martintmv-git / sdxl-lego
SDXL Fine-tune on LEGO
- Public
- 625 runs
- SDXL fine-tune
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDmennpdtbps2sbuzqljyjtjlzhuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Breaking Bad lego in the style of TOK
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Breaking Bad lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "Breaking Bad lego in the style of TOK", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "Breaking Bad lego in the style of TOK", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "Breaking Bad lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T21:01:24.731041Z", "created_at": "2023-12-16T21:01:06.698310Z", "data_removed": false, "error": null, "id": "mennpdtbps2sbuzqljyjtjlzhu", "input": { "width": 1024, "height": 1024, "prompt": "Breaking Bad lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 53041\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Breaking Bad lego in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.65it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.65it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.62it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.62it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.62it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]", "metrics": { "predict_time": 16.390947, "total_time": 18.032731 }, "output": [ "https://replicate.delivery/pbxt/uzEDpGGefdjENEsvFP3PNUUdxLrVPIOQCMSgGVsF6DKjI8CSA/out-0.png" ], "started_at": "2023-12-16T21:01:08.340094Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mennpdtbps2sbuzqljyjtjlzhu", "cancel": "https://api.replicate.com/v1/predictions/mennpdtbps2sbuzqljyjtjlzhu/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 53041 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Breaking Bad lego in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.65it/s] 6%|▌ | 3/50 [00:00<00:12, 3.65it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.63it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s] 50%|█████ | 25/50 [00:06<00:06, 3.62it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s] 60%|██████ | 30/50 [00:08<00:05, 3.62it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s] 70%|███████ | 35/50 [00:09<00:04, 3.62it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s] 80%|████████ | 40/50 [00:11<00:02, 3.62it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.62it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s]
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDtqfrs4tbyjvypf6iebtunkl4miStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Sylvester Stallone in an action lego scene, in the style of TOK
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Sylvester Stallone in an action lego scene, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "Sylvester Stallone in an action lego scene, in the style of TOK", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "Sylvester Stallone in an action lego scene, in the style of TOK", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "Sylvester Stallone in an action lego scene, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T21:03:16.582991Z", "created_at": "2023-12-16T21:02:55.668153Z", "data_removed": false, "error": null, "id": "tqfrs4tbyjvypf6iebtunkl4mi", "input": { "width": 1024, "height": 1024, "prompt": "Sylvester Stallone in an action lego scene, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 65211\nEnsuring enough disk space...\nFree disk space: 1587100958720\nDownloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.287s (647 MB/s)\\nExtracted 186 MB in 0.058s (3.2 GB/s)\\n'\nDownloaded weights in 0.45952653884887695 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Sylvester Stallone in an action lego scene, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.66it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 17.026453, "total_time": 20.914838 }, "output": [ "https://replicate.delivery/pbxt/Hygpe3o39FVRNKvAzKr8fCvnRmlt2RVWV43v4HWLNSQTK8CSA/out-0.png" ], "started_at": "2023-12-16T21:02:59.556538Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tqfrs4tbyjvypf6iebtunkl4mi", "cancel": "https://api.replicate.com/v1/predictions/tqfrs4tbyjvypf6iebtunkl4mi/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 65211 Ensuring enough disk space... Free disk space: 1587100958720 Downloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar b'Downloaded 186 MB bytes in 0.287s (647 MB/s)\nExtracted 186 MB in 0.058s (3.2 GB/s)\n' Downloaded weights in 0.45952653884887695 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Sylvester Stallone in an action lego scene, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.66it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.66it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDcu7mj6tbaeanpoa5syisforopeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Barbie movie lego in the style of TOK
