peter65374 / sdxl-cat
human-like cat sdxl-lora model (Updated 1 year, 6 months ago)
- Public
- 1.7K runs
- SDXL fine-tune
Prediction
peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2edIDqiaifcdbihvkezlxrnegqf6n5aStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of TOK, smiling cat walking on the hill
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 1024, "height": 1024, "prompt": "A photo of TOK, smiling cat walking on the hill", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run peter65374/sdxl-cat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", { input: { width: 1024, height: 1024, prompt: "A photo of TOK, smiling cat walking on the hill", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun peter65374/sdxl-cat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", input={ "width": 1024, "height": 1024, "prompt": "A photo of TOK, smiling cat walking on the hill", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run peter65374/sdxl-cat 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": "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK, smiling cat walking on the hill", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-01T10:24:47.552340Z", "created_at": "2023-12-01T10:24:28.407523Z", "data_removed": false, "error": null, "id": "qiaifcdbihvkezlxrnegqf6n5a", "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK, smiling cat walking on the hill", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 92\nEnsuring enough disk space...\nFree disk space: 1933190082560\nDownloading weights: https://replicate.delivery/pbxt/MqZe51l1ASSuZKIVF843Z9ZAYynhaGYxtACGxaFv6wblJ7eRA/trained_model.tar\nb'Downloaded 186 MB bytes in 2.858s (65 MB/s)\\nExtracted 186 MB in 0.066s (2.8 GB/s)\\n'\nDownloaded weights in 3.2346155643463135 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1>, smiling cat walking on the hill\ntxt2img mode\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:07, 3.72it/s]\n 7%|▋ | 2/30 [00:00<00:07, 3.71it/s]\n 10%|█ | 3/30 [00:00<00:07, 3.70it/s]\n 13%|█▎ | 4/30 [00:01<00:07, 3.70it/s]\n 17%|█▋ | 5/30 [00:01<00:06, 3.70it/s]\n 20%|██ | 6/30 [00:01<00:06, 3.70it/s]\n 23%|██▎ | 7/30 [00:01<00:06, 3.70it/s]\n 27%|██▋ | 8/30 [00:02<00:05, 3.71it/s]\n 30%|███ | 9/30 [00:02<00:05, 3.71it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.71it/s]\n 37%|███▋ | 11/30 [00:02<00:05, 3.71it/s]\n 40%|████ | 12/30 [00:03<00:04, 3.71it/s]\n 43%|████▎ | 13/30 [00:03<00:04, 3.71it/s]\n 47%|████▋ | 14/30 [00:03<00:04, 3.71it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.71it/s]\n 53%|█████▎ | 16/30 [00:04<00:03, 3.71it/s]\n 57%|█████▋ | 17/30 [00:04<00:03, 3.70it/s]\n 60%|██████ | 18/30 [00:04<00:03, 3.71it/s]\n 63%|██████▎ | 19/30 [00:05<00:02, 3.70it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.70it/s]\n 70%|███████ | 21/30 [00:05<00:02, 3.70it/s]\n 73%|███████▎ | 22/30 [00:05<00:02, 3.70it/s]\n 77%|███████▋ | 23/30 [00:06<00:01, 3.70it/s]\n 80%|████████ | 24/30 [00:06<00:01, 3.70it/s]\n 83%|████████▎ | 25/30 [00:06<00:01, 3.70it/s]\n 87%|████████▋ | 26/30 [00:07<00:01, 3.70it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.70it/s]\n 93%|█████████▎| 28/30 [00:07<00:00, 3.70it/s]\n 97%|█████████▋| 29/30 [00:07<00:00, 3.70it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.70it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.70it/s]", "metrics": { "predict_time": 13.448108, "total_time": 