akhil20187 / zoozoo
ZooZoo Images
- Public
- 43 runs
-
L40S
- SDXL fine-tune
Prediction
akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646IDw0y2jsxat9rgp0cfrqvv30w9ngStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- vodzo, cartoon style, one character, dressed as superman
- refine
- base_image_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.8
- num_outputs
- 1
- refine_steps
- 0
- guidance_scale
- 6
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- blur, deformed, ugly,
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, one character, dressed as superman", "refine": "base_image_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 0, "guidance_scale": 6, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "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 akhil20187/zoozoo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", { input: { width: 1024, height: 1024, prompt: "vodzo, cartoon style, one character, dressed as superman", refine: "base_image_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.8, num_outputs: 1, refine_steps: 0, guidance_scale: 6, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "blur, deformed, ugly, ", 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 akhil20187/zoozoo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", input={ "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, one character, dressed as superman", "refine": "base_image_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 0, "guidance_scale": 6, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "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 akhil20187/zoozoo 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": "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", "input": { "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, one character, dressed as superman", "refine": "base_image_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 0, "guidance_scale": 6, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "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": "2024-05-29T20:08:35.115194Z", "created_at": "2024-05-29T20:08:13.778000Z", "data_removed": false, "error": null, "id": "w0y2jsxat9rgp0cfrqvv30w9ng", "input": { "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, one character, dressed as superman", "refine": "base_image_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.8, "num_outputs": 1, "refine_steps": 0, "guidance_scale": 6, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 27017\nskipping loading .. weights already loaded\nPrompt: vodzo, cartoon style, one character, dressed as superman\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:07, 6.31it/s]\n 4%|▍ | 2/50 [00:00<00:09, 4.90it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.58it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.44it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.36it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.31it/s]\n 14%|█▍ | 7/50 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[00:11<00:00, 4.28it/s]\n 0%| | 0/15 [00:00<?, ?it/s]\n 7%|▋ | 1/15 [00:00<00:03, 4.19it/s]\n 13%|█▎ | 2/15 [00:00<00:03, 4.18it/s]\n 20%|██ | 3/15 [00:00<00:02, 4.18it/s]\n 27%|██▋ | 4/15 [00:00<00:02, 4.17it/s]\n 33%|███▎ | 5/15 [00:01<00:02, 4.17it/s]\n 40%|████ | 6/15 [00:01<00:02, 4.18it/s]\n 47%|████▋ | 7/15 [00:01<00:01, 4.19it/s]\n 53%|█████▎ | 8/15 [00:01<00:01, 4.18it/s]\n 60%|██████ | 9/15 [00:02<00:01, 4.17it/s]\n 67%|██████▋ | 10/15 [00:02<00:01, 4.18it/s]\n 73%|███████▎ | 11/15 [00:02<00:00, 4.19it/s]\n 80%|████████ | 12/15 [00:02<00:00, 4.19it/s]\n 