23, 1.65it/s] 9%|▊ | 13/150 [00:07<01:23, 1.65it/s] 9%|▉ | 14/150 [00:08<01:22, 1.65it/s] 10%|█ | 15/150 [00:08<01:22, 1.64it/s] 11%|█ | 16/150 [00:09<01:21, 1.64it/s] 11%|█▏ | 17/150 [00:10<01:21, 1.64it/s] 12%|█▏ | 18/150 [00:10<01:20, 1.63it/s] 13%|█▎ | 19/150 [00:11<01:20, 1.63it/s] 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1.50it/s] 100%|██████████| 27/27 [00:17<00:00, 1.50it/s] 100%|██████████| 27/27 [00:17<00:00, 1.50it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.
- side_x
- "64"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "1"
- guidance_scale
- 8
- timestep_respacing
- 50
{ "prompt": "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 8, "timestep_respacing": "50" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", side_x: "64", side_y: "64", batch_size: "1", upsample_temp: "1", guidance_scale: 8, timestep_respacing: "50" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 8, "timestep_respacing": "50" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 8, "timestep_respacing": "50" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-02T00:17:15.900120Z", "created_at": "2022-02-02T00:16:52.869521Z", "data_removed": false, "error": null, "id": "kawhsclf2facngqdfcc7awaggq", "input": { "prompt": "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 8, "timestep_respacing": "50" }, "logs": "\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.43it/s]\n 4%|▍ | 2/50 [00:00<00:09, 5.04it/s]\n 6%|▌ | 3/50 [00:00<00:09, 5.21it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.22it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.23it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.25it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 5.26it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.28it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.25it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.29it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.26it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 5.31it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.30it/s]\n 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5.71it/s]", "metrics": { "predict_time": 22.862624, "total_time": 23.030599 }, "output": [ { "file": "https://replicate.delivery/mgxm/e2fd284b-1e89-4572-a8c7-17876cc4c52e/upsample_predictions.png" } ], "started_at": "2022-02-02T00:16:53.037496Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kawhsclf2facngqdfcc7awaggq", "cancel": "https://api.replicate.com/v1/predictions/kawhsclf2facngqdfcc7awaggq/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.43it/s] 4%|▍ | 2/50 [00:00<00:09, 5.04it/s] 6%|▌ | 3/50 [00:00<00:09, 5.21it/s] 8%|▊ | 4/50 [00:00<00:08, 5.22it/s] 10%|█ | 5/50 [00:00<00:08, 5.23it/s] 12%|█▏ | 6/50 [00:01<00:08, 5.25it/s] 14%|█▍ | 7/50 [00:01<00:08, 5.26it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.28it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.25it/s] 20%|██ | 10/50 [00:01<00:07, 5.29it/s] 22%|██▏ | 11/50 [00:02<00:07, 5.26it/s] 24%|██▍ | 12/50 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.
- side_x
- "64"
- side_y
- "64"
- batch_size
- "4"
- upsample_temp
- "1"
- guidance_scale
- 7
- timestep_respacing
- 150
{ "prompt": "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", side_x: "64", side_y: "64", batch_size: "4", upsample_temp: "1", guidance_scale: 7, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-02T00:19:49.361981Z", "created_at": "2022-02-02T00:17:46.845655Z", "data_removed": false, "error": null, "id": "g6wg2if7efag7mx2lq2htvnyam", "input": { "prompt": "close up shot of hoardes of geese canadian geese attacking the camera. thousands of canadian geese running on the ground.", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:47, 3.12it/s]\n 1%|▏ | 2/150 [00:00<01:09, 2.12it/s]\n 2%|▏ | 3/150 [00:01<01:18, 1.87it/s]\n 3%|▎ | 4/150 [00:02<01:21, 1.79it/s]\n 3%|▎ | 5/150 [00:02<01:23, 1.74it/s]\n 4%|▍ | 6/150 [00:03<01:23, 1.72it/s]\n 5%|▍ | 7/150 [00:03<01:24, 1.70it/s]\n 5%|▌ | 8/150 [00:04<01:24, 1.69it/s]\n 6%|▌ | 9/150 [00:05<01:24, 1.68it/s]\n 7%|▋ | 10/150 [00:05<01:23, 1.67it/s]\n 7%|▋ | 11/150 [00:06<01:23, 1.67it/s]\n 8%|▊ | 12/150 [00:06<01:22, 1.67it/s]\n 9%|▊ | 13/150 [00:07<01:22, 1.66it/s]\n 9%|▉ | 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1.49it/s]\n 56%|█████▌ | 15/27 [00:10<00:08, 1.49it/s]\n 59%|█████▉ | 16/27 [00:10<00:07, 1.49it/s]\n 63%|██████▎ | 17/27 [00:11<00:06, 1.49it/s]\n 67%|██████▋ | 18/27 [00:12<00:06, 1.49it/s]\n 70%|███████ | 19/27 [00:12<00:05, 1.49it/s]\n 74%|███████▍ | 20/27 [00:13<00:04, 1.49it/s]\n 78%|███████▊ | 21/27 [00:14<00:04, 1.49it/s]\n 81%|████████▏ | 22/27 [00:14<00:03, 1.48it/s]\n 85%|████████▌ | 23/27 [00:15<00:02, 1.48it/s]\n 89%|████████▉ | 24/27 [00:16<00:02, 1.48it/s]\n 93%|█████████▎| 25/27 [00:16<00:01, 1.48it/s]\n 96%|█████████▋| 26/27 [00:17<00:00, 1.48it/s]\n100%|██████████| 27/27 [00:18<00:00, 1.48it/s]\n100%|██████████| 27/27 [00:18<00:00, 1.49it/s]", "metrics": { "predict_time": 122.305134, "total_time": 122.516326 }, "output": [ { "file": "https://replicate.delivery/mgxm/d8a10895-0a38-4e16-8d9e-19d10a178529/upsample_predictions.png" } ], "started_at": "2022-02-02T00:17:47.056847Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/g6wg2if7efag7mx2lq2htvnyam", "cancel": "https://api.replicate.com/v1/predictions/g6wg2if7efag7mx2lq2htvnyam/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:47, 3.12it/s] 1%|▏ | 2/150 [00:00<01:09, 2.12it/s] 2%|▏ | 3/150 [00:01<01:18, 1.87it/s] 3%|▎ | 4/150 [00:02<01:21, 1.79it/s] 3%|▎ | 5/150 [00:02<01:23, 1.74it/s] 4%|▍ | 6/150 [00:03<01:23, 1.72it/s] 5%|▍ | 7/150 [00:03<01:24, 1.70it/s] 5%|▌ | 8/150 [00:04<01:24, 1.69it/s] 6%|▌ | 9/150 [00:05<01:24, 1.68it/s] 7%|▋ | 10/150 [00:05<01:23, 1.67it/s] 7%|▋ | 11/150 [00:06<01:23, 1.67it/s] 8%|▊ | 12/150 [00:06<01:22, 1.67it/s] 9%|▊ | 13/150 [00:07<01:22, 1.66it/s] 9%|▉ | 14/150 [00:08<01:21, 1.66it/s] 10%|█ | 15/150 [00:08<01:21, 1.66it/s] 11%|█ | 16/150 [00:09<01:20, 1.66it/s] 11%|█▏ | 17/150 [00:09<01:20, 1.65it/s] 12%|█▏ | 18/150 [00:10<01:19, 1.65it/s] 13%|█▎ | 19/150 [00:11<01:19, 1.65it/s] 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3/27 [00:02<00:16, 1.50it/s] 15%|█▍ | 4/27 [00:02<00:15, 1.50it/s] 19%|█▊ | 5/27 [00:03<00:14, 1.49it/s] 22%|██▏ | 6/27 [00:04<00:14, 1.49it/s] 26%|██▌ | 7/27 [00:04<00:13, 1.49it/s] 30%|██▉ | 8/27 [00:05<00:12, 1.49it/s] 33%|███▎ | 9/27 [00:06<00:12, 1.49it/s] 37%|███▋ | 10/27 [00:06<00:11, 1.49it/s] 41%|████ | 11/27 [00:07<00:10, 1.49it/s] 44%|████▍ | 12/27 [00:08<00:10, 1.49it/s] 48%|████▊ | 13/27 [00:08<00:09, 1.49it/s] 52%|█████▏ | 14/27 [00:09<00:08, 1.49it/s] 56%|█████▌ | 15/27 [00:10<00:08, 1.49it/s] 59%|█████▉ | 16/27 [00:10<00:07, 1.49it/s] 63%|██████▎ | 17/27 [00:11<00:06, 1.49it/s] 67%|██████▋ | 18/27 [00:12<00:06, 1.49it/s] 70%|███████ | 19/27 [00:12<00:05, 1.49it/s] 74%|███████▍ | 20/27 [00:13<00:04, 1.49it/s] 78%|███████▊ | 21/27 [00:14<00:04, 1.49it/s] 81%|████████▏ | 22/27 [00:14<00:03, 1.48it/s] 85%|████████▌ | 23/27 [00:15<00:02, 1.48it/s] 89%|████████▉ | 24/27 [00:16<00:02, 1.48it/s] 93%|█████████▎| 25/27 [00:16<00:01, 1.48it/s] 96%|█████████▋| 26/27 [00:17<00:00, 1.48it/s] 100%|██████████| 27/27 [00:18<00:00, 1.48it/s] 100%|██████████| 27/27 [00:18<00:00, 1.49it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDhm2pejuufjeeni5ptc2ehur2teStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- trippy lsd acid flower album cover abstract art trending on art station
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "1"
- guidance_scale
- 7
- timestep_respacing
- 150
{ "prompt": "trippy lsd acid flower album cover abstract art trending on art station", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "trippy lsd acid flower album cover abstract art trending on art station", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "1", guidance_scale: 7, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "trippy lsd acid flower album cover abstract art trending on art station", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "trippy lsd acid flower album cover abstract art trending on art station", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-02T00:30:00.225575Z", "created_at": "2022-02-02T00:28:23.938334Z", "data_removed": false, "error": null, "id": "hm2pejuufjeeni5ptc2ehur2te", "input": { "prompt": "trippy lsd acid flower album cover abstract art trending on art station", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:41, 3.57it/s]\n 1%|▏ | 2/150 [00:00<00:56, 2.61it/s]\n 2%|▏ | 3/150 [00:01<01:01, 2.38it/s]\n 3%|▎ | 4/150 [00:01<01:04, 2.28it/s]\n 3%|▎ | 5/150 [00:02<01:04, 2.23it/s]\n 4%|▍ | 6/150 [00:02<01:05, 2.18it/s]\n 5%|▍ | 7/150 [00:03<01:05, 2.17it/s]\n 5%|▌ | 8/150 [00:03<01:06, 2.15it/s]\n 6%|▌ | 9/150 [00:04<01:05, 2.14it/s]\n 7%|▋ | 10/150 [00:04<01:05, 2.13it/s]\n 7%|▋ | 11/150 [00:04<01:05, 2.13it/s]\n 8%|▊ | 12/150 [00:05<01:05, 2.12it/s]\n 9%|▊ | 13/150 [00:05<01:04, 2.11it/s]\n 9%|▉ | 14/150 [00:06<01:04, 2.11it/s]\n 10%|█ | 15/150 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| 16/27 [00:08<00:05, 1.93it/s]\n 63%|██████▎ | 17/27 [00:08<00:05, 1.94it/s]\n 67%|██████▋ | 18/27 [00:09<00:04, 1.93it/s]\n 70%|███████ | 19/27 [00:09<00:04, 1.93it/s]\n 74%|███████▍ | 20/27 [00:10<00:03, 1.93it/s]\n 78%|███████▊ | 21/27 [00:10<00:03, 1.93it/s]\n 81%|████████▏ | 22/27 [00:11<00:02, 1.93it/s]\n 85%|████████▌ | 23/27 [00:11<00:02, 1.93it/s]\n 89%|████████▉ | 24/27 [00:12<00:01, 1.93it/s]\n 93%|█████████▎| 25/27 [00:12<00:01, 1.93it/s]\n 96%|█████████▋| 26/27 [00:13<00:00, 1.93it/s]\n100%|██████████| 27/27 [00:13<00:00, 1.93it/s]\n100%|██████████| 27/27 [00:13<00:00, 1.93it/s]", "metrics": { "predict_time": 96.083523, "total_time": 96.287241 }, "output": [ { "file": "https://replicate.delivery/mgxm/990413e4-e6f5-405d-b2f3-d8f38615c2e8/upsample_predictions.png" } ], "started_at": "2022-02-02T00:28:24.142052Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hm2pejuufjeeni5ptc2ehur2te", "cancel": "https://api.replicate.com/v1/predictions/hm2pejuufjeeni5ptc2ehur2te/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:41, 3.57it/s] 1%|▏ | 2/150 [00:00<00:56, 2.61it/s] 2%|▏ | 3/150 [00:01<01:01, 2.38it/s] 3%|▎ | 4/150 [00:01<01:04, 2.28it/s] 3%|▎ | 5/150 [00:02<01:04, 2.23it/s] 4%|▍ | 6/150 [00:02<01:05, 2.18it/s] 5%|▍ | 7/150 [00:03<01:05, 2.17it/s] 5%|▌ | 8/150 [00:03<01:06, 2.15it/s] 6%|▌ | 9/150 [00:04<01:05, 2.14it/s] 7%|▋ | 10/150 [00:04<01:05, 2.13it/s] 7%|▋ | 11/150 [00:04<01:05, 2.13it/s] 8%|▊ | 12/150 [00:05<01:05, 2.12it/s] 9%|▊ | 13/150 [00:05<01:04, 2.11it/s] 9%|▉ | 14/150 [00:06<01:04, 2.11it/s] 10%|█ | 15/150 [00:06<01:04, 2.10it/s] 11%|█ | 16/150 [00:07<01:03, 2.10it/s] 11%|█▏ | 17/150 [00:07<01:03, 2.10it/s] 12%|█▏ | 18/150 [00:08<01:03, 2.09it/s] 13%|█▎ | 19/150 [00:08<01:02, 2.09it/s] 13%|█▎ | 20/150 [00:09<01:02, 2.08it/s] 14%|█▍ | 21/150 [00:09<01:01, 2.09it/s] 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1.93it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aID6gfutj7lt5c43hlw6k6ier3it4StatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- trippy lsd acid flower abstract art trending on art station
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "1"
- guidance_scale
- 7
- timestep_respacing
- 150
{ "prompt": "trippy lsd acid flower abstract art trending on art station", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "trippy lsd acid flower abstract art trending on art station", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "1", guidance_scale: 7, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "trippy lsd acid flower abstract art trending on art station", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "trippy lsd acid flower abstract art trending on art station", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-02T00:40:29.611023Z", "created_at": "2022-02-02T00:38:52.540232Z", "data_removed": false, "error": null, "id": "6gfutj7lt5c43hlw6k6ier3it4", "input": { "prompt": "trippy lsd acid flower abstract art trending on art station", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:41, 3.57it/s]\n 1%|▏ | 2/150 [00:00<00:57, 2.55it/s]\n 2%|▏ | 3/150 [00:01<01:03, 2.31it/s]\n 3%|▎ | 4/150 [00:01<01:06, 2.19it/s]\n 3%|▎ | 5/150 [00:02<01:07, 2.15it/s]\n 4%|▍ | 6/150 [00:02<01:08, 2.10it/s]\n 5%|▍ | 7/150 [00:03<01:08, 2.09it/s]\n 5%|▌ | 8/150 [00:03<01:08, 2.06it/s]\n 6%|▌ | 9/150 [00:04<01:08, 2.06it/s]\n 7%|▋ | 10/150 [00:04<01:08, 2.05it/s]\n 7%|▋ | 11/150 [00:05<01:08, 2.04it/s]\n 8%|▊ | 12/150 [00:05<01:08, 2.03it/s]\n 9%|▊ | 13/150 [00:06<01:07, 2.03it/s]\n 9%|▉ | 14/150 [00:06<01:07, 2.02it/s]\n 10%|█ | 15/150 [00:07<01:06, 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2.11it/s]\n 97%|█████████▋| 146/150 [01:11<00:01, 2.10it/s]\n 98%|█████████▊| 147/150 [01:11<00:01, 2.10it/s]\n 99%|█████████▊| 148/150 [01:12<00:00, 2.10it/s]\n 99%|█████████▉| 149/150 [01:12<00:00, 2.10it/s]\n100%|██████████| 150/150 [01:13<00:00, 2.10it/s]\n100%|██████████| 150/150 [01:13<00:00, 2.05it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:13, 1.98it/s]\n 7%|▋ | 2/27 [00:01<00:13, 1.90it/s]\n 11%|█ | 3/27 [00:01<00:12, 1.92it/s]\n 15%|█▍ | 4/27 [00:02<00:12, 1.91it/s]\n 19%|█▊ | 5/27 [00:02<00:11, 1.93it/s]\n 22%|██▏ | 6/27 [00:03<00:10, 1.92it/s]\n 26%|██▌ | 7/27 [00:03<00:10, 1.93it/s]\n 30%|██▉ | 8/27 [00:04<00:09, 1.92it/s]\n 33%|███▎ | 9/27 [00:04<00:09, 1.93it/s]\n 37%|███▋ | 10/27 [00:05<00:08, 1.92it/s]\n 41%|████ | 11/27 [00:05<00:08, 1.93it/s]\n 44%|████▍ | 12/27 [00:06<00:07, 1.93it/s]\n 48%|████▊ | 13/27 [00:06<00:07, 1.92it/s]\n 52%|█████▏ | 14/27 [00:07<00:06, 1.92it/s]\n 56%|█████▌ | 15/27 [00:07<00:06, 1.92it/s]\n 59%|█████▉ | 16/27 [00:08<00:05, 1.92it/s]\n 63%|██████▎ | 17/27 [00:08<00:05, 1.92it/s]\n 67%|██████▋ | 18/27 [00:09<00:04, 1.92it/s]\n 70%|███████ | 19/27 [00:09<00:04, 1.91it/s]\n 74%|███████▍ | 20/27 [00:10<00:03, 1.92it/s]\n 78%|███████▊ | 21/27 [00:10<00:03, 1.91it/s]\n 81%|████████▏ | 22/27 [00:11<00:02, 1.91it/s]\n 85%|████████▌ | 23/27 [00:11<00:02, 1.91it/s]\n 89%|████████▉ | 24/27 [00:12<00:01, 1.91it/s]\n 93%|█████████▎| 25/27 [00:13<00:01, 1.91it/s]\n 96%|█████████▋| 26/27 [00:13<00:00, 1.91it/s]\n100%|██████████| 27/27 [00:14<00:00, 1.90it/s]\n100%|██████████| 27/27 [00:14<00:00, 1.92it/s]", "metrics": { "predict_time": 96.85988, "total_time": 97.070791 }, "output": [ { "file": "https://replicate.delivery/mgxm/3dc0e7c7-7721-47d5-9dff-b5a37a45b177/upsample_predictions.png" } ], "started_at": "2022-02-02T00:38:52.751143Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6gfutj7lt5c43hlw6k6ier3it4", "cancel": "https://api.replicate.com/v1/predictions/6gfutj7lt5c43hlw6k6ier3it4/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:41, 3.57it/s] 1%|▏ | 2/150 [00:00<00:57, 2.55it/s] 2%|▏ | 3/150 [00:01<01:03, 2.31it/s] 3%|▎ | 4/150 [00:01<01:06, 2.19it/s] 3%|▎ | 5/150 [00:02<01:07, 2.15it/s] 4%|▍ | 6/150 [00:02<01:08, 2.10it/s] 5%|▍ | 7/150 [00:03<01:08, 2.09it/s] 5%|▌ | 8/150 [00:03<01:08, 2.06it/s] 6%|▌ | 9/150 [00:04<01:08, 2.06it/s] 7%|▋ | 10/150 [00:04<01:08, 2.05it/s] 7%|▋ | 11/150 [00:05<01:08, 2.04it/s] 8%|▊ | 12/150 [00:05<01:08, 2.03it/s] 9%|▊ | 13/150 [00:06<01:07, 2.03it/s] 9%|▉ | 14/150 [00:06<01:07, 2.02it/s] 10%|█ | 15/150 [00:07<01:06, 2.02it/s] 11%|█ | 16/150 [00:07<01:06, 2.01it/s] 11%|█▏ | 17/150 [00:08<01:06, 2.01it/s] 12%|█▏ | 18/150 [00:08<01:06, 2.00it/s] 13%|█▎ | 19/150 [00:09<01:05, 1.99it/s] 13%|█▎ | 20/150 [00:09<01:05, 1.99it/s] 14%|█▍ | 21/150 [00:10<01:05, 1.98it/s] 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1.92it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDphnre2xjxbgenp2s3tywdacd64StatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a Pembroke Welsh Corgi made from isometric chess piece
- side_x
- "64"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "0.997"
- guidance_scale
- 4
- timestep_respacing
- 150