19.144817 }, "output": [ "https://replicate.delivery/pbxt/B3ioVqRSpSbdJVvMGH34sJEPvFPuF3XnuqWSWafGa7l3M7eRA/out-0.png" ], "started_at": "2023-12-01T10:24:34.104232Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qiaifcdbihvkezlxrnegqf6n5a", "cancel": "https://api.replicate.com/v1/predictions/qiaifcdbihvkezlxrnegqf6n5a/cancel" }, "version": "0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed" }Generated inUsing seed: 92 Ensuring enough disk space... Free disk space: 1933190082560 Downloading weights: https://replicate.delivery/pbxt/MqZe51l1ASSuZKIVF843Z9ZAYynhaGYxtACGxaFv6wblJ7eRA/trained_model.tar b'Downloaded 186 MB bytes in 2.858s (65 MB/s)\nExtracted 186 MB in 0.066s (2.8 GB/s)\n' Downloaded weights in 3.2346155643463135 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of <s0><s1>, smiling cat walking on the hill txt2img mode 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:07, 3.72it/s] 7%|▋ | 2/30 [00:00<00:07, 3.71it/s] 10%|█ | 3/30 [00:00<00:07, 3.70it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.70it/s] 17%|█▋ | 5/30 [00:01<00:06, 3.70it/s] 20%|██ | 6/30 [00:01<00:06, 3.70it/s] 23%|██▎ | 7/30 [00:01<00:06, 3.70it/s] 27%|██▋ | 8/30 [00:02<00:05, 3.71it/s] 30%|███ | 9/30 [00:02<00:05, 3.71it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.71it/s] 37%|███▋ | 11/30 [00:02<00:05, 3.71it/s] 40%|████ | 12/30 [00:03<00:04, 3.71it/s] 43%|████▎ | 13/30 [00:03<00:04, 3.71it/s] 47%|████▋ | 14/30 [00:03<00:04, 3.71it/s] 50%|█████ | 15/30 [00:04<00:04, 3.71it/s] 53%|█████▎ | 16/30 [00:04<00:03, 3.71it/s] 57%|█████▋ | 17/30 [00:04<00:03, 3.70it/s] 60%|██████ | 18/30 [00:04<00:03, 3.71it/s] 63%|██████▎ | 19/30 [00:05<00:02, 3.70it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.70it/s] 70%|███████ | 21/30 [00:05<00:02, 3.70it/s] 73%|███████▎ | 22/30 [00:05<00:02, 3.70it/s] 77%|███████▋ | 23/30 [00:06<00:01, 3.70it/s] 80%|████████ | 24/30 [00:06<00:01, 3.70it/s] 83%|████████▎ | 25/30 [00:06<00:01, 3.70it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.70it/s] 90%|█████████ | 27/30 [00:07<00:00, 3.70it/s] 93%|█████████▎| 28/30 [00:07<00:00, 3.70it/s] 97%|█████████▋| 29/30 [00:07<00:00, 3.70it/s] 100%|██████████| 30/30 [00:08<00:00, 3.70it/s] 100%|██████████| 30/30 [00:08<00:00, 3.70it/s]Prediction
peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2edIDl4qgd7tbj26oap6o4had5dlzwqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of TOK, angry cat is disappointed in dorm
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 1024, "height": 1024, "prompt": "A photo of TOK, angry cat is disappointed in dorm", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run peter65374/sdxl-cat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", { input: { width: 1024, height: 1024, prompt: "A photo of TOK, angry cat is disappointed in dorm", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun peter65374/sdxl-cat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", input={ "width": 1024, "height": 1024, "prompt": "A photo of TOK, angry cat is disappointed in dorm", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run peter65374/sdxl-cat 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": "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK, angry cat is disappointed in dorm", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-01T10:26:58.467897Z", "created_at": "2023-12-01T10:26:38.907068Z", "data_removed": false, "error": null, "id": "l4qgd7tbj26oap6o4had5dlzwq", "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK, angry cat is disappointed in dorm", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 27717\nEnsuring enough disk space...