fofr
/
sdxl-googly-eyes
👀 SDXL fine-tune to add googly eyes to anything
- Public
- 1K runs
-
L40S
- SDXL fine-tune
Prediction
fofr/sdxl-googly-eyes:3239d84bIDydgt2ptb2k2xypskbnhv7te2g4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 768
- prompt
- A photo of TOK eyes on a Xmas stocking
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a Xmas stocking", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", { input: { width: 768, height: 768, prompt: "A photo of TOK eyes on a Xmas stocking", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 25 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", input={ "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a Xmas stocking", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-googly-eyes 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": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a Xmas stocking", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-09T17:25:52.500681Z", "created_at": "2023-12-09T17:25:43.004604Z", "data_removed": false, "error": null, "id": "ydgt2ptb2k2xypskbnhv7te2g4", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a Xmas stocking", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 15425\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1> eyes on a Xmas stocking\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:03, 6.16it/s]\n 8%|▊ | 2/25 [00:00<00:03, 6.12it/s]\n 12%|█▏ | 3/25 [00:00<00:03, 6.12it/s]\n 16%|█▌ | 4/25 [00:00<00:03, 6.13it/s]\n 20%|██ | 5/25 [00:00<00:03, 6.12it/s]\n 24%|██▍ | 6/25 [00:00<00:03, 6.12it/s]\n 28%|██▊ | 7/25 [00:01<00:02, 6.12it/s]\n 32%|███▏ | 8/25 [00:01<00:02, 6.11it/s]\n 36%|███▌ | 9/25 [00:01<00:02, 6.11it/s]\n 40%|████ | 10/25 [00:01<00:02, 6.12it/s]\n 44%|████▍ | 11/25 [00:01<00:02, 6.11it/s]\n 48%|████▊ | 12/25 [00:01<00:02, 6.11it/s]\n 52%|█████▏ | 13/25 [00:02<00:01, 6.10it/s]\n 56%|█████▌ | 14/25 [00:02<00:01, 6.10it/s]\n 60%|██████ | 15/25 [00:02<00:01, 6.11it/s]\n 64%|██████▍ | 16/25 [00:02<00:01, 6.11it/s]\n 68%|██████▊ | 17/25 [00:02<00:01, 6.11it/s]\n 72%|███████▏ | 18/25 [00:02<00:01, 6.11it/s]\n 76%|███████▌ | 19/25 [00:03<00:00, 6.10it/s]\n 80%|████████ | 20/25 [00:03<00:00, 6.11it/s]\n 84%|████████▍ | 21/25 [00:03<00:00, 6.10it/s]\n 88%|████████▊ | 22/25 [00:03<00:00, 6.10it/s]\n 92%|█████████▏| 23/25 [00:03<00:00, 6.10it/s]\n 96%|█████████▌| 24/25 [00:03<00:00, 6.10it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.10it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.11it/s]", "metrics": { "predict_time": 6.012221, "total_time": 9.496077 }, "output": [ "https://replicate.delivery/pbxt/8KEJH5i8naqwGFYmBe5b4oCSS1AiM5CISdCWeyQ4wtGfoKBkA/out-0.png" ], "started_at": "2023-12-09T17:25:46.488460Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ydgt2ptb2k2xypskbnhv7te2g4", "cancel": "https://api.replicate.com/v1/predictions/ydgt2ptb2k2xypskbnhv7te2g4/cancel" }, "version": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f" }Generated inUsing seed: 15425 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of <s0><s1> eyes on a Xmas stocking txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:03, 6.16it/s] 8%|▊ | 2/25 [00:00<00:03, 6.12it/s] 12%|█▏ | 3/25 [00:00<00:03, 6.12it/s] 16%|█▌ | 4/25 [00:00<00:03, 6.13it/s] 20%|██ | 5/25 [00:00<00:03, 6.12it/s] 24%|██▍ | 6/25 [00:00<00:03, 6.12it/s] 28%|██▊ | 7/25 [00:01<00:02, 6.12it/s] 32%|███▏ | 8/25 [00:01<00:02, 6.11it/s] 36%|███▌ | 9/25 [00:01<00:02, 6.11it/s] 40%|████ | 10/25 [00:01<00:02, 6.12it/s] 44%|████▍ | 11/25 [00:01<00:02, 6.11it/s] 48%|████▊ | 12/25 [00:01<00:02, 6.11it/s] 52%|█████▏ | 13/25 [00:02<00:01, 6.10it/s] 56%|█████▌ | 14/25 [00:02<00:01, 6.10it/s] 60%|██████ | 15/25 [00:02<00:01, 6.11it/s] 64%|██████▍ | 16/25 [00:02<00:01, 6.11it/s] 68%|██████▊ | 17/25 [00:02<00:01, 6.11it/s] 72%|███████▏ | 18/25 [00:02<00:01, 6.11it/s] 76%|███████▌ | 19/25 [00:03<00:00, 6.10it/s] 80%|████████ | 20/25 [00:03<00:00, 6.11it/s] 84%|████████▍ | 21/25 [00:03<00:00, 6.10it/s] 88%|████████▊ | 22/25 [00:03<00:00, 6.10it/s] 92%|█████████▏| 23/25 [00:03<00:00, 6.10it/s] 96%|█████████▌| 24/25 [00:03<00:00, 6.10it/s] 100%|██████████| 25/25 [00:04<00:00, 6.10it/s] 100%|██████████| 25/25 [00:04<00:00, 6.11it/s]Prediction
fofr/sdxl-googly-eyes:3239d84bIDex47kadbppdri6v4zwl2ue7m3iStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 768
- prompt
- A photo of TOK eyes on a clown fish
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a clown fish", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", { input: { width: 768, height: 768, prompt: "A photo of TOK eyes on a clown fish", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 25 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", input={ "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a clown fish", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-googly-eyes 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": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a clown fish", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-09T17:28:23.067131Z", "created_at": "2023-12-09T17:28:14.441466Z", "data_removed": false, "error": null, "id": "ex47kadbppdri6v4zwl2ue7m3i", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a clown fish", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 49069\nEnsuring enough disk space...\nFree disk space: 1816823230464\nDownloading weights: https://replicate.delivery/pbxt/5ecT4YGgj3wjFCOxsHhKWwwsSP1rvaEsKFqOkoCIWDzaJRAJA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.316s (589 MB/s)\\nExtracted 186 MB in 0.056s (3.3 GB/s)\\n'\nDownloaded weights in 0.4944286346435547 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1> eyes on a clown fish\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:04, 5.25it/s]\n 8%|▊ | 2/25 [00:00<00:04, 5.75it/s]\n 12%|█▏ | 3/25 [00:00<00:03, 5.90it/s]\n 16%|█▌ | 4/25 [00:00<00:03, 5.98it/s]\n 20%|██ | 5/25 [00:00<00:03, 6.01it/s]\n 24%|██▍ | 6/25 [00:01<00:03, 6.03it/s]\n 28%|██▊ | 7/25 [00:01<00:02, 6.05it/s]\n 32%|███▏ | 8/25 [00:01<00:02, 6.06it/s]\n 36%|███▌ | 9/25 [00:01<00:02, 6.07it/s]\n 40%|████ | 10/25 [00:01<00:02, 6.08it/s]\n 44%|████▍ | 11/25 [00:01<00:02, 6.08it/s]\n 48%|████▊ | 12/25 [00:01<00:02, 6.09it/s]\n 52%|█████▏ | 13/25 [00:02<00:01, 6.09it/s]\n 56%|█████▌ | 14/25 [00:02<00:01, 6.09it/s]\n 60%|██████ | 15/25 [00:02<00:01, 6.08it/s]\n 64%|██████▍ | 16/25 [00:02<00:01, 6.08it/s]\n 68%|██████▊ | 17/25 [00:02<00:01, 6.08it/s]\n 72%|███████▏ | 18/25 [00:02<00:01, 6.09it/s]\n 76%|███████▌ | 19/25 [00:03<00:00, 6.10it/s]\n 80%|████████ | 20/25 [00:03<00:00, 6.10it/s]\n 84%|████████▍ | 