hunterkamerman
/
sdxl-pop
SDXL trained on pop art images
- Public
- 425 runs
-
L40S
- SDXL fine-tune
Prediction
hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0IDacdd6rtbqywofhgcyuvds6olvyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, pop art of taylor swift black and white with graffiti
- 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": "TOK, pop art of taylor swift black and white with graffiti ", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", { input: { width: 1024, height: 1024, prompt: "TOK, pop art of taylor swift black and white with graffiti ", 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 hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", input={ "width": 1024, "height": 1024, "prompt": "TOK, pop art of taylor swift black and white with graffiti ", "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 hunterkamerman/sdxl-pop 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": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", "input": { "width": 1024, "height": 1024, "prompt": "TOK, pop art of taylor swift black and white with graffiti ", "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": "2023-11-13T12:58:09.316102Z", "created_at": "2023-11-13T12:57:25.946067Z", "data_removed": false, "error": null, "id": "acdd6rtbqywofhgcyuvds6olvy", "input": { "width": 1024, "height": 1024, "prompt": "TOK, pop art of taylor swift black and white with graffiti ", "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: 54609\nEnsuring enough disk space...\nFree disk space: 1960921542656\nDownloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.758s (245 MB/s)\\nExtracted 186 MB in 0.071s (2.6 GB/s)\\n'\nDownloaded weights in 1.0317702293395996 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, pop art of taylor swift black and white with graffiti\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, 3.63it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.63it/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, 3.62it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.61it/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, 3.61it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.61it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.61it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.61it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.61it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.60it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.60it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.60it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.60it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.60it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.60it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.60it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.60it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.60it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.60it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.60it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.60it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 17.811241, "total_time": 43.370035 }, "output": [ "https://replicate.delivery/pbxt/eRUrjzwmbfuolEbT8kOM3tlnKevDPrnTfGoHCfK0YN6EsnfdE/out-0.png" ], "started_at": "2023-11-13T12:57:51.504861Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/acdd6rtbqywofhgcyuvds6olvy", "cancel": "https://api.replicate.com/v1/predictions/acdd6rtbqywofhgcyuvds6olvy/cancel" }, "version": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0" }
Generated inUsing seed: 54609 Ensuring enough disk space... Free disk space: 1960921542656 Downloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar b'Downloaded 186 MB bytes in 0.758s (245 MB/s)\nExtracted 186 MB in 0.071s (2.6 GB/s)\n' Downloaded weights in 1.0317702293395996 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, pop art of taylor swift black and white with graffiti 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, 3.63it/s] 6%|▌ | 3/50 [00:00<00:12, 3.63it/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, 3.62it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s] 20%|██ | 10/50 [00:02<00:11, 3.62it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.61it/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, 3.61it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.61it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s] 40%|████ | 20/50 [00:05<00:08, 3.61it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s] 50%|█████ | 25/50 [00:06<00:06, 3.61it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s] 60%|██████ | 30/50 [00:08<00:05, 3.61it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.60it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.60it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.60it/s] 70%|███████ | 35/50 [00:09<00:04, 3.60it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.60it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.60it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.60it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.60it/s] 80%|████████ | 40/50 [00:11<00:02, 3.60it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.60it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.60it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.60it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]
Prediction
hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0IDrixwobtbq5t4qwmn4y3gxdiwi4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, pop art of a clone trooper from star wars
- 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": "TOK, pop art of a clone trooper from star wars", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", { input: { width: 1024, height: 1024, prompt: "TOK, pop art of a clone trooper from star wars", 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 hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", input={ "width": 1024, "height": 1024, "prompt": "TOK, pop art of a clone trooper from star wars", "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 hunterkamerman/sdxl-pop 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": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", "input": { "width": 1024, "height": 1024, "prompt": "TOK, pop art of a clone trooper from star wars", "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": "2023-11-12T20:09:43.941061Z", "created_at": "2023-11-12T20:07:51.867286Z", "data_removed": false, "error": null, "id": "rixwobtbq5t4qwmn4y3gxdiwi4", "input": { "width": 1024, "height": 1024, "prompt": "TOK, pop art of a clone trooper from star wars", "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: 16403\nEnsuring enough disk space...