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Barbie movie lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "Barbie movie lego in the style of TOK", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "Barbie movie lego in the style of TOK", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "Barbie movie lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T21:12:29.652116Z", "created_at": "2023-12-16T21:12:07.759060Z", "data_removed": false, "error": null, "id": "cu7mj6tbaeanpoa5syisforope", "input": { "width": 1024, "height": 1024, "prompt": "Barbie movie lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 12261\nEnsuring enough disk space...\nFree disk space: 1646230069248\nDownloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.298s (624 MB/s)\\nExtracted 186 MB in 0.060s (3.1 GB/s)\\n'\nDownloaded weights in 0.500551700592041 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Barbie movie lego in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.66it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.65it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.64it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.63it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.63it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 16.745004, "total_time": 21.893056 }, "output": [ "https://replicate.delivery/pbxt/VlDzegbZG7xSYCN9Ta3gSvf2ez2NQFjEOeTndoCnHh5wLxLIB/out-0.png" ], "started_at": "2023-12-16T21:12:12.907112Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cu7mj6tbaeanpoa5syisforope", "cancel": "https://api.replicate.com/v1/predictions/cu7mj6tbaeanpoa5syisforope/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 12261 Ensuring enough disk space... Free disk space: 1646230069248 Downloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar b'Downloaded 186 MB bytes in 0.298s (624 MB/s)\nExtracted 186 MB in 0.060s (3.1 GB/s)\n' Downloaded weights in 0.500551700592041 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Barbie movie lego in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.66it/s] 4%|▍ | 2/50 [00:00<00:13, 3.65it/s] 6%|▌ | 3/50 [00:00<00:12, 3.64it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/s] 10%|█ | 5/50 [00:01<00:12, 3.63it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s] 30%|███ | 15/50 [00:04<00:09, 3.63it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.64it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s] 50%|█████ | 25/50 [00:06<00:06, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s] 70%|███████ | 35/50 [00:09<00:04, 3.63it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:11<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDxhgqyptbymsltmcrq4dzbaxqgmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Darth Vader riding a pony lego, in the style of TOK
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Darth Vader riding a pony lego, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "Darth Vader riding a pony lego, in the style of TOK", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "Darth Vader riding a pony lego, in the style of TOK", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "Darth Vader riding a pony lego, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T21:15:50.592494Z", "created_at": "2023-12-16T21:15:28.164034Z", "data_removed": false, "error": null, "id": "xhgqyptbymsltmcrq4dzbaxqgm", "input": { "width": 1024, "height": 1024, "prompt": "Darth Vader riding a pony lego, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 20526\nEnsuring enough disk space...\nFree disk space: 1523153334272\nDownloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.384s (485 MB/s)\\nExtracted 186 MB in 0.051s (3.7 GB/s)\\n'\nDownloaded weights in 0.5816051959991455 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Darth Vader riding a pony lego, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 16.663922, "total_time": 22.42846 }, "output": [ "https://replicate.delivery/pbxt/Y6ZYZ52EqBJIC9LlMq0IjzeZlkWvR1MxPLwDb2A6fhOFW8CSA/out-0.png" ], "started_at": "2023-12-16T21:15:33.928572Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xhgqyptbymsltmcrq4dzbaxqgm", "cancel": "https://api.replicate.com/v1/predictions/xhgqyptbymsltmcrq4dzbaxqgm/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 20526 Ensuring enough disk space... Free disk space: 1523153334272 Downloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar b'Downloaded 186 MB bytes in 0.384s (485 MB/s)\nExtracted 186 MB in 0.051s (3.7 GB/s)\n' Downloaded weights in 0.5816051959991455 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Darth Vader riding a pony lego, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.66it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.65it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s] 50%|█████ | 25/50 [00:06<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDwftd3qdbkv3qsbjune5tme6bb4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Kanye West lego model in front of a Jeep SUV on the street, in the style of TOK
