Stable Diffusion fine-tuned of the Codex Borgia, a 16th century Meso-American manuscript.
Prediction
venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040ID5xs2dtdmlvbvbmbibiw2v7bmgmStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a portrait of elon musk, from the pre-columbian codex borgia, 16th century
- scheduler
- K-LMS
- num_outputs
- "4"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a portrait of elon musk, from the pre-columbian codex borgia, 16th century", "scheduler": "K-LMS", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run venkr/tonalamatl-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", { input: { width: 512, height: 512, prompt: "a portrait of elon musk, from the pre-columbian codex borgia, 16th century", scheduler: "K-LMS", 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 venkr/tonalamatl-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", input={ "width": 512, "height": 512, "prompt": "a portrait of elon musk, from the pre-columbian codex borgia, 16th century", "scheduler": "K-LMS", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run venkr/tonalamatl-diffusion 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": "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", "input": { "width": 512, "height": 512, "prompt": "a portrait of elon musk, from the pre-columbian codex borgia, 16th century", "scheduler": "K-LMS", "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.
Output
{ "completed_at": "2022-11-09T15:02:00.830127Z", "created_at": "2022-11-09T15:01:51.249142Z", "data_removed": false, "error": null, "id": "5xs2dtdmlvbvbmbibiw2v7bmgm", "input": { "width": 512, "height": 512, "prompt": "a portrait of elon musk, from the pre-columbian codex borgia, 16th century", "scheduler": "K-LMS", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 54866\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:10, 4.72it/s]\n 4%|▍ | 2/50 [00:00<00:09, 5.25it/s]\n 6%|▌ | 3/50 [00:00<00:08, 5.45it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.54it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.60it/s]\n 12%|█▏ | 6/50 [00:01<00:07, 5.64it/s]\n 14%|█▍ | 7/50 [00:01<00:07, 5.66it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.67it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.68it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.65it/s]\n 22%|██▏ | 11/50 [00:01<00:06, 5.66it/s]\n 24%|██▍ | 12/50 [00:02<00:06, 5.67it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.68it/s]\n 28%|██▊ 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5.67it/s]\n 74%|███████▍ | 37/50 [00:06<00:02, 5.64it/s]\n 76%|███████▌ | 38/50 [00:06<00:02, 5.65it/s]\n 78%|███████▊ | 39/50 [00:06<00:01, 5.67it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.65it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.66it/s]\n 84%|████████▍ | 42/50 [00:07<00:01, 5.67it/s]\n 86%|████████▌ | 43/50 [00:07<00:01, 5.68it/s]\n 88%|████████▊ | 44/50 [00:07<00:01, 5.69it/s]\n 90%|█████████ | 45/50 [00:07<00:00, 5.69it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.70it/s]\n 94%|█████████▍| 47/50 [00:08<00:00, 5.70it/s]\n 96%|█████████▌| 48/50 [00:08<00:00, 5.69it/s]\n 98%|█████████▊| 49/50 [00:08<00:00, 5.70it/s]\n100%|██████████| 50/50 [00:08<00:00, 5.70it/s]\n100%|██████████| 50/50 [00:08<00:00, 5.67it/s]", "metrics": { "predict_time": 9.543149, "total_time": 9.580985 }, "output": [ "https://replicate.delivery/pbxt/xRCegWfi5Wpki0BtwYmfOzoHrrvfGdqm7PXufSd3wL1Fd5yfD/out-0.png", "https://replicate.delivery/pbxt/WjLZPVcD29aRJJWBBMJnn1eWOo7lncssDq2SL2cRXt90lLfPA/out-1.png", "https://replicate.delivery/pbxt/NP9CxaOTKeUfREOTUUirVeSmCRBlROaeyTWfhx4B48RTd5yfD/out-2.png", "https://replicate.delivery/pbxt/xAzrQYyfjMSINSG4O66vNRBgYebIbfN8EUIoioXfluyouc5fB/out-3.png" ], "started_at": "2022-11-09T15:01:51.286978Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5xs2dtdmlvbvbmbibiw2v7bmgm", "cancel": "https://api.replicate.com/v1/predictions/5xs2dtdmlvbvbmbibiw2v7bmgm/cancel" }, "version": "8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040" }Generated