{ "prompt": "a Pembroke Welsh Corgi made from isometric chess piece", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a Pembroke Welsh Corgi made from isometric chess piece", side_x: "64", side_y: "64", batch_size: "1", upsample_temp: "0.997", guidance_scale: 4, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a Pembroke Welsh Corgi made from isometric chess piece", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a Pembroke Welsh Corgi made from isometric chess piece", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-02T01:29:22.810705Z", "created_at": "2022-02-02T01:28:39.317094Z", "data_removed": false, "error": null, "id": "phnre2xjxbgenp2s3tywdacd64", "input": { "prompt": "a Pembroke Welsh Corgi made from isometric chess piece", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:26, 5.69it/s]\n 1%|▏ | 2/150 [00:00<00:29, 5.05it/s]\n 2%|▏ | 3/150 [00:00<00:28, 5.10it/s]\n 3%|▎ | 4/150 [00:00<00:28, 5.18it/s]\n 3%|▎ | 5/150 [00:00<00:27, 5.20it/s]\n 4%|▍ | 6/150 [00:01<00:27, 5.18it/s]\n 5%|▍ | 7/150 [00:01<00:27, 5.19it/s]\n 5%|▌ | 8/150 [00:01<00:27, 5.17it/s]\n 6%|▌ | 9/150 [00:01<00:27, 5.19it/s]\n 7%|▋ | 10/150 [00:01<00:27, 5.15it/s]\n 7%|▋ | 11/150 [00:02<00:26, 5.16it/s]\n 8%|▊ | 12/150 [00:02<00:26, 5.19it/s]\n 9%|▊ | 13/150 [00:02<00:26, 5.18it/s]\n 9%|▉ | 14/150 [00:02<00:26, 5.21it/s]\n 10%|█ | 15/150 [00:02<00:25, 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5.43it/s]\n 63%|██████▎ | 17/27 [00:03<00:01, 5.43it/s]\n 67%|██████▋ | 18/27 [00:03<00:01, 5.42it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.42it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 5.43it/s]\n 78%|███████▊ | 21/27 [00:03<00:01, 5.44it/s]\n 81%|████████▏ | 22/27 [00:04<00:00, 5.44it/s]\n 85%|████████▌ | 23/27 [00:04<00:00, 5.45it/s]\n 89%|████████▉ | 24/27 [00:04<00:00, 5.45it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.46it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.45it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.46it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.43it/s]", "metrics": { "predict_time": 43.280495, "total_time": 43.493611 }, "output": [ { "file": "https://replicate.delivery/mgxm/491b69d8-1ca7-4ce8-8e46-f4b6625f90f5/upsample_predictions.png" } ], "started_at": "2022-02-02T01:28:39.530210Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/phnre2xjxbgenp2s3tywdacd64", "cancel": "https://api.replicate.com/v1/predictions/phnre2xjxbgenp2s3tywdacd64/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:26, 5.69it/s] 1%|▏ | 2/150 [00:00<00:29, 5.05it/s] 2%|▏ | 3/150 [00:00<00:28, 5.10it/s] 3%|▎ | 4/150 [00:00<00:28, 5.18it/s] 3%|▎ | 5/150 [00:00<00:27, 5.20it/s] 4%|▍ | 6/150 [00:01<00:27, 5.18it/s] 5%|▍ | 7/150 [00:01<00:27, 5.19it/s] 5%|▌ | 8/150 [00:01<00:27, 5.17it/s] 6%|▌ | 9/150 [00:01<00:27, 5.19it/s] 7%|▋ | 10/150 [00:01<00:27, 5.15it/s] 7%|▋ | 11/150 [00:02<00:26, 5.16it/s] 8%|▊ | 12/150 [00:02<00:26, 5.19it/s] 9%|▊ | 13/150 [00:02<00:26, 5.18it/s] 9%|▉ | 14/150 [00:02<00:26, 5.21it/s] 10%|█ | 15/150 [00:02<00:25, 5.20it/s] 11%|█ | 16/150 [00:03<00:25, 5.19it/s] 11%|█▏ | 17/150 [00:03<00:25, 5.19it/s] 12%|█▏ | 18/150 [00:03<00:25, 5.19it/s] 13%|█▎ | 19/150 [00:03<00:25, 5.21it/s] 13%|█▎ | 20/150 [00:03<00:25, 5.16it/s] 14%|█▍ | 21/150 [00:04<00:24, 5.24it/s] 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5.43it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aID4ks3egfhnjh2xikcn3zqup72iyStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a Pembroke Welsh Corgi plush toy
- side_x
- "64"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "0.997"
- guidance_scale
- 4
- timestep_respacing
- 150
{ "prompt": "a Pembroke Welsh Corgi plush toy", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a Pembroke Welsh Corgi plush toy", side_x: "64", side_y: "64", batch_size: "1", upsample_temp: "0.997", guidance_scale: 4, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a Pembroke Welsh Corgi plush toy", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a Pembroke Welsh Corgi plush toy", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-02T01:32:42.544021Z", "created_at": "2022-02-02T01:31:59.414137Z", "data_removed": false, "error": null, "id": "4ks3egfhnjh2xikcn3zqup72iy", "input": { "prompt": "a Pembroke Welsh Corgi plush toy", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:25, 5.74it/s]\n 1%|▏ | 2/150 [00:00<00:28, 5.20it/s]\n 2%|▏ | 3/150 [00:00<00:28, 5.19it/s]\n 3%|▎ | 4/150 [00:00<00:27, 5.21it/s]\n 3%|▎ | 5/150 [00:00<00:27, 5.24it/s]\n 4%|▍ | 6/150 [00:01<00:27, 5.25it/s]\n 5%|▍ | 7/150 [00:01<00:27, 5.25it/s]\n 5%|▌ | 8/150 [00:01<00:26, 5.27it/s]\n 6%|▌ | 9/150 [00:01<00:26, 5.26it/s]\n 7%|▋ | 10/150 [00:01<00:26, 5.26it/s]\n 7%|▋ | 11/150 [00:02<00:26, 5.24it/s]\n 8%|▊ | 12/150 [00:02<00:26, 5.24it/s]\n 9%|▊ | 13/150 [00:02<00:26, 5.21it/s]\n 9%|▉ | 14/150 [00:02<00:26, 5.21it/s]\n 10%|█ | 15/150 [00:02<00:26, 5.19it/s]\n 11%|█ | 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63%|██████▎ | 17/27 [00:03<00:01, 5.47it/s]\n 67%|██████▋ | 18/27 [00:03<00:01, 5.47it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.49it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 5.49it/s]\n 78%|███████▊ | 21/27 [00:03<00:01, 5.49it/s]\n 81%|████████▏ | 22/27 [00:04<00:00, 5.49it/s]\n 85%|████████▌ | 23/27 [00:04<00:00, 5.49it/s]\n 89%|████████▉ | 24/27 [00:04<00:00, 5.48it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.49it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.48it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.48it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.46it/s]", "metrics": { "predict_time": 42.911023, "total_time": 43.129884 }, "output": [ { "file": "https://replicate.delivery/mgxm/6af06a03-79d6-4713-acdb-7dff495d21e3/upsample_predictions.png" } ], "started_at": "2022-02-02T01:31:59.632998Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4ks3egfhnjh2xikcn3zqup72iy", "cancel": "https://api.replicate.com/v1/predictions/4ks3egfhnjh2xikcn3zqup72iy/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:25, 5.74it/s] 1%|▏ | 2/150 [00:00<00:28, 5.20it/s] 2%|▏ | 3/150 [00:00<00:28, 5.19it/s] 3%|▎ | 4/150 [00:00<00:27, 5.21it/s] 3%|▎ | 5/150 [00:00<00:27, 5.24it/s] 4%|▍ | 6/150 [00:01<00:27, 5.25it/s] 5%|▍ | 7/150 [00:01<00:27, 5.25it/s] 5%|▌ | 8/150 [00:01<00:26, 5.27it/s] 6%|▌ | 9/150 [00:01<00:26, 5.26it/s] 7%|▋ | 10/150 [00:01<00:26, 5.26it/s] 7%|▋ | 11/150 [00:02<00:26, 5.24it/s] 8%|▊ | 12/150 [00:02<00:26, 5.24it/s] 9%|▊ | 13/150 [00:02<00:26, 5.21it/s] 9%|▉ | 14/150 [00:02<00:26, 5.21it/s] 10%|█ | 15/150 [00:02<00:26, 5.19it/s] 11%|█ | 16/150 [00:03<00:26, 5.15it/s] 11%|█▏ | 17/150 [00:03<00:25, 5.16it/s] 12%|█▏ | 18/150 [00:03<00:25, 5.18it/s] 13%|█▎ | 19/150 [00:03<00:25, 5.13it/s] 13%|█▎ | 20/150 [00:03<00:25, 5.16it/s] 14%|█▍ | 21/150 [00:04<00:24, 5.16it/s] 15%|█▍ | 22/150 [00:04<00:24, 5.15it/s] 15%|█▌ | 23/150 [00:04<00:24, 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[00:01<00:03, 5.40it/s] 30%|██▉ | 8/27 [00:01<00:03, 5.43it/s] 33%|███▎ | 9/27 [00:01<00:03, 5.46it/s] 37%|███▋ | 10/27 [00:01<00:03, 5.45it/s] 41%|████ | 11/27 [00:02<00:02, 5.42it/s] 44%|████▍ | 12/27 [00:02<00:02, 5.43it/s] 48%|████▊ | 13/27 [00:02<00:02, 5.45it/s] 52%|█████▏ | 14/27 [00:02<00:02, 5.46it/s] 56%|█████▌ | 15/27 [00:02<00:02, 5.47it/s] 59%|█████▉ | 16/27 [00:02<00:02, 5.46it/s] 63%|██████▎ | 17/27 [00:03<00:01, 5.47it/s] 67%|██████▋ | 18/27 [00:03<00:01, 5.47it/s] 70%|███████ | 19/27 [00:03<00:01, 5.49it/s] 74%|███████▍ | 20/27 [00:03<00:01, 5.49it/s] 78%|███████▊ | 21/27 [00:03<00:01, 5.49it/s] 81%|████████▏ | 22/27 [00:04<00:00, 5.49it/s] 85%|████████▌ | 23/27 [00:04<00:00, 5.49it/s] 89%|████████▉ | 24/27 [00:04<00:00, 5.48it/s] 93%|█████████▎| 25/27 [00:04<00:00, 5.49it/s] 96%|█████████▋| 26/27 [00:04<00:00, 5.48it/s] 100%|██████████| 27/27 [00:04<00:00, 5.48it/s] 100%|██████████| 27/27 [00:04<00:00, 5.46it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- a ketchup plush toy
- side_x
- "80"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.997"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "prompt": "a ketchup plush toy", "side_x": "80", "side_y": "64", "batch_size": "3", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a ketchup plush toy", side_x: "80", side_y: "64", batch_size: "3", upsample_temp: "0.997", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a ketchup plush toy", "side_x": "80", "side_y": "64", "batch_size": "3", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a ketchup plush toy", "side_x": "80", "side_y": "64", "batch_size": "3", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-02T01:37:15.404234Z", "created_at": "2022-02-02T01:36:26.667102Z", "data_removed": false, "error": null, "id": "zvkdabyms5cwzeem2ady542cda", "input": { "prompt": "a ketchup plush toy", "side_x": "80", "side_y": "64", "batch_size": "3", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:01<01:51, 1.13s/it]\n 2%|▏ | 2/100 [00:01<00:58, 1.67it/s]\n 3%|▎ | 3/100 [00:01<00:41, 2.33it/s]\n 4%|▍ | 4/100 [00:01<00:33, 2.85it/s]\n 5%|▌ | 5/100 [00:02<00:29, 3.26it/s]\n 6%|▌ | 6/100 [00:02<00:26, 3.56it/s]\n 7%|▋ | 7/100 [00:02<00:24, 3.79it/s]\n 8%|▊ | 8/100 [00:02<00:23, 3.95it/s]\n 9%|▉ | 9/100 [00:02<00:22, 4.06it/s]\n 10%|█ | 10/100 [00:03<00:21, 4.14it/s]\n 11%|█ | 11/100 [00:03<00:21, 4.20it/s]\n 12%|█▏ | 12/100 [00:03<00:20, 4.25it/s]\n 13%|█▎ | 13/100 [00:03<00:20, 4.28it/s]\n 14%|█▍ | 14/100 [00:04<00:20, 4.29it/s]\n 15%|█▌ | 15/100 [00:04<00:19, 4.29it/s]\n 16%|█▌ | 16/100 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[00:09<00:14, 4.30it/s]Caught SIGTERM, exiting...\n\n 40%|████ | 40/100 [00:10<00:14, 4.28it/s]\n 41%|████ | 41/100 [00:10<00:13, 4.28it/s]\n 42%|████▏ | 42/100 [00:10<00:13, 4.29it/s]\n 43%|████▎ | 43/100 [00:10<00:13, 4.28it/s]\n 44%|████▍ | 44/100 [00:11<00:13, 4.29it/s]\n 45%|████▌ | 45/100 [00:11<00:12, 4.27it/s]\n 46%|████▌ | 46/100 [00:11<00:12, 4.27it/s]\n 47%|████▋ | 47/100 [00:11<00:12, 4.27it/s]\n 48%|████▊ | 48/100 [00:12<00:12, 4.29it/s]\n 49%|████▉ | 49/100 [00:12<00:11, 4.28it/s]\n 50%|█████ | 50/100 [00:12<00:11, 4.27it/s]\n 51%|█████ | 51/100 [00:12<00:11, 4.28it/s]\n 52%|█████▏ | 52/100 [00:12<00:11, 4.28it/s]\n 53%|█████▎ | 53/100 [00:13<00:10, 4.29it/s]\n 54%|█████▍ | 54/100 [00:13<00:10, 4.28it/s]\n 55%|█████▌ | 55/100 [00:13<00:10, 4.29it/s]\n 56%|█████▌ | 56/100 [00:13<00:10, 4.29it/s]\n 57%|█████▋ | 57/100 [00:14<00:10, 4.29it/s]\n 58%|█████▊ | 58/100 [00:14<00:09, 4.30it/s]\n 59%|█████▉ | 59/100 [00:14<00:09, 4.29it/s]\n 60%|██████ | 60/100 [00:14<00:09, 4.28it/s]\n 61%|██████ | 61/100 [00:15<00:09, 4.28it/s]\n 62%|██████▏ | 62/100 [00:15<00:08, 4.28it/s]\n 63%|██████▎ | 63/100 [00:15<00:08, 4.28it/s]\n 64%|██████▍ | 64/100 [00:15<00:08, 4.29it/s]\n 65%|██████▌ | 65/100 [00:15<00:08, 4.29it/s]\n 66%|██████▌ | 66/100 [00:16<00:07, 4.27it/s]\n 67%|██████▋ | 67/100 [00:16<00:07, 4.27it/s]\n 68%|██████▊ | 68/100 [00:16<00:07, 4.26it/s]\n 69%|██████▉ | 69/100 [00:16<00:07, 4.27it/s]\n 70%|███████ | 70/100 [00:17<00:07, 4.27it/s]\n 71%|███████ | 71/100 [00:17<00:06, 4.26it/s]\n 72%|███████▏ | 72/100 [00:17<00:06, 4.26it/s]\n 73%|███████▎ | 73/100 [00:17<00:06, 4.26it/s]\n 74%|███████▍ | 74/100 [00:18<00:06, 4.25it/s]\n 75%|███████▌ | 75/100 [00:18<00:05, 4.25it/s]\n 76%|███████▌ | 76/100 [00:18<00:05, 4.26it/s]\n 77%|███████▋ | 77/100 [00:18<00:05, 4.25it/s]\n 78%|███████▊ | 78/100 [00:19<00:05, 4.26it/s]\n 79%|███████▉ | 79/100 [00:19<00:04, 4.25it/s]\n 80%|████████ | 80/100 [00:19<00:04, 4.24it/s]\n 81%|████████ | 81/100 [00:19<00:04, 4.25it/s]\n 82%|████████▏ | 82/100 [00:19<00:04, 4.25it/s]\n 83%|████████▎ | 83/100 [00:20<00:04, 4.25it/s]\n 84%|████████▍ | 84/100 [00:20<00:03, 4.24it/s]\n 85%|████████▌ | 85/100 [00:20<00:03, 4.24it/s]\n 86%|████████▌ | 86/100 [00:20<00:03, 4.24it/s]\n 87%|████████▋ | 87/100 [00:21<00:03, 4.23it/s]\n 88%|████████▊ | 88/100 [00:21<00:02, 4.23it/s]\n 89%|████████▉ | 89/100 [00:21<00:02, 4.22it/s]\n 90%|█████████ | 90/100 [00:21<00:02, 4.22it/s]\n 91%|█████████ | 91/100 [00:22<00:02, 4.23it/s]\n 92%|█████████▏| 92/100 [00:22<00:01, 4.22it/s]\n 93%|█████████▎| 93/100 [00:22<00:01, 4.22it/s]\n 94%|█████████▍| 94/100 [00:22<00:01, 4.23it/s]\n 95%|█████████▌| 95/100 [00:23<00:01, 4.23it/s]\n 96%|█████████▌| 96/100 [00:23<00:00, 4.23it/s]\n 97%|█████████▋| 97/100 [00:23<00:00, 4.22it/s]\n 98%|█████████▊| 98/100 [00:23<00:00, 4.22it/s]\n 99%|█████████▉| 99/100 [00:24<00:00, 4.23it/s]\n100%|██████████| 100/100 [00:24<00:00, 4.22it/s]\n100%|██████████| 100/100 [00:24<00:00, 4.12it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:16, 1.58it/s]\n 7%|▋ | 2/27 [00:01<00:15, 1.57it/s]\n 11%|█ | 3/27 [00:01<00:15, 1.59it/s]\n 15%|█▍ | 4/27 [00:02<00:14, 1.59it/s]\n 19%|█▊ | 5/27 [00:03<00:13, 1.59it/s]\n 22%|██▏ | 6/27 [00:03<00:13, 1.59it/s]\n 26%|██▌ | 7/27 [00:04<00:12, 1.59it/s]\n 30%|██▉ | 8/27 [00:05<00:11, 1.59it/s]\n 33%|███▎ | 9/27 [00:05<00:11, 1.59it/s]\n 37%|███▋ | 10/27 [00:06<00:10, 1.59it/s]\n 41%|████ | 11/27 [00:06<00:10, 1.59it/s]\n 44%|████▍ | 12/27 [00:07<00:09, 1.59it/s]\n 48%|████▊ | 13/27 [00:08<00:08, 1.59it/s]\n 52%|█████▏ | 14/27 [00:08<00:08, 1.59it/s]\n 56%|█████▌ | 15/27 [00:09<00:07, 1.59it/s]\n 59%|█████▉ | 16/27 [00:10<00:06, 1.59it/s]\n 63%|██████▎ | 17/27 [00:10<00:06, 1.59it/s]\n 67%|██████▋ | 18/27 [00:11<00:05, 1.59it/s]\n 70%|███████ | 19/27 [00:11<00:05, 1.58it/s]\n 74%|███████▍ | 20/27 [00:12<00:04, 1.58it/s]\n 78%|███████▊ | 21/27 [00:13<00:03, 1.58it/s]\n 81%|████████▏ | 22/27 [00:13<00:03, 1.58it/s]\n 85%|████████▌ | 23/27 [00:14<00:02, 1.58it/s]\n 89%|████████▉ | 24/27 [00:15<00:01, 1.58it/s]\n 93%|█████████▎| 25/27 [00:15<00:01, 1.57it/s]\n 96%|█████████▋| 26/27 [00:16<00:00, 1.57it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.57it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.58it/s]", "metrics": { "predict_time": 48.563797, "total_time": 48.737132 }, "output": [ { "file": "https://replicate.delivery/mgxm/0ac95b3e-95bd-4169-8935-749f66279345/upsample_predictions.png" } ], "started_at": "2022-02-02T01:36:26.840437Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zvkdabyms5cwzeem2ady542cda", "cancel": "https://api.replicate.com/v1/predictions/zvkdabyms5cwzeem2ady542cda/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:01<01:51, 1.13s/it] 2%|▏ | 2/100 [00:01<00:58, 1.67it/s] 3%|▎ | 3/100 [00:01<00:41, 2.33it/s] 4%|▍ | 4/100 [00:01<00:33, 2.85it/s] 5%|▌ | 5/100 [00:02<00:29, 3.26it/s] 6%|▌ | 6/100 [00:02<00:26, 3.56it/s] 7%|▋ | 7/100 [00:02<00:24, 3.79it/s] 8%|▊ | 8/100 [00:02<00:23, 3.95it/s] 9%|▉ | 9/100 [00:02<00:22, 4.06it/s] 10%|█ | 10/100 [00:03<00:21, 4.14it/s] 11%|█ | 11/100 [00:03<00:21, 4.20it/s] 12%|█▏ | 12/100 [00:03<00:20, 4.25it/s] 13%|█▎ | 13/100 [00:03<00:20, 4.28it/s] 14%|█▍ | 14/100 [00:04<00:20, 4.29it/s] 15%|█▌ | 15/100 [00:04<00:19, 4.29it/s] 16%|█▌ | 16/100 [00:04<00:19, 4.30it/s] 17%|█▋ | 17/100 [00:04<00:19, 4.31it/s] 18%|█▊ | 18/100 [00:05<00:18, 4.32it/s] 19%|█▉ | 19/100 [00:05<00:18, 4.31it/s] 20%|██ | 20/100 [00:05<00:18, 4.30it/s] 21%|██ | 21/100 [00:05<00:18, 4.30it/s] 22%|██▏ | 22/100 [00:05<00:18, 4.30it/s] 23%|██▎ | 23/100 [00:06<00:17, 4.30it/s] 24%|██▍ | 24/100 [00:06<00:17, 4.30it/s] 25%|██▌ | 25/100 [00:06<00:17, 4.30it/s] 26%|██▌ | 26/100 [00:06<00:17, 4.30it/s] 27%|██▋ | 27/100 [00:07<00:16, 4.30it/s] 28%|██▊ | 28/100 [00:07<00:16, 4.31it/s] 29%|██▉ | 29/100 [00:07<00:16, 4.31it/s] 30%|███ | 30/100 [00:07<00:16, 4.30it/s] 31%|███ | 31/100 [00:08<00:16, 4.30it/s] 32%|███▏ | 32/100 [00:08<00:15, 4.30it/s] 33%|███▎ | 33/100 [00:08<00:15, 4.30it/s] 34%|███▍ | 34/100 [00:08<00:15, 4.30it/s] 35%|███▌ | 35/100 [00:08<00:15, 4.29it/s] 36%|███▌ | 36/100 [00:09<00:14, 4.30it/s] 37%|███▋ | 37/100 [00:09<00:14, 4.30it/s] 38%|███▊ | 38/100 [00:09<00:14, 4.30it/s] 39%|███▉ | 39/100 [00:09<00:14, 4.30it/s]Caught SIGTERM, exiting... 