87%|████████▋ | 13/15 [00:03<00:00, 4.18it/s]\n 93%|█████████▎| 14/15 [00:03<00:00, 4.18it/s]\n100%|██████████| 15/15 [00:03<00:00, 4.18it/s]\n100%|██████████| 15/15 [00:03<00:00, 4.18it/s]", "metrics": { "predict_time": 16.715372, "total_time": 21.337194 }, "output": [ "https://replicate.delivery/pbxt/el97X243GR2uMa8XxL6di4fmiIKfjLwBARDu6Me3Z0sKUPlLB/out-0.png" ], "started_at": "2024-05-29T20:08:18.399822Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w0y2jsxat9rgp0cfrqvv30w9ng", "cancel": "https://api.replicate.com/v1/predictions/w0y2jsxat9rgp0cfrqvv30w9ng/cancel" }, "version": "ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646" }Generated inUsing seed: 27017 skipping loading .. weights already loaded Prompt: vodzo, cartoon style, one character, dressed as superman txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:07, 6.31it/s] 4%|▍ | 2/50 [00:00<00:09, 4.90it/s] 6%|▌ | 3/50 [00:00<00:10, 4.58it/s] 8%|▊ | 4/50 [00:00<00:10, 4.44it/s] 10%|█ | 5/50 [00:01<00:10, 4.36it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.31it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.30it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.29it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.28it/s] 20%|██ | 10/50 [00:02<00:09, 4.28it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.26it/s] 26%|██▌ | 13/50 [00:02<00:08, 4.26it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.26it/s] 30%|███ | 15/50 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67%|██████▋ | 10/15 [00:02<00:01, 4.18it/s] 73%|███████▎ | 11/15 [00:02<00:00, 4.19it/s] 80%|████████ | 12/15 [00:02<00:00, 4.19it/s] 87%|████████▋ | 13/15 [00:03<00:00, 4.18it/s] 93%|█████████▎| 14/15 [00:03<00:00, 4.18it/s] 100%|██████████| 15/15 [00:03<00:00, 4.18it/s] 100%|██████████| 15/15 [00:03<00:00, 4.18it/s]Prediction
akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646ID1nmncsph85rgj0cfrqt94e8smwStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- vodzo, cartoon style, playing cricket
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER_ANCESTRAL
- lora_scale
- 0.9
- num_outputs
- 1
- guidance_scale
- 6
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- blur, deformed, ugly,
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, playing cricket", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.9, "num_outputs": 1, "guidance_scale": 6, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "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 akhil20187/zoozoo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", { input: { width: 1024, height: 1024, prompt: "vodzo, cartoon style, playing cricket", refine: "expert_ensemble_refiner", scheduler: "K_EULER_ANCESTRAL", lora_scale: 0.9, num_outputs: 1, guidance_scale: 6, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "blur, deformed, ugly, ", 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 akhil20187/zoozoo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", input={ "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, playing cricket", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.9, "num_outputs": 1, "guidance_scale": 6, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "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 akhil20187/zoozoo 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": "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", "input": { "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, playing cricket", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.9, "num_outputs": 1, "guidance_scale": 6, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "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": "2024-05-29T20:05:22.128647Z", "created_at": "2024-05-29T20:05:07.009000Z", "data_removed": false, "error": null, "id": "1nmncsph85rgj0cfrqt94e8smw", "input": { "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, playing cricket", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER_ANCESTRAL", "lora_scale": 0.9, "num_outputs": 1, "guidance_scale": 6, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 28375\nskipping loading .. weights already loaded\nPrompt: vodzo, cartoon style, playing cricket\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:06, 6.30it/s]\n 5%|▌ | 2/40 [00:00<00:07, 4.91it/s]\n 8%|▊ | 3/40 [00:00<00:08, 4.57it/s]\n 10%|█ | 4/40 [00:00<00:08, 4.43it/s]\n 12%|█▎ | 5/40 [00:01<00:08, 4.36it/s]\n 15%|█▌ | 6/40 [00:01<00:07, 4.33it/s]\n 18%|█▊ | 7/40 [00:01<00:07, 4.31it/s]\n 20%|██ | 8/40 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78%|███████▊ | 31/40 [00:07<00:02, 4.24it/s]\n 80%|████████ | 32/40 [00:07<00:01, 4.25it/s]\n 82%|████████▎ | 33/40 [00:07<00:01, 4.25it/s]\n 85%|████████▌ | 34/40 [00:07<00:01, 4.25it/s]\n 88%|████████▊ | 35/40 [00:08<00:01, 4.25it/s]\n 90%|█████████ | 36/40 [00:08<00:00, 4.25it/s]\n 92%|█████████▎| 37/40 [00:08<00:00, 4.25it/s]\n 95%|█████████▌| 38/40 [00:08<00:00, 4.25it/s]\n 98%|█████████▊| 39/40 [00:09<00:00, 4.24it/s]\n100%|██████████| 40/40 [00:09<00:00, 4.24it/s]\n100%|██████████| 40/40 [00:09<00:00, 4.29it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.26it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.21it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.21it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.20it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.20it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.20it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.19it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.19it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.18it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.18it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.19it/s]", "metrics": { "predict_time": 13.068921, "total_time": 15.119647 }, "output": [ "https://replicate.delivery/pbxt/Wqm5vdo9DOKtPxMLuAlLeodlxNzIHtsOmLtZEQxwjV4A5pcJA/out-0.png" ], "started_at": "2024-05-29T20:05:09.059726Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/1nmncsph85rgj0cfrqt94e8smw", "cancel": "https://api.replicate.com/v1/predictions/1nmncsph85rgj0cfrqt94e8smw/cancel" }, "version": "ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646" }Generated inUsing seed: 28375 skipping loading .. weights already loaded Prompt: vodzo, cartoon style, playing cricket txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:06, 6.30it/s] 5%|▌ | 2/40 [00:00<00:07, 4.91it/s] 8%|▊ | 3/40 [00:00<00:08, 4.57it/s] 10%|█ | 4/40 [00:00<00:08, 4.43it/s] 12%|█▎ | 5/40 [00:01<00:08, 4.36it/s] 15%|█▌ | 6/40 [00:01<00:07, 4.33it/s] 18%|█▊ | 7/40 [00:01<00:07, 4.31it/s] 20%|██ | 8/40 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80%|████████ | 32/40 [00:07<00:01, 4.25it/s] 82%|████████▎ | 33/40 [00:07<00:01, 4.25it/s] 85%|████████▌ | 34/40 [00:07<00:01, 4.25it/s] 88%|████████▊ | 35/40 [00:08<00:01, 4.25it/s] 90%|█████████ | 36/40 [00:08<00:00, 4.25it/s] 92%|█████████▎| 37/40 [00:08<00:00, 4.25it/s] 95%|█████████▌| 38/40 [00:08<00:00, 4.25it/s] 98%|█████████▊| 39/40 [00:09<00:00, 4.24it/s] 100%|██████████| 40/40 [00:09<00:00, 4.24it/s] 100%|██████████| 40/40 [00:09<00:00, 4.29it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.26it/s] 20%|██ | 2/10 [00:00<00:01, 4.21it/s] 30%|███ | 3/10 [00:00<00:01, 4.21it/s] 40%|████ | 4/10 [00:00<00:01, 4.20it/s] 50%|█████ | 5/10 [00:01<00:01, 4.20it/s] 60%|██████ | 6/10 [00:01<00:00, 4.20it/s] 70%|███████ | 7/10 [00:01<00:00, 4.19it/s] 80%|████████ | 8/10 [00:01<00:00, 4.19it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.18it/s] 100%|██████████| 10/10 [00:02<00:00, 4.18it/s] 100%|██████████| 10/10 [00:02<00:00, 4.19it/s]Prediction
akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646IDn0wwqg8d3hrgm0cfrqsb4zf1hgStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- vodzo, cartoon style, driving a car
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- blur, deformed, ugly,
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, driving a car", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "prompt_strength": 0.8, "num_inference_steps": 30 }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 akhil20187/zoozoo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", { input: { width: 1024, height: 1024, prompt: "vodzo, cartoon style, driving a car", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "blur, deformed, ugly, ", prompt_strength: 0.8, num_inference_steps: 30 } } ); // 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 akhil20187/zoozoo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", input={ "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, driving a car", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "prompt_strength": 0.8, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run akhil20187/zoozoo 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": "akhil20187/zoozoo:ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646", "input": { "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, driving a car", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T20:02:25.431823Z", "created_at": "2024-05-29T20:02:05.724000Z", "data_removed": false, "error": null, "id": "n0wwqg8d3hrgm0cfrqsb4zf1hg", "input": { "width": 1024, "height": 1024, "prompt": "vodzo, cartoon style, driving a car", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "blur, deformed, ugly, ", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 24797\nEnsuring enough disk space...\nFree disk space: 1713540243456\nDownloading weights: https://replicate.delivery/pbxt/UnuZXksvP2ZiMJHneFaGp5qqfIX2XI74Fs9ecGz454jPTnylA/trained_model.tar\n2024-05-29T20:02:08Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/bfa04952c16f98d5 url=https://replicate.delivery/pbxt/UnuZXksvP2ZiMJHneFaGp5qqfIX2XI74Fs9ecGz454jPTnylA/trained_model.tar\n2024-05-29T20:02:14Z | INFO | [ Complete ] dest=/src/weights-cache/bfa04952c16f98d5 size=\"186 MB\" total_elapsed=5.272s url=https://replicate.delivery/pbxt/UnuZXksvP2ZiMJHneFaGp5qqfIX2XI74Fs9ecGz454jPTnylA/trained_model.tar\nb''\nDownloaded weights in 5.394346475601196 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: vodzo, cartoon style, driving a car\ntxt2img mode\n 0%| | 0/30 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1946: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights`\ndeprecate(\n 3%|▎ | 1/30 [00:00<00:06, 4.29it/s]\n 7%|▋ | 2/30 [00:00<00:06, 4.26it/s]\n 10%|█ | 3/30 [00:00<00:06, 4.27it/s]\n 13%|█▎ | 4/30 [00:00<00:06, 4.26it/s]\n 17%|█▋ | 5/30 [00:01<00:05, 4.26it/s]\n 20%|██ | 6/30 [00:01<00:05, 4.26it/s]\n 23%|██▎ | 7/30 [00:01<00:05, 4.26it/s]\n 27%|██▋ | 8/30 [00:01<00:05, 4.26it/s]\n 30%|███ | 9/30 [00:02<00:04, 4.26it/s]\n 33%|███▎ | 10/30 [00:02<00:04, 4.26it/s]\n 37%|███▋ | 11/30 [00:02<00:04, 4.26it/s]\n 40%|████ | 12/30 [00:02<00:04, 4.26it/s]\n 43%|████▎ | 13/30 [00:03<00:03, 