workroomprds / jamesbooth1
To explore stablebooth (training and prompts) trained on pictures of James!
- Public
- 261 runs
-
T4
Prediction
workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdIDskrhbiio7ncrjflnhu7iahawbyStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a compelling HDR silverpoint photo of a workroomprds person
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a compelling HDR silverpoint photo of a workroomprds person", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", { input: { width: 512, height: 512, prompt: "a compelling HDR silverpoint photo of a workroomprds person", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", input={ "width": 512, "height": 512, "prompt": "a compelling HDR silverpoint photo of a workroomprds person", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR silverpoint photo of a workroomprds person", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a compelling HDR silverpoint photo of a workroomprds person"' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "a compelling HDR silverpoint photo of a workroomprds person", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-22T20:21:23.312878Z", "created_at": "2023-02-22T20:17:22.665126Z", "data_removed": false, "error": null, "id": "skrhbiio7ncrjflnhu7iahawby", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR silverpoint photo of a workroomprds person", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 10448\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<02:14, 2.75s/it]\n 4%|▍ | 2/50 [00:02<01:00, 1.25s/it]\n 6%|▌ | 3/50 [00:03<00:35, 1.31it/s]\n 8%|▊ | 4/50 [00:03<00:24, 1.88it/s]\n 10%|█ | 5/50 [00:03<00:18, 2.48it/s]\n 12%|█▏ | 6/50 [00:03<00:14, 3.06it/s]\n 14%|█▍ | 7/50 [00:03<00:12, 3.57it/s]\n 16%|█▌ | 8/50 [00:04<00:10, 4.03it/s]\n 18%|█▊ | 9/50 [00:04<00:09, 4.42it/s]\n 20%|██ | 10/50 [00:04<00:08, 4.72it/s]\n 22%|██▏ | 11/50 [00:04<00:07, 4.96it/s]\n 24%|██▍ | 12/50 [00:04<00:07, 5.14it/s]\n 26%|██▌ | 13/50 [00:04<00:07, 5.27it/s]\n 28%|██▊ | 14/50 [00:05<00:06, 5.37it/s]\n 30%|███ | 15/50 [00:05<00:06, 5.42it/s]\n 32%|███▏ | 16/50 [00:05<00:06, 5.45it/s]\n 34%|███▍ | 17/50 [00:05<00:06, 5.46it/s]\n 36%|███▌ | 18/50 [00:05<00:05, 5.47it/s]\n 38%|███▊ | 19/50 [00:06<00:05, 5.49it/s]\n 40%|████ | 20/50 [00:06<00:05, 5.54it/s]\n 42%|████▏ | 21/50 [00:06<00:05, 5.56it/s]\n 44%|████▍ | 22/50 [00:06<00:05, 5.57it/s]\n 46%|████▌ | 23/50 [00:06<00:04, 5.54it/s]\n 48%|████▊ | 24/50 [00:06<00:04, 5.52it/s]\n 50%|█████ | 25/50 [00:07<00:04, 5.52it/s]\n 52%|█████▏ | 26/50 [00:07<00:04, 5.53it/s]\n 54%|█████▍ | 27/50 [00:07<00:04, 5.53it/s]\n 56%|█████▌ | 28/50 [00:07<00:03, 5.52it/s]\n 58%|█████▊ | 29/50 [00:07<00:03, 5.51it/s]\n 60%|██████ | 30/50 [00:07<00:03, 5.51it/s]\n 62%|██████▏ | 31/50 [00:08<00:03, 5.53it/s]\n 64%|██████▍ | 32/50 [00:08<00:03, 5.56it/s]\n 66%|██████▌ | 33/50 [00:08<00:03, 5.57it/s]\n 68%|██████▊ | 34/50 [00:08<00:02, 5.56it/s]\n 70%|███████ | 35/50 [00:08<00:02, 5.55it/s]\n 72%|███████▏ | 36/50 [00:09<00:02, 5.55it/s]\n 74%|███████▍ | 37/50 [00:09<00:02, 5.53it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 5.52it/s]\n 78%|███████▊ | 39/50 [00:09<00:01, 5.52it/s]\n 80%|████████ | 40/50 [00:09<00:01, 5.51it/s]\n 82%|████████▏ | 41/50 [00:09<00:01, 5.50it/s]\n 84%|████████▍ | 42/50 [00:10<00:01, 5.50it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 5.50it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 5.50it/s]\n 90%|█████████ | 45/50 [00:10<00:00, 5.50it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 5.50it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 5.50it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 5.52it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 5.55it/s]\n100%|██████████| 50/50 [00:11<00:00, 5.57it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.31it/s]", "metrics": { "predict_time": 15.193982, "total_time": 240.647752 }, "output": [ "https://replicate.delivery/pbxt/Ftrs34oudl7CD5qn2DBJXcUZ7ehlHfrF3L6EPfa35kFFaFChA/out-0.png" ], "started_at": "2023-02-22T20:21:08.118896Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/skrhbiio7ncrjflnhu7iahawby", "cancel": "https://api.replicate.com/v1/predictions/skrhbiio7ncrjflnhu7iahawby/cancel" }, "version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd" }Generated inUsing seed: 10448 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<02:14, 2.75s/it] 4%|▍ | 2/50 [00:02<01:00, 1.25s/it] 6%|▌ | 3/50 [00:03<00:35, 1.31it/s] 8%|▊ | 4/50 [00:03<00:24, 1.88it/s] 10%|█ | 5/50 [00:03<00:18, 2.48it/s] 12%|█▏ | 6/50 [00:03<00:14, 3.06it/s] 14%|█▍ | 7/50 [00:03<00:12, 3.57it/s] 16%|█▌ | 8/50 [00:04<00:10, 4.03it/s] 18%|█▊ | 9/50 [00:04<00:09, 4.42it/s] 20%|██ | 10/50 [00:04<00:08, 4.72it/s] 22%|██▏ | 11/50 [00:04<00:07, 4.96it/s] 24%|██▍ | 12/50 [00:04<00:07, 5.14it/s] 26%|██▌ | 13/50 [00:04<00:07, 5.27it/s] 28%|██▊ | 14/50 [00:05<00:06, 5.37it/s] 30%|███ | 15/50 [00:05<00:06, 5.42it/s] 32%|███▏ | 16/50 [00:05<00:06, 5.45it/s] 34%|███▍ | 17/50 [00:05<00:06, 5.46it/s] 36%|███▌ | 18/50 [00:05<00:05, 5.47it/s] 38%|███▊ | 19/50 [00:06<00:05, 5.49it/s] 40%|████ | 20/50 [00:06<00:05, 5.54it/s] 42%|████▏ | 21/50 [00:06<00:05, 5.56it/s] 44%|████▍ | 22/50 [00:06<00:05, 5.57it/s] 46%|████▌ | 23/50 [00:06<00:04, 5.54it/s] 48%|████▊ | 24/50 [00:06<00:04, 5.52it/s] 50%|█████ | 25/50 [00:07<00:04, 5.52it/s] 52%|█████▏ | 26/50 [00:07<00:04, 5.53it/s] 54%|█████▍ | 27/50 [00:07<00:04, 5.53it/s] 56%|█████▌ | 28/50 [00:07<00:03, 5.52it/s] 58%|█████▊ | 29/50 [00:07<00:03, 5.51it/s] 60%|██████ | 30/50 [00:07<00:03, 5.51it/s] 62%|██████▏ | 31/50 [00:08<00:03, 5.53it/s] 64%|██████▍ | 32/50 [00:08<00:03, 5.56it/s] 66%|██████▌ | 33/50 [00:08<00:03, 5.57it/s] 68%|██████▊ | 34/50 [00:08<00:02, 5.56it/s] 70%|███████ | 35/50 [00:08<00:02, 5.55it/s] 72%|███████▏ | 36/50 [00:09<00:02, 5.55it/s] 74%|███████▍ | 37/50 [00:09<00:02, 5.53it/s] 76%|███████▌ | 38/50 [00:09<00:02, 5.52it/s] 78%|███████▊ | 39/50 [00:09<00:01, 5.52it/s] 80%|████████ | 40/50 [00:09<00:01, 5.51it/s] 82%|████████▏ | 41/50 [00:09<00:01, 5.50it/s] 84%|████████▍ | 42/50 [00:10<00:01, 5.50it/s] 86%|████████▌ | 43/50 [00:10<00:01, 5.50it/s] 88%|████████▊ | 44/50 [00:10<00:01, 5.50it/s] 90%|█████████ | 45/50 [00:10<00:00, 5.50it/s] 92%|█████████▏| 46/50 [00:10<00:00, 5.50it/s] 94%|█████████▍| 47/50 [00:11<00:00, 5.50it/s] 96%|█████████▌| 48/50 [00:11<00:00, 5.52it/s] 98%|█████████▊| 49/50 [00:11<00:00, 5.55it/s] 100%|██████████| 50/50 [00:11<00:00, 5.57it/s] 100%|██████████| 50/50 [00:11<00:00, 4.31it/s]Prediction
workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdID4kkvyaj2svf6riim2duotuyhbmStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a compelling HDR portrait of a workroomprds person in 1950 film noir
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1950 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", { input: { width: 512, height: 512, prompt: "a compelling HDR portrait of a workroomprds person in 1950 film noir", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", input={ "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1950 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1950 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a compelling HDR portrait of a workroomprds person in 1950 film noir"' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1950 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-22T20:24:29.715346Z", "created_at": "2023-02-22T20:24:19.225174Z", "data_removed": false, "error": null, "id": "4kkvyaj2svf6riim2duotuyhbm", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1950 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5284\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.94it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.61it/s]\n 6%|▌ | 3/50 [00:00<00:09, 4.97it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.15it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.25it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.29it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 5.30it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.37it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.42it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.45it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.46it/s]\n 24%|██▍ | 12/50 [00:02<00:06, 5.43it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.45it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.47it/s]\n 30%|███ | 15/50 [00:02<00:06, 5.49it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 5.50it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 5.48it/s]\n 36%|███▌ | 18/50 [00:03<00:05, 5.46it/s]\n 38%|███▊ | 19/50 [00:03<00:05, 5.46it/s]\n 40%|████ | 20/50 [00:03<00:05, 5.48it/s]\n 42%|████▏ | 21/50 [00:03<00:05, 5.49it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 5.49it/s]\n 46%|████▌ | 23/50 [00:04<00:04, 5.48it/s]\n 48%|████▊ | 24/50 [00:04<00:04, 5.47it/s]\n 50%|█████ | 25/50 [00:04<00:04, 5.47it/s]\n 52%|█████▏ | 26/50 [00:04<00:04, 5.48it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 5.49it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 5.47it/s]\n 58%|█████▊ | 29/50 [00:05<00:03, 5.47it/s]\n 60%|██████ | 30/50 [00:05<00:03, 5.47it/s]\n 62%|██████▏ | 31/50 [00:05<00:03, 5.47it/s]\n 64%|██████▍ | 32/50 [00:05<00:03, 5.49it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 5.48it/s]\n 68%|██████▊ | 34/50 [00:06<00:02, 5.49it/s]\n 70%|███████ | 35/50 [00:06<00:02, 5.48it/s]\n 72%|███████▏ | 36/50 [00:06<00:02, 5.48it/s]\n 74%|███████▍ | 37/50 [00:06<00:02, 5.48it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 5.48it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 5.47it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.44it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.41it/s]\n 84%|████████▍ | 42/50 [00:07<00:01, 5.42it/s]\n 86%|████████▌ | 43/50 [00:07<00:01, 5.41it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 5.43it/s]\n 90%|█████████ | 45/50 [00:08<00:00, 5.44it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.44it/s]\n 94%|█████████▍| 47/50 [00:08<00:00, 5.45it/s]\n 96%|█████████▌| 48/50 [00:08<00:00, 5.46it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 5.46it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.46it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.42it/s]", "metrics": { "predict_time": 10.421526, "total_time": 10.490172 }, "output": [ "https://replicate.delivery/pbxt/UqKnsX3CLEIhARjVtggjAs895ZD6xpwqGVAuf0TKjuievChQA/out-0.png" ], "started_at": "2023-02-22T20:24:19.293820Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4kkvyaj2svf6riim2duotuyhbm", "cancel": "https://api.replicate.com/v1/predictions/4kkvyaj2svf6riim2duotuyhbm/cancel" }, "version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd" }Generated inUsing seed: 5284 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:12, 3.94it/s] 4%|▍ | 2/50 [00:00<00:10, 4.61it/s] 6%|▌ | 3/50 [00:00<00:09, 4.97it/s] 8%|▊ | 4/50 [00:00<00:08, 5.15it/s] 10%|█ | 5/50 [00:00<00:08, 5.25it/s] 12%|█▏ | 6/50 [00:01<00:08, 5.29it/s] 14%|█▍ | 7/50 [00:01<00:08, 5.30it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.37it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.42it/s] 20%|██ | 10/50 [00:01<00:07, 5.45it/s] 22%|██▏ | 11/50 [00:02<00:07, 5.46it/s] 24%|██▍ | 12/50 [00:02<00:06, 5.43it/s] 26%|██▌ | 13/50 [00:02<00:06, 5.45it/s] 28%|██▊ | 14/50 [00:02<00:06, 5.47it/s] 30%|███ | 15/50 [00:02<00:06, 5.49it/s] 32%|███▏ | 16/50 [00:03<00:06, 5.50it/s] 34%|███▍ | 17/50 [00:03<00:06, 5.48it/s] 36%|███▌ | 18/50 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workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdIDxshb2bomhvgjxoudo4kk6ecrbmStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a compelling HDR portrait of a workroomprds person in 1930 film noir