40%|████ | 40/100 [00:10<00:14, 4.28it/s] 41%|████ | 41/100 [00:10<00:13, 4.28it/s] 42%|████▏ | 42/100 [00:10<00:13, 4.29it/s] 43%|████▎ | 43/100 [00:10<00:13, 4.28it/s] 44%|████▍ | 44/100 [00:11<00:13, 4.29it/s] 45%|████▌ | 45/100 [00:11<00:12, 4.27it/s] 46%|████▌ | 46/100 [00:11<00:12, 4.27it/s] 47%|████▋ | 47/100 [00:11<00:12, 4.27it/s] 48%|████▊ | 48/100 [00:12<00:12, 4.29it/s] 49%|████▉ | 49/100 [00:12<00:11, 4.28it/s] 50%|█████ | 50/100 [00:12<00:11, 4.27it/s] 51%|█████ | 51/100 [00:12<00:11, 4.28it/s] 52%|█████▏ | 52/100 [00:12<00:11, 4.28it/s] 53%|█████▎ | 53/100 [00:13<00:10, 4.29it/s] 54%|█████▍ | 54/100 [00:13<00:10, 4.28it/s] 55%|█████▌ | 55/100 [00:13<00:10, 4.29it/s] 56%|█████▌ | 56/100 [00:13<00:10, 4.29it/s] 57%|█████▋ | 57/100 [00:14<00:10, 4.29it/s] 58%|█████▊ | 58/100 [00:14<00:09, 4.30it/s] 59%|█████▉ | 59/100 [00:14<00:09, 4.29it/s] 60%|██████ | 60/100 [00:14<00:09, 4.28it/s] 61%|██████ | 61/100 [00:15<00:09, 4.28it/s] 62%|██████▏ | 62/100 [00:15<00:08, 4.28it/s] 63%|██████▎ | 63/100 [00:15<00:08, 4.28it/s] 64%|██████▍ | 64/100 [00:15<00:08, 4.29it/s] 65%|██████▌ | 65/100 [00:15<00:08, 4.29it/s] 66%|██████▌ | 66/100 [00:16<00:07, 4.27it/s] 67%|██████▋ | 67/100 [00:16<00:07, 4.27it/s] 68%|██████▊ | 68/100 [00:16<00:07, 4.26it/s] 69%|██████▉ | 69/100 [00:16<00:07, 4.27it/s] 70%|███████ | 70/100 [00:17<00:07, 4.27it/s] 71%|███████ | 71/100 [00:17<00:06, 4.26it/s] 72%|███████▏ | 72/100 [00:17<00:06, 4.26it/s] 73%|███████▎ | 73/100 [00:17<00:06, 4.26it/s] 74%|███████▍ | 74/100 [00:18<00:06, 4.25it/s] 75%|███████▌ | 75/100 [00:18<00:05, 4.25it/s] 76%|███████▌ | 76/100 [00:18<00:05, 4.26it/s] 77%|███████▋ | 77/100 [00:18<00:05, 4.25it/s] 78%|███████▊ | 78/100 [00:19<00:05, 4.26it/s] 79%|███████▉ | 79/100 [00:19<00:04, 4.25it/s] 80%|████████ | 80/100 [00:19<00:04, 4.24it/s] 81%|████████ | 81/100 [00:19<00:04, 4.25it/s] 82%|████████▏ | 82/100 [00:19<00:04, 4.25it/s] 83%|████████▎ | 83/100 [00:20<00:04, 4.25it/s] 84%|████████▍ | 84/100 [00:20<00:03, 4.24it/s] 85%|████████▌ | 85/100 [00:20<00:03, 4.24it/s] 86%|████████▌ | 86/100 [00:20<00:03, 4.24it/s] 87%|████████▋ | 87/100 [00:21<00:03, 4.23it/s] 88%|████████▊ | 88/100 [00:21<00:02, 4.23it/s] 89%|████████▉ | 89/100 [00:21<00:02, 4.22it/s] 90%|█████████ | 90/100 [00:21<00:02, 4.22it/s] 91%|█████████ | 91/100 [00:22<00:02, 4.23it/s] 92%|█████████▏| 92/100 [00:22<00:01, 4.22it/s] 93%|█████████▎| 93/100 [00:22<00:01, 4.22it/s] 94%|█████████▍| 94/100 [00:22<00:01, 4.23it/s] 95%|█████████▌| 95/100 [00:23<00:01, 4.23it/s] 96%|█████████▌| 96/100 [00:23<00:00, 4.23it/s] 97%|█████████▋| 97/100 [00:23<00:00, 4.22it/s] 98%|█████████▊| 98/100 [00:23<00:00, 4.22it/s] 99%|█████████▉| 99/100 [00:24<00:00, 4.23it/s] 100%|██████████| 100/100 [00:24<00:00, 4.22it/s] 100%|██████████| 100/100 [00:24<00:00, 4.12it/s] 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:16, 1.58it/s] 7%|▋ | 2/27 [00:01<00:15, 1.57it/s] 11%|█ | 3/27 [00:01<00:15, 1.59it/s] 15%|█▍ | 4/27 [00:02<00:14, 1.59it/s] 19%|█▊ | 5/27 [00:03<00:13, 1.59it/s] 22%|██▏ | 6/27 [00:03<00:13, 1.59it/s] 26%|██▌ | 7/27 [00:04<00:12, 1.59it/s] 30%|██▉ | 8/27 [00:05<00:11, 1.59it/s] 33%|███▎ | 9/27 [00:05<00:11, 1.59it/s] 37%|███▋ | 10/27 [00:06<00:10, 1.59it/s] 41%|████ | 11/27 [00:06<00:10, 1.59it/s] 44%|████▍ | 12/27 [00:07<00:09, 1.59it/s] 48%|████▊ | 13/27 [00:08<00:08, 1.59it/s] 52%|█████▏ | 14/27 [00:08<00:08, 1.59it/s] 56%|█████▌ | 15/27 [00:09<00:07, 1.59it/s] 59%|█████▉ | 16/27 [00:10<00:06, 1.59it/s] 63%|██████▎ | 17/27 [00:10<00:06, 1.59it/s] 67%|██████▋ | 18/27 [00:11<00:05, 1.59it/s] 70%|███████ | 19/27 [00:11<00:05, 1.58it/s] 74%|███████▍ | 20/27 [00:12<00:04, 1.58it/s] 78%|███████▊ | 21/27 [00:13<00:03, 1.58it/s] 81%|████████▏ | 22/27 [00:13<00:03, 1.58it/s] 85%|████████▌ | 23/27 [00:14<00:02, 1.58it/s] 89%|████████▉ | 24/27 [00:15<00:01, 1.58it/s] 93%|█████████▎| 25/27 [00:15<00:01, 1.57it/s] 96%|█████████▋| 26/27 [00:16<00:00, 1.57it/s] 100%|██████████| 27/27 [00:17<00:00, 1.57it/s] 100%|██████████| 27/27 [00:17<00:00, 1.58it/s]Prediction
afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0Input
- prompt
- a delorean from the 80's with a fire red coat of paint
- side_x
- 64
- side_y
- 64
- batch_size
- "1"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 150
{ "prompt": "a delorean from the 80's with a fire red coat of paint", "side_x": 64, "side_y": 64, "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", { input: { prompt: "a delorean from the 80's with a fire red coat of paint", side_x: 64, side_y: 64, batch_size: "1", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", input={ "prompt": "a delorean from the 80's with a fire red coat of paint", "side_x": 64, "side_y": 64, "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", "input": { "prompt": "a delorean from the 80\'s with a fire red coat of paint", "side_x": 64, "side_y": 64, "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T02:29:25.237178Z", "created_at": "2022-01-31T02:28:43.308689Z", "data_removed": false, "error": null, "id": "jahbzw6tkbeobgaltokwcznj7i", "input": { "prompt": "a delorean from the 80's with a fire red coat of paint", "side_x": 64, "side_y": 64, "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:24, 6.09it/s]\n 1%|▏ | 2/150 [00:00<00:27, 5.34it/s]\n 2%|▏ | 3/150 [00:00<00:27, 5.37it/s]\n 3%|▎ | 4/150 [00:00<00:27, 5.38it/s]\n 3%|▎ | 5/150 [00:00<00:27, 5.34it/s]\n 4%|▍ | 6/150 [00:01<00:26, 5.36it/s]\n 5%|▍ | 7/150 [00:01<00:26, 5.39it/s]\n 5%|▌ | 8/150 [00:01<00:26, 5.38it/s]\n 6%|▌ | 9/150 [00:01<00:26, 5.41it/s]\n 7%|▋ | 10/150 [00:01<00:26, 5.37it/s]\n 7%|▋ | 11/150 [00:02<00:26, 5.32it/s]\n 8%|▊ | 12/150 [00:02<00:25, 5.31it/s]\n 9%|▊ | 13/150 [00:02<00:25, 5.41it/s]\n 9%|▉ | 14/150 [00:02<00:25, 5.40it/s]\n 10%|█ | 15/150 [00:02<00:24, 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5.26it/s]\n 97%|█████████▋| 146/150 [00:27<00:00, 5.23it/s]\n 98%|█████████▊| 147/150 [00:27<00:00, 5.28it/s]\n 99%|█████████▊| 148/150 [00:27<00:00, 5.29it/s]\n 99%|█████████▉| 149/150 [00:28<00:00, 5.24it/s]\n100%|██████████| 150/150 [00:28<00:00, 5.26it/s]\n100%|██████████| 150/150 [00:28<00:00, 5.31it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:04, 5.67it/s]\n 7%|▋ | 2/27 [00:00<00:04, 5.68it/s]\n 11%|█ | 3/27 [00:00<00:04, 5.70it/s]\n 15%|█▍ | 4/27 [00:00<00:04, 5.69it/s]\n 19%|█▊ | 5/27 [00:00<00:03, 5.66it/s]\n 22%|██▏ | 6/27 [00:01<00:03, 5.62it/s]\n 26%|██▌ | 7/27 [00:01<00:03, 5.64it/s]\n 30%|██▉ | 8/27 [00:01<00:03, 5.64it/s]\n 33%|███▎ | 9/27 [00:01<00:03, 5.66it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 5.68it/s]\n 41%|████ | 11/27 [00:01<00:02, 5.67it/s]\n 44%|████▍ | 12/27 [00:02<00:02, 5.64it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 5.67it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 5.68it/s]\n 56%|█████▌ | 15/27 [00:02<00:02, 5.67it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 5.69it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 5.68it/s]\n 67%|██████▋ | 18/27 [00:03<00:01, 5.67it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.68it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 5.69it/s]\n 78%|███████▊ | 21/27 [00:03<00:01, 5.67it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 5.69it/s]\n 85%|████████▌ | 23/27 [00:04<00:00, 5.69it/s]\n 89%|████████▉ | 24/27 [00:04<00:00, 5.68it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.69it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.68it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.67it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.67it/s]", "metrics": { "predict_time": 41.712119, "total_time": 41.928489 }, "output": [ { "file": "https://replicate.delivery/mgxm/a25c436b-b470-47ba-a08e-b5045ad7c3ee/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/2bb6c6e5-36a5-4435-92df-8409e10885eb/upsample_predictions.png" } ], "started_at": "2022-01-31T02:28:43.525059Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jahbzw6tkbeobgaltokwcznj7i", "cancel": "https://api.replicate.com/v1/predictions/jahbzw6tkbeobgaltokwcznj7i/cancel" }, "version": "b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:24, 6.09it/s] 1%|▏ | 2/150 [00:00<00:27, 5.34it/s] 2%|▏ | 3/150 [00:00<00:27, 5.37it/s] 3%|▎ | 4/150 [00:00<00:27, 5.38it/s] 3%|▎ | 5/150 [00:00<00:27, 5.34it/s] 4%|▍ | 6/150 [00:01<00:26, 5.36it/s] 5%|▍ | 7/150 [00:01<00:26, 5.39it/s] 5%|▌ | 8/150 [00:01<00:26, 5.38it/s] 6%|▌ | 9/150 [00:01<00:26, 5.41it/s] 7%|▋ | 10/150 [00:01<00:26, 5.37it/s] 7%|▋ | 11/150 [00:02<00:26, 5.32it/s] 8%|▊ | 12/150 [00:02<00:25, 5.31it/s] 9%|▊ | 13/150 [00:02<00:25, 5.41it/s] 9%|▉ | 14/150 [00:02<00:25, 5.40it/s] 10%|█ | 15/150 [00:02<00:24, 5.43it/s] 11%|█ | 16/150 [00:02<00:24, 5.43it/s] 11%|█▏ | 17/150 [00:03<00:24, 5.39it/s] 12%|█▏ | 18/150 [00:03<00:24, 5.36it/s] 13%|█▎ | 19/150 [00:03<00:24, 5.38it/s] 13%|█▎ | 20/150 [00:03<00:24, 5.31it/s] 14%|█▍ | 21/150 [00:03<00:24, 5.33it/s] 15%|█▍ | 22/150 [00:04<00:23, 5.35it/s] 15%|█▌ | 23/150 [00:04<00:23, 5.38it/s] 16%|█▌ | 24/150 [00:04<00:23, 5.41it/s] 17%|█▋ | 25/150 [00:04<00:23, 5.37it/s] 17%|█▋ | 26/150 [00:04<00:23, 5.35it/s] 18%|█▊ | 27/150 [00:05<00:23, 5.31it/s] 19%|█▊ | 28/150 [00:05<00:23, 5.30it/s] 19%|█▉ | 29/150 [00:05<00:22, 5.28it/s] 20%|██ | 30/150 [00:05<00:22, 5.23it/s] 21%|██ | 31/150 [00:05<00:22, 5.22it/s] 21%|██▏ | 32/150 [00:05<00:22, 5.24it/s] 22%|██▏ | 33/150 [00:06<00:22, 5.28it/s] 23%|██▎ | 34/150 [00:06<00:21, 5.33it/s] 23%|██▎ | 35/150 [00:06<00:21, 5.36it/s] 24%|██▍ | 36/150 [00:06<00:21, 5.39it/s] 25%|██▍ | 37/150 [00:06<00:21, 5.28it/s] 25%|██▌ | 38/150 [00:07<00:21, 5.32it/s] 26%|██▌ | 39/150 [00:07<00:20, 5.33it/s] 27%|██▋ | 40/150 [00:07<00:20, 5.34it/s] 27%|██▋ | 41/150 [00:07<00:20, 5.35it/s] 28%|██▊ | 42/150 [00:07<00:20, 5.30it/s] 29%|██▊ | 43/150 [00:08<00:20, 5.33it/s] 29%|██▉ | 44/150 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5.68it/s] 100%|██████████| 27/27 [00:04<00:00, 5.67it/s] 100%|██████████| 27/27 [00:04<00:00, 5.67it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- a mirror positioned at the bottom of a tall mountain
- side_x
- "64"
- side_y
- "96"
- batch_size
- "1"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 150
{ "prompt": "a mirror positioned at the bottom of a tall mountain", "side_x": "64", "side_y": "96", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a mirror positioned at the bottom of a tall mountain", side_x: "64", side_y: "96", batch_size: "1", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a mirror positioned at the bottom of a tall mountain", "side_x": "64", "side_y": "96", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a mirror positioned at the bottom of a tall mountain", "side_x": "64", "side_y": "96", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T02:57:23.749859Z", "created_at": "2022-01-31T02:52:13.326164Z", "data_removed": false, "error": null, "id": "ididelp4y5ga7hhzrb5c6icwni", "input": { "prompt": "a mirror positioned at the bottom of a tall mountain", "side_x": "64", "side_y": "96", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:01<02:31, 1.01s/it]\n 1%|▏ | 2/150 [00:01<01:12, 2.03it/s]\n 2%|▏ | 3/150 [00:01<00:47, 3.12it/s]\n 3%|▎ | 4/150 [00:01<00:35, 4.17it/s]\n 3%|▎ | 5/150 [00:01<00:28, 5.13it/s]\n 4%|▍ | 6/150 [00:01<00:24, 5.94it/s]\n 5%|▍ | 7/150 [00:01<00:21, 6.62it/s]\n 5%|▌ | 8/150 [00:01<00:19, 7.15it/s]\n 6%|▌ | 9/150 [00:01<00:18, 7.55it/s]\n 7%|▋ | 10/150 [00:02<00:17, 7.82it/s]\n 7%|▋ | 11/150 [00:02<00:17, 8.03it/s]\n 8%|▊ | 12/150 [00:02<00:16, 8.20it/s]\n 9%|▊ | 13/150 [00:02<00:16, 8.33it/s]\n 9%|▉ | 14/150 [00:02<00:16, 8.40it/s]\n 10%|█ | 15/150 [00:02<00:15, 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8.49it/s]\n 97%|█████████▋| 146/150 [00:18<00:00, 8.50it/s]\n 98%|█████████▊| 147/150 [00:18<00:00, 8.50it/s]\n 99%|█████████▊| 148/150 [00:18<00:00, 8.51it/s]\n 99%|█████████▉| 149/150 [00:18<00:00, 8.46it/s]\n100%|██████████| 150/150 [00:18<00:00, 8.48it/s]\n100%|██████████| 150/150 [00:18<00:00, 8.11it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:06, 3.98it/s]\n 7%|▋ | 2/27 [00:00<00:06, 4.16it/s]\n 11%|█ | 3/27 [00:00<00:05, 4.22it/s]\n 15%|█▍ | 4/27 [00:00<00:05, 4.18it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 4.21it/s]\n 22%|██▏ | 6/27 [00:01<00:04, 4.23it/s]\n 26%|██▌ | 7/27 [00:01<00:04, 4.25it/s]\n 30%|██▉ | 8/27 [00:01<00:04, 4.23it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 4.23it/s]\n 37%|███▋ | 10/27 [00:02<00:04, 4.23it/s]\n 41%|████ | 11/27 [00:02<00:03, 4.24it/s]\n 44%|████▍ | 12/27 [00:02<00:03, 4.24it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 4.24it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 4.24it/s]\n 56%|█████▌ | 15/27 [00:03<00:02, 4.24it/s]\n 59%|█████▉ | 16/27 [00:03<00:02, 4.24it/s]\n 63%|██████▎ | 17/27 [00:04<00:02, 4.24it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 4.23it/s]\n 70%|███████ | 19/27 [00:04<00:01, 4.23it/s]\n 74%|███████▍ | 20/27 [00:04<00:01, 4.23it/s]\n 78%|███████▊ | 21/27 [00:04<00:01, 4.23it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 4.22it/s]\n 85%|████████▌ | 23/27 [00:05<00:00, 4.23it/s]\n 89%|████████▉ | 24/27 [00:05<00:00, 4.23it/s]\n 93%|█████████▎| 25/27 [00:05<00:00, 4.24it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 4.24it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.23it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.23it/s]", "metrics": { "predict_time": 36.684759, "total_time": 310.423695 }, "output": [ { "file": "https://replicate.delivery/mgxm/ca26da61-3f46-416d-bbde-70aaa7353907/upsample_predictions.png" } ], "started_at": "2022-01-31T02:56:47.065100Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ididelp4y5ga7hhzrb5c6icwni", "cancel": "https://api.replicate.com/v1/predictions/ididelp4y5ga7hhzrb5c6icwni/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:01<02:31, 1.01s/it] 1%|▏ | 2/150 [00:01<01:12, 2.03it/s] 2%|▏ | 3/150 [00:01<00:47, 3.12it/s] 3%|▎ | 4/150 [00:01<00:35, 4.17it/s] 3%|▎ | 5/150 [00:01<00:28, 5.13it/s] 4%|▍ | 6/150 [00:01<00:24, 5.94it/s] 5%|▍ | 7/150 [00:01<00:21, 6.62it/s] 5%|▌ | 8/150 [00:01<00:19, 7.15it/s] 6%|▌ | 9/150 [00:01<00:18, 7.55it/s] 7%|▋ | 10/150 [00:02<00:17, 7.82it/s] 7%|▋ | 11/150 [00:02<00:17, 8.03it/s] 8%|▊ | 12/150 [00:02<00:16, 8.20it/s] 9%|▊ | 13/150 [00:02<00:16, 8.33it/s] 9%|▉ | 14/150 [00:02<00:16, 8.40it/s] 10%|█ | 15/150 [00:02<00:15, 8.47it/s] 11%|█ | 16/150 [00:02<00:15, 8.48it/s] 11%|█▏ | 17/150 [00:02<00:15, 8.49it/s] 12%|█▏ | 18/150 [00:03<00:15, 8.50it/s] 13%|█▎ | 19/150 [00:03<00:15, 8.55it/s] 13%|█▎ | 20/150 [00:03<00:15, 8.58it/s] 14%|█▍ | 21/150 [00:03<00:15, 8.59it/s] 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4.23it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDonx5hbcdozck7fuwekjp5dtdhyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- a pirate cat shooting laser guns while riding a unicorn that is breathing fire
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "0.997"
- guidance_scale
- 8
- timestep_respacing
- 100