inUsing seed: 54866 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:10, 4.72it/s] 4%|▍ | 2/50 [00:00<00:09, 5.25it/s] 6%|▌ | 3/50 [00:00<00:08, 5.45it/s] 8%|▊ | 4/50 [00:00<00:08, 5.54it/s] 10%|█ | 5/50 [00:00<00:08, 5.60it/s] 12%|█▏ | 6/50 [00:01<00:07, 5.64it/s] 14%|█▍ | 7/50 [00:01<00:07, 5.66it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.67it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.68it/s] 20%|██ | 10/50 [00:01<00:07, 5.65it/s] 22%|██▏ | 11/50 [00:01<00:06, 5.66it/s] 24%|██▍ | 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72%|███████▏ | 36/50 [00:06<00:02, 5.67it/s] 74%|███████▍ | 37/50 [00:06<00:02, 5.64it/s] 76%|███████▌ | 38/50 [00:06<00:02, 5.65it/s] 78%|███████▊ | 39/50 [00:06<00:01, 5.67it/s] 80%|████████ | 40/50 [00:07<00:01, 5.65it/s] 82%|████████▏ | 41/50 [00:07<00:01, 5.66it/s] 84%|████████▍ | 42/50 [00:07<00:01, 5.67it/s] 86%|████████▌ | 43/50 [00:07<00:01, 5.68it/s] 88%|████████▊ | 44/50 [00:07<00:01, 5.69it/s] 90%|█████████ | 45/50 [00:07<00:00, 5.69it/s] 92%|█████████▏| 46/50 [00:08<00:00, 5.70it/s] 94%|█████████▍| 47/50 [00:08<00:00, 5.70it/s] 96%|█████████▌| 48/50 [00:08<00:00, 5.69it/s] 98%|█████████▊| 49/50 [00:08<00:00, 5.70it/s] 100%|██████████| 50/50 [00:08<00:00, 5.70it/s] 100%|██████████| 50/50 [00:08<00:00, 5.67it/s]Prediction
venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040ID5zekvizkxbcbfhsdd6ffy6am7iStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a pokemon from the codex borgia
- scheduler
- K-LMS
- num_outputs
- "4"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a pokemon from the codex borgia", "scheduler": "K-LMS", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run venkr/tonalamatl-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", { input: { width: 512, height: 512, prompt: "a pokemon from the codex borgia", scheduler: "K-LMS", 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 venkr/tonalamatl-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", input={ "width": 512, "height": 512, "prompt": "a pokemon from the codex borgia", "scheduler": "K-LMS", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run venkr/tonalamatl-diffusion 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": "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", "input": { "width": 512, "height": 512, "prompt": "a pokemon from the codex borgia", "scheduler": "K-LMS", "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.
Output
{ "completed_at": "2022-11-09T15:04:52.461305Z", "created_at": "2022-11-09T15:04:42.779640Z", "data_removed": false, "error": null, "id": "5zekvizkxbcbfhsdd6ffy6am7i", "input": { "width": 512, "height": 512, "prompt": "a pokemon from the codex borgia", "scheduler": "K-LMS", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5679\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:09, 5.03it/s]\n 4%|▍ | 2/50 [00:00<00:08, 5.46it/s]\n 6%|▌ | 3/50 [00:00<00:08, 5.60it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.64it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.62it/s]\n 12%|█▏ | 6/50 [00:01<00:07, 5.65it/s]\n 14%|█▍ | 7/50 [00:01<00:07, 5.67it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.68it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.69it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.69it/s]\n 22%|██▏ | 11/50 [00:01<00:06, 5.69it/s]\n 24%|██▍ | 12/50 [00:02<00:06, 5.69it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.69it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.69it/s]\n 30%|███ | 15/50 [00:02<00:06, 5.69it/s]\n 32%|███▏ | 16/50 [00:02<00:05, 5.70it/s]\n 34%|███▍ | 17/50 [00:03<00:05, 5.70it/s]\n 36%|███▌ | 18/50 [00:03<00:05, 5.69it/s]\n 38%|███▊ | 19/50 [00:03<00:05, 5.69it/s]\n 40%|████ | 20/50 [00:03<00:05, 5.69it/s]\n 42%|████▏ | 21/50 [00:03<00:05, 5.69it/s]\n 44%|████▍ | 