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1930 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", { input: { width: 512, height: 512, prompt: "a compelling HDR portrait of a workroomprds person in 1930 film noir", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", input={ "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1930 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1930 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a compelling HDR portrait of a workroomprds person in 1930 film noir"' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1930 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-22T20:24:52.265098Z", "created_at": "2023-02-22T20:24:41.650136Z", "data_removed": false, "error": null, "id": "xshb2bomhvgjxoudo4kk6ecrbm", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR portrait of a workroomprds person in 1930 film noir", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 13986\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.95it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.76it/s]\n 6%|▌ | 3/50 [00:00<00:09, 5.07it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.23it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.32it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.33it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 5.35it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.36it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.36it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.37it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.38it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 5.38it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.39it/s]\n 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[00:06<00:02, 5.36it/s]\n 74%|███████▍ | 37/50 [00:06<00:02, 5.38it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 5.39it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 5.38it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.34it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.36it/s]\n 84%|████████▍ | 42/50 [00:07<00:01, 5.38it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 5.40it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 5.38it/s]\n 90%|█████████ | 45/50 [00:08<00:00, 5.35it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.35it/s]\n 94%|█████████▍| 47/50 [00:08<00:00, 5.37it/s]\n 96%|█████████▌| 48/50 [00:08<00:00, 5.37it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 5.36it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.35it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.35it/s]", "metrics": { "predict_time": 10.545126, "total_time": 10.614962 }, "output": [ "https://replicate.delivery/pbxt/Qhti9Ie3fMnCbkU2d45yrXg0w3ReVauJoeV9o9veqFSeEsQIE/out-0.png" ], "started_at": "2023-02-22T20:24:41.719972Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xshb2bomhvgjxoudo4kk6ecrbm", "cancel": "https://api.replicate.com/v1/predictions/xshb2bomhvgjxoudo4kk6ecrbm/cancel" }, "version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd" }Generated inUsing seed: 13986 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:12, 3.95it/s] 4%|▍ | 2/50 [00:00<00:10, 4.76it/s] 6%|▌ | 3/50 [00:00<00:09, 5.07it/s] 8%|▊ | 4/50 [00:00<00:08, 5.23it/s] 10%|█ | 5/50 [00:00<00:08, 5.32it/s] 12%|█▏ | 6/50 [00:01<00:08, 5.33it/s] 14%|█▍ | 7/50 [00:01<00:08, 5.35it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.36it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.36it/s] 20%|██ | 10/50 [00:01<00:07, 5.37it/s] 22%|██▏ | 11/50 [00:02<00:07, 5.38it/s] 24%|██▍ | 12/50 [00:02<00:07, 5.38it/s] 26%|██▌ | 13/50 [00:02<00:06, 5.39it/s] 28%|██▊ | 14/50 [00:02<00:06, 5.40it/s] 30%|███ | 15/50 [00:02<00:06, 5.41it/s] 32%|███▏ | 16/50 [00:03<00:06, 5.39it/s] 34%|███▍ | 17/50 [00:03<00:06, 5.36it/s] 36%|███▌ | 18/50 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[00:07<00:01, 5.36it/s] 84%|████████▍ | 42/50 [00:07<00:01, 5.38it/s] 86%|████████▌ | 43/50 [00:08<00:01, 5.40it/s] 88%|████████▊ | 44/50 [00:08<00:01, 5.38it/s] 90%|█████████ | 45/50 [00:08<00:00, 5.35it/s] 92%|█████████▏| 46/50 [00:08<00:00, 5.35it/s] 94%|█████████▍| 47/50 [00:08<00:00, 5.37it/s] 96%|█████████▌| 48/50 [00:08<00:00, 5.37it/s] 98%|█████████▊| 49/50 [00:09<00:00, 5.36it/s] 100%|██████████| 50/50 [00:09<00:00, 5.35it/s] 100%|██████████| 50/50 [00:09<00:00, 5.35it/s]Prediction
workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdIDvqapghh5dbae3h6daxtyofq4u4StatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a delightful technicolour film still from the Wizard of Oz of a workroomprds person
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a delightful technicolour film still from the Wizard of Oz of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", { input: { width: 512, height: 512, prompt: "a delightful technicolour film still from the Wizard of Oz of a workroomprds person ", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", input={ "width": 512, "height": 512, "prompt": "a delightful technicolour film still from the Wizard of Oz of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", "input": { "width": 512, "height": 512, "prompt": "a delightful technicolour film still from the Wizard of Oz of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a delightful technicolour film still from the Wizard of Oz of a workroomprds person "' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "a delightful technicolour film still from the Wizard of Oz of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-22T20:27:01.108603Z", "created_at": "2023-02-22T20:26:50.238910Z", "data_removed": false, "error": null, "id": "vqapghh5dbae3h6daxtyofq4u4", "input": { "width": 