hunterkamerman
/
sdxl-cats
A fine-tuned SDXL LoRA trained on cats being human like
- Public
- 76 runs
-
L40S
- SDXL fine-tune
Prediction
hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8cIDckgxf3tbncsp23ihrx6l4nhgl4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- tok a cute kitten inspired by tracer
- 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 a cute kitten inspired by tracer", "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 replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-cats using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", { input: { width: 1024, height: 1024, prompt: "tok a cute kitten inspired by tracer", 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 replicateImport the client:import replicateRun hunterkamerman/sdxl-cats using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", input={ "width": 1024, "height": 1024, "prompt": "tok a cute kitten inspired by tracer", "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-cats 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": "a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", "input": { "width": 1024, "height": 1024, "prompt": "tok a cute kitten inspired by tracer", "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/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-04T15:53:10.015529Z", "created_at": "2023-12-04T15:52:45.485750Z", "data_removed": false, "error": null, "id": "ckgxf3tbncsp23ihrx6l4nhgl4", "input": { "width": 1024, "height": 1024, "prompt": "tok a cute kitten inspired by tracer", "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: 51681\nEnsuring enough disk space...\nFree disk space: 450182795264\nDownloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar\nb'Downloaded 186 MB bytes in 3.272s (57 MB/s)\\nExtracted 186 MB in 0.057s (3.2 GB/s)\\n'\nDownloaded weights in 3.816222906112671 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: tok a cute kitten inspired by tracer\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, 3.67it/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, 3.65it/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, 3.65it/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, 3.63it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/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, 3.66it/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, 3.66it/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, 3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 20.318444, "total_time": 24.529779 }, "output": [ "https://replicate.delivery/pbxt/d9Vz7f0DiegbZUFZhceftrx1wy1UmTdgnWfppEDC6hbs8T3PC/out-0.png" ], "started_at": "2023-12-04T15:52:49.697085Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ckgxf3tbncsp23ihrx6l4nhgl4", "cancel": "https://api.replicate.com/v1/predictions/ckgxf3tbncsp23ihrx6l4nhgl4/cancel" }, "version": "a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c" }Generated inUsing seed: 51681 Ensuring enough disk space... Free disk space: 450182795264 Downloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar b'Downloaded 186 MB bytes in 3.272s (57 MB/s)\nExtracted 186 MB in 0.057s (3.2 GB/s)\n' Downloaded weights in 3.816222906112671 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: tok a cute kitten inspired by tracer 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, 3.67it/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, 3.65it/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, 3.65it/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, 3.63it/s] 40%|████ | 20/50 [00:05<00:08, 3.64it/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, 3.66it/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, 3.66it/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, 3.64it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8cIDycgn65lbk3spb3n75w4awudagiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- tok a cute kitten inspired by five nights at freddys
- 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 a cute kitten inspired by five nights at freddys", "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 replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-cats using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", { input: { width: 1024, height: 1024, prompt: "tok a cute kitten inspired by five nights at freddys", 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 replicateImport the client:import replicateRun hunterkamerman/sdxl-cats using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", input={ "width": 1024, "height": 1024, "prompt": "tok a cute kitten inspired by five nights at freddys", "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-cats 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": "a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", "input": { "width": 1024, "height": 1024, "prompt": "tok a cute kitten inspired by five nights at freddys", "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/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-04T15:54:14.789105Z", "created_at": "2023-12-04T15:53:42.249585Z", "data_removed": false, "error": null, "id": "ycgn65lbk3spb3n75w4awudagi", "input": { "width": 1024, "height": 1024, "prompt": "tok a cute kitten inspired by five nights at freddys", "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: 45140\nEnsuring enough disk space...\nFree disk space: 1569824854016\nDownloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar\nb'Downloaded 186 MB bytes in 3.509s (53 MB/s)\\nExtracted 186 MB in 0.081s (2.3 GB/s)\\n'\nDownloaded weights in 3.9538285732269287 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: tok a cute kitten inspired by five nights at freddys\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.50it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.48it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.47it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.47it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.47it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.47it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.46it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.46it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.46it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.46it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.46it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.45it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.45it/s]\n 28%|██▊ | 14/50 [00:04<00:10, 3.45it/s]\n 30%|███ | 15/50 [00:04<00:10, 3.46it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.45it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.45it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.42it/s]\n 38%|███▊ | 19/50 [00:05<00:09, 3.41it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.43it/s]\n 42%|████▏ | 21/50 [00:06<00:08, 3.44it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.45it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.45it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.45it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.45it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.46it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.45it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.45it/s]\n 58%|█████▊ | 29/50 [00:08<00:06, 3.45it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.45it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.45it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.45it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.45it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.45it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.45it/s]\n 72%|███████▏ | 36/50 [00:10<00:04, 