\nFree disk space: 2101700825088\nDownloading weights: https://replicate.delivery/pbxt/MqZe51l1ASSuZKIVF843Z9ZAYynhaGYxtACGxaFv6wblJ7eRA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.256s (726 MB/s)\\nExtracted 186 MB in 0.062s (3.0 GB/s)\\n'\nDownloaded weights in 0.7456281185150146 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1>, angry cat is disappointed in dorm\ntxt2img mode\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:07, 3.73it/s]\n 7%|▋ | 2/30 [00:00<00:07, 3.72it/s]\n 10%|█ | 3/30 [00:00<00:07, 3.71it/s]\n 13%|█▎ | 4/30 [00:01<00:07, 3.70it/s]\n 17%|█▋ | 5/30 [00:01<00:06, 3.71it/s]\n 20%|██ | 6/30 [00:01<00:06, 3.70it/s]\n 23%|██▎ | 7/30 [00:01<00:06, 3.70it/s]\n 27%|██▋ | 8/30 [00:02<00:05, 3.70it/s]\n 30%|███ | 9/30 [00:02<00:05, 3.71it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.71it/s]\n 37%|███▋ | 11/30 [00:02<00:05, 3.72it/s]\n 40%|████ | 12/30 [00:03<00:04, 3.72it/s]\n 43%|████▎ | 13/30 [00:03<00:04, 3.71it/s]\n 47%|████▋ | 14/30 [00:03<00:04, 3.71it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.71it/s]\n 53%|█████▎ | 16/30 [00:04<00:03, 3.72it/s]\n 57%|█████▋ | 17/30 [00:04<00:03, 3.71it/s]\n 60%|██████ | 18/30 [00:04<00:03, 3.71it/s]\n 63%|██████▎ | 19/30 [00:05<00:02, 3.72it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.71it/s]\n 70%|███████ | 21/30 [00:05<00:02, 3.71it/s]\n 73%|███████▎ | 22/30 [00:05<00:02, 3.71it/s]\n 77%|███████▋ | 23/30 [00:06<00:01, 3.71it/s]\n 80%|████████ | 24/30 [00:06<00:01, 3.71it/s]\n 83%|████████▎ | 25/30 [00:06<00:01, 3.71it/s]\n 87%|████████▋ | 26/30 [00:07<00:01, 3.71it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.71it/s]\n 93%|█████████▎| 28/30 [00:07<00:00, 3.71it/s]\n 97%|█████████▋| 29/30 [00:07<00:00, 3.71it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.71it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.71it/s]", "metrics": { "predict_time": 11.036358, "total_time": 19.560829 }, "output": [ "https://replicate.delivery/pbxt/BQuZpxS8SnKVEFdvGjv5nF3UA91tCuQp4IV1vlL10qV8mdfIA/out-0.png" ], "started_at": "2023-12-01T10:26:47.431539Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/l4qgd7tbj26oap6o4had5dlzwq", "cancel": "https://api.replicate.com/v1/predictions/l4qgd7tbj26oap6o4had5dlzwq/cancel" }, "version": "0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed" }Generated inUsing seed: 27717 Ensuring enough disk space... Free disk space: 2101700825088 Downloading weights: https://replicate.delivery/pbxt/MqZe51l1ASSuZKIVF843Z9ZAYynhaGYxtACGxaFv6wblJ7eRA/trained_model.tar b'Downloaded 186 MB bytes in 0.256s (726 MB/s)\nExtracted 186 MB in 0.062s (3.0 GB/s)\n' Downloaded weights in 0.7456281185150146 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of <s0><s1>, angry cat is disappointed in dorm txt2img mode 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:07, 3.73it/s] 7%|▋ | 2/30 [00:00<00:07, 3.72it/s] 10%|█ | 3/30 [00:00<00:07, 3.71it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.70it/s] 17%|█▋ | 5/30 [00:01<00:06, 3.71it/s] 20%|██ | 6/30 [00:01<00:06, 3.70it/s] 23%|██▎ | 7/30 [00:01<00:06, 3.70it/s] 27%|██▋ | 8/30 [00:02<00:05, 3.70it/s] 30%|███ | 9/30 [00:02<00:05, 