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Kanye West lego model in front of a Jeep SUV on the street, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "Kanye West lego model in front of a Jeep SUV on the street, in the style of TOK", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "Kanye West lego model in front of a Jeep SUV on the street, in the style of TOK", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "Kanye West lego model in front of a Jeep SUV on the street, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-17T11:41:15.690238Z", "created_at": "2023-12-17T11:40:54.513901Z", "data_removed": false, "error": null, "id": "wftd3qdbkv3qsbjune5tme6bb4", "input": { "width": 1024, "height": 1024, "prompt": "Kanye West lego model in front of a Jeep SUV on the street, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 24888\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Kanye West lego model in front of a Jeep SUV on the street, in the style of <s0><s1>\ntxt2img mode\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<00:12, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 16.564318, "total_time": 21.176337 }, "output": [ "https://replicate.delivery/pbxt/TxULq6r8Zy4bORR3I1K87XdjasRV6aSCSieeQOTR7ylaBJDSA/out-0.png" ], "started_at": "2023-12-17T11:40:59.125920Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wftd3qdbkv3qsbjune5tme6bb4", "cancel": "https://api.replicate.com/v1/predictions/wftd3qdbkv3qsbjune5tme6bb4/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 24888 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Kanye West lego model in front of a Jeep SUV on the street, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:12, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDbnzqpldbg4wtcwjzoueoumwxmiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Elon Musk lego model on Mars with SpaceX rocket in the background, in the style of TOK
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Elon Musk lego model on Mars with SpaceX rocket in the background, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "Elon Musk lego model on Mars with SpaceX rocket in the background, in the style of TOK", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "Elon Musk lego model on Mars with SpaceX rocket in the background, in the style of TOK", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "Elon Musk lego model on Mars with SpaceX rocket in the background, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-17T11:54:06.838690Z", "created_at": "2023-12-17T11:53:45.983705Z", "data_removed": false, "error": null, "id": "bnzqpldbg4wtcwjzoueoumwxmi", "input": { "width": 1024, "height": 1024, "prompt": "Elon Musk lego model on Mars with SpaceX rocket in the background, in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 59360\nskipping loading .. weights already loaded\nPrompt: Elon Musk lego model on Mars with SpaceX rocket in the background, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 15.299938, "total_time": 20.854985 }, "output": [ "https://replicate.delivery/pbxt/YKsYpx4vuxL0Ape8RLEtl0ihSg8C7qAGDoVcDMQQgUNvmkBJA/out-0.png" ], "started_at": "2023-12-17T11:53:51.538752Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bnzqpldbg4wtcwjzoueoumwxmi", "cancel": "https://api.replicate.com/v1/predictions/bnzqpldbg4wtcwjzoueoumwxmi/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 59360 skipping loading .. weights already loaded Prompt: Elon Musk lego model on Mars with SpaceX rocket in the background, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s] 30%|███ | 15/50 [00:04<00:09, 3.63it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.65it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDwcbwcw3bdbiz3fogbtfz656rn4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- anatomical brain lego in the style of TOK
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "anatomical brain lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "anatomical brain lego in the style of TOK", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "anatomical brain lego in the style of TOK", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "anatomical brain lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T21:46:27.233379Z", "created_at": "2023-12-16T21:46:12.248879Z", "data_removed": false, "error": null, "id": "wcbwcw3bdbiz3fogbtfz656rn4", "input": { "width": 1024, "height": 1024, "prompt": "anatomical brain lego in the style of TOK", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 51659\nskipping loading .. weights already loaded\nPrompt: anatomical brain lego in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.70it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.69it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.69it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.69it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 14.977147, "total_time": 14.9845 }, "output": [ "https://replicate.delivery/pbxt/GB2UIKzRQIqKH9sKt1MHkfAPS8kydPDt70VcQfeSefsRWmXQC/out-0.png" ], "started_at": "2023-12-16T21:46:12.256232Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wcbwcw3bdbiz3fogbtfz656rn4", "cancel": "https://api.replicate.com/v1/predictions/wcbwcw3bdbiz3fogbtfz656rn4/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 51659 skipping loading .. weights already loaded Prompt: anatomical brain lego in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:12, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.70it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.70it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.69it/s] 40%|████ | 20/50 [00:05<00:08, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s] 50%|█████ | 25/50 [00:06<00:06, 3.69it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDzkv2xklb6jml6rhmvrdjnq3jemStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A lego cat wearing VR glasses