{ "seed": 0, "prompt": "a pirate cat shooting laser guns while riding a unicorn that is breathing fire", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.997", "guidance_scale": 8, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "a pirate cat shooting laser guns while riding a unicorn that is breathing fire", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "0.997", guidance_scale: 8, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "a pirate cat shooting laser guns while riding a unicorn that is breathing fire", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.997", "guidance_scale": 8, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "a pirate cat shooting laser guns while riding a unicorn that is breathing fire", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.997", "guidance_scale": 8, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T03:52:12.543842Z", "created_at": "2022-01-31T03:51:03.957767Z", "data_removed": false, "error": null, "id": "onx5hbcdozck7fuwekjp5dtdhy", "input": { "seed": 0, "prompt": "a pirate cat shooting laser guns while riding a unicorn that is breathing fire", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "0.997", "guidance_scale": 8, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:25, 3.83it/s]\n 2%|▏ | 2/100 [00:00<00:35, 2.75it/s]\n 3%|▎ | 3/100 [00:01<00:38, 2.51it/s]\n 4%|▍ | 4/100 [00:01<00:40, 2.39it/s]\n 5%|▌ | 5/100 [00:02<00:40, 2.35it/s]\n 6%|▌ | 6/100 [00:02<00:40, 2.30it/s]\n 7%|▋ | 7/100 [00:02<00:40, 2.28it/s]\n 8%|▊ | 8/100 [00:03<00:40, 2.27it/s]\n 9%|▉ | 9/100 [00:03<00:40, 2.26it/s]\n 10%|█ | 10/100 [00:04<00:39, 2.25it/s]\n 11%|█ | 11/100 [00:04<00:39, 2.25it/s]\n 12%|█▏ | 12/100 [00:05<00:39, 2.25it/s]\n 13%|█▎ | 13/100 [00:05<00:38, 2.24it/s]\n 14%|█▍ | 14/100 [00:06<00:38, 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[00:11<00:02, 2.00it/s]\n 85%|████████▌ | 23/27 [00:11<00:02, 2.00it/s]\n 89%|████████▉ | 24/27 [00:12<00:01, 2.00it/s]\n 93%|█████████▎| 25/27 [00:12<00:00, 2.00it/s]\n 96%|█████████▋| 26/27 [00:13<00:00, 2.00it/s]\n100%|██████████| 27/27 [00:13<00:00, 2.01it/s]\n100%|██████████| 27/27 [00:13<00:00, 1.99it/s]", "metrics": { "predict_time": 68.386553, "total_time": 68.586075 }, "output": [ { "file": "https://replicate.delivery/mgxm/a5df6ec9-caeb-4558-8e5f-356248a7af0b/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/584cef83-7520-4053-bbc1-76958b4e7b69/upsample_predictions.png" } ], "started_at": "2022-01-31T03:51:04.157289Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/onx5hbcdozck7fuwekjp5dtdhy", "cancel": "https://api.replicate.com/v1/predictions/onx5hbcdozck7fuwekjp5dtdhy/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:25, 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[00:08<00:05, 2.00it/s] 63%|██████▎ | 17/27 [00:08<00:05, 1.99it/s] 67%|██████▋ | 18/27 [00:09<00:04, 1.99it/s] 70%|███████ | 19/27 [00:09<00:04, 2.00it/s] 74%|███████▍ | 20/27 [00:10<00:03, 2.00it/s] 78%|███████▊ | 21/27 [00:10<00:03, 2.00it/s] 81%|████████▏ | 22/27 [00:11<00:02, 2.00it/s] 85%|████████▌ | 23/27 [00:11<00:02, 2.00it/s] 89%|████████▉ | 24/27 [00:12<00:01, 2.00it/s] 93%|█████████▎| 25/27 [00:12<00:00, 2.00it/s] 96%|█████████▋| 26/27 [00:13<00:00, 2.00it/s] 100%|██████████| 27/27 [00:13<00:00, 2.01it/s] 100%|██████████| 27/27 [00:13<00:00, 1.99it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- the river is long and winding, ufo invasion
- side_x
- "48"
- side_y
- "112"
- batch_size
- "4"
- upsample_temp
- "1"
- guidance_scale
- 9
- timestep_respacing
- 250
{ "prompt": "the river is long and winding, ufo invasion", "side_x": "48", "side_y": "112", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 9, "timestep_respacing": "250" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "the river is long and winding, ufo invasion", side_x: "48", side_y: "112", batch_size: "4", upsample_temp: "1", guidance_scale: 9, timestep_respacing: "250" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "the river is long and winding, ufo invasion", "side_x": "48", "side_y": "112", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 9, "timestep_respacing": "250" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "the river is long and winding, ufo invasion", "side_x": "48", "side_y": "112", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 9, "timestep_respacing": "250" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T05:59:09.824193Z", "created_at": "2022-01-31T05:57:03.442721Z", "data_removed": false, "error": null, "id": "frouipnmlzbdtdftbcxe3zhw4y", "input": { "prompt": "the river is long and winding, ufo invasion", "side_x": "48", "side_y": "112", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 9, "timestep_respacing": "250" }, "logs": "\n 0%| | 0/250 [00:00<?, ?it/s]\n 0%| | 1/250 [00:00<01:39, 2.51it/s]\n 1%| | 2/250 [00:00<01:29, 2.77it/s]\n 1%| | 3/250 [00:01<01:26, 2.86it/s]\n 2%|▏ | 4/250 [00:01<01:25, 2.88it/s]\n 2%|▏ | 5/250 [00:01<01:24, 2.91it/s]\n 2%|▏ | 6/250 [00:02<01:23, 2.93it/s]\n 3%|▎ | 7/250 [00:02<01:22, 2.94it/s]\n 3%|▎ | 8/250 [00:02<01:22, 2.94it/s]\n 4%|▎ | 9/250 [00:03<01:21, 2.95it/s]\n 4%|▍ | 10/250 [00:03<01:21, 2.95it/s]\n 4%|▍ | 11/250 [00:03<01:21, 2.94it/s]\n 5%|▍ | 12/250 [00:04<01:20, 2.94it/s]\n 5%|▌ | 13/250 [00:04<01:20, 2.94it/s]\n 6%|▌ | 14/250 [00:04<01:20, 2.94it/s]\n 6%|▌ | 15/250 [00:05<01:19, 2.94it/s]\n 6%|▋ | 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[00:01<00:28, 1.10s/it]\n 7%|▋ | 2/27 [00:02<00:27, 1.10s/it]\n 11%|█ | 3/27 [00:03<00:26, 1.10s/it]\n 15%|█▍ | 4/27 [00:04<00:25, 1.10s/it]\n 19%|█▊ | 5/27 [00:05<00:24, 1.10s/it]\n 22%|██▏ | 6/27 [00:06<00:23, 1.10s/it]\n 26%|██▌ | 7/27 [00:07<00:21, 1.10s/it]\n 30%|██▉ | 8/27 [00:08<00:20, 1.10s/it]\n 33%|███▎ | 9/27 [00:09<00:19, 1.10s/it]\n 37%|███▋ | 10/27 [00:10<00:18, 1.10s/it]\n 41%|████ | 11/27 [00:12<00:17, 1.10s/it]\n 44%|████▍ | 12/27 [00:13<00:16, 1.10s/it]\n 48%|████▊ | 13/27 [00:14<00:15, 1.10s/it]\n 52%|█████▏ | 14/27 [00:15<00:14, 1.10s/it]\n 56%|█████▌ | 15/27 [00:16<00:13, 1.10s/it]\n 59%|█████▉ | 16/27 [00:17<00:12, 1.10s/it]\n 63%|██████▎ | 17/27 [00:18<00:11, 1.10s/it]\n 67%|██████▋ | 18/27 [00:19<00:09, 1.10s/it]\n 70%|███████ | 19/27 [00:20<00:08, 1.10s/it]\n 74%|███████▍ | 20/27 [00:21<00:07, 1.10s/it]\n 78%|███████▊ | 21/27 [00:23<00:06, 1.10s/it]\n 81%|████████▏ | 22/27 [00:24<00:05, 1.10s/it]\n 85%|████████▌ | 23/27 [00:25<00:04, 1.11s/it]\n 89%|████████▉ | 24/27 [00:26<00:03, 1.11s/it]\n 93%|█████████▎| 25/27 [00:27<00:02, 1.11s/it]\n 96%|█████████▋| 26/27 [00:28<00:01, 1.11s/it]\n100%|██████████| 27/27 [00:29<00:00, 1.11s/it]\n100%|██████████| 27/27 [00:29<00:00, 1.10s/it]", "metrics": { "predict_time": 126.174431, "total_time": 126.381472 }, "output": [ { "file": "https://replicate.delivery/mgxm/eabf648d-99e1-46c5-8f3c-68ecc93a79de/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/4ee6f63d-2537-49c4-8fec-ec24420f6e41/upsample_predictions.png" } ], "started_at": "2022-01-31T05:57:03.649762Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/frouipnmlzbdtdftbcxe3zhw4y", "cancel": "https://api.replicate.com/v1/predictions/frouipnmlzbdtdftbcxe3zhw4y/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/250 [00:00<?, ?it/s] 0%| | 1/250 [00:00<01:39, 2.51it/s] 1%| | 2/250 [00:00<01:29, 2.77it/s] 1%| | 3/250 [00:01<01:26, 2.86it/s] 2%|▏ 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[00:02<00:27, 1.10s/it] 11%|█ | 3/27 [00:03<00:26, 1.10s/it] 15%|█▍ | 4/27 [00:04<00:25, 1.10s/it] 19%|█▊ | 5/27 [00:05<00:24, 1.10s/it] 22%|██▏ | 6/27 [00:06<00:23, 1.10s/it] 26%|██▌ | 7/27 [00:07<00:21, 1.10s/it] 30%|██▉ | 8/27 [00:08<00:20, 1.10s/it] 33%|███▎ | 9/27 [00:09<00:19, 1.10s/it] 37%|███▋ | 10/27 [00:10<00:18, 1.10s/it] 41%|████ | 11/27 [00:12<00:17, 1.10s/it] 44%|████▍ | 12/27 [00:13<00:16, 1.10s/it] 48%|████▊ | 13/27 [00:14<00:15, 1.10s/it] 52%|█████▏ | 14/27 [00:15<00:14, 1.10s/it] 56%|█████▌ | 15/27 [00:16<00:13, 1.10s/it] 59%|█████▉ | 16/27 [00:17<00:12, 1.10s/it] 63%|██████▎ | 17/27 [00:18<00:11, 1.10s/it] 67%|██████▋ | 18/27 [00:19<00:09, 1.10s/it] 70%|███████ | 19/27 [00:20<00:08, 1.10s/it] 74%|███████▍ | 20/27 [00:21<00:07, 1.10s/it] 78%|███████▊ | 21/27 [00:23<00:06, 1.10s/it] 81%|████████▏ | 22/27 [00:24<00:05, 1.10s/it] 85%|████████▌ | 23/27 [00:25<00:04, 1.11s/it] 89%|████████▉ | 24/27 [00:26<00:03, 1.11s/it] 93%|█████████▎| 25/27 [00:27<00:02, 1.11s/it] 96%|█████████▋| 26/27 [00:28<00:01, 1.11s/it] 100%|██████████| 27/27 [00:29<00:00, 1.11s/it] 100%|██████████| 27/27 [00:29<00:00, 1.10s/it]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDml4zm6slubcs7gsc7ppusky5iaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- This is not just a car. This is a lifestyle. This is an Eddie Bauer edition. Tortoise-shell interior, super-charged twin cam, European luxury sports utility vehicle, my friend. It's a classic, okay?
- side_x
- "64"
- side_y
- "64"
- batch_size
- "4"
- upsample_temp
- "1"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "seed": 0, "prompt": "This is not just a car. This is a lifestyle. This is an Eddie Bauer edition. Tortoise-shell interior, super-charged twin cam, European luxury sports utility vehicle, my friend. It's a classic, okay?", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "This is not just a car. This is a lifestyle. This is an Eddie Bauer edition. Tortoise-shell interior, super-charged twin cam, European luxury sports utility vehicle, my friend. It's a classic, okay?", side_x: "64", side_y: "64", batch_size: "4", upsample_temp: "1", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "This is not just a car. This is a lifestyle. This is an Eddie Bauer edition. Tortoise-shell interior, super-charged twin cam, European luxury sports utility vehicle, my friend. It's a classic, okay?", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "This is not just a car. This is a lifestyle. This is an Eddie Bauer edition. Tortoise-shell interior, super-charged twin cam, European luxury sports utility vehicle, my friend. It\'s a classic, okay?", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T07:16:10.603708Z", "created_at": "2022-01-31T07:14:44.313574Z", "data_removed": false, "error": null, "id": "ml4zm6slubcs7gsc7ppusky5ia", "input": { "seed": 0, "prompt": "This is not just a car. This is a lifestyle. This is an Eddie Bauer edition. Tortoise-shell interior, super-charged twin cam, European luxury sports utility vehicle, my friend. It's a classic, okay?", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:31, 3.13it/s]\n 2%|▏ | 2/100 [00:00<00:45, 2.15it/s]\n 3%|▎ | 3/100 [00:01<00:50, 1.91it/s]\n 4%|▍ | 4/100 [00:02<00:52, 1.83it/s]\n 5%|▌ | 5/100 [00:02<00:53, 1.78it/s]\n 6%|▌ | 6/100 [00:03<00:53, 1.75it/s]\n 7%|▋ | 7/100 [00:03<00:53, 1.74it/s]\n 8%|▊ | 8/100 [00:04<00:53, 1.72it/s]\n 9%|▉ | 9/100 [00:05<00:53, 1.72it/s]\n 10%|█ | 10/100 [00:05<00:52, 1.71it/s]\n 11%|█ | 11/100 [00:06<00:52, 1.70it/s]\n 12%|█▏ | 12/100 [00:06<00:51, 1.70it/s]\n 13%|█▎ | 13/100 [00:07<00:51, 1.69it/s]\n 14%|█▍ | 14/100 [00:07<00:50, 1.69it/s]\n 15%|█▌ | 15/100 [00:08<00:50, 1.69it/s]\n 16%|█▌ | 16/100 [00:09<00:49, 1.68it/s]\n 17%|█▋ | 17/100 [00:09<00:49, 1.68it/s]\n 18%|█▊ | 18/100 [00:10<00:48, 1.68it/s]\n 19%|█▉ | 19/100 [00:10<00:48, 1.68it/s]\n 20%|██ | 20/100 [00:11<00:47, 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1.52it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.52it/s]", "metrics": { "predict_time": 86.05856, "total_time": 86.290134 }, "output": [ { "file": "https://replicate.delivery/mgxm/346120aa-4855-4280-b5dd-639ebcc443e1/upsample_predictions.png" } ], "started_at": "2022-01-31T07:14:44.545148Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ml4zm6slubcs7gsc7ppusky5ia", "cancel": "https://api.replicate.com/v1/predictions/ml4zm6slubcs7gsc7ppusky5ia/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:31, 3.13it/s] 2%|▏ | 2/100 [00:00<00:45, 2.15it/s] 3%|▎ | 3/100 [00:01<00:50, 1.91it/s] 4%|▍ | 4/100 [00:02<00:52, 1.83it/s] 5%|▌ | 5/100 [00:02<00:53, 1.78it/s] 6%|▌ | 6/100 [00:03<00:53, 1.75it/s] 7%|▋ | 7/100 [00:03<00:53, 1.74it/s] 8%|▊ | 8/100 [00:04<00:53, 1.72it/s] 9%|▉ | 9/100 [00:05<00:53, 1.72it/s] 10%|█ | 10/100 [00:05<00:52, 1.71it/s] 11%|█ | 11/100 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[00:15<00:01, 1.51it/s] 93%|█████████▎| 25/27 [00:16<00:01, 1.51it/s] 96%|█████████▋| 26/27 [00:17<00:00, 1.51it/s] 100%|██████████| 27/27 [00:17<00:00, 1.52it/s] 100%|██████████| 27/27 [00:17<00:00, 1.52it/s]Prediction
afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0IDyubhsvs7qvd7bjwkmeunkmvs3iStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- a t-shirt with a meme in it
- side_x
- "64"
- side_y
- 64
- batch_size
- "1"
- upsample_temp
- "0.997"
- guidance_scale
- 4
- timestep_respacing
- 50
{ "prompt": "a t-shirt with a meme in it", "side_x": "64", "side_y": 64, "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "50" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", { input: { prompt: "a t-shirt with a meme in it", side_x: "64", side_y: 64, batch_size: "1", upsample_temp: "0.997", guidance_scale: 4, timestep_respacing: "50" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", input={ "prompt": "a t-shirt with a meme in it", "side_x": "64", "side_y": 64, "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "50" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0", "input": { "prompt": "a t-shirt with a meme in it", "side_x": "64", "side_y": 64, "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "50" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T01:57:32.810837Z", "created_at": "2022-01-31T01:57:09.683357Z", "data_removed": false, "error": null, "id": "yubhsvs7qvd7bjwkmeunkmvs3i", "input": { "prompt": "a t-shirt with a meme in it", "side_x": "64", "side_y": 64, "batch_size": "1", "upsample_temp": "0.997", "guidance_scale": 4, "timestep_respacing": "50" }, "logs": "\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:07, 6.27it/s]\n 4%|▍ | 2/50 [00:00<00:08, 5.66it/s]\n 6%|▌ | 3/50 [00:00<00:08, 5.45it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.41it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.45it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.43it/s]\n 14%|█▍ | 7/50 [00:01<00:07, 5.42it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.34it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.32it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.33it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.39it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 5.42it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.41it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.40it/s]\n 30%|███ | 15/50 [00:02<00:06, 5.45it/s]\n 32%|███▏ | 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33%|███▎ | 9/27 [00:01<00:03, 5.72it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 5.71it/s]\n 41%|████ | 11/27 [00:01<00:02, 5.72it/s]\n 44%|████▍ | 12/27 [00:02<00:02, 5.69it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 5.70it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 5.72it/s]\n 56%|█████▌ | 15/27 [00:02<00:02, 5.72it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 5.71it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 5.71it/s]\n 67%|██████▋ | 18/27 [00:03<00:01, 5.71it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.74it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 5.73it/s]\n 78%|███████▊ | 21/27 [00:03<00:01, 5.74it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 5.72it/s]\n 85%|████████▌ | 23/27 [00:04<00:00, 5.72it/s]\n 89%|████████▉ | 24/27 [00:04<00:00, 5.71it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.70it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.70it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.72it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.71it/s]", "metrics": { "predict_time": 22.947316, "total_time": 23.12748 }, "output": [ { "file": "https://replicate.delivery/mgxm/3a9badc2-650f-4d31-8edb-81d87646e2b1/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/67cf36c7-3ea2-4ac8-b648-9781f4688a99/upsample_predictions.png" } ], "started_at": "2022-01-31T01:57:09.863521Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yubhsvs7qvd7bjwkmeunkmvs3i", "cancel": "https://api.replicate.com/v1/predictions/yubhsvs7qvd7bjwkmeunkmvs3i/cancel" }, "version": "b1701399f42b18f18309a14ac09051e8a52d11f134910ec06588565eaac9efb0" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:07, 6.27it/s] 4%|▍ | 2/50 [00:00<00:08, 5.66it/s] 6%|▌ | 3/50 [00:00<00:08, 5.45it/s] 8%|▊ | 4/50 [00:00<00:08, 5.41it/s] 10%|█ | 5/50 [00:00<00:08, 5.45it/s] 12%|█▏ | 6/50 [00:01<00:08, 5.43it/s] 14%|█▍ | 7/50 [00:01<00:07, 5.42it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.34it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.32it/s] 20%|██ | 10/50 [00:01<00:07, 5.33it/s] 22%|██▏ | 11/50 [00:02<00:07, 5.39it/s] 24%|██▍ | 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- prompt
- a canadian goose made from car. goosecar.