22/50 [00:03<00:04, 5.62it/s]\n 46%|████▌ | 23/50 [00:04<00:04, 5.64it/s]\n 48%|████▊ | 24/50 [00:04<00:04, 5.66it/s]\n 50%|█████ | 25/50 [00:04<00:04, 5.67it/s]\n 52%|█████▏ | 26/50 [00:04<00:04, 5.67it/s]\n 54%|█████▍ | 27/50 [00:04<00:04, 5.68it/s]\n 56%|█████▌ | 28/50 [00:04<00:03, 5.68it/s]\n 58%|█████▊ | 29/50 [00:05<00:03, 5.68it/s]\n 60%|██████ | 30/50 [00:05<00:03, 5.68it/s]\n 62%|██████▏ | 31/50 [00:05<00:03, 5.66it/s]\n 64%|██████▍ | 32/50 [00:05<00:03, 5.66it/s]\n 66%|██████▌ | 33/50 [00:05<00:03, 5.66it/s]\n 68%|██████▊ | 34/50 [00:06<00:02, 5.64it/s]\n 70%|███████ | 35/50 [00:06<00:02, 5.64it/s]\n 72%|███████▏ | 36/50 [00:06<00:02, 5.65it/s]\n 74%|███████▍ | 37/50 [00:06<00:02, 5.64it/s]\n 76%|███████▌ | 38/50 [00:06<00:02, 5.62it/s]\n 78%|███████▊ | 39/50 [00:06<00:01, 5.62it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.63it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.64it/s]\n 84%|████████▍ | 42/50 [00:07<00:01, 5.64it/s]\n 86%|████████▌ | 43/50 [00:07<00:01, 5.65it/s]\n 88%|████████▊ | 44/50 [00:07<00:01, 5.66it/s]\n 90%|█████████ | 45/50 [00:07<00:00, 5.66it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.66it/s]\n 94%|█████████▍| 47/50 [00:08<00:00, 5.67it/s]\n 96%|█████████▌| 48/50 [00:08<00:00, 5.67it/s]\n 98%|█████████▊| 49/50 [00:08<00:00, 5.66it/s]\n100%|██████████| 50/50 [00:08<00:00, 5.66it/s]\n100%|██████████| 50/50 [00:08<00:00, 5.66it/s]", "metrics": { "predict_time": 9.644552, "total_time": 9.681665 }, "output": [ "https://replicate.delivery/pbxt/sFEVo7WfYt37aKVs6uxHsfqeHsi7p5X8P6O55EBTMl1pcu8fA/out-0.png", "https://replicate.delivery/pbxt/PVgtkfUFZ6UiO63SjfP4uYAM2FWy0a7DRFE2JYMu44EVOXefA/out-1.png", "https://replicate.delivery/pbxt/ANWD5jIuWPLKOB9faqS93flzta2JaeY6BORkQZQvv4brcu8fA/out-2.png", "https://replicate.delivery/pbxt/U7K5fv48NcwAPqJPCiK2Myh9SN3s0t0kd0NrItKrzUKLnLfPA/out-3.png" ], "started_at": "2022-11-09T15:04:42.816753Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5zekvizkxbcbfhsdd6ffy6am7i", "cancel": "https://api.replicate.com/v1/predictions/5zekvizkxbcbfhsdd6ffy6am7i/cancel" }, "version": "8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040" }Generated inUsing seed: 5679 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:09, 5.03it/s] 4%|▍ | 2/50 [00:00<00:08, 5.46it/s] 6%|▌ | 3/50 [00:00<00:08, 5.60it/s] 8%|▊ | 4/50 [00:00<00:08, 5.64it/s] 10%|█ | 5/50 [00:00<00:08, 5.62it/s] 12%|█▏ | 6/50 [00:01<00:07, 5.65it/s] 14%|█▍ | 7/50 [00:01<00:07, 5.67it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.68it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.69it/s] 20%|██ | 10/50 [00:01<00:07, 5.69it/s] 22%|██▏ | 11/50 [00:01<00:06, 5.69it/s] 24%|██▍ | 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venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040IDaxeq5h4ufnghxmacm5nmkixqrqStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a plate of nachos from the codex borgia
- scheduler
- K-LMS
- num_outputs
- "4"
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a plate of nachos from the codex borgia", "scheduler": "K-LMS", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run venkr/tonalamatl-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", { input: { width: 512, height: 512, prompt: "a plate of nachos from the codex borgia", scheduler: "K-LMS", 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 venkr/tonalamatl-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", input={ "width": 512, "height": 512, "prompt": "a plate of nachos from the codex borgia", "scheduler": "K-LMS", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run venkr/tonalamatl-diffusion 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": "venkr/tonalamatl-diffusion:8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040", "input": { "width": 512, "height": 512, "prompt": "a plate of nachos from the codex borgia", "scheduler": "K-LMS", "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.