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A lego cat wearing VR glasses", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "A lego cat wearing VR glasses", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "A lego cat wearing VR glasses", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "A lego cat wearing VR glasses", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T22:38:28.538380Z", "created_at": "2023-12-16T22:38:05.106264Z", "data_removed": false, "error": null, "id": "zkv2xklb6jml6rhmvrdjnq3jem", "input": { "width": 1024, "height": 1024, "prompt": "A lego cat wearing VR glasses", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 31839\nEnsuring enough disk space...\nFree disk space: 1994210701312\nDownloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.242s (768 MB/s)\\nExtracted 186 MB in 0.050s (3.7 GB/s)\\n'\nDownloaded weights in 0.4087944030761719 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A lego cat wearing VR glasses\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.66it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.63it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 15.804132, "total_time": 23.432116 }, "output": [ "https://replicate.delivery/pbxt/M0feDZtEULkqLEiPydGamFeDeoVveIwk3T0mXQGxMruncsXQC/out-0.png" ], "started_at": "2023-12-16T22:38:12.734248Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zkv2xklb6jml6rhmvrdjnq3jem", "cancel": "https://api.replicate.com/v1/predictions/zkv2xklb6jml6rhmvrdjnq3jem/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 31839 Ensuring enough disk space... Free disk space: 1994210701312 Downloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar b'Downloaded 186 MB bytes in 0.242s (768 MB/s)\nExtracted 186 MB in 0.050s (3.7 GB/s)\n' Downloaded weights in 0.4087944030761719 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A lego cat wearing VR glasses txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.66it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.63it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s]
Prediction
martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488IDlc7eprlba2gwdiqkiysqfdcejqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A lego capybara wearing a hat in a green grass field
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A lego capybara wearing a hat in a green grass field", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", { input: { width: 1024, height: 1024, prompt: "A lego capybara wearing a hat in a green grass field", 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: "", 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 replicate
Import the client:import replicate
Run martintmv-git/sdxl-lego using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", input={ "width": 1024, "height": 1024, "prompt": "A lego capybara wearing a hat in a green grass field", "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": "", "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 martintmv-git/sdxl-lego 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": "martintmv-git/sdxl-lego:4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488", "input": { "width": 1024, "height": 1024, "prompt": "A lego capybara wearing a hat in a green grass field", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-16T22:40:51.919037Z", "created_at": "2023-12-16T22:40:05.892642Z", "data_removed": false, "error": null, "id": "lc7eprlba2gwdiqkiysqfdcejq", "input": { "width": 1024, "height": 1024, "prompt": "A lego capybara wearing a hat in a green grass field", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 65193\nEnsuring enough disk space...\nFree disk space: 1429784051712\nDownloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.438s (425 MB/s)\\nExtracted 186 MB in 0.063s (3.0 GB/s)\\n'\nDownloaded weights in 0.5996050834655762 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A lego capybara wearing a hat in a green grass field\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.65it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.64it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.64it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.63it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 17.342251, "total_time": 46.026395 }, "output": [ "https://replicate.delivery/pbxt/IqRwjgGh9GY5Kdm9KC1UIv9WTc8e05VIehrGkeMVfbCMX2LIB/out-0.png" ], "started_at": "2023-12-16T22:40:34.576786Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lc7eprlba2gwdiqkiysqfdcejq", "cancel": "https://api.replicate.com/v1/predictions/lc7eprlba2gwdiqkiysqfdcejq/cancel" }, "version": "4c082ddfd3e666f9b07f603de33832736b30d27240b67f9a7abc4adc504b6488" }
Generated inUsing seed: 65193 Ensuring enough disk space... Free disk space: 1429784051712 Downloading weights: https://replicate.delivery/pbxt/rZTCzIywOTbBF9457UWzMSb5buucjnsebfwrtMRe0meJ4vLIB/trained_model.tar b'Downloaded 186 MB bytes in 0.438s (425 MB/s)\nExtracted 186 MB in 0.063s (3.0 GB/s)\n' Downloaded weights in 0.5996050834655762 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A lego capybara wearing a hat in a green grass field txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.65it/s] 4%|▍ | 2/50 [00:00<00:13, 3.64it/s] 6%|▌ | 3/50 [00:00<00:12, 3.64it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/s] 10%|█ | 5/50 [00:01<00:12, 3.63it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s] 80%|████████ | 40/50 [00:11<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]
Want to make some of these yourself?
Run this model