- side_x
- "64"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "0.996"
- guidance_scale
- 10
- timestep_respacing
- 100
{ "prompt": "a canadian goose made from car. goosecar.", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 10, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "a canadian goose made from car. goosecar.", side_x: "64", side_y: "64", batch_size: "1", upsample_temp: "0.996", guidance_scale: 10, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "a canadian goose made from car. goosecar.", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 10, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "a canadian goose made from car. goosecar.", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 10, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T03:02:47.850645Z", "created_at": "2022-01-31T03:02:15.898980Z", "data_removed": false, "error": null, "id": "egldrlp37bestlfytoactf5iiq", "input": { "prompt": "a canadian goose made from car. goosecar.", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 10, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:15, 6.41it/s]\n 2%|▏ | 2/100 [00:00<00:17, 5.62it/s]\n 3%|▎ | 3/100 [00:00<00:17, 5.52it/s]\n 4%|▍ | 4/100 [00:00<00:17, 5.59it/s]\n 5%|▌ | 5/100 [00:00<00:16, 5.59it/s]\n 6%|▌ | 6/100 [00:01<00:17, 5.52it/s]\n 7%|▋ | 7/100 [00:01<00:16, 5.58it/s]\n 8%|▊ | 8/100 [00:01<00:16, 5.57it/s]\n 9%|▉ | 9/100 [00:01<00:16, 5.48it/s]\n 10%|█ | 10/100 [00:01<00:16, 5.53it/s]\n 11%|█ | 11/100 [00:01<00:16, 5.51it/s]\n 12%|█▏ | 12/100 [00:02<00:15, 5.51it/s]\n 13%|█▎ | 13/100 [00:02<00:15, 5.53it/s]\n 14%|█▍ | 14/100 [00:02<00:15, 5.50it/s]\n 15%|█▌ | 15/100 [00:02<00:15, 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| 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:04, 5.74it/s]\n 7%|▋ | 2/27 [00:00<00:04, 5.77it/s]\n 11%|█ | 3/27 [00:00<00:04, 5.77it/s]\n 15%|█▍ | 4/27 [00:00<00:03, 5.78it/s]\n 19%|█▊ | 5/27 [00:00<00:03, 5.78it/s]\n 22%|██▏ | 6/27 [00:01<00:03, 5.73it/s]\n 26%|██▌ | 7/27 [00:01<00:03, 5.74it/s]\n 30%|██▉ | 8/27 [00:01<00:03, 5.75it/s]\n 33%|███▎ | 9/27 [00:01<00:03, 5.77it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 5.76it/s]\n 41%|████ | 11/27 [00:01<00:02, 5.75it/s]\n 44%|████▍ | 12/27 [00:02<00:02, 5.76it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 5.74it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 5.75it/s]\n 56%|█████▌ | 15/27 [00:02<00:02, 5.74it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 5.76it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 5.76it/s]\n 67%|██████▋ | 18/27 [00:03<00:01, 5.75it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.75it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 5.73it/s]\n 78%|███████▊ | 21/27 [00:03<00:01, 5.73it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 5.74it/s]\n 85%|████████▌ | 23/27 [00:03<00:00, 5.75it/s]\n 89%|████████▉ | 24/27 [00:04<00:00, 5.73it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.74it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.74it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.74it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.75it/s]", "metrics": { "predict_time": 31.628599, "total_time": 31.951665 }, "output": [ { "file": "https://replicate.delivery/mgxm/16fed635-77be-4db6-87eb-f1aa44825d85/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/5b6246c2-00b0-4dbb-80b6-bce4f90fe7b9/upsample_predictions.png" } ], "started_at": "2022-01-31T03:02:16.222046Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/egldrlp37bestlfytoactf5iiq", "cancel": "https://api.replicate.com/v1/predictions/egldrlp37bestlfytoactf5iiq/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:15, 6.41it/s] 2%|▏ | 2/100 [00:00<00:17, 5.62it/s] 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5.76it/s] 67%|██████▋ | 18/27 [00:03<00:01, 5.75it/s] 70%|███████ | 19/27 [00:03<00:01, 5.75it/s] 74%|███████▍ | 20/27 [00:03<00:01, 5.73it/s] 78%|███████▊ | 21/27 [00:03<00:01, 5.73it/s] 81%|████████▏ | 22/27 [00:03<00:00, 5.74it/s] 85%|████████▌ | 23/27 [00:03<00:00, 5.75it/s] 89%|████████▉ | 24/27 [00:04<00:00, 5.73it/s] 93%|█████████▎| 25/27 [00:04<00:00, 5.74it/s] 96%|█████████▋| 26/27 [00:04<00:00, 5.74it/s] 100%|██████████| 27/27 [00:04<00:00, 5.74it/s] 100%|██████████| 27/27 [00:04<00:00, 5.75it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDpb4meaalqje4zde7s6gqkhuebiStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- the river is long and winding
- side_x
- "112"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "prompt": "the river is long and winding", "side_x": "112", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "the river is long and winding", side_x: "112", side_y: "64", batch_size: "1", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "the river is long and winding", "side_x": "112", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "the river is long and winding", "side_x": "112", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T05:47:15.112927Z", "created_at": "2022-01-31T05:46:46.877600Z", "data_removed": false, "error": null, "id": "pb4meaalqje4zde7s6gqkhuebi", "input": { "prompt": "the river is long and winding", "side_x": "112", "side_y": "64", "batch_size": "1", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:19, 5.18it/s]\n 2%|▏ | 2/100 [00:00<00:16, 6.00it/s]\n 3%|▎ | 3/100 [00:00<00:14, 6.68it/s]\n 4%|▍ | 4/100 [00:00<00:13, 7.07it/s]\n 5%|▌ | 5/100 [00:00<00:13, 7.27it/s]\n 6%|▌ | 6/100 [00:00<00:12, 7.42it/s]\n 7%|▋ | 7/100 [00:00<00:12, 7.51it/s]\n 8%|▊ | 8/100 [00:01<00:12, 7.59it/s]\n 9%|▉ | 9/100 [00:01<00:11, 7.64it/s]\n 10%|█ | 10/100 [00:01<00:11, 7.67it/s]\n 11%|█ | 11/100 [00:01<00:11, 7.69it/s]\n 12%|█▏ | 12/100 [00:01<00:11, 7.69it/s]\n 13%|█▎ | 13/100 [00:01<00:11, 7.72it/s]\n 14%|█▍ | 14/100 [00:01<00:11, 7.72it/s]\n 15%|█▌ | 15/100 [00:02<00:11, 7.71it/s]\n 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4%|▎ | 1/27 [00:00<00:07, 3.70it/s]\n 7%|▋ | 2/27 [00:00<00:06, 3.74it/s]\n 11%|█ | 3/27 [00:00<00:06, 3.75it/s]\n 15%|█▍ | 4/27 [00:01<00:06, 3.74it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 3.75it/s]\n 22%|██▏ | 6/27 [00:01<00:05, 3.75it/s]\n 26%|██▌ | 7/27 [00:01<00:05, 3.76it/s]\n 30%|██▉ | 8/27 [00:02<00:05, 3.76it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 3.76it/s]\n 37%|███▋ | 10/27 [00:02<00:04, 3.77it/s]\n 41%|████ | 11/27 [00:02<00:04, 3.76it/s]\n 44%|████▍ | 12/27 [00:03<00:03, 3.75it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 3.76it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 3.76it/s]\n 56%|█████▌ | 15/27 [00:03<00:03, 3.76it/s]\n 59%|█████▉ | 16/27 [00:04<00:02, 3.76it/s]\n 63%|██████▎ | 17/27 [00:04<00:02, 3.75it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 3.75it/s]\n 70%|███████ | 19/27 [00:05<00:02, 3.75it/s]\n 74%|███████▍ | 20/27 [00:05<00:01, 3.75it/s]\n 78%|███████▊ | 21/27 [00:05<00:01, 3.76it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 3.76it/s]\n 85%|████████▌ | 23/27 [00:06<00:01, 3.77it/s]\n 89%|████████▉ | 24/27 [00:06<00:00, 3.76it/s]\n 93%|█████████▎| 25/27 [00:06<00:00, 3.75it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 3.75it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.76it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.76it/s]", "metrics": { "predict_time": 28.055894, "total_time": 28.235327 }, "output": [ { "file": "https://replicate.delivery/mgxm/fefc1a8f-c5e6-43a7-b11d-dad63c3dceca/upsample_predictions.png" } ], "started_at": "2022-01-31T05:46:47.057033Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pb4meaalqje4zde7s6gqkhuebi", "cancel": "https://api.replicate.com/v1/predictions/pb4meaalqje4zde7s6gqkhuebi/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:19, 5.18it/s] 2%|▏ | 2/100 [00:00<00:16, 6.00it/s] 3%|▎ | 3/100 [00:00<00:14, 6.68it/s] 4%|▍ | 4/100 [00:00<00:13, 7.07it/s] 5%|▌ | 5/100 [00:00<00:13, 7.27it/s] 6%|▌ | 6/100 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[00:05<00:01, 3.75it/s] 78%|███████▊ | 21/27 [00:05<00:01, 3.76it/s] 81%|████████▏ | 22/27 [00:05<00:01, 3.76it/s] 85%|████████▌ | 23/27 [00:06<00:01, 3.77it/s] 89%|████████▉ | 24/27 [00:06<00:00, 3.76it/s] 93%|█████████▎| 25/27 [00:06<00:00, 3.75it/s] 96%|█████████▋| 26/27 [00:06<00:00, 3.75it/s] 100%|██████████| 27/27 [00:07<00:00, 3.76it/s] 100%|██████████| 27/27 [00:07<00:00, 3.76it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDtup3suvhwng2dbrl2eamwsalniStatusSucceededSourceWebHardware–Total durationCreatedInput
- prompt
- "Have you heard the expression room temperature? This is the room. This is the room temperature room.
- side_x
- "112"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "1"
- guidance_scale
- 6
- timestep_respacing
- 150
{ "prompt": "\"Have you heard the expression room temperature? This is the room. This is the room temperature room.", "side_x": "112", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 6, "timestep_respacing": "150" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { prompt: "\"Have you heard the expression room temperature? This is the room. This is the room temperature room.", side_x: "112", side_y: "64", batch_size: "1", upsample_temp: "1", guidance_scale: 6, timestep_respacing: "150" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "prompt": "\"Have you heard the expression room temperature? This is the room. This is the room temperature room.", "side_x": "112", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 6, "timestep_respacing": "150" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "prompt": "\\"Have you heard the expression room temperature? This is the room. This is the room temperature room.", "side_x": "112", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 6, "timestep_respacing": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T06:48:27.286848Z", "created_at": "2022-01-31T06:47:51.112493Z", "data_removed": false, "error": null, "id": "tup3suvhwng2dbrl2eamwsalni", "input": { "prompt": "\"Have you heard the expression room temperature? This is the room. This is the room temperature room.", "side_x": "112", "side_y": "64", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 6, "timestep_respacing": "150" }, "logs": "\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%| | 1/150 [00:00<00:29, 5.02it/s]\n 1%|▏ | 2/150 [00:00<00:23, 6.33it/s]\n 2%|▏ | 3/150 [00:00<00:21, 6.90it/s]\n 3%|▎ | 4/150 [00:00<00:20, 7.20it/s]\n 3%|▎ | 5/150 [00:00<00:19, 7.37it/s]\n 4%|▍ | 6/150 [00:00<00:19, 7.49it/s]\n 5%|▍ | 7/150 [00:00<00:18, 7.56it/s]\n 5%|▌ | 8/150 [00:01<00:18, 7.62it/s]\n 6%|▌ | 9/150 [00:01<00:18, 7.65it/s]\n 7%|▋ | 10/150 [00:01<00:18, 7.67it/s]\n 7%|▋ | 11/150 [00:01<00:18, 7.69it/s]\n 8%|▊ | 12/150 [00:01<00:17, 7.68it/s]\n 9%|▊ | 13/150 [00:01<00:17, 7.69it/s]\n 9%|▉ | 14/150 [00:01<00:17, 7.72it/s]\n 10%|█ | 15/150 [00:02<00:17, 7.71it/s]\n 11%|█ | 16/150 [00:02<00:17, 7.72it/s]\n 11%|█▏ | 17/150 [00:02<00:17, 7.70it/s]\n 12%|█▏ | 18/150 [00:02<00:17, 7.70it/s]\n 13%|█▎ | 19/150 [00:02<00:17, 7.69it/s]\n 13%|█▎ | 20/150 [00:02<00:16, 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[00:19<00:00, 7.60it/s]\n100%|██████████| 150/150 [00:19<00:00, 7.61it/s]\n100%|██████████| 150/150 [00:19<00:00, 7.61it/s]\n\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:07, 3.69it/s]\n 7%|▋ | 2/27 [00:00<00:06, 3.73it/s]\n 11%|█ | 3/27 [00:00<00:06, 3.72it/s]\n 15%|█▍ | 4/27 [00:01<00:06, 3.71it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 3.73it/s]\n 22%|██▏ | 6/27 [00:01<00:05, 3.73it/s]\n 26%|██▌ | 7/27 [00:01<00:05, 3.72it/s]\n 30%|██▉ | 8/27 [00:02<00:05, 3.73it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 3.74it/s]\n 37%|███▋ | 10/27 [00:02<00:04, 3.74it/s]\n 41%|████ | 11/27 [00:02<00:04, 3.74it/s]\n 44%|████▍ | 12/27 [00:03<00:04, 3.73it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 3.73it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 3.73it/s]\n 56%|█████▌ | 15/27 [00:04<00:03, 3.71it/s]\n 59%|█████▉ | 16/27 [00:04<00:02, 3.71it/s]\n 63%|██████▎ | 17/27 [00:04<00:02, 3.71it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 3.72it/s]\n 70%|███████ | 19/27 [00:05<00:02, 3.72it/s]\n 74%|███████▍ | 20/27 [00:05<00:01, 3.72it/s]\n 78%|███████▊ | 21/27 [00:05<00:01, 3.72it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 3.71it/s]\n 85%|████████▌ | 23/27 [00:06<00:01, 3.72it/s]\n 89%|████████▉ | 24/27 [00:06<00:00, 3.73it/s]\n 93%|█████████▎| 25/27 [00:06<00:00, 3.72it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 3.72it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.72it/s]\n100%|██████████| 27/27 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 35.972636, "total_time": 36.174355 }, "output": [ { "file": "https://replicate.delivery/mgxm/b900a259-1293-4a83-a8cb-85e1cecb3b90/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/d96371cf-e952-4703-9fd3-dd7c22bf4b75/upsample_predictions.png" } ], "started_at": "2022-01-31T06:47:51.314212Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tup3suvhwng2dbrl2eamwsalni", "cancel": "https://api.replicate.com/v1/predictions/tup3suvhwng2dbrl2eamwsalni/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/150 [00:00<?, ?it/s] 1%| | 1/150 [00:00<00:29, 5.02it/s] 1%|▏ | 2/150 [00:00<00:23, 6.33it/s] 2%|▏ | 3/150 [00:00<00:21, 6.90it/s] 3%|▎ | 4/150 [00:00<00:20, 7.20it/s] 3%|▎ | 5/150 [00:00<00:19, 7.37it/s] 4%|▍ | 6/150 [00:00<00:19, 7.49it/s] 5%|▍ | 7/150 [00:00<00:18, 7.56it/s] 5%|▌ | 8/150 [00:01<00:18, 7.62it/s] 6%|▌ | 9/150 [00:01<00:18, 7.65it/s] 7%|▋ | 10/150 [00:01<00:18, 7.67it/s] 7%|▋ | 11/150 [00:01<00:18, 7.69it/s] 8%|▊ | 12/150 [00:01<00:17, 7.68it/s] 9%|▊ | 13/150 [00:01<00:17, 7.69it/s] 9%|▉ | 14/150 [00:01<00:17, 7.72it/s] 10%|█ | 15/150 [00:02<00:17, 7.71it/s] 11%|█ | 16/150 [00:02<00:17, 7.72it/s] 11%|█▏ | 17/150 [00:02<00:17, 7.70it/s] 12%|█▏ | 18/150 [00:02<00:17, 7.70it/s] 13%|█▎ | 19/150 [00:02<00:17, 7.69it/s] 13%|█▎ | 20/150 [00:02<00:16, 7.65it/s] 14%|█▍ | 21/150 [00:02<00:16, 7.63it/s] 15%|█▍ | 22/150 [00:02<00:16, 7.63it/s] 15%|█▌ | 23/150 [00:03<00:16, 7.61it/s] 16%|█▌ | 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDtegwwqk37zfqfhloo2j2mw4354StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 3211321321
- prompt
- Sticker of a cartoon washing machine. A creative illustrated sticker of a cartoon washing machine white background royalty free illustration
- side_x
- "64"
- side_y
- "64"
- batch_size
- "4"
- upsample_temp
- "1"
- guidance_scale
- 7
- timestep_respacing
- 100
{ "seed": 3211321321, "prompt": "Sticker of a cartoon washing machine. A creative illustrated sticker of a cartoon washing machine white background royalty free illustration", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 3211321321, prompt: "Sticker of a cartoon washing machine. A creative illustrated sticker of a cartoon washing machine white background royalty free illustration", side_x: "64", side_y: "64", batch_size: "4", upsample_temp: "1", guidance_scale: 7, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 3211321321, "prompt": "Sticker of a cartoon washing machine. A creative illustrated sticker of a cartoon washing machine white background royalty free illustration", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 3211321321, "prompt": "Sticker of a cartoon washing machine. A creative illustrated sticker of a cartoon washing machine white background royalty free illustration", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T06:52:36.010311Z", "created_at": "2022-01-31T06:51:10.402393Z", "data_removed": false, "error": null, "id": "tegwwqk37zfqfhloo2j2mw4354", "input": { "seed": 3211321321, "prompt": "Sticker of a cartoon washing machine. A creative illustrated sticker of a cartoon washing machine white background royalty free illustration", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "1", "guidance_scale": 7, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<01:04, 1.52it/s]\n 2%|▏ | 2/100 [00:00<00:42, 2.33it/s]\n 3%|▎ | 3/100 [00:01<00:47, 2.05it/s]\n 4%|▍ | 4/100 [00:02<00:49, 1.93it/s]\n 5%|▌ | 5/100 [00:02<00:50, 1.88it/s]\n 6%|▌ | 6/100 [00:03<00:51, 1.84it/s]\n 7%|▋ | 7/100 [00:03<00:51, 1.82it/s]\n 8%|▊ | 8/100 [00:04<00:51, 1.80it/s]\n 9%|▉ | 9/100 [00:04<00:50, 1.79it/s]\n 10%|█ | 10/100 [00:05<00:50, 1.78it/s]\n 11%|█ | 11/100 [00:05<00:50, 1.78it/s]\n 12%|█▏ | 12/100 [00:06<00:49, 1.78it/s]\n 13%|█▎ | 13/100 [00:07<00:49, 1.77it/s]\n 14%|█▍ | 14/100 [00:07<00:48, 1.77it/s]\n 15%|█▌ | 15/100 [00:08<00:47, 1.78it/s]\n 16%|█▌ | 16/100 [00:08<00:47, 1.77it/s]\n 17%|█▋ | 17/100 [00:09<00:46, 1.77it/s]\n 18%|█▊ | 18/100 [00:09<00:46, 1.77it/s]\n 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96%|█████████▋| 26/27 [00:16<00:00, 1.55it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.55it/s]\n100%|██████████| 27/27 [00:17<00:00, 1.56it/s]", "metrics": { "predict_time": 85.389951, "total_time": 85.607918 }, "output": [ { "file": "https://replicate.delivery/mgxm/2ba95dc4-c6c7-4abf-baa7-ab102341fa88/upsample_predictions.png" } ], "started_at": "2022-01-31T06:51:10.620360Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tegwwqk37zfqfhloo2j2mw4354", "cancel": "https://api.replicate.com/v1/predictions/tegwwqk37zfqfhloo2j2mw4354/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<01:04, 1.52it/s] 2%|▏ | 2/100 [00:00<00:42, 2.33it/s] 3%|▎ | 3/100 [00:01<00:47, 2.05it/s] 4%|▍ | 4/100 [00:02<00:49, 1.93it/s] 5%|▌ | 5/100 [00:02<00:50, 1.88it/s] 6%|▌ | 6/100 [00:03<00:51, 1.84it/s] 7%|▋ | 7/100 [00:03<00:51, 1.82it/s] 8%|▊ | 8/100 [00:04<00:51, 1.80it/s] 9%|▉ 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aID4rm67xzwi5hedoomuv7jd62sueStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- latte art of infinity symbol ∞∞∞∞∞∞∞∞∞∞∞∞∞
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "1"
- guidance_scale
- 10
- timestep_respacing
- 250
{ "seed": 0, "prompt": "latte art of infinity symbol ∞∞∞∞∞∞∞∞∞∞∞∞∞", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 10, "timestep_respacing": "250" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "latte art of infinity symbol ∞∞∞∞∞∞∞∞∞∞∞∞∞", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "1", guidance_scale: 10, timestep_respacing: "250" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "latte art of infinity symbol ∞∞∞∞∞∞∞∞∞∞∞∞∞", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 10, "timestep_respacing": "250" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "latte art of infinity symbol ∞∞∞∞∞∞∞∞∞∞∞∞∞", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 10, "timestep_respacing": "250" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T07:27:16.225013Z", "created_at": "2022-01-31T07:24:59.465714Z", "data_removed": false, "error": null, "id": "4rm67xzwi5hedoomuv7jd62sue", "input": { "seed": 0, "prompt": "latte art of infinity symbol ∞∞∞∞∞∞∞∞∞∞∞∞∞", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1", "guidance_scale": 10, "timestep_respacing": "250" }, "logs": "\n 0%| | 0/250 [00:00<?, ?it/s]\n 0%| | 1/250 [00:00<01:09, 3.57it/s]\n 1%| | 2/250 [00:00<01:31, 2.72it/s]\n 1%| | 3/250 [00:01<01:38, 2.50it/s]\n 2%|▏ | 4/250 [00:01<01:42, 2.41it/s]\n 2%|▏ | 5/250 [00:02<01:43, 2.36it/s]\n 2%|▏ | 6/250 [00:02<01:45, 2.32it/s]\n 3%|▎ | 7/250 [00:02<01:45, 2.31it/s]\n 3%|▎ | 8/250 [00:03<01:45, 2.29it/s]\n 4%|▎ | 9/250 [00:03<01:45, 2.29it/s]\n 4%|▍ | 10/250 [00:04<01:45, 2.28it/s]\n 4%|▍ | 11/250 [00:04<01:44, 2.28it/s]\n 5%|▍ | 12/250 [00:05<01:45, 2.27it/s]\n 5%|▌ | 13/250 [00:05<01:44, 2.28it/s]\n 6%|▌ | 14/250 [00:05<01:43, 2.27it/s]\n 6%|▌ | 15/250 [00:06<01:43, 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[00:00<00:12, 2.01it/s]\n 7%|▋ | 2/27 [00:01<00:12, 1.95it/s]\n 11%|█ | 3/27 [00:01<00:12, 1.97it/s]\n 15%|█▍ | 4/27 [00:02<00:11, 1.96it/s]\n 19%|█▊ | 5/27 [00:02<00:11, 1.97it/s]\n 22%|██▏ | 6/27 [00:03<00:10, 1.96it/s]\n 26%|██▌ | 7/27 [00:03<00:10, 1.97it/s]\n 30%|██▉ | 8/27 [00:04<00:09, 1.97it/s]\n 33%|███▎ | 9/27 [00:04<00:09, 1.97it/s]\n 37%|███▋ | 10/27 [00:05<00:08, 1.97it/s]\n 41%|████ | 11/27 [00:05<00:08, 1.97it/s]\n 44%|████▍ | 12/27 [00:06<00:07, 1.97it/s]\n 48%|████▊ | 13/27 [00:06<00:07, 1.97it/s]\n 52%|█████▏ | 14/27 [00:07<00:06, 1.97it/s]\n 56%|█████▌ | 15/27 [00:07<00:06, 1.97it/s]\n 59%|█████▉ | 16/27 [00:08<00:05, 1.97it/s]\n 63%|██████▎ | 17/27 [00:08<00:05, 1.97it/s]\n 67%|██████▋ | 18/27 [00:09<00:04, 1.97it/s]\n 70%|███████ | 19/27 [00:09<00:04, 1.98it/s]\n 74%|███████▍ | 20/27 [00:10<00:03, 1.97it/s]\n 78%|███████▊ | 21/27 [00:10<00:03, 1.97it/s]\n 81%|████████▏ | 22/27 [00:11<00:02, 1.96it/s]\n 85%|████████▌ | 23/27 [00:11<00:02, 1.96it/s]\n 89%|████████▉ | 24/27 [00:12<00:01, 1.97it/s]\n 93%|█████████▎| 25/27 [00:12<00:01, 1.96it/s]\n 96%|█████████▋| 26/27 [00:13<00:00, 1.96it/s]\n100%|██████████| 27/27 [00:13<00:00, 1.97it/s]\n100%|██████████| 27/27 [00:13<00:00, 1.97it/s]", "metrics": { "predict_time": 136.55932, "total_time": 136.759299 }, "output": [ { "file": "https://replicate.delivery/mgxm/dbf6a819-7e44-414d-8aa5-ac9571370cf4/upsample_predictions.png" } ], "started_at": "2022-01-31T07:24:59.665693Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4rm67xzwi5hedoomuv7jd62sue", "cancel": "https://api.replicate.com/v1/predictions/4rm67xzwi5hedoomuv7jd62sue/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/250 [00:00<?, ?it/s] 0%| | 1/250 [00:00<01:09, 3.57it/s] 1%| | 2/250 [00:00<01:31, 2.72it/s] 1%| | 3/250 [00:01<01:38, 2.50it/s] 2%|▏ | 4/250 [00:01<01:42, 2.41it/s] 2%|▏ | 5/250 [00:02<01:43, 2.36it/s] 2%|▏ | 6/250 [00:02<01:45, 2.32it/s] 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27/27 [00:13<00:00, 1.97it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDsccfjwscjrgijbkk2jkp7smdbeStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- a panorama photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.
