chuanzi
/
fanhu_style_lora_sdxl
繁花 style 测试
- Public
- 109 runs
-
L40S
- SDXL fine-tune
Prediction
chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47IDmbyx36x9k5rgp0cfg12ashyjm8StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a cat in style of fanhua
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a cat in style of fanhua", "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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", { input: { width: 1024, height: 1024, prompt: "a cat in style of fanhua", 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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", input={ "width": 1024, "height": 1024, "prompt": "a cat in style of fanhua", "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 chuanzi/fanhu_style_lora_sdxl 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": "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", "input": { "width": 1024, "height": 1024, "prompt": "a cat in style of fanhua", "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": "2024-05-16T07:19:49.119790Z", "created_at": "2024-05-16T07:18:54.617000Z", "data_removed": false, "error": null, "id": "mbyx36x9k5rgp0cfg12ashyjm8", "input": { "width": 1024, "height": 1024, "prompt": "a cat in style of fanhua", "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: 24111\nEnsuring enough disk space...\nFree disk space: 3417957826560\nDownloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-05-16T07:19:28Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/3569e431a241683a url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-05-16T07:19:32Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size=\"186 MB\" total_elapsed=3.806s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\nb''\nDownloaded weights in 4.261150598526001 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a cat in style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.63it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.62it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.62it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.61it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.61it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.61it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.60it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.60it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.60it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.60it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.60it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.60it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.60it/s]\n 28%|██▊ | 14/50 [00:03<00:10, 3.60it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.60it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.60it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.59it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.59it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.60it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.60it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.59it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.59it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.59it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.59it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.60it/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, 3.60it/s]", "metrics": { "predict_time": 20.507053, "total_time": 54.50279 }, "output": [ "https://replicate.delivery/pbxt/h5gFn8eiLpUkSyqZrA7oQSeJrX058drJhFEIlZyKvUBUW20SA/out-0.png" ], "started_at": "2024-05-16T07:19:28.612737Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/mbyx36x9k5rgp0cfg12ashyjm8", "cancel": "https://api.replicate.com/v1/predictions/mbyx36x9k5rgp0cfg12ashyjm8/cancel" }, "version": "a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47" }Generated inUsing seed: 24111 Ensuring enough disk space... Free disk space: 3417957826560 Downloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-05-16T07:19:28Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/3569e431a241683a url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-05-16T07:19:32Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size="186 MB" total_elapsed=3.806s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar b'' Downloaded weights in 4.261150598526001 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a cat in style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.63it/s] 4%|▍ | 2/50 [00:00<00:13, 3.62it/s] 6%|▌ | 3/50 [00:00<00:12, 3.62it/s] 8%|▊ | 4/50 [00:01<00:12, 3.61it/s] 10%|█ | 5/50 [00:01<00:12, 3.61it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.61it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.60it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.60it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.60it/s] 20%|██ | 10/50 [00:02<00:11, 3.60it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.60it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.60it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.60it/s] 28%|██▊ | 14/50 [00:03<00:10, 3.60it/s] 30%|███ | 15/50 [00:04<00:09, 3.60it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.60it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.59it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.59it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.60it/s] 40%|████ | 20/50 [00:05<00:08, 3.60it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.59it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.59it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.59it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.59it/s] 50%|█████ | 25/50 [00:06<00:06, 3.60it/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, 3.60it/s]Prediction
chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47IDvzh6d905jxrgj0ceqrbrx8r7ymStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a sportscar in style of fanhua