4.26it/s]\n 47%|████▋ | 14/30 [00:03<00:03, 4.25it/s]\n 50%|█████ | 15/30 [00:03<00:03, 4.25it/s]\n 53%|█████▎ | 16/30 [00:03<00:03, 4.25it/s]\n 57%|█████▋ | 17/30 [00:03<00:03, 4.25it/s]\n 60%|██████ | 18/30 [00:04<00:02, 4.25it/s]\n 63%|██████▎ | 19/30 [00:04<00:02, 4.24it/s]\n 67%|██████▋ | 20/30 [00:04<00:02, 4.25it/s]\n 70%|███████ | 21/30 [00:04<00:02, 4.25it/s]\n 73%|███████▎ | 22/30 [00:05<00:01, 4.25it/s]\n 77%|███████▋ | 23/30 [00:05<00:01, 4.24it/s]\n 80%|████████ | 24/30 [00:05<00:01, 4.24it/s]\n 83%|████████▎ | 25/30 [00:05<00:01, 4.24it/s]\n 87%|████████▋ | 26/30 [00:06<00:00, 4.24it/s]\n 90%|█████████ | 27/30 [00:06<00:00, 4.24it/s]\n 93%|█████████▎| 28/30 [00:06<00:00, 4.24it/s]\n 97%|█████████▋| 29/30 [00:06<00:00, 4.24it/s]\n100%|██████████| 30/30 [00:07<00:00, 4.24it/s]\n100%|██████████| 30/30 [00:07<00:00, 4.25it/s]", "metrics": { "predict_time": 16.648544, "total_time": 19.707823 }, "output": [ "https://replicate.delivery/pbxt/CfxnjQWU8k0ObS2zEDFZjR3NrD1LirBtEaU2rjnESqUo3pcJA/out-0.png" ], "started_at": "2024-05-29T20:02:08.783279Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/n0wwqg8d3hrgm0cfrqsb4zf1hg", "cancel": "https://api.replicate.com/v1/predictions/n0wwqg8d3hrgm0cfrqsb4zf1hg/cancel" }, "version": "ca549059ac63914f28a5a65b16f551d0ae234ed4a768a646a6ad529bdbe12646" }Generated inUsing seed: 24797 Ensuring enough disk space... Free disk space: 1713540243456 Downloading weights: https://replicate.delivery/pbxt/UnuZXksvP2ZiMJHneFaGp5qqfIX2XI74Fs9ecGz454jPTnylA/trained_model.tar 2024-05-29T20:02:08Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/bfa04952c16f98d5 url=https://replicate.delivery/pbxt/UnuZXksvP2ZiMJHneFaGp5qqfIX2XI74Fs9ecGz454jPTnylA/trained_model.tar 2024-05-29T20:02:14Z | INFO | [ Complete ] dest=/src/weights-cache/bfa04952c16f98d5 size="186 MB" total_elapsed=5.272s url=https://replicate.delivery/pbxt/UnuZXksvP2ZiMJHneFaGp5qqfIX2XI74Fs9ecGz454jPTnylA/trained_model.tar b'' Downloaded weights in 5.394346475601196 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: vodzo, cartoon style, driving a car txt2img mode 0%| | 0/30 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1946: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights` deprecate( 3%|▎ | 1/30 [00:00<00:06, 4.29it/s] 7%|▋ | 2/30 [00:00<00:06, 4.26it/s] 10%|█ | 3/30 [00:00<00:06, 4.27it/s] 13%|█▎ | 4/30 [00:00<00:06, 4.26it/s] 17%|█▋ | 5/30 [00:01<00:05, 4.26it/s] 20%|██ | 6/30 [00:01<00:05, 4.26it/s] 23%|██▎ | 7/30 [00:01<00:05, 4.26it/s] 27%|██▋ | 8/30 [00:01<00:05, 4.26it/s] 30%|███ | 9/30 [00:02<00:04, 4.26it/s] 33%|███▎ | 10/30 [00:02<00:04, 4.26it/s] 37%|███▋ | 11/30 [00:02<00:04, 4.26it/s] 40%|████ | 12/30 [00:02<00:04, 4.26it/s] 43%|████▎ | 13/30 [00:03<00:03, 4.26it/s] 47%|████▋ | 14/30 [00:03<00:03, 4.25it/s] 50%|█████ | 15/30 [00:03<00:03, 4.25it/s] 53%|█████▎ | 16/30 [00:03<00:03, 4.25it/s] 57%|█████▋ | 17/30 [00:03<00:03, 4.25it/s] 60%|██████ | 18/30 [00:04<00:02, 4.25it/s] 63%|██████▎ | 19/30 [00:04<00:02, 4.24it/s] 67%|██████▋ | 20/30 [00:04<00:02, 4.25it/s] 70%|███████ | 21/30 [00:04<00:02, 4.25it/s] 73%|███████▎ | 22/30 [00:05<00:01, 4.25it/s] 77%|███████▋ | 23/30 [00:05<00:01, 4.24it/s] 80%|████████ | 24/30 [00:05<00:01, 4.24it/s] 83%|████████▎ | 25/30 [00:05<00:01, 4.24it/s] 87%|████████▋ | 26/30 [00:06<00:00, 4.24it/s] 90%|█████████ | 27/30 [00:06<00:00, 4.24it/s] 93%|█████████▎| 28/30 [00:06<00:00, 4.24it/s] 97%|█████████▋| 29/30 [00:06<00:00, 4.24it/s] 100%|██████████| 30/30 [00:07<00:00, 4.24it/s] 100%|██████████| 30/30 [00:07<00:00, 4.25it/s]
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