- side_x
- "112"
- side_y
- "64"
- batch_size
- "2"
- upsample_temp
- "1"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "seed": 0, "prompt": "a panorama photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", "side_x": "112", "side_y": "64", "batch_size": "2", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "a panorama photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", side_x: "112", side_y: "64", batch_size: "2", upsample_temp: "1", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "a panorama photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", "side_x": "112", "side_y": "64", "batch_size": "2", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "a panorama photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", "side_x": "112", "side_y": "64", "batch_size": "2", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T07:46:58.941167Z", "created_at": "2022-01-31T07:45:20.739437Z", "data_removed": false, "error": null, "id": "sccfjwscjrgijbkk2jkp7smdbe", "input": { "seed": 0, "prompt": "a panorama photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", "side_x": "112", "side_y": "64", "batch_size": "2", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:30, 3.27it/s]\n 2%|▏ | 2/100 [00:00<00:25, 3.79it/s]\n 3%|▎ | 3/100 [00:00<00:24, 3.98it/s]\n 4%|▍ | 4/100 [00:01<00:23, 4.09it/s]\n 5%|▌ | 5/100 [00:01<00:22, 4.14it/s]\n 6%|▌ | 6/100 [00:01<00:22, 4.18it/s]\n 7%|▋ | 7/100 [00:01<00:22, 4.21it/s]\n 8%|▊ | 8/100 [00:01<00:21, 4.22it/s]\n 9%|▉ | 9/100 [00:02<00:21, 4.23it/s]\n 10%|█ | 10/100 [00:02<00:21, 4.23it/s]\n 11%|█ | 11/100 [00:02<00:20, 4.24it/s]\n 12%|█▏ | 12/100 [00:02<00:20, 4.25it/s]\n 13%|█▎ 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78%|███████▊ | 21/27 [00:11<00:03, 1.89it/s]\n 81%|████████▏ | 22/27 [00:11<00:02, 1.89it/s]\n 85%|████████▌ | 23/27 [00:12<00:02, 1.89it/s]\n 89%|████████▉ | 24/27 [00:12<00:01, 1.89it/s]\n 93%|█████████▎| 25/27 [00:13<00:01, 1.88it/s]\n 96%|█████████▋| 26/27 [00:13<00:00, 1.89it/s]\n100%|██████████| 27/27 [00:14<00:00, 1.89it/s]\n100%|██████████| 27/27 [00:14<00:00, 1.89it/s]", "metrics": { "predict_time": 46.797726, "total_time": 98.20173 }, "output": [ { "file": "https://replicate.delivery/mgxm/ff30b544-301a-4abd-a198-0ba33c09361d/upsample_predictions.png" } ], "started_at": "2022-01-31T07:46:12.143441Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sccfjwscjrgijbkk2jkp7smdbe", "cancel": "https://api.replicate.com/v1/predictions/sccfjwscjrgijbkk2jkp7smdbe/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:30, 3.27it/s] 2%|▏ | 2/100 [00:00<00:25, 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afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- seed
- 0
- prompt
- a panorama stitched photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.
- side_x
- "128"
- side_y
- "32"
- batch_size
- "1"
- upsample_temp
- "1"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "seed": 0, "prompt": "a panorama stitched photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", "side_x": "128", "side_y": "32", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "a panorama stitched photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", side_x: "128", side_y: "32", batch_size: "1", upsample_temp: "1", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "a panorama stitched photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", "side_x": "128", "side_y": "32", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "a panorama stitched photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", "side_x": "128", "side_y": "32", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T07:50:33.371058Z", "created_at": "2022-01-31T07:49:31.129346Z", "data_removed": false, "error": null, "id": "vmbw2zmozrb3fienje4aaog7mq", "input": { "seed": 0, "prompt": "a panorama stitched photo of a bustling town, repetitive, made from wooden buildens, town made from more town. city made from more city. like tears in rain.", "side_x": "128", "side_y": "32", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<01:29, 1.10it/s]\n 2%|▏ | 2/100 [00:01<00:42, 2.31it/s]\n 3%|▎ | 3/100 [00:01<00:27, 3.53it/s]\n 5%|▌ | 5/100 [00:01<00:16, 5.63it/s]\n 7%|▋ | 7/100 [00:01<00:12, 7.24it/s]\n 9%|▉ | 9/100 [00:01<00:10, 8.41it/s]\n 11%|█ | 11/100 [00:01<00:09, 9.27it/s]\n 13%|█▎ | 13/100 [00:02<00:09, 9.63it/s]\n 15%|█▌ | 15/100 [00:02<00:08, 9.81it/s]\n 17%|█▋ | 17/100 [00:02<00:08, 10.11it/s]\n 19%|█▉ | 19/100 [00:02<00:07, 10.43it/s]\n 21%|██ | 21/100 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2/27 [00:00<00:04, 5.81it/s]\n 11%|█ | 3/27 [00:00<00:04, 5.97it/s]\n 15%|█▍ | 4/27 [00:00<00:03, 5.96it/s]\n 19%|█▊ | 5/27 [00:00<00:03, 6.04it/s]\n 22%|██▏ | 6/27 [00:01<00:03, 6.09it/s]\n 26%|██▌ | 7/27 [00:01<00:03, 6.11it/s]\n 30%|██▉ | 8/27 [00:01<00:03, 6.13it/s]\n 33%|███▎ | 9/27 [00:01<00:02, 6.15it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 6.14it/s]\n 41%|████ | 11/27 [00:01<00:02, 6.15it/s]\n 44%|████▍ | 12/27 [00:01<00:02, 6.14it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 6.15it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 6.15it/s]\n 56%|█████▌ | 15/27 [00:02<00:01, 6.15it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 6.15it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 6.17it/s]\n 67%|██████▋ | 18/27 [00:02<00:01, 6.17it/s]\n 70%|███████ | 19/27 [00:03<00:01, 6.15it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 6.14it/s]\n 78%|███████▊ | 21/27 [00:03<00:00, 6.13it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 6.15it/s]\n 85%|████████▌ | 23/27 [00:03<00:00, 6.15it/s]\n 89%|████████▉ | 24/27 [00:03<00:00, 6.15it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 6.15it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 6.15it/s]\n100%|██████████| 27/27 [00:04<00:00, 6.16it/s]\n100%|██████████| 27/27 [00:04<00:00, 6.12it/s]", "metrics": { "predict_time": 21.838434, "total_time": 62.241712 }, "output": [ { "file": "https://replicate.delivery/mgxm/ea590069-049b-4820-9258-6c08bdad16eb/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/a0dbea5d-e476-4508-a715-d85e0cd48a48/upsample_predictions.png" } ], "started_at": "2022-01-31T07:50:11.532624Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vmbw2zmozrb3fienje4aaog7mq", "cancel": "https://api.replicate.com/v1/predictions/vmbw2zmozrb3fienje4aaog7mq/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<01:29, 1.10it/s] 2%|▏ | 2/100 [00:01<00:42, 2.31it/s] 3%|▎ | 3/100 [00:01<00:27, 3.53it/s] 5%|▌ | 5/100 [00:01<00:16, 5.63it/s] 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| 21/27 [00:03<00:00, 6.13it/s] 81%|████████▏ | 22/27 [00:03<00:00, 6.15it/s] 85%|████████▌ | 23/27 [00:03<00:00, 6.15it/s] 89%|████████▉ | 24/27 [00:03<00:00, 6.15it/s] 93%|█████████▎| 25/27 [00:04<00:00, 6.15it/s] 96%|█████████▋| 26/27 [00:04<00:00, 6.15it/s] 100%|██████████| 27/27 [00:04<00:00, 6.16it/s] 100%|██████████| 27/27 [00:04<00:00, 6.12it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aInput
- seed
- 0
- prompt
- a panorama stitched photo of a science fiction futuristic town. the ominous monolith in the center of the city is scary.
- side_x
- "128"
- side_y
- "32"
- batch_size
- "1"
- upsample_temp
- "1"
- guidance_scale
- 4
- timestep_respacing
- 100
{ "seed": 0, "prompt": "a panorama stitched photo of a science fiction futuristic town. the ominous monolith in the center of the city is scary.", "side_x": "128", "side_y": "32", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "a panorama stitched photo of a science fiction futuristic town. the ominous monolith in the center of the city is scary.", side_x: "128", side_y: "32", batch_size: "1", upsample_temp: "1", guidance_scale: 4, timestep_respacing: "100" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "a panorama stitched photo of a science fiction futuristic town. the ominous monolith in the center of the city is scary.", "side_x": "128", "side_y": "32", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "a panorama stitched photo of a science fiction futuristic town. the ominous monolith in the center of the city is scary.", "side_x": "128", "side_y": "32", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-01-31T07:53:11.566085Z", "created_at": "2022-01-31T07:52:48.979279Z", "data_removed": false, "error": null, "id": "gzkdxkrnwbb6pkaewpkgs4j5su", "input": { "seed": 0, "prompt": "a panorama stitched photo of a science fiction futuristic town. the ominous monolith in the center of the city is scary.", "side_x": "128", "side_y": "32", "batch_size": "1", "upsample_temp": "1", "guidance_scale": 4, "timestep_respacing": "100" }, "logs": "\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:13, 7.19it/s]\n 2%|▏ | 2/100 [00:00<00:11, 8.61it/s]\n 4%|▍ | 4/100 [00:00<00:09, 9.70it/s]\n 6%|▌ | 6/100 [00:00<00:09, 9.96it/s]\n 8%|▊ | 8/100 [00:00<00:09, 10.21it/s]\n 10%|█ | 10/100 [00:01<00:08, 10.30it/s]\n 12%|█▏ | 12/100 [00:01<00:08, 10.40it/s]\n 14%|█▍ | 14/100 [00:01<00:08, 10.48it/s]\n 16%|█▌ | 16/100 [00:01<00:07, 10.54it/s]\n 18%|█▊ | 18/100 [00:01<00:07, 10.61it/s]\n 20%|██ | 20/100 [00:01<00:07, 10.66it/s]\n 22%|██▏ | 22/100 [00:02<00:07, 10.63it/s]\n 24%|██▍ | 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11%|█ | 3/27 [00:00<00:04, 5.92it/s]\n 15%|█▍ | 4/27 [00:00<00:03, 5.94it/s]\n 19%|█▊ | 5/27 [00:00<00:03, 5.97it/s]\n 22%|██▏ | 6/27 [00:01<00:03, 5.93it/s]\n 26%|██▌ | 7/27 [00:01<00:03, 5.95it/s]\n 30%|██▉ | 8/27 [00:01<00:03, 5.97it/s]\n 33%|███▎ | 9/27 [00:01<00:03, 6.00it/s]\n 37%|███▋ | 10/27 [00:01<00:02, 5.97it/s]\n 41%|████ | 11/27 [00:01<00:02, 5.92it/s]\n 44%|████▍ | 12/27 [00:02<00:02, 5.93it/s]\n 48%|████▊ | 13/27 [00:02<00:02, 5.95it/s]\n 52%|█████▏ | 14/27 [00:02<00:02, 5.95it/s]\n 56%|█████▌ | 15/27 [00:02<00:02, 5.94it/s]\n 59%|█████▉ | 16/27 [00:02<00:01, 5.95it/s]\n 63%|██████▎ | 17/27 [00:02<00:01, 5.95it/s]\n 67%|██████▋ | 18/27 [00:03<00:01, 5.96it/s]\n 70%|███████ | 19/27 [00:03<00:01, 5.96it/s]\n 74%|███████▍ | 20/27 [00:03<00:01, 5.96it/s]\n 78%|███████▊ | 21/27 [00:03<00:01, 5.96it/s]\n 81%|████████▏ | 22/27 [00:03<00:00, 5.96it/s]\n 85%|████████▌ | 23/27 [00:03<00:00, 5.96it/s]\n 89%|████████▉ | 24/27 [00:04<00:00, 5.97it/s]\n 93%|█████████▎| 25/27 [00:04<00:00, 5.96it/s]\n 96%|█████████▋| 26/27 [00:04<00:00, 5.95it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.96it/s]\n100%|██████████| 27/27 [00:04<00:00, 5.95it/s]", "metrics": { "predict_time": 22.375024, "total_time": 22.586806 }, "output": [ { "file": "https://replicate.delivery/mgxm/bed3e186-db32-41ff-b2da-52f0aa8cdcf4/upsample_predictions.png" } ], "started_at": "2022-01-31T07:52:49.191061Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gzkdxkrnwbb6pkaewpkgs4j5su", "cancel": "https://api.replicate.com/v1/predictions/gzkdxkrnwbb6pkaewpkgs4j5su/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:13, 7.19it/s] 2%|▏ | 2/100 [00:00<00:11, 8.61it/s] 4%|▍ | 4/100 [00:00<00:09, 9.70it/s] 6%|▌ | 6/100 [00:00<00:09, 9.96it/s] 8%|▊ | 8/100 [00:00<00:09, 10.21it/s] 10%|█ | 10/100 [00:01<00:08, 10.30it/s] 12%|█▏ | 12/100 [00:01<00:08, 10.40it/s] 14%|█▍ | 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5.96it/s] 89%|████████▉ | 24/27 [00:04<00:00, 5.97it/s] 93%|█████████▎| 25/27 [00:04<00:00, 5.96it/s] 96%|█████████▋| 26/27 [00:04<00:00, 5.95it/s] 100%|██████████| 27/27 [00:04<00:00, 5.96it/s] 100%|██████████| 27/27 [00:04<00:00, 5.95it/s]Prediction
afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6aIDxgv5oqau2nhqtoyjmuqun4leqqStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- a vector art illustration of a new type of pokemon
- side_x
- "64"
- side_y
- "64"
- batch_size
- "4"
- upsample_temp
- "0.996"
- guidance_scale
- 4
- timestep_respacing
- 50
{ "seed": 0, "prompt": "a vector art illustration of a new type of pokemon", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "50" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", { input: { seed: 0, prompt: "a vector art illustration of a new type of pokemon", side_x: "64", side_y: "64", batch_size: "4", upsample_temp: "0.996", guidance_scale: 4, timestep_respacing: "50" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", input={ "seed": 0, "prompt": "a vector art illustration of a new type of pokemon", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "50" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a", "input": { "seed": 0, "prompt": "a vector art illustration of a new type of pokemon", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "50" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-05T04:42:57.228102Z", "created_at": "2022-02-05T04:41:23.206157Z", "data_removed": false, "error": null, "id": "xgv5oqau2nhqtoyjmuqun4leqq", "input": { "seed": 0, "prompt": "a vector art illustration of a new type of pokemon", "side_x": "64", "side_y": "64", "batch_size": "4", "upsample_temp": "0.996", "guidance_scale": 4, "timestep_respacing": "50" }, "logs": "\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:37, 1.31it/s]\n 4%|▍ | 2/50 [00:01<00:22, 2.12it/s]\n 6%|▌ | 3/50 [00:01<00:23, 1.96it/s]\n 8%|▊ | 4/50 [00:02<00:24, 1.89it/s]\n 10%|█ | 5/50 [00:02<00:24, 1.86it/s]\n 12%|█▏ | 6/50 [00:03<00:24, 1.83it/s]\n 14%|█▍ | 7/50 [00:03<00:23, 1.81it/s]\n 16%|█▌ | 8/50 [00:04<00:23, 1.81it/s]\n 18%|█▊ | 9/50 [00:04<00:22, 1.80it/s]\n 20%|██ | 10/50 [00:05<00:22, 1.80it/s]\n 22%|██▏ | 11/50 [00:06<00:21, 1.79it/s]\n 24%|██▍ | 12/50 [00:06<00:21, 1.79it/s]\n 26%|██▌ | 13/50 [00:07<00:20, 1.79it/s]\n 28%|██▊ | 14/50 [00:07<00:20, 1.79it/s]\n 30%|███ | 15/50 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"total_time": 94.021945 }, "output": [ { "file": "https://replicate.delivery/mgxm/96477c0f-569b-4dcc-8abd-7ee7ca270ce3/upsample_predictions.png" } ], "started_at": "2022-02-05T04:42:00.067236Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xgv5oqau2nhqtoyjmuqun4leqq", "cancel": "https://api.replicate.com/v1/predictions/xgv5oqau2nhqtoyjmuqun4leqq/cancel" }, "version": "60b29272ea379262575938178e018f34492fb017fb60b63abab586f17c181d6a" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:37, 1.31it/s] 4%|▍ | 2/50 [00:01<00:22, 2.12it/s] 6%|▌ | 3/50 [00:01<00:23, 1.96it/s] 8%|▊ | 4/50 [00:02<00:24, 1.89it/s] 10%|█ | 5/50 [00:02<00:24, 1.86it/s] 12%|█▏ | 6/50 [00:03<00:24, 1.83it/s] 14%|█▍ | 7/50 [00:03<00:23, 1.81it/s] 16%|█▌ | 8/50 [00:04<00:23, 1.81it/s] 18%|█▊ | 9/50 [00:04<00:22, 1.80it/s] 20%|██ | 10/50 [00:05<00:22, 1.80it/s] 22%|██▏ | 11/50 [00:06<00:21, 1.79it/s] 24%|██▍ | 12/50 [00:06<00:21, 1.79it/s] 26%|██▌ | 13/50 [00:07<00:20, 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afiaka87/pyglide:83369f5ab8cbe48301e6ac12c328b72ba25d8507027eadf2e81e8974e02ebbb7Input
- prompt
- a goose made of paper. paper goose.