3.71it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.71it/s] 37%|███▋ | 11/30 [00:02<00:05, 3.72it/s] 40%|████ | 12/30 [00:03<00:04, 3.72it/s] 43%|████▎ | 13/30 [00:03<00:04, 3.71it/s] 47%|████▋ | 14/30 [00:03<00:04, 3.71it/s] 50%|█████ | 15/30 [00:04<00:04, 3.71it/s] 53%|█████▎ | 16/30 [00:04<00:03, 3.72it/s] 57%|█████▋ | 17/30 [00:04<00:03, 3.71it/s] 60%|██████ | 18/30 [00:04<00:03, 3.71it/s] 63%|██████▎ | 19/30 [00:05<00:02, 3.72it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.71it/s] 70%|███████ | 21/30 [00:05<00:02, 3.71it/s] 73%|███████▎ | 22/30 [00:05<00:02, 3.71it/s] 77%|███████▋ | 23/30 [00:06<00:01, 3.71it/s] 80%|████████ | 24/30 [00:06<00:01, 3.71it/s] 83%|████████▎ | 25/30 [00:06<00:01, 3.71it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.71it/s] 90%|█████████ | 27/30 [00:07<00:00, 3.71it/s] 93%|█████████▎| 28/30 [00:07<00:00, 3.71it/s] 97%|█████████▋| 29/30 [00:07<00:00, 3.71it/s] 100%|██████████| 30/30 [00:08<00:00, 3.71it/s] 100%|██████████| 30/30 [00:08<00:00, 3.71it/s]Prediction
peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2edIDz3nvwttb6fq4eymbxd4fzocgguStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of TOK, dumb cat with two hands up sitting a table
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 1024, "height": 1024, "prompt": "A photo of TOK, dumb cat with two hands up sitting a table", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run peter65374/sdxl-cat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", { input: { width: 1024, height: 1024, prompt: "A photo of TOK, dumb cat with two hands up sitting a table", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun peter65374/sdxl-cat using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", input={ "width": 1024, "height": 1024, "prompt": "A photo of TOK, dumb cat with two hands up sitting a table", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run peter65374/sdxl-cat 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": "peter65374/sdxl-cat:0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed", "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK, dumb cat with two hands up sitting a table", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-12-01T10:28:01.557411Z", "created_at": "2023-12-01T10:27:42.283567Z", "data_removed": false, "error": null, "id": "z3nvwttb6fq4eymbxd4fzocggu", "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK, dumb cat with two hands up sitting a table", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 57738\nEnsuring enough disk space...\nFree disk space: 1579524763648\nDownloading weights: https://replicate.delivery/pbxt/MqZe51l1ASSuZKIVF843Z9ZAYynhaGYxtACGxaFv6wblJ7eRA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.279s (666 MB/s)\\nExtracted 186 MB in 0.052s (3.6 GB/s)\\n'\nDownloaded weights in 0.4638402462005615 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1>, dumb cat with two hands up sitting a table\ntxt2img mode\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:00<00:07, 3.65it/s]\n 7%|▋ | 2/30 [00:00<00:07, 3.64it/s]\n 10%|█ | 3/30 [00:00<00:07, 3.64it/s]\n 13%|█▎ | 4/30 [00:01<00:07, 3.63it/s]\n 17%|█▋ | 5/30 [00:01<00:06, 3.63it/s]\n 20%|██ | 6/30 [00:01<00:06, 3.63it/s]\n 23%|██▎ | 7/30 [00:01<00:06, 3.63it/s]\n 27%|██▋ | 8/30 [00:02<00:06, 3.63it/s]\n 30%|███ | 9/30 [00:02<00:05, 3.62it/s]\n 33%|███▎ | 10/30 [00:02<00:05, 3.62it/s]\n 37%|███▋ | 