21/25 [00:03<00:00, 6.10it/s]\n 88%|████████▊ | 22/25 [00:03<00:00, 6.11it/s]\n 92%|█████████▏| 23/25 [00:03<00:00, 6.11it/s]\n 96%|█████████▌| 24/25 [00:03<00:00, 6.12it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.12it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.06it/s]", "metrics": { "predict_time": 6.609414, "total_time": 8.625665 }, "output": [ "https://replicate.delivery/pbxt/eop3WMklh90BCSaY4Y0QNFAtmgZGqEM7d3Jx2vEFyUe2WlASA/out-0.png" ], "started_at": "2023-12-09T17:28:16.457717Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ex47kadbppdri6v4zwl2ue7m3i", "cancel": "https://api.replicate.com/v1/predictions/ex47kadbppdri6v4zwl2ue7m3i/cancel" }, "version": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f" }Generated inUsing seed: 49069 Ensuring enough disk space... Free disk space: 1816823230464 Downloading weights: https://replicate.delivery/pbxt/5ecT4YGgj3wjFCOxsHhKWwwsSP1rvaEsKFqOkoCIWDzaJRAJA/trained_model.tar b'Downloaded 186 MB bytes in 0.316s (589 MB/s)\nExtracted 186 MB in 0.056s (3.3 GB/s)\n' Downloaded weights in 0.4944286346435547 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of <s0><s1> eyes on a clown fish txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:04, 5.25it/s] 8%|▊ | 2/25 [00:00<00:04, 5.75it/s] 12%|█▏ | 3/25 [00:00<00:03, 5.90it/s] 16%|█▌ | 4/25 [00:00<00:03, 5.98it/s] 20%|██ | 5/25 [00:00<00:03, 6.01it/s] 24%|██▍ | 6/25 [00:01<00:03, 6.03it/s] 28%|██▊ | 7/25 [00:01<00:02, 6.05it/s] 32%|███▏ | 8/25 [00:01<00:02, 6.06it/s] 36%|███▌ | 9/25 [00:01<00:02, 6.07it/s] 40%|████ | 10/25 [00:01<00:02, 6.08it/s] 44%|████▍ | 11/25 [00:01<00:02, 6.08it/s] 48%|████▊ | 12/25 [00:01<00:02, 6.09it/s] 52%|█████▏ | 13/25 [00:02<00:01, 6.09it/s] 56%|█████▌ | 14/25 [00:02<00:01, 6.09it/s] 60%|██████ | 15/25 [00:02<00:01, 6.08it/s] 64%|██████▍ | 16/25 [00:02<00:01, 6.08it/s] 68%|██████▊ | 17/25 [00:02<00:01, 6.08it/s] 72%|███████▏ | 18/25 [00:02<00:01, 6.09it/s] 76%|███████▌ | 19/25 [00:03<00:00, 6.10it/s] 80%|████████ | 20/25 [00:03<00:00, 6.10it/s] 84%|████████▍ | 21/25 [00:03<00:00, 6.10it/s] 88%|████████▊ | 22/25 [00:03<00:00, 6.11it/s] 92%|█████████▏| 23/25 [00:03<00:00, 6.11it/s] 96%|█████████▌| 24/25 [00:03<00:00, 6.12it/s] 100%|██████████| 25/25 [00:04<00:00, 6.12it/s] 100%|██████████| 25/25 [00:04<00:00, 6.06it/s]Prediction
fofr/sdxl-googly-eyes:3239d84bIDw2nqa6lbo76tkivb2kwb4gxh34StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 768
- prompt
- A photo of TOK eyes on a soup
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.7
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- old, rusty, decayed
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a soup", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "old, rusty, decayed", "prompt_strength": 0.8, "num_inference_steps": 25 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", { input: { width: 768, height: 768, prompt: "A photo of TOK eyes on a soup", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.7, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "old, rusty, decayed", prompt_strength: 0.8, num_inference_steps: 25 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", input={ "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a