- side_x
- "64"
- side_y
- "64"
- batch_size
- "8"
- upsample_temp
- "0.997"
- guidance_scale
- 10
- upsample_stage
- timestep_respacing
- 30
- sr_timestep_respacing
- 17
{ "prompt": "a goose made of paper. paper goose.", "side_x": "64", "side_y": "64", "batch_size": "8", "upsample_temp": "0.997", "guidance_scale": 10, "upsample_stage": true, "timestep_respacing": "30", "sr_timestep_respacing": "17" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:83369f5ab8cbe48301e6ac12c328b72ba25d8507027eadf2e81e8974e02ebbb7", { input: { prompt: "a goose made of paper. paper goose.", side_x: "64", side_y: "64", batch_size: "8", upsample_temp: "0.997", guidance_scale: 10, upsample_stage: true, timestep_respacing: "30", sr_timestep_respacing: "17" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:83369f5ab8cbe48301e6ac12c328b72ba25d8507027eadf2e81e8974e02ebbb7", input={ "prompt": "a goose made of paper. paper goose.", "side_x": "64", "side_y": "64", "batch_size": "8", "upsample_temp": "0.997", "guidance_scale": 10, "upsample_stage": True, "timestep_respacing": "30", "sr_timestep_respacing": "17" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "83369f5ab8cbe48301e6ac12c328b72ba25d8507027eadf2e81e8974e02ebbb7", "input": { "prompt": "a goose made of paper. paper goose.", "side_x": "64", "side_y": "64", "batch_size": "8", "upsample_temp": "0.997", "guidance_scale": 10, "upsample_stage": true, "timestep_respacing": "30", "sr_timestep_respacing": "17" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-16T19:48:41.185581Z", "created_at": "2022-02-16T19:47:23.410786Z", "data_removed": false, "error": null, "id": "eoz5brtp7beapipjyizmqrtx2y", "input": { "prompt": "a goose made of paper. paper goose.", "side_x": "64", "side_y": "64", "batch_size": "8", "upsample_temp": "0.997", "guidance_scale": 10, "upsample_stage": true, "timestep_respacing": "30", "sr_timestep_respacing": "17" }, "logs": "\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:02<00:56, 2.10s/it]\n 7%|▋ | 2/28 [00:03<00:48, 1.85s/it]\n 11%|█ | 3/28 [00:05<00:44, 1.78s/it]\n 14%|█▍ | 4/28 [00:05<00:29, 1.24s/it]\n 18%|█▊ | 5/28 [00:06<00:21, 1.05it/s]\n 21%|██▏ | 6/28 [00:06<00:16, 1.30it/s]\n 25%|██▌ | 7/28 [00:07<00:13, 1.52it/s]\n 29%|██▊ | 8/28 [00:07<00:11, 1.72it/s]\n 32%|███▏ | 9/28 [00:08<00:10, 1.88it/s]\n 36%|███▌ | 10/28 [00:08<00:08, 2.00it/s]\n 39%|███▉ | 11/28 [00:08<00:08, 2.10it/s]\n 43%|████▎ | 12/28 [00:09<00:07, 2.17it/s]\n 46%|████▋ | 13/28 [00:09<00:06, 2.22it/s]\n 50%|█████ | 14/28 [00:10<00:06, 2.25it/s]\n 54%|█████▎ | 15/28 [00:10<00:05, 2.28it/s]\n 57%|█████▋ | 16/28 [00:11<00:05, 2.30it/s]\n 61%|██████ | 17/28 [00:11<00:04, 2.31it/s]\n 64%|██████▍ | 18/28 [00:11<00:04, 2.32it/s]\n 68%|██████▊ | 19/28 [00:12<00:03, 2.33it/s]\n 71%|███████▏ | 20/28 [00:12<00:03, 2.33it/s]\n 75%|███████▌ | 21/28 [00:13<00:02, 2.34it/s]\n 79%|███████▊ | 22/28 [00:13<00:02, 2.34it/s]\n 82%|████████▏ | 23/28 [00:13<00:02, 2.34it/s]\n 86%|████████▌ | 24/28 [00:14<00:01, 2.34it/s]\n 89%|████████▉ | 25/28 [00:14<00:01, 2.33it/s]\n 93%|█████████▎| 26/28 [00:15<00:00, 2.33it/s]\n 96%|█████████▋| 27/28 [00:15<00:00, 2.32it/s]\n100%|██████████| 28/28 [00:16<00:00, 2.32it/s]\n100%|██████████| 28/28 [00:16<00:00, 1.73it/s]\n\n 0%| | 0/15 [00:00<?, ?it/s]\n 7%|▋ | 1/15 [00:04<01:07, 4.79s/it]\n 13%|█▎ | 2/15 [00:09<01:02, 4.81s/it]\n 20%|██ | 3/15 [00:14<00:58, 4.84s/it]\n 27%|██▋ | 4/15 [00:15<00:37, 3.41s/it]\n 33%|███▎ | 5/15 [00:16<00:26, 2.63s/it]\n 40%|████ | 6/15 [00:18<00:19, 2.15s/it]\n 47%|████▋ | 7/15 [00:19<00:14, 1.85s/it]\n 53%|█████▎ | 8/15 [00:20<00:11, 1.66s/it]\n 60%|██████ | 9/15 [00:21<00:09, 1.53s/it]\n 67%|██████▋ | 10/15 [00:23<00:07, 1.44s/it]\n 73%|███████▎ | 11/15 [00:24<00:05, 1.39s/it]\n 80%|████████ | 12/15 [00:25<00:04, 1.35s/it]\n 87%|████████▋ | 13/15 [00:26<00:02, 1.32s/it]\n 93%|█████████▎| 14/15 [00:28<00:01, 1.31s/it]\n100%|██████████| 15/15 [00:29<00:00, 1.29s/it]\n100%|██████████| 15/15 [00:29<00:00, 1.96s/it]", "metrics": { "predict_time": 62.450349, "total_time": 77.774795 }, "output": [ { "file": "https://replicate.delivery/mgxm/99e0bbd4-db34-4a75-8621-103003c56c37/upsample_predictions.png" } ], "started_at": "2022-02-16T19:47:38.735232Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/eoz5brtp7beapipjyizmqrtx2y", "cancel": "https://api.replicate.com/v1/predictions/eoz5brtp7beapipjyizmqrtx2y/cancel" }, "version": "83369f5ab8cbe48301e6ac12c328b72ba25d8507027eadf2e81e8974e02ebbb7" }Generated in0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:02<00:56, 2.10s/it] 7%|▋ | 2/28 [00:03<00:48, 1.85s/it] 11%|█ | 3/28 [00:05<00:44, 1.78s/it] 14%|█▍ | 4/28 [00:05<00:29, 1.24s/it] 18%|█▊ | 5/28 [00:06<00:21, 1.05it/s] 21%|██▏ | 6/28 [00:06<00:16, 1.30it/s] 25%|██▌ | 7/28 [00:07<00:13, 1.52it/s] 29%|██▊ | 8/28 [00:07<00:11, 1.72it/s] 32%|███▏ | 9/28 [00:08<00:10, 1.88it/s] 36%|███▌ | 10/28 [00:08<00:08, 2.00it/s] 39%|███▉ | 11/28 [00:08<00:08, 2.10it/s] 43%|████▎ | 12/28 [00:09<00:07, 2.17it/s] 46%|████▋ | 13/28 [00:09<00:06, 2.22it/s] 50%|█████ | 14/28 [00:10<00:06, 2.25it/s] 54%|█████▎ | 15/28 [00:10<00:05, 2.28it/s] 57%|█████▋ | 16/28 [00:11<00:05, 2.30it/s] 61%|██████ | 17/28 [00:11<00:04, 2.31it/s] 64%|██████▍ | 18/28 [00:11<00:04, 2.32it/s] 68%|██████▊ | 19/28 [00:12<00:03, 2.33it/s] 71%|███████▏ | 20/28 [00:12<00:03, 2.33it/s] 75%|███████▌ | 21/28 [00:13<00:02, 2.34it/s] 79%|███████▊ | 22/28 [00:13<00:02, 2.34it/s] 82%|████████▏ | 23/28 [00:13<00:02, 2.34it/s] 86%|████████▌ | 24/28 [00:14<00:01, 2.34it/s] 89%|████████▉ | 25/28 [00:14<00:01, 2.33it/s] 93%|█████████▎| 26/28 [00:15<00:00, 2.33it/s] 96%|█████████▋| 27/28 [00:15<00:00, 2.32it/s] 100%|██████████| 28/28 [00:16<00:00, 2.32it/s] 100%|██████████| 28/28 [00:16<00:00, 1.73it/s] 0%| | 0/15 [00:00<?, ?it/s] 7%|▋ | 1/15 [00:04<01:07, 4.79s/it] 13%|█▎ | 2/15 [00:09<01:02, 4.81s/it] 20%|██ | 3/15 [00:14<00:58, 4.84s/it] 27%|██▋ | 4/15 [00:15<00:37, 3.41s/it] 33%|███▎ | 5/15 [00:16<00:26, 2.63s/it] 40%|████ | 6/15 [00:18<00:19, 2.15s/it] 47%|████▋ | 7/15 [00:19<00:14, 1.85s/it] 53%|█████▎ | 8/15 [00:20<00:11, 1.66s/it] 60%|██████ | 9/15 [00:21<00:09, 1.53s/it] 67%|██████▋ | 10/15 [00:23<00:07, 1.44s/it] 73%|███████▎ | 11/15 [00:24<00:05, 1.39s/it] 80%|████████ | 12/15 [00:25<00:04, 1.35s/it] 87%|████████▋ | 13/15 [00:26<00:02, 1.32s/it] 93%|█████████▎| 14/15 [00:28<00:01, 1.31s/it] 100%|██████████| 15/15 [00:29<00:00, 1.29s/it] 100%|██████████| 15/15 [00:29<00:00, 1.96s/it]Prediction
afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4IDfxjuxfbjnrgvppyr466ug2psreStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 234
- prompt
- detailed oil painting of a pembroke welsh corgi
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "1.0"
- guidance_scale
- 4
- upsample_stage
- timestep_respacing
- 35
- sr_timestep_respacing
- 27
{ "seed": 234, "prompt": "detailed oil painting of a pembroke welsh corgi", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "35", "sr_timestep_respacing": "27" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", { input: { seed: 234, prompt: "detailed oil painting of a pembroke welsh corgi", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "1.0", guidance_scale: 4, upsample_stage: true, timestep_respacing: "35", sr_timestep_respacing: "27" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", input={ "seed": 234, "prompt": "detailed oil painting of a pembroke welsh corgi", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": True, "timestep_respacing": "35", "sr_timestep_respacing": "27" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", "input": { "seed": 234, "prompt": "detailed oil painting of a pembroke welsh corgi", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "35", "sr_timestep_respacing": "27" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-16T21:13:19.703037Z", "created_at": "2022-02-16T21:12:43.381788Z", "data_removed": false, "error": null, "id": "fxjuxfbjnrgvppyr466ug2psre", "input": { "seed": 234, "prompt": "detailed oil painting of a pembroke welsh corgi", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "35", "sr_timestep_respacing": "27" }, "logs": "\n 0%| | 0/33 [00:00<?, ?it/s]\n 3%|▎ | 1/33 [00:00<00:24, 1.31it/s]\n 6%|▌ | 2/33 [00:01<00:22, 1.38it/s]\n 9%|▉ | 3/33 [00:02<00:21, 1.40it/s]\n 12%|█▏ | 4/33 [00:02<00:14, 1.99it/s]\n 15%|█▌ | 5/33 [00:02<00:10, 2.59it/s]\n 18%|█▊ | 6/33 [00:02<00:08, 3.18it/s]\n 21%|██ | 7/33 [00:02<00:07, 3.71it/s]\n 24%|██▍ | 8/33 [00:03<00:05, 4.17it/s]\n 27%|██▋ | 9/33 [00:03<00:05, 4.54it/s]\n 30%|███ | 10/33 [00:03<00:04, 4.82it/s]\n 33%|███▎ | 11/33 [00:03<00:04, 5.06it/s]\n 36%|███▋ | 12/33 [00:03<00:04, 5.23it/s]\n 39%|███▉ | 13/33 [00:03<00:03, 5.36it/s]\n 42%|████▏ | 14/33 [00:04<00:03, 5.44it/s]\n 45%|████▌ | 15/33 [00:04<00:03, 5.50it/s]\n 48%|████▊ | 16/33 [00:04<00:03, 5.54it/s]\n 52%|█████▏ | 17/33 [00:04<00:02, 5.56it/s]\n 55%|█████▍ | 18/33 [00:04<00:02, 5.59it/s]\n 58%|█████▊ | 19/33 [00:04<00:02, 5.60it/s]\n 61%|██████ | 20/33 [00:05<00:02, 5.61it/s]\n 64%|██████▎ | 21/33 [00:05<00:02, 5.63it/s]\n 67%|██████▋ | 22/33 [00:05<00:01, 5.63it/s]\n 70%|██████▉ | 23/33 [00:05<00:01, 5.63it/s]\n 73%|███████▎ | 24/33 [00:05<00:01, 5.62it/s]\n 76%|███████▌ | 25/33 [00:06<00:01, 5.64it/s]\n 79%|███████▉ | 26/33 [00:06<00:01, 5.63it/s]\n 82%|████████▏ | 27/33 [00:06<00:01, 5.63it/s]\n 85%|████████▍ | 28/33 [00:06<00:00, 5.63it/s]\n 88%|████████▊ | 29/33 [00:06<00:00, 5.63it/s]\n 91%|█████████ | 30/33 [00:06<00:00, 5.63it/s]\n 94%|█████████▍| 31/33 [00:07<00:00, 5.62it/s]\n 97%|█████████▋| 32/33 [00:07<00:00, 5.63it/s]\n100%|██████████| 33/33 [00:07<00:00, 5.62it/s]\n100%|██████████| 33/33 [00:07<00:00, 4.41it/s]\n\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:01<00:45, 1.91s/it]\n 8%|▊ | 2/25 [00:03<00:43, 1.91s/it]\n 12%|█▏ | 3/25 [00:05<00:42, 1.91s/it]\n 16%|█▌ | 4/25 [00:06<00:28, 1.35s/it]\n 20%|██ | 5/25 [00:06<00:20, 1.03s/it]\n 24%|██▍ | 6/25 [00:07<00:16, 1.18it/s]\n 28%|██▊ | 7/25 [00:07<00:13, 1.38it/s]\n 32%|███▏ | 8/25 [00:08<00:10, 1.55it/s]\n 36%|███▌ | 9/25 [00:08<00:09, 1.69it/s]\n 40%|████ | 10/25 [00:09<00:08, 1.79it/s]\n 44%|████▍ | 11/25 [00:09<00:07, 1.87it/s]\n 48%|████▊ | 12/25 [00:10<00:06, 1.93it/s]\n 52%|█████▏ | 13/25 [00:10<00:06, 1.98it/s]\n 56%|█████▌ | 14/25 [00:11<00:05, 2.01it/s]\n 60%|██████ | 15/25 [00:11<00:04, 2.03it/s]\n 64%|██████▍ | 16/25 [00:11<00:04, 2.05it/s]\n 68%|██████▊ | 17/25 [00:12<00:03, 2.06it/s]\n 72%|███████▏ | 18/25 [00:12<00:03, 2.06it/s]\n 76%|███████▌ | 19/25 [00:13<00:02, 2.06it/s]\n 80%|████████ | 20/25 [00:13<00:02, 2.07it/s]\n 84%|████████▍ | 21/25 [00:14<00:01, 2.07it/s]\n 88%|████████▊ | 22/25 [00:14<00:01, 2.07it/s]\n 92%|█████████▏| 23/25 [00:15<00:00, 2.07it/s]\n 96%|█████████▌| 24/25 [00:15<00:00, 2.07it/s]\n100%|██████████| 25/25 [00:16<00:00, 2.07it/s]\n100%|██████████| 25/25 [00:16<00:00, 1.53it/s]", "metrics": { "predict_time": 36.103013, "total_time": 36.321249 }, "output": [ { "file": "https://replicate.delivery/mgxm/fcddf21c-6531-4295-8a29-813674150d98/upsample_predictions.png" } ], "started_at": "2022-02-16T21:12:43.600024Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fxjuxfbjnrgvppyr466ug2psre", "cancel": "https://api.replicate.com/v1/predictions/fxjuxfbjnrgvppyr466ug2psre/cancel" }, "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4" }Generated in0%| | 0/33 [00:00<?, ?it/s] 3%|▎ | 1/33 [00:00<00:24, 1.31it/s] 6%|▌ | 2/33 [00:01<00:22, 1.38it/s] 9%|▉ | 3/33 [00:02<00:21, 1.40it/s] 12%|█▏ | 4/33 [00:02<00:14, 1.99it/s] 15%|█▌ | 5/33 [00:02<00:10, 2.59it/s] 18%|█▊ | 6/33 [00:02<00:08, 3.18it/s] 21%|██ | 7/33 [00:02<00:07, 3.71it/s] 24%|██▍ | 8/33 [00:03<00:05, 4.17it/s] 27%|██▋ | 9/33 [00:03<00:05, 4.54it/s] 30%|███ | 10/33 [00:03<00:04, 4.82it/s] 33%|███▎ | 11/33 [00:03<00:04, 5.06it/s] 36%|███▋ | 12/33 [00:03<00:04, 5.23it/s] 39%|███▉ | 13/33 [00:03<00:03, 5.36it/s] 42%|████▏ | 14/33 [00:04<00:03, 5.44it/s] 45%|████▌ | 15/33 [00:04<00:03, 5.50it/s] 48%|████▊ | 16/33 [00:04<00:03, 5.54it/s] 52%|█████▏ | 17/33 [00:04<00:02, 5.56it/s] 55%|█████▍ | 18/33 [00:04<00:02, 5.59it/s] 58%|█████▊ | 19/33 [00:04<00:02, 5.60it/s] 61%|██████ | 20/33 [00:05<00:02, 5.61it/s] 64%|██████▎ | 21/33 [00:05<00:02, 5.63it/s] 67%|██████▋ | 22/33 [00:05<00:01, 5.63it/s] 70%|██████▉ | 23/33 [00:05<00:01, 5.63it/s] 73%|███████▎ | 24/33 [00:05<00:01, 5.62it/s] 76%|███████▌ | 25/33 [00:06<00:01, 5.64it/s] 79%|███████▉ | 26/33 [00:06<00:01, 5.63it/s] 82%|████████▏ | 27/33 [00:06<00:01, 5.63it/s] 85%|████████▍ | 28/33 [00:06<00:00, 5.63it/s] 88%|████████▊ | 29/33 [00:06<00:00, 5.63it/s] 91%|█████████ | 30/33 [00:06<00:00, 5.63it/s] 94%|█████████▍| 31/33 [00:07<00:00, 5.62it/s] 97%|█████████▋| 32/33 [00:07<00:00, 5.63it/s] 100%|██████████| 33/33 [00:07<00:00, 5.62it/s] 100%|██████████| 33/33 [00:07<00:00, 4.41it/s] 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:01<00:45, 1.91s/it] 8%|▊ | 2/25 [00:03<00:43, 1.91s/it] 12%|█▏ | 3/25 [00:05<00:42, 1.91s/it] 16%|█▌ | 4/25 [00:06<00:28, 1.35s/it] 20%|██ | 5/25 [00:06<00:20, 1.03s/it] 24%|██▍ | 6/25 [00:07<00:16, 1.18it/s] 28%|██▊ | 7/25 [00:07<00:13, 1.38it/s] 32%|███▏ | 8/25 [00:08<00:10, 1.55it/s] 36%|███▌ | 9/25 [00:08<00:09, 1.69it/s] 40%|████ | 10/25 [00:09<00:08, 1.79it/s] 44%|████▍ | 11/25 [00:09<00:07, 1.87it/s] 48%|████▊ | 12/25 [00:10<00:06, 1.93it/s] 52%|█████▏ | 13/25 [00:10<00:06, 1.98it/s] 56%|█████▌ | 14/25 [00:11<00:05, 2.01it/s] 60%|██████ | 15/25 [00:11<00:04, 2.03it/s] 64%|██████▍ | 16/25 [00:11<00:04, 2.05it/s] 68%|██████▊ | 17/25 [00:12<00:03, 2.06it/s] 72%|███████▏ | 18/25 [00:12<00:03, 2.06it/s] 76%|███████▌ | 19/25 [00:13<00:02, 2.06it/s] 80%|████████ | 20/25 [00:13<00:02, 2.07it/s] 84%|████████▍ | 21/25 [00:14<00:01, 2.07it/s] 88%|████████▊ | 22/25 [00:14<00:01, 2.07it/s] 92%|█████████▏| 23/25 [00:15<00:00, 2.07it/s] 96%|█████████▌| 24/25 [00:15<00:00, 2.07it/s] 100%|██████████| 25/25 [00:16<00:00, 2.07it/s] 100%|██████████| 25/25 [00:16<00:00, 1.53it/s]Prediction
afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4IDm7gdprpdbjezpbvmyd75u6dom4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- an armchair in the form of a croissant. croissant is imitating an armchair. croissantchair
- side_x
- "64"
- side_y
- "64"
- batch_size
- "1"
- upsample_temp
- "0.998"
- guidance_scale
- 8
- upsample_stage
- timestep_respacing
- 27
- sr_timestep_respacing
- 15
{ "seed": 0, "prompt": "an armchair in the form of a croissant. croissant is imitating an armchair. croissantchair", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.998", "guidance_scale": 8, "upsample_stage": true, "timestep_respacing": "27", "sr_timestep_respacing": "15" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", { input: { seed: 0, prompt: "an armchair in the form of a croissant. croissant is imitating an armchair. croissantchair", side_x: "64", side_y: "64", batch_size: "1", upsample_temp: "0.998", guidance_scale: 8, upsample_stage: true, timestep_respacing: "27", sr_timestep_respacing: "15" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", input={ "seed": 0, "prompt": "an armchair in the form of a croissant. croissant is imitating an armchair. croissantchair", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.998", "guidance_scale": 8, "upsample_stage": True, "timestep_respacing": "27", "sr_timestep_respacing": "15" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", "input": { "seed": 0, "prompt": "an armchair in the form of a croissant. croissant is imitating an armchair. croissantchair", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.998", "guidance_scale": 8, "upsample_stage": true, "timestep_respacing": "27", "sr_timestep_respacing": "15" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-16T21:24:05.821683Z", "created_at": "2022-02-16T21:23:44.867093Z", "data_removed": false, "error": null, "id": "m7gdprpdbjezpbvmyd75u6dom4", "input": { "seed": 0, "prompt": "an armchair in the form of a croissant. croissant is imitating an armchair. croissantchair", "side_x": "64", "side_y": "64", "batch_size": "1", "upsample_temp": "0.998", "guidance_scale": 8, "upsample_stage": true, "timestep_respacing": "27", "sr_timestep_respacing": "15" }, "logs": "\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:10, 2.27it/s]\n 8%|▊ | 2/25 [00:00<00:09, 2.51it/s]\n 12%|█▏ | 3/25 [00:01<00:08, 2.55it/s]\n 20%|██ | 5/25 [00:01<00:04, 4.50it/s]\n 24%|██▍ | 6/25 [00:01<00:03, 5.31it/s]\n 32%|███▏ | 8/25 [00:01<00:02, 6.84it/s]\n 40%|████ | 10/25 [00:01<00:01, 7.96it/s]\n 48%|████▊ | 12/25 [00:02<00:01, 8.77it/s]\n 56%|█████▌ | 14/25 [00:02<00:01, 9.39it/s]\n 64%|██████▍ | 16/25 [00:02<00:00, 9.82it/s]\n 72%|███████▏ | 18/25 [00:02<00:00, 10.10it/s]\n 80%|████████ | 20/25 [00:02<00:00, 10.12it/s]\n 88%|████████▊ | 22/25 [00:02<00:00, 10.23it/s]\n 96%|█████████▌| 24/25 [00:03<00:00, 10.36it/s]\n100%|██████████| 25/25 [00:03<00:00, 7.65it/s]\n\n 0%| | 0/13 [00:00<?, ?it/s]\n 8%|▊ | 1/13 [00:00<00:08, 1.42it/s]\n 15%|█▌ | 2/13 [00:01<00:07, 1.41it/s]\n 23%|██▎ | 3/13 [00:02<00:07, 1.41it/s]\n 31%|███ | 4/13 [00:02<00:04, 2.01it/s]\n 38%|███▊ | 5/13 [00:02<00:03, 2.62it/s]\n 46%|████▌ | 6/13 [00:02<00:02, 3.20it/s]\n 54%|█████▍ | 7/13 [00:02<00:01, 3.72it/s]\n 62%|██████▏ | 8/13 [00:03<00:01, 4.16it/s]\n 69%|██████▉ | 9/13 [00:03<00:00, 4.53it/s]\n 77%|███████▋ | 10/13 [00:03<00:00, 4.81it/s]\n 85%|████████▍ | 11/13 [00:03<00:00, 5.03it/s]\n 92%|█████████▏| 12/13 [00:03<00:00, 5.18it/s]\n100%|██████████| 13/13 [00:03<00:00, 5.30it/s]\n100%|██████████| 13/13 [00:03<00:00, 3.33it/s]", "metrics": { "predict_time": 20.769402, "total_time": 20.95459 }, "output": [ { "file": "https://replicate.delivery/mgxm/8ca25c9d-1bde-49f7-9b5a-3d4a18b1b39c/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/21923ae3-facb-40ae-bc66-5563d83502fd/upsample_predictions.png" } ], "started_at": "2022-02-16T21:23:45.052281Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m7gdprpdbjezpbvmyd75u6dom4", "cancel": "https://api.replicate.com/v1/predictions/m7gdprpdbjezpbvmyd75u6dom4/cancel" }, "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4" }Generated in0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:10, 2.27it/s] 8%|▊ | 2/25 [00:00<00:09, 2.51it/s] 12%|█▏ | 3/25 [00:01<00:08, 2.55it/s] 20%|██ | 5/25 [00:01<00:04, 4.50it/s] 24%|██▍ | 6/25 [00:01<00:03, 5.31it/s] 32%|███▏ | 8/25 [00:01<00:02, 6.84it/s] 40%|████ | 10/25 [00:01<00:01, 7.96it/s] 48%|████▊ | 12/25 [00:02<00:01, 8.77it/s] 56%|█████▌ | 14/25 [00:02<00:01, 9.39it/s] 64%|██████▍ | 16/25 [00:02<00:00, 9.82it/s] 72%|███████▏ | 18/25 [00:02<00:00, 10.10it/s] 80%|████████ | 20/25 [00:02<00:00, 10.12it/s] 88%|████████▊ | 22/25 [00:02<00:00, 10.23it/s] 96%|█████████▌| 24/25 [00:03<00:00, 10.36it/s] 100%|██████████| 25/25 [00:03<00:00, 7.65it/s] 0%| | 0/13 [00:00<?, ?it/s] 8%|▊ | 1/13 [00:00<00:08, 1.42it/s] 15%|█▌ | 2/13 [00:01<00:07, 1.41it/s] 23%|██▎ | 3/13 [00:02<00:07, 1.41it/s] 31%|███ | 4/13 [00:02<00:04, 2.01it/s] 38%|███▊ | 5/13 [00:02<00:03, 2.62it/s] 46%|████▌ | 6/13 [00:02<00:02, 3.20it/s] 54%|█████▍ | 7/13 [00:02<00:01, 3.72it/s] 62%|██████▏ | 8/13 [00:03<00:01, 4.16it/s] 69%|██████▉ | 9/13 [00:03<00:00, 4.53it/s] 77%|███████▋ | 10/13 [00:03<00:00, 4.81it/s] 85%|████████▍ | 11/13 [00:03<00:00, 5.03it/s] 92%|█████████▏| 12/13 [00:03<00:00, 5.18it/s] 100%|██████████| 13/13 [00:03<00:00, 5.30it/s] 100%|██████████| 13/13 [00:03<00:00, 3.33it/s]Prediction
afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4IDrybz3e3x6bfyvid7pmkavpoquaStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- a goose made of paper. paper goose.