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a sportscar in style of fanhua", "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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", { input: { width: 1024, height: 1024, prompt: "a sportscar in style of fanhua", 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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", input={ "width": 1024, "height": 1024, "prompt": "a sportscar in style of fanhua", "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 chuanzi/fanhu_style_lora_sdxl 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": "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", "input": { "width": 1024, "height": 1024, "prompt": "a sportscar in style of fanhua", "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": "2024-04-08T14:23:03.029859Z", "created_at": "2024-04-08T14:22:43.607000Z", "data_removed": false, "error": null, "id": "vzh6d905jxrgj0ceqrbrx8r7ym", "input": { "width": 1024, "height": 1024, "prompt": "a sportscar in style of fanhua", "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: 38523\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a sportscar in style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/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, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/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": 16.673963, "total_time": 19.422859 }, "output": [ "https://replicate.delivery/pbxt/eH9n1ayITuxgMiBsFk6xVBfYdyI9vaeiJsw8UUzFI86LerhKB/out-0.png" ], "started_at": "2024-04-08T14:22:46.355896Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vzh6d905jxrgj0ceqrbrx8r7ym", "cancel": "https://api.replicate.com/v1/predictions/vzh6d905jxrgj0ceqrbrx8r7ym/cancel" }, "version": "a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47" }Generated inUsing seed: 38523 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a sportscar in style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/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, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s] 40%|████ | 20/50 [00:05<00:08, 3.65it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s] 50%|█████ | 25/50 [00:06<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s] 70%|███████ | 35/50 [00:09<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s] 80%|████████ | 40/50 [00:10<00:02, 3.64it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/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
chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47ID6s6p75ac95rgj0ceqrctz24p08StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a modern bedroom in style of fanhua
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a modern bedroom in style of fanhua", "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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", { input: { width: 1024, height: 1024, prompt: "a modern bedroom in style of fanhua", 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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", input={ "width": 1024, "height": 1024, "prompt": "a modern bedroom in style of fanhua", "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 chuanzi/fanhu_style_lora_sdxl 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": "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", "input": { "width": 1024, "height": 1024, "prompt": "a modern bedroom in style of fanhua", "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": "2024-04-08T14:25:35.568435Z", "created_at": "2024-04-08T14:25:12.777000Z", "data_removed": false, "error": null, "id": "6s6p75ac95rgj0ceqrctz24p08", "input": { "width": 1024, "height": 1024, "prompt": "a modern bedroom in style of fanhua", "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: 22821\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a modern bedroom in style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.602361, "total_time": 22.791435 }, "output": [ "https://replicate.delivery/pbxt/dfAXKjDJGEWfn0zC3XfRjBhKilILe1uK8NMWa75eXCxeXwGqE/out-0.png" ], "started_at": "2024-04-08T14:25:19.966074Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6s6p75ac95rgj0ceqrctz24p08", "cancel": "https://api.replicate.com/v1/predictions/6s6p75ac95rgj0ceqrctz24p08/cancel" }, "version": "a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47" }Generated inUsing seed: 22821 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a modern bedroom in style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47ID61r18gbczdrgg0ceqrdb30y1v4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a phone in style of fanhua
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a phone in style of fanhua", "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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", { input: { width: 1024, height: 1024, prompt: "a phone in style of fanhua", 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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", input={ "width": 1024, "height": 1024, "prompt": "a phone in style of fanhua", "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 chuanzi/fanhu_style_lora_sdxl 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": "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", "input": { "width": 1024, "height": 1024, "prompt": "a phone in style of fanhua", "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": "2024-04-08T14:26:48.141679Z", "created_at": "2024-04-08T14:26:26.683000Z", "data_removed": false, "error": null, "id": "61r18gbczdrgg0ceqrdb30y1v4", "input": { "width": 1024, "height": 1024, "prompt": "a phone in style of fanhua", "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: 28837\nEnsuring enough disk space...