soup", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "old, rusty, decayed", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-googly-eyes 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": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a soup", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "old, rusty, decayed", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-09T16:04:23.247076Z", "created_at": "2023-12-09T16:04:13.068659Z", "data_removed": false, "error": null, "id": "w2nqa6lbo76tkivb2kwb4gxh34", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a soup", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "old, rusty, decayed", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 14588\nEnsuring enough disk space...\nFree disk space: 1682980356096\nDownloading weights: https://replicate.delivery/pbxt/5ecT4YGgj3wjFCOxsHhKWwwsSP1rvaEsKFqOkoCIWDzaJRAJA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.246s (756 MB/s)\\nExtracted 186 MB in 0.066s (2.8 GB/s)\\n'\nDownloaded weights in 0.5667271614074707 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1> eyes on a soup\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:03, 6.17it/s]\n 8%|▊ | 2/25 [00:00<00:03, 6.17it/s]\n 12%|█▏ | 3/25 [00:00<00:03, 6.17it/s]\n 16%|█▌ | 4/25 [00:00<00:03, 6.16it/s]\n 20%|██ | 5/25 [00:00<00:03, 6.16it/s]\n 24%|██▍ | 6/25 [00:00<00:03, 6.15it/s]\n 28%|██▊ | 7/25 [00:01<00:02, 6.15it/s]\n 32%|███▏ | 8/25 [00:01<00:02, 6.14it/s]\n 36%|███▌ | 9/25 [00:01<00:02, 6.15it/s]\n 40%|████ | 10/25 [00:01<00:02, 6.15it/s]\n 44%|████▍ | 11/25 [00:01<00:02, 6.15it/s]\n 48%|████▊ | 12/25 [00:01<00:02, 6.15it/s]\n 52%|█████▏ | 13/25 [00:02<00:01, 6.14it/s]\n 56%|█████▌ | 14/25 [00:02<00:01, 6.14it/s]\n 60%|██████ | 15/25 [00:02<00:01, 6.14it/s]\n 64%|██████▍ | 16/25 [00:02<00:01, 6.14it/s]\n 68%|██████▊ | 17/25 [00:02<00:01, 6.15it/s]\n 72%|███████▏ | 18/25 [00:02<00:01, 6.14it/s]\n 76%|███████▌ | 19/25 [00:03<00:00, 6.14it/s]\n 80%|████████ | 20/25 [00:03<00:00, 6.14it/s]\n 84%|████████▍ | 21/25 [00:03<00:00, 6.14it/s]\n 88%|████████▊ | 22/25 [00:03<00:00, 6.14it/s]\n 92%|█████████▏| 23/25 [00:03<00:00, 6.14it/s]\n 96%|█████████▌| 24/25 [00:03<00:00, 6.15it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.16it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.15it/s]", "metrics": { "predict_time": 6.062584, "total_time": 10.178417 }, "output": [ "https://replicate.delivery/pbxt/TO2oTPwJcWLeEaM3811Yz9IwrZgPrGNGcn7zNxDaeMsGIkASA/out-0.png" ], "started_at": "2023-12-09T16:04:17.184492Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w2nqa6lbo76tkivb2kwb4gxh34", "cancel": "https://api.replicate.com/v1/predictions/w2nqa6lbo76tkivb2kwb4gxh34/cancel" }, "version": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f" }Generated inUsing seed: 14588 Ensuring enough disk space... Free disk space: 1682980356096 Downloading weights: https://replicate.delivery/pbxt/5ecT4YGgj3wjFCOxsHhKWwwsSP1rvaEsKFqOkoCIWDzaJRAJA/trained_model.tar b'Downloaded 186 MB bytes in 0.246s (756 MB/s)\nExtracted 186 MB in 0.066s (2.8 GB/s)\n' Downloaded weights in 0.5667271614074707 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of <s0><s1> eyes on a soup txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:03, 6.17it/s] 8%|▊ | 2/25 [00:00<00:03, 6.17it/s] 12%|█▏ | 3/25 [00:00<00:03, 6.17it/s] 16%|█▌ | 4/25 [00:00<00:03, 6.16it/s] 20%|██ | 5/25 [00:00<00:03, 6.16it/s] 24%|██▍ | 6/25 [00:00<00:03, 6.15it/s] 28%|██▊ | 7/25 [00:01<00:02, 6.15it/s] 32%|███▏ | 8/25 [00:01<00:02, 6.14it/s] 36%|███▌ | 9/25 [00:01<00:02, 6.15it/s] 40%|████ | 10/25 [00:01<00:02, 6.15it/s] 44%|████▍ | 11/25 [00:01<00:02, 6.15it/s] 48%|████▊ | 12/25 [00:01<00:02, 6.15it/s] 52%|█████▏ | 13/25 [00:02<00:01, 6.14it/s] 56%|█████▌ | 14/25 [00:02<00:01, 6.14it/s] 60%|██████ | 15/25 [00:02<00:01, 6.14it/s] 64%|██████▍ | 16/25 [00:02<00:01, 6.14it/s] 68%|██████▊ | 17/25 [00:02<00:01, 6.15it/s] 72%|███████▏ | 18/25 [00:02<00:01, 6.14it/s] 76%|███████▌ | 19/25 [00:03<00:00, 6.14it/s] 80%|████████ | 20/25 [00:03<00:00, 6.14it/s] 84%|████████▍ | 21/25 [00:03<00:00, 6.14it/s] 88%|████████▊ | 22/25 [00:03<00:00, 6.14it/s] 92%|█████████▏| 23/25 [00:03<00:00, 6.14it/s] 96%|█████████▌| 24/25 [00:03<00:00, 6.15it/s] 100%|██████████| 25/25 [00:04<00:00, 6.16it/s] 100%|██████████| 25/25 [00:04<00:00, 6.15it/s]Prediction
fofr/sdxl-googly-eyes:3239d84bIDqa5l53lbyucqp7e2b62w3vtuauStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 768
- prompt
- A photo of TOK eyes
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 768, "height": 768, "prompt": "A photo of TOK eyes", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", { input: { width: 768, height: 768, prompt: "A photo of TOK eyes", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 25 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", input={ "width": 768, "height": 768, "prompt": "A photo of TOK eyes", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-googly-eyes 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": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-09T14:07:20.465650Z", "created_at": "2023-12-09T14:07:13.799636Z", "data_removed": false, "error": null, "id": "qa5l53lbyucqp7e2b62w3vtuau", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 22560\nEnsuring enough disk space...\nFree disk space: 2262383398912\nDownloading weights: https://replicate.delivery/pbxt/5ecT4YGgj3wjFCOxsHhKWwwsSP1rvaEsKFqOkoCIWDzaJRAJA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.434s (428 MB/s)\\nExtracted 186 MB in 0.061s (3.0 GB/s)\\n'\nDownloaded weights in 0.5974154472351074 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1> eyes\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:03, 6.19it/s]\n 8%|▊ | 2/25 [00:00<00:03, 6.16it/s]\n 12%|█▏ | 3/25 [00:00<00:03, 6.16it/s]\n 16%|█▌ | 4/25 [00:00<00:03, 6.15it/s]\n 20%|██ | 5/25 [00:00<00:03, 6.12it/s]\n 24%|██▍ | 6/25 [00:00<00:03, 6.12it/s]\n 28%|██▊ | 7/25 [00:01<00:02, 6.12it/s]\n 32%|███▏ | 8/25 [00:01<00:02, 6.12it/s]\n 36%|███▌ | 9/25 [00:01<00:02, 6.12it/s]\n 40%|████ | 10/25 [00:01<00:02, 6.12it/s]\n 44%|████▍ | 11/25 [00:01<00:02, 6.11it/s]\n 48%|████▊ | 12/25 [00:01<00:02, 6.11it/s]\n 52%|█████▏ | 13/25 [00:02<00:01, 6.11it/s]\n 56%|█████▌ | 14/25 [00:02<00:01, 6.11it/s]\n 60%|██████ | 15/25 [00:02<00:01, 6.11it/s]\n 64%|██████▍ | 16/25 [00:02<00:01, 6.13it/s]\n 68%|██████▊ | 17/25 [00:02<00:01, 6.13it/s]\n 72%|███████▏ | 18/25 [00:02<00:01, 6.14it/s]\n 76%|███████▌ | 19/25 [00:03<00:00, 6.14it/s]\n 80%|████████ | 20/25 [00:03<00:00, 6.15it/s]\n 84%|████████▍ | 21/25 [00:03<00:00, 6.15it/s]\n 88%|████████▊ | 22/25 [00:03<00:00, 6.16it/s]\n 