fofr
/
sdxl-black-light
SDXL fine-tuned on black light imagery
- Public
- 3.8K runs
-
L40S
- SDXL fine-tune
Prediction
fofr/sdxl-black-light:0b682d57IDh4uucelbgfk2iok7o6vpfvxpg4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 1152
- prompt
- A dark neon portrait photo in the style of TOK
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.9
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 768, "height": 1152, "prompt": "A dark neon portrait photo in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", { input: { width: 768, height: 1152, prompt: "A dark neon portrait photo in the style of TOK", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.9, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", input={ "width": 768, "height": 1152, "prompt": "A dark neon portrait photo in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.9, "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-black-light 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": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", "input": { "width": 768, "height": 1152, "prompt": "A dark neon portrait photo in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-29T10:05:56.576556Z", "created_at": "2023-10-29T10:05:46.186140Z", "data_removed": false, "error": null, "id": "h4uucelbgfk2iok7o6vpfvxpg4", "input": { "width": 768, "height": 1152, "prompt": "A dark neon portrait photo in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 26292\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A dark neon portrait photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:06, 4.32it/s]\n 7%|▋ | 2/27 [00:00<00:05, 4.31it/s]\n 11%|█ | 3/27 [00:00<00:05, 4.31it/s]\n 15%|█▍ | 4/27 [00:00<00:05, 4.30it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 4.30it/s]\n 22%|██▏ | 6/27 [00:01<00:04, 4.30it/s]\n 26%|██▌ | 7/27 [00:01<00:04, 4.31it/s]\n 30%|██▉ | 8/27 [00:01<00:04, 4.31it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 4.31it/s]\n 37%|███▋ | 10/27 [00:02<00:03, 4.31it/s]\n 41%|████ | 11/27 [00:02<00:03, 4.31it/s]\n 44%|████▍ | 12/27 [00:02<00:03, 4.31it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 4.31it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 4.30it/s]\n 56%|█████▌ | 15/27 [00:03<00:02, 4.31it/s]\n 59%|█████▉ | 16/27 [00:03<00:02, 4.31it/s]\n 63%|██████▎ | 17/27 [00:03<00:02, 4.31it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 4.30it/s]\n 70%|███████ | 19/27 [00:04<00:01, 4.30it/s]\n 74%|███████▍ | 20/27 [00:04<00:01, 4.30it/s]\n 78%|███████▊ | 21/27 [00:04<00:01, 4.30it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 4.30it/s]\n 85%|████████▌ | 23/27 [00:05<00:00, 4.30it/s]\n 89%|████████▉ | 24/27 [00:05<00:00, 4.30it/s]\n 93%|█████████▎| 25/27 [00:05<00:00, 4.30it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 4.30it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.30it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.30it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.51it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.47it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.47it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.48it/s]", "metrics": { "predict_time": 9.343797, "total_time": 10.390416 }, "output": [ "https://pbxt.replicate.delivery/oFXWIdZcDppuMhTal2QKl6V1kCqBYCiAjf9dmpjQfS0DCeljA/out-0.png" ], "started_at": "2023-10-29T10:05:47.232759Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/h4uucelbgfk2iok7o6vpfvxpg4", "cancel": "https://api.replicate.com/v1/predictions/h4uucelbgfk2iok7o6vpfvxpg4/cancel" }, "version": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95" }
Generated inUsing seed: 26292 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A dark neon portrait photo in the style of <s0><s1> txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:06, 4.32it/s] 7%|▋ | 2/27 [00:00<00:05, 4.31it/s] 11%|█ | 3/27 [00:00<00:05, 4.31it/s] 15%|█▍ | 4/27 [00:00<00:05, 4.30it/s] 19%|█▊ | 5/27 [00:01<00:05, 4.30it/s] 22%|██▏ | 6/27 [00:01<00:04, 4.30it/s] 26%|██▌ | 7/27 [00:01<00:04, 4.31it/s] 30%|██▉ | 8/27 [00:01<00:04, 4.31it/s] 33%|███▎ | 9/27 [00:02<00:04, 4.31it/s] 37%|███▋ | 10/27 [00:02<00:03, 4.31it/s] 41%|████ | 11/27 [00:02<00:03, 4.31it/s] 44%|████▍ | 12/27 [00:02<00:03, 4.31it/s] 48%|████▊ | 13/27 [00:03<00:03, 4.31it/s] 52%|█████▏ | 14/27 [00:03<00:03, 4.30it/s] 56%|█████▌ | 15/27 [00:03<00:02, 4.31it/s] 59%|█████▉ | 16/27 [00:03<00:02, 4.31it/s] 63%|██████▎ | 17/27 [00:03<00:02, 4.31it/s] 67%|██████▋ | 18/27 [00:04<00:02, 4.30it/s] 70%|███████ | 19/27 [00:04<00:01, 4.30it/s] 74%|███████▍ | 20/27 [00:04<00:01, 4.30it/s] 78%|███████▊ | 21/27 [00:04<00:01, 4.30it/s] 81%|████████▏ | 22/27 [00:05<00:01, 4.30it/s] 85%|████████▌ | 23/27 [00:05<00:00, 4.30it/s] 89%|████████▉ | 24/27 [00:05<00:00, 4.30it/s] 93%|█████████▎| 25/27 [00:05<00:00, 4.30it/s] 96%|█████████▋| 26/27 [00:06<00:00, 4.30it/s] 100%|██████████| 27/27 [00:06<00:00, 4.30it/s] 100%|██████████| 27/27 [00:06<00:00, 4.30it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.51it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.47it/s] 100%|██████████| 3/3 [00:00<00:00, 5.47it/s] 100%|██████████| 3/3 [00:00<00:00, 5.48it/s]