\nFree disk space: 1629951995904\nDownloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-04-08T14:26:29Z | INFO | [ Initiating ] dest=/src/weights-cache/3569e431a241683a minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-04-08T14:26:30Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size=\"186 MB\" total_elapsed=0.770s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\nb''\nDownloaded weights in 0.9190170764923096 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a phone in style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:00<00:31, 1.57it/s]\n 4%|▍ | 2/50 [00:00<00:20, 2.36it/s]\n 6%|▌ | 3/50 [00:01<00:16, 2.82it/s]\n 8%|▊ | 4/50 [00:01<00:14, 3.10it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.28it/s]\n 12%|█▏ | 6/50 [00:02<00:12, 3.39it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.47it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.52it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.56it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.59it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.60it/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 [00:04<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 [00:05<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.64it/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 [00:06<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:09<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 [00:10<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 [00:11<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:11<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 [00:12<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 [00:13<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 [00:14<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.55it/s]", "metrics": { "predict_time": 18.809181, "total_time": 21.458679 }, "output": [ "https://replicate.delivery/pbxt/ifcMtUlffRYXApQ6ceQEFaXNbbLH9Is3qHLFhvReZ351UYDVC/out-0.png" ], "started_at": "2024-04-08T14:26:29.332498Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/61r18gbczdrgg0ceqrdb30y1v4", "cancel": "https://api.replicate.com/v1/predictions/61r18gbczdrgg0ceqrdb30y1v4/cancel" }, "version": "a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47" }Generated inUsing seed: 28837 Ensuring enough disk space... Free disk space: 1629951995904 Downloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-04-08T14:26:29Z | INFO | [ Initiating ] dest=/src/weights-cache/3569e431a241683a minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-04-08T14:26:30Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size="186 MB" total_elapsed=0.770s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar b'' Downloaded weights in 0.9190170764923096 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a phone in style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:00<00:31, 1.57it/s] 4%|▍ | 2/50 [00:00<00:20, 2.36it/s] 6%|▌ | 3/50 [00:01<00:16, 2.82it/s] 8%|▊ | 4/50 [00:01<00:14, 3.10it/s] 10%|█ | 5/50 [00:01<00:13, 3.28it/s] 12%|█▏ | 6/50 [00:02<00:12, 3.39it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.47it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.52it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.56it/s] 20%|██ | 10/50 [00:03<00:11, 3.59it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.60it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:04<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 [00:05<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s] 50%|█████ | 25/50 [00:07<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:09<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 [00:10<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 [00:11<00:03, 3.65it/s] 80%|████████ | 40/50 [00:11<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 [00:12<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 [00:13<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 [00:14<00:00, 3.65it/s] 100%|██████████| 50/50 [00:14<00:00, 3.55it/s]Prediction
chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47IDv6vkmxmqxdrgg0ceqrdtb90j80StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- in style of fanhua,a fashion girl, pov
- 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": "in style of fanhua,a fashion girl, pov", "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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", { input: { width: 1024, height: 1024, prompt: "in style of fanhua,a fashion girl, pov", 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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", input={ "width": 1024, "height": 1024, "prompt": "in style of fanhua,a fashion girl, pov", "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 chuanzi/fanhu_style_lora_sdxl 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": "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", "input": { "width": 1024, "height": 1024, "prompt": "in style of fanhua,a fashion girl, pov", "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": "2024-04-08T14:28:01.339173Z", "created_at": "2024-04-08T14:27:43.211000Z", "data_removed": false, "error": null, "id": "v6vkmxmqxdrgg0ceqrdtb90j80", "input": { "width": 1024, "height": 1024, "prompt": "in style of fanhua,a fashion girl, pov", "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: 24481\nEnsuring enough disk space...\nFree disk space: 1573664862208\nDownloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-04-08T14:27:43Z | INFO | [ Initiating ] dest=/src/weights-cache/3569e431a241683a minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-04-08T14:27:44Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size=\"186 MB\" total_elapsed=0.656s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\nb''\nDownloaded weights in 0.7448842525482178 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in style of <s0><s1>,a fashion girl, pov\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, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/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, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.63it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 17.639601, "total_time": 18.128173 }, "output": [ "https://replicate.delivery/pbxt/sDxTSx3J01a8OJ2UaUfDaNw31V58jnm1LU8qMHSqRnp3hNUJA/out-0.png" ], "started_at": "2024-04-08T14:27:43.699572Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/v6vkmxmqxdrgg0ceqrdtb90j80", "cancel": "https://api.replicate.com/v1/predictions/v6vkmxmqxdrgg0ceqrdtb90j80/cancel" }, "version": "a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47" }Generated inUsing seed: 24481 Ensuring enough disk space... Free disk space: 1573664862208 Downloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-04-08T14:27:43Z | INFO | [ Initiating ] dest=/src/weights-cache/3569e431a241683a minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-04-08T14:27:44Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size="186 MB" total_elapsed=0.656s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar b'' Downloaded weights in 0.7448842525482178 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in style of <s0><s1>,a fashion girl, pov 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, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/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, 3.65it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s] 40%|████ | 20/50 [00:05<00:08, 3.64it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s] 50%|█████ | 25/50 [00:06<00:06, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.63it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:10<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s]Prediction
chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47ID6g2cvkr97nrgj0ceqrjv2713r4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- city street in the morning
- 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": "city street in the morning", "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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", { input: { width: 1024, height: 1024, prompt: "city street in the morning", 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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", input={ "width": 1024, "height": 1024, "prompt": "city street in the morning", "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 chuanzi/fanhu_style_lora_sdxl 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": "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", "input": { "width": 1024, "height": 1024, "prompt": "city street in the morning", "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": "2024-04-08T14:38:25.031341Z", "created_at": "2024-04-08T14:38:02.045000Z", "data_removed": false, "error": null, "id": "6g2cvkr97nrgj0ceqrjv2713r4", "input": { "width": 1024, "height": 1024, "prompt": "city street in the morning", "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: 22762\nEnsuring enough disk space...\nFree disk space: 2287358267392\nDownloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-04-08T14:38:04Z | INFO | [ Initiating ] dest=/src/weights-cache/3569e431a241683a minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-04-08T14:38:09Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size=\"186 MB\" total_elapsed=5.284s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\nb''\nDownloaded weights in 5.3567235469818115 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: city street in the morning\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:05<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, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/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, 3.55it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 20.99995, "total_time": 22.986341 }, "output": [ "https://replicate.delivery/pbxt/flwGbcVLtvXCe003ihnDHJWQCxzLVvhJqIMKpBy23EdgNboSA/out-0.png" ], "started_at": "2024-04-08T14:38:04.031391Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6g2cvkr97nrgj0ceqrjv2713r4", "cancel": "https://api.replicate.com/v1/predictions/6g2cvkr97nrgj0ceqrjv2713r4/cancel" }, "version": "a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47" }Generated inUsing seed: 22762 Ensuring enough disk space... Free disk space: 2287358267392 Downloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-04-08T14:38:04Z | INFO | [ Initiating ] dest=/src/weights-cache/3569e431a241683a minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-04-08T14:38:09Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size="186 MB" total_elapsed=5.284s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar b'' Downloaded weights in 5.3567235469818115 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: city street in the morning txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:05<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, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/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, 3.55it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47IDhw9n58c049rgj0ceqrn802b26gStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- in style of fanhua,a fashion girl, pov
- 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
- 0.7
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "in style of fanhua,a fashion girl, pov", "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": 0.7, "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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", { input: { width: 1024, height: 1024, prompt: "in style of fanhua,a fashion girl, pov", 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: 0.7, 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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", input={ "width": 1024, "height": 1024, "prompt": "in style of fanhua,a fashion girl, pov", "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": 0.7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run chuanzi/fanhu_style_lora_sdxl 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": "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", "input": { "width": 1024, "height": 1024, "prompt": "in style of fanhua,a fashion girl, pov", "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": 0.7, "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": "2024-04-08T14:44:17.451921Z", "created_at": "2024-04-08T14:44:00.162000Z", "data_removed": false, "error": null, "id": "hw9n58c049rgj0ceqrn802b26g", "input": { "width": 1024, "height": 1024, "prompt": "in style of fanhua,a fashion girl, pov", "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": 0.7, "num_inference_steps": 50 }, "logs": "Using seed: 59859\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in style of <s0><s1>,a fashion