11/30 [00:03<00:05, 3.62it/s]\n 40%|████ | 12/30 [00:03<00:04, 3.62it/s]\n 43%|████▎ | 13/30 [00:03<00:04, 3.62it/s]\n 47%|████▋ | 14/30 [00:03<00:04, 3.62it/s]\n 50%|█████ | 15/30 [00:04<00:04, 3.62it/s]\n 53%|█████▎ | 16/30 [00:04<00:03, 3.62it/s]\n 57%|█████▋ | 17/30 [00:04<00:03, 3.62it/s]\n 60%|██████ | 18/30 [00:04<00:03, 3.61it/s]\n 63%|██████▎ | 19/30 [00:05<00:03, 3.61it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.61it/s]\n 70%|███████ | 21/30 [00:05<00:02, 3.61it/s]\n 73%|███████▎ | 22/30 [00:06<00:02, 3.61it/s]\n 77%|███████▋ | 23/30 [00:06<00:01, 3.61it/s]\n 80%|████████ | 24/30 [00:06<00:01, 3.61it/s]\n 83%|████████▎ | 25/30 [00:06<00:01, 3.61it/s]\n 87%|████████▋ | 26/30 [00:07<00:01, 3.61it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.61it/s]\n 93%|█████████▎| 28/30 [00:07<00:00, 3.61it/s]\n 97%|█████████▋| 29/30 [00:08<00:00, 3.61it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.61it/s]\n100%|██████████| 30/30 [00:08<00:00, 3.62it/s]", "metrics": { "predict_time": 11.69378, "total_time": 19.273844 }, "output": [ "https://replicate.delivery/pbxt/SiDfWWDmKXXidaqeCGmJR7f5YoKhFMUzu6MU1sU1Ydug5s7jA/out-0.png" ], "started_at": "2023-12-01T10:27:49.863631Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z3nvwttb6fq4eymbxd4fzocggu", "cancel": "https://api.replicate.com/v1/predictions/z3nvwttb6fq4eymbxd4fzocggu/cancel" }, "version": "0ac4841eb414b90346e08e27c48be8e3b03f6e8281d5844f965be8347614d2ed" }Generated inUsing seed: 57738 Ensuring enough disk space... Free disk space: 1579524763648 Downloading weights: https://replicate.delivery/pbxt/MqZe51l1ASSuZKIVF843Z9ZAYynhaGYxtACGxaFv6wblJ7eRA/trained_model.tar b'Downloaded 186 MB bytes in 0.279s (666 MB/s)\nExtracted 186 MB in 0.052s (3.6 GB/s)\n' Downloaded weights in 0.4638402462005615 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of <s0><s1>, dumb cat with two hands up sitting a table txt2img mode 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:07, 3.65it/s] 7%|▋ | 2/30 [00:00<00:07, 3.64it/s] 10%|█ | 3/30 [00:00<00:07, 3.64it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.63it/s] 17%|█▋ | 5/30 [00:01<00:06, 3.63it/s] 20%|██ | 6/30 [00:01<00:06, 3.63it/s] 23%|██▎ | 7/30 [00:01<00:06, 3.63it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.63it/s] 30%|███ | 9/30 [00:02<00:05, 3.62it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.62it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.62it/s] 40%|████ | 12/30 [00:03<00:04, 3.62it/s] 43%|████▎ | 13/30 [00:03<00:04, 3.62it/s] 47%|████▋ | 14/30 [00:03<00:04, 3.62it/s] 50%|█████ | 15/30 [00:04<00:04, 3.62it/s] 53%|█████▎ | 16/30 [00:04<00:03, 3.62it/s] 57%|█████▋ | 17/30 [00:04<00:03, 3.62it/s] 60%|██████ | 18/30 [00:04<00:03, 3.61it/s] 63%|██████▎ | 19/30 [00:05<00:03, 3.61it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.61it/s] 70%|███████ | 21/30 [00:05<00:02, 3.61it/s] 73%|███████▎ | 22/30 [00:06<00:02, 3.61it/s] 77%|███████▋ | 23/30 [00:06<00:01, 3.61it/s] 80%|████████ | 24/30 [00:06<00:01, 3.61it/s] 83%|████████▎ | 25/30 [00:06<00:01, 3.61it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.61it/s] 90%|█████████ | 27/30 [00:07<00:00, 3.61it/s] 93%|█████████▎| 28/30 [00:07<00:00, 3.61it/s] 97%|█████████▋| 29/30 [00:08<00:00, 3.61it/s] 100%|██████████| 30/30 [00:08<00:00, 3.61it/s] 100%|██████████| 30/30 [00:08<00:00, 3.62it/s]
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