Output
{ "completed_at": "2022-11-09T15:07:36.047205Z", "created_at": "2022-11-09T15:07:26.327097Z", "data_removed": false, "error": null, "id": "axeq5h4ufnghxmacm5nmkixqrq", "input": { "width": 512, "height": 512, "prompt": "a plate of nachos from the codex borgia", "scheduler": "K-LMS", "num_outputs": "4", "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 40913\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:10, 4.66it/s]\n 4%|▍ | 2/50 [00:00<00:09, 5.16it/s]\n 6%|▌ | 3/50 [00:00<00:08, 5.39it/s]\n 8%|▊ | 4/50 [00:00<00:08, 5.41it/s]\n 10%|█ | 5/50 [00:00<00:08, 5.49it/s]\n 12%|█▏ | 6/50 [00:01<00:07, 5.54it/s]\n 14%|█▍ | 7/50 [00:01<00:07, 5.57it/s]\n 16%|█▌ | 8/50 [00:01<00:07, 5.60it/s]\n 18%|█▊ | 9/50 [00:01<00:07, 5.62it/s]\n 20%|██ | 10/50 [00:01<00:07, 5.63it/s]\n 22%|██▏ | 11/50 [00:01<00:06, 5.63it/s]\n 24%|██▍ | 12/50 [00:02<00:06, 5.64it/s]\n 26%|██▌ | 13/50 [00:02<00:06, 5.65it/s]\n 28%|██▊ | 14/50 [00:02<00:06, 5.65it/s]\n 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[00:06<00:02, 5.67it/s]\n 76%|███████▌ | 38/50 [00:06<00:02, 5.68it/s]\n 78%|███████▊ | 39/50 [00:06<00:01, 5.68it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.69it/s]\n 82%|████████▏ | 41/50 [00:07<00:01, 5.69it/s]\n 84%|████████▍ | 42/50 [00:07<00:01, 5.69it/s]\n 86%|████████▌ | 43/50 [00:07<00:01, 5.69it/s]\n 88%|████████▊ | 44/50 [00:07<00:01, 5.69it/s]\n 90%|█████████ | 45/50 [00:07<00:00, 5.69it/s]\n 92%|█████████▏| 46/50 [00:08<00:00, 5.69it/s]\n 94%|█████████▍| 47/50 [00:08<00:00, 5.69it/s]\n 96%|█████████▌| 48/50 [00:08<00:00, 5.69it/s]\n 98%|█████████▊| 49/50 [00:08<00:00, 5.68it/s]\n100%|██████████| 50/50 [00:08<00:00, 5.68it/s]\n100%|██████████| 50/50 [00:08<00:00, 5.64it/s]", "metrics": { "predict_time": 9.681843, "total_time": 9.720108 }, "output": [ "https://replicate.delivery/pbxt/QzhSk1e7wgVUKCBClJeAUTaNVSf3uLCiDuBbhN8mKZiwhu8fA/out-0.png", "https://replicate.delivery/pbxt/yg1skiZJiuIeJicgr7og7dp6bAzpReV2tt7zx94oRr64QXefA/out-1.png", "https://replicate.delivery/pbxt/2wC2UWmfGmzeu0WZ9whEQxKh1funk5u5PG06QBkrBWSyhu8fA/out-2.png", "https://replicate.delivery/pbxt/XVBGeJCi0P2RZqbrew6nq0wrRdffLcFWgwdNwXJF9eCTH6yfD/out-3.png" ], "started_at": "2022-11-09T15:07:26.365362Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/axeq5h4ufnghxmacm5nmkixqrq", "cancel": "https://api.replicate.com/v1/predictions/axeq5h4ufnghxmacm5nmkixqrq/cancel" }, "version": "8948e199254cd5165159e7475360fa4039b93548f49a1f6b712a63519b70f040" }Generated inUsing seed: 40913 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:10, 4.66it/s] 4%|▍ | 2/50 [00:00<00:09, 5.16it/s] 6%|▌ | 3/50 [00:00<00:08, 5.39it/s] 8%|▊ | 4/50 [00:00<00:08, 5.41it/s] 10%|█ | 5/50 [00:00<00:08, 5.49it/s] 12%|█▏ | 6/50 [00:01<00:07, 5.54it/s] 14%|█▍ | 7/50 [00:01<00:07, 5.57it/s] 16%|█▌ | 8/50 [00:01<00:07, 5.60it/s] 18%|█▊ | 9/50 [00:01<00:07, 5.62it/s] 20%|██ | 10/50 [00:01<00:07, 5.63it/s] 22%|██▏ | 11/50 [00:01<00:06, 5.63it/s] 24%|██▍ | 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