girl, pov\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.66it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/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, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/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, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.63it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/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, 3.64it/s]", "metrics": { "predict_time": 15.888297, "total_time": 17.289921 }, "output": [ "https://replicate.delivery/pbxt/2SNSz9Ayc6IhFxvmRLn1OWiqvHYBEdhPbw8o9kK8m7Vw0GqE/out-0.png" ], "started_at": "2024-04-08T14:44:01.563624Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hw9n58c049rgj0ceqrn802b26g", "cancel": "https://api.replicate.com/v1/predictions/hw9n58c049rgj0ceqrn802b26g/cancel" }, "version": "a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47" }Generated inUsing seed: 59859 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in style of <s0><s1>,a fashion girl, pov txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.66it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 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chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47IDnn6bthcybxrgg0ceqrntwkqjcmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a girl is looking away and stands in the street
- 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
- 0.7
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a girl is looking away and stands in the street", "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": 0.7, "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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", { input: { width: 1024, height: 1024, prompt: "a girl is looking away and stands in the street", 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: 0.7, 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 chuanzi/fanhu_style_lora_sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", input={ "width": 1024, "height": 1024, "prompt": "a girl is looking away and stands in the street", "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": 0.7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run chuanzi/fanhu_style_lora_sdxl 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": "chuanzi/fanhu_style_lora_sdxl:a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47", "input": { "width": 1024, "height": 1024, "prompt": "a girl is looking away and stands in the street", "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": 0.7, "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": "2024-04-08T14:45:32.584173Z", "created_at": "2024-04-08T14:45:13.439000Z", "data_removed": false, "error": null, "id": "nn6bthcybxrgg0ceqrntwkqjcm", "input": { "width": 1024, "height": 1024, "prompt": "a girl is looking away and stands in the street", "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": 0.7, "num_inference_steps": 50 }, "logs": "Using seed: 16932\nEnsuring enough disk space...\nFree disk space: 1846869872640\nDownloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-04-08T14:45:16Z | INFO | [ Initiating ] dest=/src/weights-cache/3569e431a241683a minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\n2024-04-08T14:45:16Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size=\"186 MB\" total_elapsed=0.595s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar\nb''\nDownloaded weights in 0.6781527996063232 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a girl is looking away and stands in the street\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.70it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.69it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.69it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.69it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]", "metrics": { "predict_time": 16.373936, "total_time": 19.145173 }, "output": [ "https://replicate.delivery/pbxt/6twTZ4jfkemNBUYo7gNAKsZxFEBuCVMKkLQDXhbPzHfYo2QlA/out-0.png" ], "started_at": "2024-04-08T14:45:16.210237Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nn6bthcybxrgg0ceqrntwkqjcm", "cancel": "https://api.replicate.com/v1/predictions/nn6bthcybxrgg0ceqrntwkqjcm/cancel" }, "version": "a8bcc7412cfadcbf1a16642dc0993c7b50b0ce0f960f60e1359dfc1be5723e47" }Generated inUsing seed: 16932 Ensuring enough disk space... Free disk space: 1846869872640 Downloading weights: https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-04-08T14:45:16Z | INFO | [ Initiating ] dest=/src/weights-cache/3569e431a241683a minimum_chunk_size=150M url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar 2024-04-08T14:45:16Z | INFO | [ Complete ] dest=/src/weights-cache/3569e431a241683a size="186 MB" total_elapsed=0.595s url=https://replicate.delivery/pbxt/nH2w5oI9hUKYGZUESlvpi951TV5lgEwdxYtZu7kutMQltGqE/trained_model.tar b'' Downloaded weights in 0.6781527996063232 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a girl is looking away and stands in the street txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.70it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s] 30%|███ | 15/50 [00:04<00:09, 3.70it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.70it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.70it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.70it/s] 40%|████ | 20/50 [00:05<00:08, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.69it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.69it/s] 50%|█████ | 25/50 [00:06<00:06, 3.69it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.69it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.69it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.69it/s] 60%|██████ | 30/50 [00:08<00:05, 3.69it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.69it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.69it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.69it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.68it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.68it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.68it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s]
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