3.45it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.45it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.45it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.45it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.45it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.45it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.44it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.44it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.44it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.44it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.44it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.44it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.44it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.44it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.44it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.45it/s]", "metrics": { "predict_time": 21.054492, "total_time": 32.53952 }, "output": [ "https://replicate.delivery/pbxt/ujXxHWYKRNKmG1NsRRdAu4lspRFQIQoUtDvoydxFLOVJoufIA/out-0.png" ], "started_at": "2023-12-04T15:53:53.734613Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ycgn65lbk3spb3n75w4awudagi", "cancel": "https://api.replicate.com/v1/predictions/ycgn65lbk3spb3n75w4awudagi/cancel" }, "version": "a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c" }Generated inUsing seed: 45140 Ensuring enough disk space... Free disk space: 1569824854016 Downloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar b'Downloaded 186 MB bytes in 3.509s (53 MB/s)\nExtracted 186 MB in 0.081s (2.3 GB/s)\n' Downloaded weights in 3.9538285732269287 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: tok a cute kitten inspired by five nights at freddys txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.50it/s] 4%|▍ | 2/50 [00:00<00:13, 3.48it/s] 6%|▌ | 3/50 [00:00<00:13, 3.47it/s] 8%|▊ | 4/50 [00:01<00:13, 3.47it/s] 10%|█ | 5/50 [00:01<00:12, 3.47it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.47it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.46it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.46it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.46it/s] 20%|██ | 10/50 [00:02<00:11, 3.46it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.46it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.45it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.45it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.45it/s] 30%|███ | 15/50 [00:04<00:10, 3.46it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.45it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.45it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.42it/s] 38%|███▊ | 19/50 [00:05<00:09, 3.41it/s] 40%|████ | 20/50 [00:05<00:08, 3.43it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.44it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.45it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.45it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.45it/s] 50%|█████ | 25/50 [00:07<00:07, 3.45it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.46it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.45it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.45it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.45it/s] 60%|██████ | 30/50 [00:08<00:05, 3.45it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.45it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.45it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.45it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.45it/s] 70%|███████ | 35/50 [00:10<00:04, 3.45it/s] 72%|███████▏ | 36/50 [00:10<00:04, 3.45it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.45it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.45it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.45it/s] 80%|████████ | 40/50 [00:11<00:02, 3.45it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.45it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.44it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.44it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.44it/s] 90%|█████████ | 45/50 [00:13<00:01, 3.44it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.44it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.44it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.44it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.44it/s] 100%|██████████| 50/50 [00:14<00:00, 3.44it/s] 100%|██████████| 50/50 [00:14<00:00, 3.45it/s]Prediction
hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8cIDyghr5y3b2zumjvt6ryr46kbzmeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- tok a cute kitten skiing
- 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 a cute kitten skiing", "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 replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hunterkamerman/sdxl-cats using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", { input: { width: 1024, height: 1024, prompt: "tok a cute kitten skiing", 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 replicateImport the client:import replicateRun hunterkamerman/sdxl-cats using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hunterkamerman/sdxl-cats:a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", input={ "width": 1024, "height": 1024, "prompt": "tok a cute kitten skiing", "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-cats 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": "a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c", "input": { "width": 1024, "height": 1024, "prompt": "tok a cute kitten skiing", "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/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-04T15:55:23.685594Z", "created_at": "2023-12-04T15:54:48.328751Z", "data_removed": false, "error": null, "id": "yghr5y3b2zumjvt6ryr46kbzme", "input": { "width": 1024, "height": 1024, "prompt": "tok a cute kitten skiing", "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: 47030\nEnsuring enough disk space...\nFree disk space: 1474168360960\nDownloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar\nb'Downloaded 186 MB bytes in 0.135s (1.4 GB/s)\\nExtracted 186 MB in 0.053s (3.5 GB/s)\\n'\nDownloaded weights in 0.28025150299072266 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: tok a cute kitten skiing\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, 3.64it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.59it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.61it/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, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/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, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/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, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/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, 3.65it/s]", "metrics": { "predict_time": 16.714238, "total_time": 35.356843 }, "output": [ "https://replicate.delivery/pbxt/qnbbyGuZRbLfZinlrfebqYZQlQKFtiMZe9UBnEceiH5VNU3PC/out-0.png" ], "started_at": "2023-12-04T15:55:06.971356Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yghr5y3b2zumjvt6ryr46kbzme", "cancel": "https://api.replicate.com/v1/predictions/yghr5y3b2zumjvt6ryr46kbzme/cancel" }, "version": "a62ea062fab83dc826120a9261dc3c075acf44d2f4072721b407348b47c5df8c" }Generated inUsing seed: 47030 Ensuring enough disk space... Free disk space: 1474168360960 Downloading weights: https://replicate.delivery/pbxt/Av7nF61OpVpfO6h5Ux3mKUtym0ZascMnANeeG8sg03GxwemHB/trained_model.tar b'Downloaded 186 MB bytes in 0.135s (1.4 GB/s)\nExtracted 186 MB in 0.053s (3.5 GB/s)\n' Downloaded weights in 0.28025150299072266 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: tok a cute kitten skiing 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, 3.64it/s] 6%|▌ | 3/50 [00:00<00:13, 3.59it/s] 8%|▊ | 4/50 [00:01<00:12, 3.61it/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, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/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, 3.65it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/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, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/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, 3.65it/s]
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