\nFree disk space: 1683847073792\nDownloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.082s (2.3 GB/s)\\nExtracted 186 MB in 0.054s (3.5 GB/s)\\n'\nDownloaded weights in 0.4860689640045166 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, pop art of a clone trooper from star wars\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, 3.63it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.62it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.62it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.61it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.61it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.61it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/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, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/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, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.59it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.60it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.61it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/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, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/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": 17.190592, "total_time": 112.073775 }, "output": [ "https://replicate.delivery/pbxt/JmAqQGyROgYRPZfhEeS9qbd29Pvq86Kq79KYk2Qc8hDGMu3RA/out-0.png" ], "started_at": "2023-11-12T20:09:26.750469Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rixwobtbq5t4qwmn4y3gxdiwi4", "cancel": "https://api.replicate.com/v1/predictions/rixwobtbq5t4qwmn4y3gxdiwi4/cancel" }, "version": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0" }
Generated inUsing seed: 16403 Ensuring enough disk space... Free disk space: 1683847073792 Downloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar b'Downloaded 186 MB bytes in 0.082s (2.3 GB/s)\nExtracted 186 MB in 0.054s (3.5 GB/s)\n' Downloaded weights in 0.4860689640045166 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, pop art of a clone trooper from star wars 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, 3.63it/s] 8%|▊ | 4/50 [00:01<00:12, 3.62it/s] 10%|█ | 5/50 [00:01<00:12, 3.62it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.61it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.61it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.61it/s] 20%|██ | 10/50 [00:02<00:11, 3.62it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/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, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.63it/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, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.59it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.60it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s] 60%|██████ | 30/50 [00:08<00:05, 3.61it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/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, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/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
hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0IDmi44cwdbgykxpjab6ixqo3afaaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, pop art of kanye west with sunglass
- 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": "TOK, pop art of kanye west with sunglass", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", { input: { width: 1024, height: 1024, prompt: "TOK, pop art of kanye west with sunglass", 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 hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", input={ "width": 1024, "height": 1024, "prompt": "TOK, pop art of kanye west with sunglass", "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 hunterkamerman/sdxl-pop 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": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", "input": { "width": 1024, "height": 1024, "prompt": "TOK, pop art of kanye west with sunglass", "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": "2023-11-12T20:15:04.016693Z", "created_at": "2023-11-12T20:13:07.716547Z", "data_removed": false, "error": null, "id": "mi44cwdbgykxpjab6ixqo3afaa", "input": { "width": 1024, "height": 1024, "prompt": "TOK, pop art of kanye west with sunglass", "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: 9216\nEnsuring enough disk space...\nFree disk space: 1725223661568\nDownloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.443s (420 MB/s)\\nExtracted 186 MB in 0.065s (2.8 GB/s)\\n'\nDownloaded weights in 0.687150239944458 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, pop art of kanye west with sunglass\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.47it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.46it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.45it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.45it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.45it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.45it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.44it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.44it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.44it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.44it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.44it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.44it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.44it/s]\n 28%|██▊ | 14/50 [00:04<00:10, 3.44it/s]\n 30%|███ | 15/50 [00:04<00:10, 3.44it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.44it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.44it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.44it/s]\n 38%|███▊ | 19/50 [00:05<00:09, 3.44it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.44it/s]\n 42%|████▏ | 21/50 [00:06<00:08, 3.44it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.44it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.43it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.43it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.43it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.43it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.43it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.43it/s]\n 58%|█████▊ | 29/50 [00:08<00:06, 3.43it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.43it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.43it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.43it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.43it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.43it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.43it/s]\n 72%|███████▏ | 36/50 [00:10<00:04, 