512, "height": 512, "prompt": "a delightful technicolour film still from the Wizard of Oz of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 49141\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.49it/s]\n 6%|▌ | 3/50 [00:00<00:09, 4.85it/s]\n 8%|▊ | 4/50 [00:00<00:09, 5.03it/s]\n 10%|█ | 5/50 [00:01<00:08, 5.12it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 5.09it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 5.07it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 5.13it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.17it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.21it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.21it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 5.15it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 5.18it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.23it/s]\n 30%|███ | 15/50 [00:02<00:06, 5.22it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 5.24it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 5.22it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 5.20it/s]\n 38%|███▊ | 19/50 [00:03<00:05, 5.21it/s]\n 40%|████ | 20/50 [00:03<00:05, 5.23it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 5.21it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 5.20it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 5.21it/s]\n 48%|████▊ | 24/50 [00:04<00:04, 5.22it/s]\n 50%|█████ | 25/50 [00:04<00:04, 5.23it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 5.22it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 5.19it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 5.17it/s]\n 58%|█████▊ | 29/50 [00:05<00:04, 5.17it/s]\n 60%|██████ | 30/50 [00:05<00:03, 5.21it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 5.20it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 5.21it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 5.20it/s]\n 68%|██████▊ | 34/50 [00:06<00:03, 5.19it/s]\n 70%|███████ | 35/50 [00:06<00:02, 5.20it/s]\n 72%|███████▏ | 36/50 [00:06<00:02, 5.20it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 5.20it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 5.22it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 5.20it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.20it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.22it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 5.21it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 5.20it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 5.21it/s]\n 90%|█████████ | 45/50 [00:08<00:00, 5.20it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.20it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 5.21it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 5.20it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 5.19it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.21it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.17it/s]", "metrics": { "predict_time": 10.802573, "total_time": 10.869693 }, "output": [ "https://replicate.delivery/pbxt/F2Z5M1zGs3r0AVf1nZMJMzAHP9ulPxxBX7fYnGhffQJRJLECB/out-0.png" ], "started_at": "2023-02-22T20:26:50.306030Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vqapghh5dbae3h6daxtyofq4u4", "cancel": "https://api.replicate.com/v1/predictions/vqapghh5dbae3h6daxtyofq4u4/cancel" }, "version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd" }Generated inUsing seed: 49141 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:10, 4.49it/s] 6%|▌ | 3/50 [00:00<00:09, 4.85it/s] 8%|▊ | 4/50 [00:00<00:09, 5.03it/s] 10%|█ | 5/50 [00:01<00:08, 5.12it/s] 12%|█▏ | 6/50 [00:01<00:08, 5.09it/s] 14%|█▍ | 7/50 [00:01<00:08, 5.07it/s] 16%|█▌ | 8/50 [00:01<00:08, 5.13it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.17it/s] 20%|██ | 10/50 [00:01<00:07, 5.21it/s] 22%|██▏ | 11/50 [00:02<00:07, 5.21it/s] 24%|██▍ | 12/50 [00:02<00:07, 5.15it/s] 26%|██▌ | 13/50 [00:02<00:07, 5.18it/s] 28%|██▊ | 14/50 [00:02<00:06, 5.23it/s] 30%|███ | 15/50 [00:02<00:06, 5.22it/s] 32%|███▏ | 16/50 [00:03<00:06, 5.24it/s] 34%|███▍ | 17/50 [00:03<00:06, 5.22it/s] 36%|███▌ | 18/50 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[00:07<00:01, 5.22it/s] 84%|████████▍ | 42/50 [00:08<00:01, 5.21it/s] 86%|████████▌ | 43/50 [00:08<00:01, 5.20it/s] 88%|████████▊ | 44/50 [00:08<00:01, 5.21it/s] 90%|█████████ | 45/50 [00:08<00:00, 5.20it/s] 92%|█████████▏| 46/50 [00:08<00:00, 5.20it/s] 94%|█████████▍| 47/50 [00:09<00:00, 5.21it/s] 96%|█████████▌| 48/50 [00:09<00:00, 5.20it/s] 98%|█████████▊| 49/50 [00:09<00:00, 5.19it/s] 100%|██████████| 50/50 [00:09<00:00, 5.21it/s] 100%|██████████| 50/50 [00:09<00:00, 5.17it/s]Prediction
workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdIDwqcwn25scnehzmcw4fwmiqiepeStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- an HDR promotional portrait from a 1970s New York police television series of a workroomprds person