92%|█████████▏| 23/25 [00:03<00:00, 6.15it/s]\n 96%|█████████▌| 24/25 [00:03<00:00, 6.15it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.15it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.14it/s]", "metrics": { "predict_time": 6.628585, "total_time": 6.666014 }, "output": [ "https://replicate.delivery/pbxt/5l25VS08DYq6CVjWQNTGSK28ScSWJ8bxbPP5EDe2f1bXaiASA/out-0.png" ], "started_at": "2023-12-09T14:07:13.837065Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qa5l53lbyucqp7e2b62w3vtuau", "cancel": "https://api.replicate.com/v1/predictions/qa5l53lbyucqp7e2b62w3vtuau/cancel" }, "version": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f" }Generated inUsing seed: 22560 Ensuring enough disk space... Free disk space: 2262383398912 Downloading weights: https://replicate.delivery/pbxt/5ecT4YGgj3wjFCOxsHhKWwwsSP1rvaEsKFqOkoCIWDzaJRAJA/trained_model.tar b'Downloaded 186 MB bytes in 0.434s (428 MB/s)\nExtracted 186 MB in 0.061s (3.0 GB/s)\n' Downloaded weights in 0.5974154472351074 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of <s0><s1> eyes txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:03, 6.19it/s] 8%|▊ | 2/25 [00:00<00:03, 6.16it/s] 12%|█▏ | 3/25 [00:00<00:03, 6.16it/s] 16%|█▌ | 4/25 [00:00<00:03, 6.15it/s] 20%|██ | 5/25 [00:00<00:03, 6.12it/s] 24%|██▍ | 6/25 [00:00<00:03, 6.12it/s] 28%|██▊ | 7/25 [00:01<00:02, 6.12it/s] 32%|███▏ | 8/25 [00:01<00:02, 6.12it/s] 36%|███▌ | 9/25 [00:01<00:02, 6.12it/s] 40%|████ | 10/25 [00:01<00:02, 6.12it/s] 44%|████▍ | 11/25 [00:01<00:02, 6.11it/s] 48%|████▊ | 12/25 [00:01<00:02, 6.11it/s] 52%|█████▏ | 13/25 [00:02<00:01, 6.11it/s] 56%|█████▌ | 14/25 [00:02<00:01, 6.11it/s] 60%|██████ | 15/25 [00:02<00:01, 6.11it/s] 64%|██████▍ | 16/25 [00:02<00:01, 6.13it/s] 68%|██████▊ | 17/25 [00:02<00:01, 6.13it/s] 72%|███████▏ | 18/25 [00:02<00:01, 6.14it/s] 76%|███████▌ | 19/25 [00:03<00:00, 6.14it/s] 80%|████████ | 20/25 [00:03<00:00, 6.15it/s] 84%|████████▍ | 21/25 [00:03<00:00, 6.15it/s] 88%|████████▊ | 22/25 [00:03<00:00, 6.16it/s] 92%|█████████▏| 23/25 [00:03<00:00, 6.15it/s] 96%|█████████▌| 24/25 [00:03<00:00, 6.15it/s] 100%|██████████| 25/25 [00:04<00:00, 6.15it/s] 100%|██████████| 25/25 [00:04<00:00, 6.14it/s]Prediction
fofr/sdxl-googly-eyes:3239d84bID22hxwelbj3l5dab35w7naghxo4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 768
- prompt
- A photo of TOK eyes on a dolphin
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 25
{ "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a dolphin", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", { input: { width: 768, height: 768, prompt: "A photo of TOK eyes on a dolphin", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 25 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-googly-eyes using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-googly-eyes:3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", input={ "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a dolphin", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-googly-eyes 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": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a dolphin", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-09T14:18:03.608719Z", "created_at": "2023-12-09T14:17:57.968909Z", "data_removed": false, "error": null, "id": "22hxwelbj3l5dab35w7naghxo4", "input": { "width": 