3.43it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.43it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.43it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.43it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.44it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.43it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.43it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.43it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.43it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.43it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.43it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.43it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.43it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.43it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.43it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.44it/s]", "metrics": { "predict_time": 18.155118, "total_time": 116.300146 }, "output": [ "https://replicate.delivery/pbxt/wP2UHKfj8lxBFCaKLepq49iyaeE7VkyMaiDIhh824qvOicvjA/out-0.png" ], "started_at": "2023-11-12T20:14:45.861575Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mi44cwdbgykxpjab6ixqo3afaa", "cancel": "https://api.replicate.com/v1/predictions/mi44cwdbgykxpjab6ixqo3afaa/cancel" }, "version": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0" }
Generated inUsing seed: 9216 Ensuring enough disk space... Free disk space: 1725223661568 Downloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar b'Downloaded 186 MB bytes in 0.443s (420 MB/s)\nExtracted 186 MB in 0.065s (2.8 GB/s)\n' Downloaded weights in 0.687150239944458 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, pop art of kanye west with sunglass txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.47it/s] 4%|▍ | 2/50 [00:00<00:13, 3.46it/s] 6%|▌ | 3/50 [00:00<00:13, 3.45it/s] 8%|▊ | 4/50 [00:01<00:13, 3.45it/s] 10%|█ | 5/50 [00:01<00:13, 3.45it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.45it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.44it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.44it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.44it/s] 20%|██ | 10/50 [00:02<00:11, 3.44it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.44it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.44it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.44it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.44it/s] 30%|███ | 15/50 [00:04<00:10, 3.44it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.44it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.44it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.44it/s] 38%|███▊ | 19/50 [00:05<00:09, 3.44it/s] 40%|████ | 20/50 [00:05<00:08, 3.44it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.44it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.44it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.43it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.43it/s] 50%|█████ | 25/50 [00:07<00:07, 3.43it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.43it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.43it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.43it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.43it/s] 60%|██████ | 30/50 [00:08<00:05, 3.43it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.43it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.43it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.43it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.43it/s] 70%|███████ | 35/50 [00:10<00:04, 3.43it/s] 72%|███████▏ | 36/50 [00:10<00:04, 3.43it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.43it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.43it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.43it/s] 80%|████████ | 40/50 [00:11<00:02, 3.44it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.43it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.43it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.43it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.43it/s] 90%|█████████ | 45/50 [00:13<00:01, 3.43it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.43it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.43it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.43it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.43it/s] 100%|██████████| 50/50 [00:14<00:00, 3.43it/s] 100%|██████████| 50/50 [00:14<00:00, 3.44it/s]
Prediction
hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0IDck26ss3bkpnrz7gn4p223bd64uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, 4 panel pop art of donald trump yelling
- 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": "TOK, 4 panel pop art of donald trump yelling", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", { input: { width: 1024, height: 1024, prompt: "TOK, 4 panel pop art of donald trump yelling", 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 hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", input={ "width": 1024, "height": 1024, "prompt": "TOK, 4 panel pop art of donald trump yelling", "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 hunterkamerman/sdxl-pop 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": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", "input": { "width": 1024, "height": 1024, "prompt": "TOK, 4 panel pop art of donald trump yelling", "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": "2023-11-12T20:18:12.290046Z", "created_at": "2023-11-12T20:17:34.806084Z", "data_removed": false, "error": null, "id": "ck26ss3bkpnrz7gn4p223bd64u", "input": { "width": 1024, "height": 1024, "prompt": "TOK, 4 panel pop art of donald trump yelling", "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: 62023\nEnsuring enough disk space...\nFree disk space: 1623386136576\nDownloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.272s (684 MB/s)\\nExtracted 186 MB in 0.058s (3.2 GB/s)\\n'\nDownloaded weights in 0.45954179763793945 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, 4 panel pop art of donald trump yelling\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.64it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.64it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.63it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.62it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.61it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.61it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.61it/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, 3.61it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.61it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.61it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/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, 3.61it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.61it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.61it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.61it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 17.212691, "total_time": 37.483962 }, "output": [ "https://replicate.delivery/pbxt/O6CCjlPgvqr5MxCGLbNckU2bSRdePL8P22SFPR1NT5jBK37IA/out-0.png" ], "started_at": "2023-11-12T20:17:55.077355Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ck26ss3bkpnrz7gn4p223bd64u", "cancel": "https://api.replicate.com/v1/predictions/ck26ss3bkpnrz7gn4p223bd64u/cancel" }, "version": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0" }