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "an HDR promotional portrait from a 1970s New York police television series of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", { input: { width: 512, height: 512, prompt: "an HDR promotional portrait from a 1970s New York police television series of a workroomprds person ", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", input={ "width": 512, "height": 512, "prompt": "an HDR promotional portrait from a 1970s New York police television series of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", "input": { "width": 512, "height": 512, "prompt": "an HDR promotional portrait from a 1970s New York police television series of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="an HDR promotional portrait from a 1970s New York police television series of a workroomprds person "' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "an HDR promotional portrait from a 1970s New York police television series of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-22T20:31:48.663408Z", "created_at": "2023-02-22T20:31:37.793422Z", "data_removed": false, "error": null, "id": "wqcwn25scnehzmcw4fwmiqiepe", "input": { "width": 512, "height": 512, "prompt": "an HDR promotional portrait from a 1970s New York police television series of a workroomprds person ", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 42451\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.87it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.55it/s]\n 6%|▌ | 3/50 [00:00<00:09, 4.79it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.92it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.99it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.86it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.92it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.98it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 5.00it/s]\n 20%|██ | 10/50 [00:02<00:07, 5.01it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 4.88it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.92it/s]\n 26%|██▌ | 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4.94it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.94it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.93it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 4.94it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 4.95it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.95it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.95it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.94it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 4.95it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 4.95it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.94it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.93it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.94it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.94it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 4.94it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.94it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.93it/s]", "metrics": { "predict_time": 10.791398, "total_time": 10.869986 }, "output": [ "https://replicate.delivery/pbxt/na11FYXpPiJeaSLSNvPwdwba8u1fL8W54f5nNERujDWptFChA/out-0.png" ], "started_at": "2023-02-22T20:31:37.872010Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wqcwn25scnehzmcw4fwmiqiepe", "cancel": "https://api.replicate.com/v1/predictions/wqcwn25scnehzmcw4fwmiqiepe/cancel" }, "version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd" }Generated inUsing seed: 42451 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:12, 3.87it/s] 4%|▍ | 2/50 [00:00<00:10, 4.55it/s] 6%|▌ | 3/50 [00:00<00:09, 4.79it/s] 8%|▊ | 4/50 [00:00<00:09, 4.92it/s] 10%|█ | 5/50 [00:01<00:09, 4.99it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.86it/s] 14%|█▍ | 7/50 [00:01<00:08, 4.92it/s] 16%|█▌ | 8/50 [00:01<00:08, 4.98it/s] 18%|█▊ | 9/50 [00:01<00:08, 5.00it/s] 20%|██ | 10/50 [00:02<00:07, 5.01it/s] 22%|██▏ | 11/50 [00:02<00:07, 4.88it/s] 24%|██▍ | 12/50 [00:02<00:07, 4.92it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.95it/s] 28%|██▊ | 14/50 [00:02<00:07, 4.98it/s] 30%|███ | 15/50 [00:03<00:07, 4.99it/s] 32%|███▏ | 16/50 [00:03<00:06, 4.97it/s] 34%|███▍ | 17/50 [00:03<00:06, 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4.95it/s] 82%|████████▏ | 41/50 [00:08<00:01, 4.95it/s] 84%|████████▍ | 42/50 [00:08<00:01, 4.94it/s] 86%|████████▌ | 43/50 [00:08<00:01, 4.95it/s] 88%|████████▊ | 44/50 [00:08<00:01, 4.95it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.94it/s] 92%|█████████▏| 46/50 [00:09<00:00, 4.93it/s] 94%|█████████▍| 47/50 [00:09<00:00, 4.94it/s] 96%|█████████▌| 48/50 [00:09<00:00, 4.94it/s] 98%|█████████▊| 49/50 [00:09<00:00, 4.94it/s] 100%|██████████| 50/50 [00:10<00:00, 4.94it/s] 100%|██████████| 50/50 [00:10<00:00, 4.93it/s]Prediction
workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdIDznwtj76tsjecjexzo4a4zxacr4StatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- 51805
- width
- 512
- height
- 512
- prompt
- a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson
- scheduler
- DDIM
- num_outputs
- "4"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 51805, "width": 512, "height": 512, "prompt": "a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", { input: { seed: 51805, width: 512, height: 512, prompt: "a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson", scheduler: "DDIM", num_outputs: "4", guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", input={ "seed": 51805, "width": 512, "height": 512, "prompt": "a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", "input": { "seed": 51805, "width": 512, "height": 512, "prompt": "a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd \ -i 'seed=51805' \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson"' \ -i 'scheduler="DDIM"' \ -i 'num_outputs="4"' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 51805, "width": 512, "height": 512, "prompt": "a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-02-23T14:52:35.565531Z", "created_at": "2023-02-23T14:47:57.634253Z", "data_removed": false, "error": null, "id": "znwtj76tsjecjexzo4a4zxacr4", "input": { "seed": 51805, "width": 512, "height": 512, "prompt": "a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson a magnificent, detailed and compelling rembrandt HDR portrait of a workroomprds person in space wearing a stetson", "scheduler": "DDIM", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 51805\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:03<03:00, 3.68s/it]\n 4%|▍ | 2/50 [00:04<01:31, 1.91s/it]\n 6%|▌ | 3/50 [00:04<01:02, 1.33s/it]\n 8%|▊ | 4/50 [00:05<00:49, 1.07s/it]\n 10%|█ | 5/50 [00:06<00:41, 1.08it/s]\n 12%|█▏ | 6/50 [00:06<00:36, 1.21it/s]\n 14%|█▍ | 7/50 [00:07<00:33, 1.29it/s]\n 16%|█▌ | 8/50 [00:08<00:31, 1.35it/s]\n 18%|█▊ | 9/50 [00:08<00:29, 1.40it/s]\n 20%|██ | 10/50 [00:09<00:27, 1.43it/s]\n 22%|██▏ | 11/50 [00:10<00:26, 1.46it/s]\n 24%|██▍ | 12/50 [00:10<00:25, 1.47it/s]\n 26%|██▌ | 13/50 [00:11<00:24, 1.48it/s]\n 28%|██▊ | 14/50 [00:12<00:24, 1.49it/s]\n 30%|███ | 15/50 [00:12<00:23, 1.50it/s]\n 32%|███▏ | 16/50 [00:13<00:22, 1.50it/s]\n 34%|███▍ | 17/50 [00:14<00:22, 1.50it/s]\n 36%|███▌ | 18/50 [00:14<00:21, 1.50it/s]\n 38%|███▊ | 19/50 [00:15<00:20, 1.49it/s]\n 40%|████ | 20/50 [00:16<00:20, 1.49it/s]\n 42%|████▏ | 21/50 [00:16<00:19, 1.49it/s]\n 44%|████▍ | 22/50 [00:17<00:18, 1.49it/s]\n 46%|████▌ | 23/50 [00:18<00:18, 1.49it/s]\n 48%|████▊ | 24/50 [00:18<00:17, 1.49it/s]\n 50%|█████ | 25/50 [00:19<00:16, 1.49it/s]\n 52%|█████▏ | 26/50 [00:20<00:16, 1.49it/s]\n 54%|█████▍ | 27/50 [00:20<00:15, 1.48it/s]\n 56%|█████▌ | 28/50 [00:21<00:14, 1.48it/s]\n 58%|█████▊ | 29/50 [00:22<00:14, 1.48it/s]\n 60%|██████ | 30/50 [00:23<00:13, 1.48it/s]\n 62%|██████▏ | 31/50 [00:23<00:12, 1.47it/s]\n 64%|██████▍ | 32/50 [00:24<00:12, 1.47it/s]\n 66%|██████▌ | 33/50 [00:25<00:11, 1.47it/s]\n 68%|██████▊ | 34/50 [00:25<00:10, 1.47it/s]\n 70%|███████ | 35/50 [00:26<00:10, 1.47it/s]\n 72%|███████▏ | 36/50 [00:27<00:09, 1.47it/s]\n 74%|███████▍ | 37/50 [00:27<00:08, 1.48it/s]\n 76%|███████▌ | 38/50 [00:28<00:08, 1.48it/s]\n 78%|███████▊ | 39/50 [00:29<00:07, 1.48it/s]\n 80%|████████ | 40/50 [00:29<00:06, 1.47it/s]\n 82%|████████▏ | 41/50 [00:30<00:06, 1.47it/s]\n 84%|████████▍ | 42/50 [00:31<00:05, 1.47it/s]\n 86%|████████▌ | 43/50 [00:31<00:04, 1.47it/s]\n 88%|████████▊ | 44/50 [00:32<00:04, 1.47it/s]\n 90%|█████████ | 45/50 [00:33<00:03, 1.46it/s]\n 92%|█████████▏| 46/50 [00:33<00:02, 1.46it/s]\n 94%|█████████▍| 47/50 [00:34<00:02, 1.46it/s]\n 96%|█████████▌| 48/50 [00:35<00:01, 1.45it/s]\n 98%|█████████▊| 49/50 [00:35<00:00, 1.45it/s]\n100%|██████████| 50/50 [00:36<00:00, 1.45it/s]\n100%|██████████| 50/50 [00:36<00:00, 1.36it/s]", "metrics": { "predict_time": 43.578777, "total_time": 277.931278 }, "output": [ "https://replicate.delivery/pbxt/J9Fe0kK3pPTyKCoHumRB0UNRj1kNQf4IQKWkGW7y6U6welChA/out-0.png", "https://replicate.delivery/pbxt/MDBJfwf6WIpGAUf83l93VGwooUbVWNHsi3uZ511xfTTE7LFCB/out-1.png", "https://replicate.delivery/pbxt/LAy9vCHPC0aIP5D0Gn5pDt4f3MfYbhmY9PclHtFFb8byelChA/out-2.png", "https://replicate.delivery/pbxt/VrVjCdeTrwyePkeZPAeB1nboIDbpcchtJl1V7dhhqJtK7LFCB/out-3.png" ], "started_at": "2023-02-23T14:51:51.986754Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/znwtj76tsjecjexzo4a4zxacr4", "cancel": "https://api.replicate.com/v1/predictions/znwtj76tsjecjexzo4a4zxacr4/cancel" }, "version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd" }Generated inUsing seed: 51805 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:03<03:00, 3.68s/it] 4%|▍ | 2/50 [00:04<01:31, 1.91s/it] 6%|▌ | 3/50 [00:04<01:02, 1.33s/it] 8%|▊ | 4/50 [00:05<00:49, 1.07s/it] 10%|█ | 5/50 [00:06<00:41, 1.08it/s] 12%|█▏ | 6/50 [00:06<00:36, 1.21it/s] 14%|█▍ | 7/50 [00:07<00:33, 1.29it/s] 16%|█▌ | 8/50 [00:08<00:31, 1.35it/s] 18%|█▊ | 9/50 [00:08<00:29, 1.40it/s] 20%|██ | 10/50 [00:09<00:27, 1.43it/s] 22%|██▏ | 11/50 [00:10<00:26, 1.46it/s] 24%|██▍ | 12/50 [00:10<00:25, 1.47it/s] 26%|██▌ | 13/50 [00:11<00:24, 1.48it/s] 28%|██▊ | 14/50 [00:12<00:24, 1.49it/s] 30%|███ | 15/50 [00:12<00:23, 1.50it/s] 32%|███▏ | 16/50 [00:13<00:22, 1.50it/s] 34%|███▍ | 17/50 [00:14<00:22, 1.50it/s] 36%|███▌ | 18/50 [00:14<00:21, 1.50it/s] 38%|███▊ | 19/50 [00:15<00:20, 1.49it/s] 40%|████ | 20/50 [00:16<00:20, 1.49it/s] 42%|████▏ | 21/50 [00:16<00:19, 1.49it/s] 44%|████▍ | 22/50 [00:17<00:18, 1.49it/s] 46%|████▌ | 23/50 [00:18<00:18, 1.49it/s] 48%|████▊ | 24/50 [00:18<00:17, 1.49it/s] 50%|█████ | 25/50 [00:19<00:16, 1.49it/s] 52%|█████▏ | 26/50 [00:20<00:16, 1.49it/s] 54%|█████▍ | 27/50 [00:20<00:15, 1.48it/s] 56%|█████▌ | 28/50 [00:21<00:14, 1.48it/s] 58%|█████▊ | 29/50 [00:22<00:14, 1.48it/s] 60%|██████ | 30/50 [00:23<00:13, 1.48it/s] 62%|██████▏ | 31/50 [00:23<00:12, 1.47it/s] 64%|██████▍ | 32/50 [00:24<00:12, 1.47it/s] 66%|██████▌ | 33/50 [00:25<00:11, 1.47it/s] 68%|██████▊ | 34/50 [00:25<00:10, 1.47it/s] 70%|███████ | 35/50 [00:26<00:10, 1.47it/s] 72%|███████▏ | 36/50 [00:27<00:09, 1.47it/s] 74%|███████▍ | 37/50 [00:27<00:08, 1.48it/s] 76%|███████▌ | 38/50 [00:28<00:08, 1.48it/s] 78%|███████▊ | 39/50 [00:29<00:07, 1.48it/s] 80%|████████ | 40/50 [00:29<00:06, 1.47it/s] 82%|████████▏ | 41/50 [00:30<00:06, 1.47it/s] 84%|████████▍ | 42/50 [00:31<00:05, 1.47it/s] 86%|████████▌ | 43/50 [00:31<00:04, 1.47it/s] 88%|████████▊ | 44/50 [00:32<00:04, 1.47it/s] 90%|█████████ | 45/50 [00:33<00:03, 1.46it/s] 92%|█████████▏| 46/50 [00:33<00:02, 1.46it/s] 94%|█████████▍| 47/50 [00:34<00:02, 1.46it/s] 96%|█████████▌| 48/50 [00:35<00:01, 1.45it/s] 98%|█████████▊| 49/50 [00:35<00:00, 1.45it/s] 100%|██████████| 50/50 [00:36<00:00, 1.45it/s] 100%|██████████| 50/50 [00:36<00:00, 1.36it/s]Prediction
workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdIDc56w7jrbhhf5j4y7cqcigfmjdqStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- negative_prompt
- jug, beard, fireplace