768, "height": 768, "prompt": "A photo of TOK eyes on a dolphin", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 25 }, "logs": "Using seed: 29447\nskipping loading .. weights already loaded\nPrompt: A photo of <s0><s1> eyes on a dolphin\ntxt2img mode\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:03, 6.16it/s]\n 8%|▊ | 2/25 [00:00<00:03, 6.12it/s]\n 12%|█▏ | 3/25 [00:00<00:03, 6.10it/s]\n 16%|█▌ | 4/25 [00:00<00:03, 6.10it/s]\n 20%|██ | 5/25 [00:00<00:03, 6.11it/s]\n 24%|██▍ | 6/25 [00:00<00:03, 6.12it/s]\n 28%|██▊ | 7/25 [00:01<00:02, 6.12it/s]\n 32%|███▏ | 8/25 [00:01<00:02, 6.12it/s]\n 36%|███▌ | 9/25 [00:01<00:02, 6.12it/s]\n 40%|████ | 10/25 [00:01<00:02, 6.13it/s]\n 44%|████▍ | 11/25 [00:01<00:02, 6.12it/s]\n 48%|████▊ | 12/25 [00:01<00:02, 6.13it/s]\n 52%|█████▏ | 13/25 [00:02<00:01, 6.12it/s]\n 56%|█████▌ | 14/25 [00:02<00:01, 6.11it/s]\n 60%|██████ | 15/25 [00:02<00:01, 6.12it/s]\n 64%|██████▍ | 16/25 [00:02<00:01, 6.12it/s]\n 68%|██████▊ | 17/25 [00:02<00:01, 6.12it/s]\n 72%|███████▏ | 18/25 [00:02<00:01, 6.10it/s]\n 76%|███████▌ | 19/25 [00:03<00:00, 6.11it/s]\n 80%|████████ | 20/25 [00:03<00:00, 6.11it/s]\n 84%|████████▍ | 21/25 [00:03<00:00, 6.12it/s]\n 88%|████████▊ | 22/25 [00:03<00:00, 6.11it/s]\n 92%|█████████▏| 23/25 [00:03<00:00, 6.11it/s]\n 96%|█████████▌| 24/25 [00:03<00:00, 6.10it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.10it/s]\n100%|██████████| 25/25 [00:04<00:00, 6.11it/s]", "metrics": { "predict_time": 5.60298, "total_time": 5.63981 }, "output": [ "https://replicate.delivery/pbxt/hX87sBy1Iv7hGxI7mvqHzAo1dDyl0mOJxjjALY9FO0tGpIgE/out-0.png" ], "started_at": "2023-12-09T14:17:58.005739Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/22hxwelbj3l5dab35w7naghxo4", "cancel": "https://api.replicate.com/v1/predictions/22hxwelbj3l5dab35w7naghxo4/cancel" }, "version": "3239d84b2b6e2c1ef1814f9e613534fcc338df5f8fa9f8198edaee0adde6066f" }Generated inUsing seed: 29447 skipping loading .. weights already loaded Prompt: A photo of <s0><s1> eyes on a dolphin txt2img mode 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:03, 6.16it/s] 8%|▊ | 2/25 [00:00<00:03, 6.12it/s] 12%|█▏ | 3/25 [00:00<00:03, 6.10it/s] 16%|█▌ | 4/25 [00:00<00:03, 6.10it/s] 20%|██ | 5/25 [00:00<00:03, 6.11it/s] 24%|██▍ | 6/25 [00:00<00:03, 6.12it/s] 28%|██▊ | 7/25 [00:01<00:02, 6.12it/s] 32%|███▏ | 8/25 [00:01<00:02, 6.12it/s] 36%|███▌ | 9/25 [00:01<00:02, 6.12it/s] 40%|████ | 10/25 [00:01<00:02, 6.13it/s] 44%|████▍ | 11/25 [00:01<00:02, 6.12it/s] 48%|████▊ | 12/25 [00:01<00:02, 6.13it/s] 52%|█████▏ | 13/25 [00:02<00:01, 6.12it/s] 56%|█████▌ | 14/25 [00:02<00:01, 6.11it/s] 60%|██████ | 15/25 [00:02<00:01, 6.12it/s] 64%|██████▍ | 16/25 [00:02<00:01, 6.12it/s] 68%|██████▊ | 17/25 [00:02<00:01, 6.12it/s] 72%|███████▏ | 18/25 [00:02<00:01, 6.10it/s] 76%|███████▌ | 19/25 [00:03<00:00, 6.11it/s] 80%|████████ | 20/25 [00:03<00:00, 6.11it/s] 84%|████████▍ | 21/25 [00:03<00:00, 6.12it/s] 88%|████████▊ | 22/25 [00:03<00:00, 6.11it/s] 92%|█████████▏| 23/25 [00:03<00:00, 6.11it/s] 96%|█████████▌| 24/25 [00:03<00:00, 6.10it/s] 100%|██████████| 25/25 [00:04<00:00, 6.10it/s] 100%|██████████| 25/25 [00:04<00:00, 6.11it/s]
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