Generated inUsing seed: 62023 Ensuring enough disk space... Free disk space: 1623386136576 Downloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar b'Downloaded 186 MB bytes in 0.272s (684 MB/s)\nExtracted 186 MB in 0.058s (3.2 GB/s)\n' Downloaded weights in 0.45954179763793945 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, 4 panel pop art of donald trump yelling txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.64it/s] 4%|▍ | 2/50 [00:00<00:13, 3.64it/s] 6%|▌ | 3/50 [00:00<00:12, 3.63it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/s] 10%|█ | 5/50 [00:01<00:12, 3.62it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.61it/s] 20%|██ | 10/50 [00:02<00:11, 3.61it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.61it/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, 3.61it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s] 50%|█████ | 25/50 [00:06<00:06, 3.61it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s] 60%|██████ | 30/50 [00:08<00:05, 3.61it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/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, 3.61it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s] 80%|████████ | 40/50 [00:11<00:02, 3.61it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.61it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]
Prediction
hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0IDt2bh4btbfjhde3hubr4ndtnvu4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, pop art of George washington black and white with graffiti background
- 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": "TOK, pop art of George washington black and white with graffiti background ", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", { input: { width: 1024, height: 1024, prompt: "TOK, pop art of George washington black and white with graffiti background ", 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 hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", input={ "width": 1024, "height": 1024, "prompt": "TOK, pop art of George washington black and white with graffiti background ", "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 hunterkamerman/sdxl-pop 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": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", "input": { "width": 1024, "height": 1024, "prompt": "TOK, pop art of George washington black and white with graffiti background ", "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": "2023-11-12T20:25:54.189457Z", "created_at": "2023-11-12T20:24:55.600267Z", "data_removed": false, "error": null, "id": "t2bh4btbfjhde3hubr4ndtnvu4", "input": { "width": 1024, "height": 1024, "prompt": "TOK, pop art of George washington black and white with graffiti background ", "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: 40804\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, pop art of George washington black and white with graffiti background\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.45it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.45it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.44it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.39it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.40it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.41it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.42it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.42it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.42it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.43it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.43it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.43it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.43it/s]\n 28%|██▊ | 14/50 [00:04<00:10, 3.43it/s]\n 30%|███ | 15/50 [00:04<00:10, 3.43it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.43it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.43it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.43it/s]\n 38%|███▊ | 19/50 [00:05<00:09, 3.42it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.43it/s]\n 42%|████▏ | 21/50 [00:06<00:08, 3.43it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.43it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.42it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.42it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.42it/s]\n 52%|█████▏ | 26/50 [00:07<00:07, 3.42it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.42it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.42it/s]\n 58%|█████▊ | 29/50 [00:08<00:06, 3.42it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.42it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.42it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.42it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.42it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.42it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.42it/s]\n 72%|███████▏ | 36/50 [00:10<00:04, 3.42it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.42it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.42it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.42it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.42it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.42it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.42it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.42it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.42it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.42it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.42it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.42it/s]\n 96%|█████████▌| 48/50 [00:14<00:00, 3.41it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.41it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.41it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.42it/s]", "metrics": { "predict_time": 17.61099, "total_time": 58.58919 }, "output": [ "https://replicate.delivery/pbxt/bWWEkHx1bE4QC1KxgsDeBjiJNLt5DY8tCE4mxAJk0pvoN37IA/out-0.png" ], "started_at": "2023-11-12T20:25:36.578467Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/t2bh4btbfjhde3hubr4ndtnvu4", "cancel": "https://api.replicate.com/v1/predictions/t2bh4btbfjhde3hubr4ndtnvu4/cancel" }, "version": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0" }