- side_x
- "64"
- side_y
- "64"
- batch_size
- "8"
- upsample_temp
- "1.0"
- guidance_scale
- 4
- upsample_stage
- timestep_respacing
- 100
- sr_timestep_respacing
- 27
{ "seed": 0, "prompt": "a goose made of paper. paper goose.", "side_x": "64", "side_y": "64", "batch_size": "8", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "100", "sr_timestep_respacing": "27" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", { input: { seed: 0, prompt: "a goose made of paper. paper goose.", side_x: "64", side_y: "64", batch_size: "8", upsample_temp: "1.0", guidance_scale: 4, upsample_stage: true, timestep_respacing: "100", sr_timestep_respacing: "27" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", input={ "seed": 0, "prompt": "a goose made of paper. paper goose.", "side_x": "64", "side_y": "64", "batch_size": "8", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": True, "timestep_respacing": "100", "sr_timestep_respacing": "27" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", "input": { "seed": 0, "prompt": "a goose made of paper. paper goose.", "side_x": "64", "side_y": "64", "batch_size": "8", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "100", "sr_timestep_respacing": "27" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-16T22:59:40.233119Z", "created_at": "2022-02-16T22:57:50.293289Z", "data_removed": false, "error": null, "id": "rybz3e3x6bfyvid7pmkavpoqua", "input": { "seed": 0, "prompt": "a goose made of paper. paper goose.", "side_x": "64", "side_y": "64", "batch_size": "8", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "100", "sr_timestep_respacing": "27" }, "logs": "\n 0%| | 0/98 [00:00<?, ?it/s]\n 1%| | 1/98 [00:01<03:04, 1.91s/it]\n 2%|▏ | 2/98 [00:03<03:01, 1.89s/it]\n 3%|▎ | 3/98 [00:05<02:58, 1.88s/it]\n 4%|▍ | 4/98 [00:06<02:04, 1.32s/it]\n 5%|▌ | 5/98 [00:06<01:34, 1.02s/it]\n 6%|▌ | 6/98 [00:07<01:16, 1.20it/s]\n 7%|▋ | 7/98 [00:07<01:05, 1.40it/s]\n 8%|▊ | 8/98 [00:08<00:57, 1.57it/s]\n 9%|▉ | 9/98 [00:08<00:52, 1.71it/s]\n 10%|█ | 10/98 [00:08<00:48, 1.80it/s]\n 11%|█ | 11/98 [00:09<00:46, 1.89it/s]\n 12%|█▏ | 12/98 [00:09<00:44, 1.95it/s]\n 13%|█▎ | 13/98 [00:10<00:42, 1.99it/s]\n 14%|█▍ | 14/98 [00:10<00:41, 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1.31s/it]\n100%|██████████| 25/25 [00:44<00:00, 1.77s/it]", "metrics": { "predict_time": 109.715669, "total_time": 109.93983 }, "output": [ { "file": "https://replicate.delivery/mgxm/53a380d7-7bb4-48d4-b76f-29e7c9b029b8/upsample_predictions.png" } ], "started_at": "2022-02-16T22:57:50.517450Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rybz3e3x6bfyvid7pmkavpoqua", "cancel": "https://api.replicate.com/v1/predictions/rybz3e3x6bfyvid7pmkavpoqua/cancel" }, "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4" }Generated in0%| | 0/98 [00:00<?, ?it/s] 1%| | 1/98 [00:01<03:04, 1.91s/it] 2%|▏ | 2/98 [00:03<03:01, 1.89s/it] 3%|▎ | 3/98 [00:05<02:58, 1.88s/it] 4%|▍ | 4/98 [00:06<02:04, 1.32s/it] 5%|▌ | 5/98 [00:06<01:34, 1.02s/it] 6%|▌ | 6/98 [00:07<01:16, 1.20it/s] 7%|▋ | 7/98 [00:07<01:05, 1.40it/s] 8%|▊ | 8/98 [00:08<00:57, 1.57it/s] 9%|▉ | 9/98 [00:08<00:52, 1.71it/s] 10%|█ | 10/98 [00:08<00:48, 1.80it/s] 11%|█ | 11/98 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afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4IDbcgycg3u3jhpvedcijdu4dturiStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- peanut butter
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "1.0"
- guidance_scale
- 4
- upsample_stage
- timestep_respacing
- 100
- sr_timestep_respacing
- 27
{ "seed": 0, "prompt": "peanut butter", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "100", "sr_timestep_respacing": "27" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", { input: { seed: 0, prompt: "peanut butter", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "1.0", guidance_scale: 4, upsample_stage: true, timestep_respacing: "100", sr_timestep_respacing: "27" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", input={ "seed": 0, "prompt": "peanut butter", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": True, "timestep_respacing": "100", "sr_timestep_respacing": "27" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", "input": { "seed": 0, "prompt": "peanut butter", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "100", "sr_timestep_respacing": "27" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-16T23:01:06.670549Z", "created_at": "2022-02-16T23:00:11.196050Z", "data_removed": false, "error": null, "id": "bcgycg3u3jhpvedcijdu4dturi", "input": { "seed": 0, "prompt": "peanut butter", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "100", "sr_timestep_respacing": "27" }, "logs": "\n 0%| | 0/98 [00:00<?, ?it/s]\n 1%| | 1/98 [00:00<01:17, 1.25it/s]\n 2%|▏ | 2/98 [00:01<01:13, 1.30it/s]\n 3%|▎ | 3/98 [00:02<01:11, 1.32it/s]\n 4%|▍ | 4/98 [00:02<00:49, 1.88it/s]\n 5%|▌ | 5/98 [00:02<00:37, 2.46it/s]\n 6%|▌ | 6/98 [00:02<00:30, 3.02it/s]\n 7%|▋ | 7/98 [00:03<00:25, 3.52it/s]\n 8%|▊ | 8/98 [00:03<00:22, 3.95it/s]\n 9%|▉ | 9/98 [00:03<00:20, 4.31it/s]\n 10%|█ | 10/98 [00:03<00:19, 4.58it/s]\n 11%|█ | 11/98 [00:03<00:18, 4.79it/s]\n 12%|█▏ | 12/98 [00:03<00:17, 4.95it/s]\n 13%|█▎ | 13/98 [00:04<00:16, 5.06it/s]\n 14%|█▍ | 14/98 [00:04<00:16, 5.15it/s]\n 15%|█▌ | 15/98 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[00:17<00:00, 1.40it/s]", "metrics": { "predict_time": 55.227602, "total_time": 55.474499 }, "output": [ { "file": "https://replicate.delivery/mgxm/87ab0142-a5fe-4344-a867-f82658b32832/base_predictions.png" }, { "file": "https://replicate.delivery/mgxm/b898423c-3b3f-4e17-9503-355552361081/upsample_predictions.png" } ], "started_at": "2022-02-16T23:00:11.442947Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bcgycg3u3jhpvedcijdu4dturi", "cancel": "https://api.replicate.com/v1/predictions/bcgycg3u3jhpvedcijdu4dturi/cancel" }, "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4" }Generated in0%| | 0/98 [00:00<?, ?it/s] 1%| | 1/98 [00:00<01:17, 1.25it/s] 2%|▏ | 2/98 [00:01<01:13, 1.30it/s] 3%|▎ | 3/98 [00:02<01:11, 1.32it/s] 4%|▍ | 4/98 [00:02<00:49, 1.88it/s] 5%|▌ | 5/98 [00:02<00:37, 2.46it/s] 6%|▌ | 6/98 [00:02<00:30, 3.02it/s] 7%|▋ | 7/98 [00:03<00:25, 3.52it/s] 8%|▊ | 8/98 [00:03<00:22, 3.95it/s] 9%|▉ | 9/98 [00:03<00:20, 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[00:17<00:00, 1.40it/s]Prediction
afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4IDiwqtt2xf7ndwzgzflnz4ctpgsyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 0
- prompt
- peanut butter
- side_x
- "64"
- side_y
- "64"
- batch_size
- "3"
- upsample_temp
- "1.0"
- guidance_scale
- 4
- upsample_stage
- timestep_respacing
- 50
- sr_timestep_respacing
- 21
{ "seed": 0, "prompt": "peanut butter", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "50", "sr_timestep_respacing": "21" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", { input: { seed: 0, prompt: "peanut butter", side_x: "64", side_y: "64", batch_size: "3", upsample_temp: "1.0", guidance_scale: 4, upsample_stage: true, timestep_respacing: "50", sr_timestep_respacing: "21" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/pyglide:9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", input={ "seed": 0, "prompt": "peanut butter", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": True, "timestep_respacing": "50", "sr_timestep_respacing": "21" } ) # The afiaka87/pyglide model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/pyglide/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/pyglide using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4", "input": { "seed": 0, "prompt": "peanut butter", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "50", "sr_timestep_respacing": "21" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-16T23:02:12.539888Z", "created_at": "2022-02-16T23:01:33.456534Z", "data_removed": false, "error": null, "id": "iwqtt2xf7ndwzgzflnz4ctpgsy", "input": { "seed": 0, "prompt": "peanut butter", "side_x": "64", "side_y": "64", "batch_size": "3", "upsample_temp": "1.0", "guidance_scale": 4, "upsample_stage": true, "timestep_respacing": "50", "sr_timestep_respacing": "21" }, "logs": "\n 0%| | 0/48 [00:00<?, ?it/s]\n 2%|▏ | 1/48 [00:00<00:36, 1.28it/s]\n 4%|▍ | 2/48 [00:01<00:34, 1.32it/s]\n 6%|▋ | 3/48 [00:02<00:33, 1.34it/s]\n 8%|▊ | 4/48 [00:02<00:23, 1.90it/s]\n 10%|█ | 5/48 [00:02<00:17, 2.48it/s]\n 12%|█▎ | 6/48 [00:02<00:13, 3.05it/s]\n 15%|█▍ | 7/48 [00:02<00:11, 3.56it/s]\n 17%|█▋ | 8/48 [00:03<00:10, 3.99it/s]\n 19%|█▉ | 9/48 [00:03<00:09, 4.33it/s]\n 21%|██ | 10/48 [00:03<00:08, 4.61it/s]\n 23%|██▎ | 11/48 [00:03<00:07, 4.82it/s]\n 25%|██▌ | 12/48 [00:03<00:07, 4.99it/s]\n 27%|██▋ | 13/48 [00:04<00:06, 5.11it/s]\n 29%|██▉ | 14/48 [00:04<00:06, 5.18it/s]\n 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37/48 [00:08<00:02, 5.34it/s]\n 79%|███████▉ | 38/48 [00:08<00:01, 5.35it/s]\n 81%|████████▏ | 39/48 [00:08<00:01, 5.34it/s]\n 83%|████████▎ | 40/48 [00:09<00:01, 5.35it/s]\n 85%|████████▌ | 41/48 [00:09<00:01, 5.31it/s]\n 88%|████████▊ | 42/48 [00:09<00:01, 5.32it/s]\n 90%|████████▉ | 43/48 [00:09<00:00, 5.32it/s]\n 92%|█████████▏| 44/48 [00:09<00:00, 5.34it/s]\n 94%|█████████▍| 45/48 [00:10<00:00, 5.32it/s]\n 96%|█████████▌| 46/48 [00:10<00:00, 5.31it/s]\n 98%|█████████▊| 47/48 [00:10<00:00, 5.32it/s]\n100%|██████████| 48/48 [00:10<00:00, 5.32it/s]\n100%|██████████| 48/48 [00:10<00:00, 4.50it/s]\n\n 0%| | 0/19 [00:00<?, ?it/s]\n 5%|▌ | 1/19 [00:02<00:36, 2.04s/it]\n 11%|█ | 2/19 [00:04<00:34, 2.04s/it]\n 16%|█▌ | 3/19 [00:06<00:32, 2.04s/it]\n 21%|██ | 4/19 [00:06<00:21, 1.44s/it]\n 26%|██▋ | 5/19 [00:07<00:15, 1.10s/it]\n 32%|███▏ | 6/19 [00:07<00:11, 1.11it/s]\n 37%|███▋ | 7/19 [00:08<00:09, 1.29it/s]\n 42%|████▏ | 8/19 [00:08<00:07, 1.44it/s]\n 47%|████▋ | 9/19 [00:09<00:06, 1.56it/s]\n 53%|█████▎ | 10/19 [00:09<00:05, 1.66it/s]\n 58%|█████▊ | 11/19 [00:10<00:04, 1.73it/s]\n 63%|██████▎ | 12/19 [00:10<00:03, 1.79it/s]\n 68%|██████▊ | 13/19 [00:11<00:03, 1.83it/s]\n 74%|███████▎ | 14/19 [00:11<00:02, 1.85it/s]\n 79%|███████▉ | 15/19 [00:12<00:02, 1.87it/s]\n 84%|████████▍ | 16/19 [00:12<00:01, 1.88it/s]\n 89%|████████▉ | 17/19 [00:13<00:01, 1.89it/s]\n 95%|█████████▍| 18/19 [00:13<00:00, 1.90it/s]\n100%|██████████| 19/19 [00:14<00:00, 1.90it/s]\n100%|██████████| 19/19 [00:14<00:00, 1.32it/s]", "metrics": { "predict_time": 38.849514, "total_time": 39.083354 }, "output": [ { "file": "https://replicate.delivery/mgxm/8518196c-ebe8-47a1-9eae-664330aa266a/upsample_predictions.png" } ], "started_at": "2022-02-16T23:01:33.690374Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iwqtt2xf7ndwzgzflnz4ctpgsy", "cancel": "https://api.replicate.com/v1/predictions/iwqtt2xf7ndwzgzflnz4ctpgsy/cancel" }, "version": "9218d6e9a0f5f147f53f31ab6a2d562e5e88a3dab5c2f74fce9e067b868b9ca4" }Generated in0%| | 0/48 [00:00<?, ?it/s] 2%|▏ | 1/48 [00:00<00:36, 1.28it/s] 4%|▍ | 2/48 [00:01<00:34, 1.32it/s] 6%|▋ | 3/48 [00:02<00:33, 1.34it/s] 8%|▊ | 4/48 [00:02<00:23, 1.90it/s] 10%|█ | 5/48 [00:02<00:17, 2.48it/s] 12%|█▎ | 6/48 [00:02<00:13, 3.05it/s] 15%|█▍ | 7/48 [00:02<00:11, 3.56it/s] 17%|█▋ | 8/48 [00:03<00:10, 3.99it/s] 19%|█▉ | 9/48 [00:03<00:09, 4.33it/s] 21%|██ | 10/48 [00:03<00:08, 4.61it/s] 23%|██▎ | 11/48 [00:03<00:07, 4.82it/s] 25%|██▌ | 12/48 [00:03<00:07, 4.99it/s] 27%|██▋ | 13/48 [00:04<00:06, 5.11it/s] 29%|██▉ | 14/48 [00:04<00:06, 5.18it/s] 31%|███▏ | 15/48 [00:04<00:06, 5.23it/s] 33%|███▎ | 16/48 [00:04<00:06, 5.27it/s] 35%|███▌ | 17/48 [00:04<00:05, 5.31it/s] 38%|███▊ | 18/48 [00:05<00:05, 5.33it/s] 40%|███▉ | 19/48 [00:05<00:05, 5.33it/s] 42%|████▏ | 20/48 [00:05<00:05, 5.33it/s] 44%|████▍ | 21/48 [00:05<00:05, 5.35it/s] 46%|████▌ | 22/48 [00:05<00:04, 5.36it/s] 48%|████▊ | 23/48 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