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", { input: { width: 512, height: 512, prompt: "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, negative_prompt: "jug, beard, fireplace", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", input={ "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left"' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'negative_prompt="jug, beard, fireplace"' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-07-17T12:04:08.336101Z", "created_at": "2023-07-17T12:01:06.059879Z", "data_removed": false, "error": null, "id": "c56w7jrbhhf5j4y7cqcigfmjdq", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 14578\nusing txt2img\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:08, 5.78it/s]\n 4%|▍ | 2/50 [00:00<00:08, 5.66it/s]\n 6%|▌ | 3/50 [00:00<00:08, 5.63it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.61it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.62it/s]\n 12%|█▏ | 6/50 [00:01<00:07, 5.62it/s]\n 14%|█▍ | 7/50 [00:01<00:07, 5.62it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.62it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.60it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.60it/s]\n 22%|██▏ | 11/50 [00:01<00:06, 5.60it/s]\n 24%|██▍ | 12/50 [00:02<00:06, 5.60it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.60it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.59it/s]\n 30%|███ | 15/50 [00:02<00:06, 5.57it/s]\n 32%|███▏ | 16/50 [00:02<00:06, 5.57it/s]\n 34%|███▍ | 17/50 [00:03<00:05, 5.57it/s]\n 36%|███▌ | 18/50 [00:03<00:05, 5.59it/s]\n 38%|███▊ | 19/50 [00:03<00:05, 5.60it/s]\n 40%|████ | 20/50 [00:03<00:05, 5.59it/s]\n 42%|████▏ | 21/50 [00:03<00:05, 5.59it/s]\n 44%|████▍ | 22/50 [00:03<00:05, 5.58it/s]\n 46%|████▌ | 23/50 [00:04<00:04, 5.59it/s]\n 48%|████▊ | 24/50 [00:04<00:04, 5.59it/s]\n 50%|█████ | 25/50 [00:04<00:04, 5.62it/s]\n 52%|█████▏ | 26/50 [00:04<00:04, 5.63it/s]\n 54%|█████▍ | 27/50 [00:04<00:04, 5.60it/s]\n 56%|█████▌ | 28/50 [00:04<00:03, 5.60it/s]\n 58%|█████▊ | 29/50 [00:05<00:03, 5.59it/s]\n 60%|██████ | 30/50 [00:05<00:03, 5.60it/s]\n 62%|██████▏ | 31/50 [00:05<00:03, 5.62it/s]\n 64%|██████▍ | 32/50 [00:05<00:03, 5.62it/s]\n 66%|██████▌ | 33/50 [00:05<00:03, 5.61it/s]\n 68%|██████▊ | 34/50 [00:06<00:02, 5.60it/s]\n 70%|███████ | 35/50 [00:06<00:02, 5.58it/s]\n 72%|███████▏ | 36/50 [00:06<00:02, 5.60it/s]\n 74%|███████▍ | 37/50 [00:06<00:02, 5.61it/s]\n 76%|███████▌ | 38/50 [00:06<00:02, 5.61it/s]\n 78%|███████▊ | 39/50 [00:06<00:01, 5.61it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.59it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.57it/s]\n 84%|████████▍ | 42/50 [00:07<00:01, 5.56it/s]\n 86%|████████▌ | 43/50 [00:07<00:01, 5.56it/s]\n 88%|████████▊ | 44/50 [00:07<00:01, 5.56it/s]\n 90%|█████████ | 45/50 [00:08<00:00, 5.59it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.60it/s]\n 94%|█████████▍| 47/50 [00:08<00:00, 5.60it/s]\n 96%|█████████▌| 48/50 [00:08<00:00, 5.59it/s]\n 98%|█████████▊| 49/50 [00:08<00:00, 5.58it/s]\n100%|██████████| 50/50 [00:08<00:00, 5.55it/s]\n100%|██████████| 50/50 [00:08<00:00, 5.59it/s]", "metrics": { "predict_time": 10.366554, "total_time": 182.276222 }, "output": [ "https://replicate.delivery/pbxt/ww2MxGlokeRNRadPda7yViBkyIF4ysUBFG1sEO3fddA3AuQRA/out-0.png" ], "started_at": "2023-07-17T12:03:57.969547Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c56w7jrbhhf5j4y7cqcigfmjdq", "cancel": "https://api.replicate.com/v1/predictions/c56w7jrbhhf5j4y7cqcigfmjdq/cancel" }, "version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd" }Generated inUsing seed: 14578 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:08, 5.78it/s] 4%|▍ | 2/50 [00:00<00:08, 5.66it/s] 6%|▌ | 3/50 [00:00<00:08, 5.63it/s] 8%|▊ | 4/50 [00:00<00:08, 5.61it/s] 10%|█ | 5/50 [00:00<00:08, 5.62it/s] 12%|█▏ | 6/50 [00:01<00:07, 5.62it/s] 14%|█▍ | 7/50 [00:01<00:07, 5.62it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.62it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.60it/s] 20%|██ | 10/50 [00:01<00:07, 5.60it/s] 22%|██▏ | 11/50 [00:01<00:06, 5.60it/s] 24%|██▍ | 12/50 [00:02<00:06, 5.60it/s] 26%|██▌ | 13/50 [00:02<00:06, 5.60it/s] 28%|██▊ | 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5.61it/s] 76%|███████▌ | 38/50 [00:06<00:02, 5.61it/s] 78%|███████▊ | 39/50 [00:06<00:01, 5.61it/s] 80%|████████ | 40/50 [00:07<00:01, 5.59it/s] 82%|████████▏ | 41/50 [00:07<00:01, 5.57it/s] 84%|████████▍ | 42/50 [00:07<00:01, 5.56it/s] 86%|████████▌ | 43/50 [00:07<00:01, 5.56it/s] 88%|████████▊ | 44/50 [00:07<00:01, 5.56it/s] 90%|█████████ | 45/50 [00:08<00:00, 5.59it/s] 92%|█████████▏| 46/50 [00:08<00:00, 5.60it/s] 94%|█████████▍| 47/50 [00:08<00:00, 5.60it/s] 96%|█████████▌| 48/50 [00:08<00:00, 5.59it/s] 98%|█████████▊| 49/50 [00:08<00:00, 5.58it/s] 100%|██████████| 50/50 [00:08<00:00, 5.55it/s] 100%|██████████| 50/50 [00:08<00:00, 5.59it/s]Prediction
workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdIDtmlioibbjf5q3esm2cs3amv52uStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left
- scheduler
- DDIM
- num_outputs
- 4
- guidance_scale
- 7.5
- negative_prompt
- jug, beard, fireplace
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 4, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", { input: { width: 512, height: 512, prompt: "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", scheduler: "DDIM", num_outputs: 4, guidance_scale: 7.5, negative_prompt: "jug, beard, fireplace", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 workroomprds/jamesbooth1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", input={ "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 4, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run workroomprds/jamesbooth1 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": "workroomprds/jamesbooth1:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 4, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left"' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'negative_prompt="jug, beard, fireplace"' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/workroomprds/jamesbooth1@sha256:d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bdcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 4, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-07-17T12:08:10.154153Z", "created_at": "2023-07-17T12:07:31.529907Z", "data_removed": false, "error": null, "id": "tmlioibbjf5q3esm2cs3amv52u", "input": { "width": 512, "height": 512, "prompt": "a compelling HDR photo of a workroomprds person in an 17th century household smiling broadly and looking to the left", "scheduler": "DDIM", "num_outputs": 4, "guidance_scale": 7.5, "negative_prompt": "jug, beard, fireplace", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 2035\nusing txt2img\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<00:34, 1.41it/s]\n 6%|▌ | 3/50 [00:02<00:32, 1.46it/s]\n 8%|▊ | 4/50 [00:02<00:31, 1.47it/s]\n 10%|█ | 5/50 [00:03<00:30, 1.48it/s]\n 12%|█▏ | 6/50 [00:04<00:29, 1.48it/s]\n 14%|█▍ | 7/50 [00:04<00:29, 1.48it/s]\n 16%|█▌ | 8/50 [00:05<00:28, 1.48it/s]\n 18%|█▊ | 9/50 [00:06<00:27, 1.48it/s]\n 20%|██ | 10/50 [00:06<00:27, 1.48it/s]\n 22%|██▏ | 11/50 [00:07<00:26, 1.47it/s]\n 24%|██▍ | 12/50 [00:08<00:25, 1.48it/s]\n 26%|██▌ | 13/50 [00:08<00:25, 1.47it/s]\n 28%|██▊ | 14/50 [00:09<00:24, 1.48it/s]\n 30%|███ | 15/50 [00:10<00:23, 1.48it/s]\n 32%|███▏ | 16/50 [00:10<00:23, 1.48it/s]\n 34%|███▍ | 17/50 [00:11<00:22, 1.48it/s]\n 36%|███▌ | 18/50 [00:12<00:21, 1.47it/s]\n 38%|███▊ | 19/50 [00:12<00:20, 1.48it/s]\n 40%|████ | 20/50 [00:13<00:20, 1.47it/s]\n 42%|████▏ | 21/50 [00:14<00:19, 1.47it/s]\n 44%|████▍ | 22/50 [00:14<00:19, 1.47it/s]\n 46%|████▌ | 23/50 [00:15<00:18, 1.47it/s]\n 48%|████▊ | 24/50 [00:16<00:17, 1.47it/s]\n 50%|█████ | 25/50 [00:16<00:17, 1.47it/s]\n 52%|█████▏ | 26/50 [00:17<00:16, 1.46it/s]\n 54%|█████▍ | 27/50 [00:18<00:15, 1.46it/s]\n 56%|█████▌ | 28/50 [00:19<00:15, 1.46it/s]\n 58%|█████▊ | 29/50 [00:19<00:14, 1.46it/s]\n 60%|██████ | 30/50 [00:20<00:13, 1.45it/s]\n 62%|██████▏ | 31/50 [00:21<00:13, 1.45it/s]\n 64%|██████▍ | 32/50 [00:21<00:12, 1.45it/s]\n 66%|██████▌ | 33/50 [00:22<00:11, 1.45it/s]\n 68%|██████▊ | 34/50 [00:23<00:11, 1.45it/s]\n 70%|███████ | 35/50 [00:23<00:10, 1.45it/s]\n 72%|███████▏ | 36/50 [00:24<00:09, 1.45it/s]\n 74%|███████▍ | 37/50 [00:25<00:08, 1.45it/s]\n 76%|███████▌ | 38/50 [00:25<00:08, 1.45it/s]\n 78%|███████▊ | 39/50 [00:26<00:07, 1.45it/s]\n 80%|████████ | 40/50 [00:27<00:06, 1.45it/s]\n 82%|████████▏ | 41/50 [00:28<00:06, 1.45it/s]\n 84%|████████▍ | 42/50 [00:28<00:05, 1.45it/s]\n 86%|████████▌ | 43/50 [00:29<00:04, 1.45it/s]\n 88%|████████▊ | 44/50 [00:30<00:04, 1.45it/s]\n 90%|█████████ | 45/50 [00:30<00:03, 1.45it/s]\n 92%|█████████▏| 46/50 [00:31<00:02, 1.45it/s]\n 94%|█████████▍| 47/50 [00:32<00:02, 1.44it/s]\n 96%|█████████▌| 48/50 [00:32<00:01, 1.44it/s]\n 98%|█████████▊| 49/50 [00:33<00:00, 1.44it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.43it/s]\n100%|██████████| 50/50 [00:34<00:00, 1.46it/s]", "metrics": { "predict_time": 38.646706, "total_time": 38.624246 }, "output": [ "https://replicate.delivery/pbxt/ZudEYaDjhQpIFpSSDe9m3WfGffC6pG39N2c1EOUfZX0fJhLUE/out-0.png", "https://replicate.delivery/pbxt/DgLHDQPGwpp7K1rAtovRT0J9VdO5kbYCvRXqJFZMtMBKhLUE/out-1.png", "https://replicate.delivery/pbxt/b0HfewYpBbofbJMM8YRly3AkpZx5zfwTndrsHeXPDZODlwFKC/out-2.png", "https://replicate.delivery/pbxt/66yexsuofogmnUXG2qOxF0FkMi3mDxd4aLB4Zs1vR08pEuQRA/out-3.png" ], "started_at": "2023-07-17T12:07:31.507447Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tmlioibbjf5q3esm2cs3amv52u", "cancel": "https://api.replicate.com/v1/predictions/tmlioibbjf5q3esm2cs3amv52u/cancel" }, "version": "d04969e3b5bb16a806e7c5c49a90870cb4891c629a38fda542ce77ce2368e0bd" }Generated inUsing seed: 2035 using txt2img 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:37, 1.31it/s] 4%|▍ | 2/50 [00:01<00:34, 1.41it/s] 6%|▌ | 3/50 [00:02<00:32, 1.46it/s] 8%|▊ | 4/50 [00:02<00:31, 1.47it/s] 10%|█ | 5/50 [00:03<00:30, 1.48it/s] 12%|█▏ | 6/50 [00:04<00:29, 1.48it/s] 14%|█▍ | 7/50 [00:04<00:29, 1.48it/s] 16%|█▌ | 8/50 [00:05<00:28, 1.48it/s] 18%|█▊ | 9/50 [00:06<00:27, 1.48it/s] 20%|██ | 10/50 [00:06<00:27, 1.48it/s] 22%|██▏ | 11/50 [00:07<00:26, 1.47it/s] 24%|██▍ | 12/50 [00:08<00:25, 1.48it/s] 26%|██▌ | 13/50 [00:08<00:25, 1.47it/s] 28%|██▊ | 14/50 [00:09<00:24, 1.48it/s] 30%|███ | 15/50 [00:10<00:23, 1.48it/s] 32%|███▏ | 16/50 [00:10<00:23, 1.48it/s] 34%|███▍ | 17/50 [00:11<00:22, 1.48it/s] 36%|███▌ | 18/50 [00:12<00:21, 1.47it/s] 38%|███▊ | 19/50 [00:12<00:20, 1.48it/s] 40%|████ | 20/50 [00:13<00:20, 1.47it/s] 42%|████▏ | 21/50 [00:14<00:19, 1.47it/s] 44%|████▍ | 22/50 [00:14<00:19, 1.47it/s] 46%|████▌ | 23/50 [00:15<00:18, 1.47it/s] 48%|████▊ | 24/50 [00:16<00:17, 1.47it/s] 50%|█████ | 25/50 [00:16<00:17, 1.47it/s] 52%|█████▏ | 26/50 [00:17<00:16, 1.46it/s] 54%|█████▍ | 27/50 [00:18<00:15, 1.46it/s] 56%|█████▌ | 28/50 [00:19<00:15, 1.46it/s] 58%|█████▊ | 29/50 [00:19<00:14, 1.46it/s] 60%|██████ | 30/50 [00:20<00:13, 1.45it/s] 62%|██████▏ | 31/50 [00:21<00:13, 1.45it/s] 64%|██████▍ | 32/50 [00:21<00:12, 1.45it/s] 66%|██████▌ | 33/50 [00:22<00:11, 1.45it/s] 68%|██████▊ | 34/50 [00:23<00:11, 1.45it/s] 70%|███████ | 35/50 [00:23<00:10, 1.45it/s] 72%|███████▏ | 36/50 [00:24<00:09, 1.45it/s] 74%|███████▍ | 37/50 [00:25<00:08, 1.45it/s] 76%|███████▌ | 38/50 [00:25<00:08, 1.45it/s] 78%|███████▊ | 39/50 [00:26<00:07, 1.45it/s] 80%|████████ | 40/50 [00:27<00:06, 1.45it/s] 82%|████████▏ | 41/50 [00:28<00:06, 1.45it/s] 84%|████████▍ | 42/50 [00:28<00:05, 1.45it/s] 86%|████████▌ | 43/50 [00:29<00:04, 1.45it/s] 88%|████████▊ | 44/50 [00:30<00:04, 1.45it/s] 90%|█████████ | 45/50 [00:30<00:03, 1.45it/s] 92%|█████████▏| 46/50 [00:31<00:02, 1.45it/s] 94%|█████████▍| 47/50 [00:32<00:02, 1.44it/s] 96%|█████████▌| 48/50 [00:32<00:01, 1.44it/s] 98%|█████████▊| 49/50 [00:33<00:00, 1.44it/s] 100%|██████████| 50/50 [00:34<00:00, 1.43it/s] 100%|██████████| 50/50 [00:34<00:00, 1.46it/s]
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