Generated inUsing seed: 40804 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, pop art of George washington black and white with graffiti background txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.45it/s] 4%|▍ | 2/50 [00:00<00:13, 3.45it/s] 6%|▌ | 3/50 [00:00<00:13, 3.44it/s] 8%|▊ | 4/50 [00:01<00:13, 3.39it/s] 10%|█ | 5/50 [00:01<00:13, 3.40it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.41it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.42it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.42it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.42it/s] 20%|██ | 10/50 [00:02<00:11, 3.43it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.43it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.43it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.43it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.43it/s] 30%|███ | 15/50 [00:04<00:10, 3.43it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.43it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.43it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.43it/s] 38%|███▊ | 19/50 [00:05<00:09, 3.42it/s] 40%|████ | 20/50 [00:05<00:08, 3.43it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.43it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.43it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.42it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.42it/s] 50%|█████ | 25/50 [00:07<00:07, 3.42it/s] 52%|█████▏ | 26/50 [00:07<00:07, 3.42it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.42it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.42it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.42it/s] 60%|██████ | 30/50 [00:08<00:05, 3.42it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.42it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.42it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.42it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.42it/s] 70%|███████ | 35/50 [00:10<00:04, 3.42it/s] 72%|███████▏ | 36/50 [00:10<00:04, 3.42it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.42it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.42it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.42it/s] 80%|████████ | 40/50 [00:11<00:02, 3.42it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.42it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.42it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.42it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.42it/s] 90%|█████████ | 45/50 [00:13<00:01, 3.42it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.42it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.42it/s] 96%|█████████▌| 48/50 [00:14<00:00, 3.41it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.41it/s] 100%|██████████| 50/50 [00:14<00:00, 3.41it/s] 100%|██████████| 50/50 [00:14<00:00, 3.42it/s]
Prediction
hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0ID6jz2w5dbysg6z7xaauitzn274mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, batman pop art
- 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": "TOK, batman pop art", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", { input: { width: 1024, height: 1024, prompt: "TOK, batman pop art", 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 hunterkamerman/sdxl-pop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-pop:f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", input={ "width": 1024, "height": 1024, "prompt": "TOK, batman pop art", "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 hunterkamerman/sdxl-pop 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": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0", "input": { "width": 1024, "height": 1024, "prompt": "TOK, batman pop art", "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": "2023-11-14T00:10:40.439332Z", "created_at": "2023-11-14T00:10:20.268153Z", "data_removed": false, "error": null, "id": "6jz2w5dbysg6z7xaauitzn274m", "input": { "width": 1024, "height": 1024, "prompt": "TOK, batman pop art", "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: 60996\nEnsuring enough disk space...\nFree disk space: 1478200520704\nDownloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.457s (407 MB/s)\\nExtracted 186 MB in 0.063s (2.9 GB/s)\\n'\nDownloaded weights in 0.6571033000946045 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, batman pop art\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, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.63it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/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, 3.63it/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, 3.62it/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, 3.61it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.61it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.61it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/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, 3.61it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.61it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.60it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.60it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.60it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 16.576836, "total_time": 20.171179 }, "output": [ "https://replicate.delivery/pbxt/qjafCrBkXpSAGS54NtJcfgtVseXGYlqX9GteBXvZqki8PbgHB/out-0.png" ], "started_at": "2023-11-14T00:10:23.862496Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6jz2w5dbysg6z7xaauitzn274m", "cancel": "https://api.replicate.com/v1/predictions/6jz2w5dbysg6z7xaauitzn274m/cancel" }, "version": "f75e4e20ecfb86f09ffd56e4002768593623b2105abf31fc5248f7d731844be0" }
Generated inUsing seed: 60996 Ensuring enough disk space... Free disk space: 1478200520704 Downloading weights: https://replicate.delivery/pbxt/ipFCwnIgUIqvNV0zkiOxRebkYsDZnmAjHz3u80fcOVCtFu3RA/trained_model.tar b'Downloaded 186 MB bytes in 0.457s (407 MB/s)\nExtracted 186 MB in 0.063s (2.9 GB/s)\n' Downloaded weights in 0.6571033000946045 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, batman pop art 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, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.63it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/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, 3.63it/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, 3.62it/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, 3.61it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s] 50%|█████ | 25/50 [00:06<00:06, 3.61it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s] 60%|██████ | 30/50 [00:08<00:05, 3.61it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/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, 3.61it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.60it/s] 80%|████████ | 40/50 [00:11<00:02, 3.60it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.60it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]
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