Prediction
fofr/sdxl-black-light:0b682d57IDzypuzs3b4ogexccjuth2r7dpgqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 1152
- prompt
- A dark neon mountain landscape photo in the style of TOK
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.9
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 768, "height": 1152, "prompt": "A dark neon mountain landscape photo in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", { input: { width: 768, height: 1152, prompt: "A dark neon mountain landscape photo in the style of TOK", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.9, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", input={ "width": 768, "height": 1152, "prompt": "A dark neon mountain landscape photo in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.9, "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-black-light 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": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", "input": { "width": 768, "height": 1152, "prompt": "A dark neon mountain landscape photo in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-29T10:06:38.679919Z", "created_at": "2023-10-29T10:06:28.262268Z", "data_removed": false, "error": null, "id": "zypuzs3b4ogexccjuth2r7dpgq", "input": { "width": 768, "height": 1152, "prompt": "A dark neon mountain landscape photo in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 42527\nEnsuring enough disk space...\nFree disk space: 1448470212608\nDownloading weights: https://pbxt.replicate.delivery/w93EAHCLgoL1IRX2edgMuGed88a311e2EtIuPDz6oFjZ87ljA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.243s (766 MB/s)\\nExtracted 186 MB in 0.064s (2.9 GB/s)\\n'\nDownloaded weights in 0.46370577812194824 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A dark neon mountain landscape photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:06, 4.06it/s]\n 7%|▋ | 2/27 [00:00<00:05, 4.21it/s]\n 11%|█ | 3/27 [00:00<00:05, 4.25it/s]\n 15%|█▍ | 4/27 [00:00<00:05, 4.28it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 4.30it/s]\n 22%|██▏ | 6/27 [00:01<00:04, 4.31it/s]\n 26%|██▌ | 7/27 [00:01<00:04, 4.32it/s]\n 30%|██▉ | 8/27 [00:01<00:04, 4.32it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 4.32it/s]\n 37%|███▋ | 10/27 [00:02<00:03, 4.33it/s]\n 41%|████ | 11/27 [00:02<00:03, 4.33it/s]\n 44%|████▍ | 12/27 [00:02<00:03, 4.33it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 4.32it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 4.33it/s]\n 56%|█████▌ | 15/27 [00:03<00:02, 4.33it/s]\n 59%|█████▉ | 16/27 [00:03<00:02, 4.33it/s]\n 63%|██████▎ | 17/27 [00:03<00:02, 4.33it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 4.32it/s]\n 70%|███████ | 19/27 [00:04<00:01, 4.33it/s]\n 74%|███████▍ | 20/27 [00:04<00:01, 4.32it/s]\n 78%|███████▊ | 21/27 [00:04<00:01, 4.33it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 4.32it/s]\n 85%|████████▌ | 23/27 [00:05<00:00, 4.33it/s]\n 89%|████████▉ | 24/27 [00:05<00:00, 4.32it/s]\n 93%|█████████▎| 25/27 [00:05<00:00, 4.33it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 4.32it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.32it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.31it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.17it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.42it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.38it/s]", "metrics": { "predict_time": 9.311994, "total_time": 10.417651 }, "output": [ "https://pbxt.replicate.delivery/sT1E0FeewBifFJ5Beg9f9KjH8bXfeW4dTq6cxqs54edkuCeljA/out-0.png" ], "started_at": "2023-10-29T10:06:29.367925Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zypuzs3b4ogexccjuth2r7dpgq", "cancel": "https://api.replicate.com/v1/predictions/zypuzs3b4ogexccjuth2r7dpgq/cancel" }, "version": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95" }
Generated inUsing seed: 42527 Ensuring enough disk space... Free disk space: 1448470212608 Downloading weights: https://pbxt.replicate.delivery/w93EAHCLgoL1IRX2edgMuGed88a311e2EtIuPDz6oFjZ87ljA/trained_model.tar b'Downloaded 186 MB bytes in 0.243s (766 MB/s)\nExtracted 186 MB in 0.064s (2.9 GB/s)\n' Downloaded weights in 0.46370577812194824 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A dark neon mountain landscape photo in the style of <s0><s1> txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:06, 4.06it/s] 7%|▋ | 2/27 [00:00<00:05, 4.21it/s] 11%|█ | 3/27 [00:00<00:05, 4.25it/s] 15%|█▍ | 4/27 [00:00<00:05, 4.28it/s] 19%|█▊ | 5/27 [00:01<00:05, 4.30it/s] 22%|██▏ | 6/27 [00:01<00:04, 4.31it/s] 26%|██▌ | 7/27 [00:01<00:04, 4.32it/s] 30%|██▉ | 8/27 [00:01<00:04, 4.32it/s] 33%|███▎ | 9/27 [00:02<00:04, 4.32it/s] 37%|███▋ | 10/27 [00:02<00:03, 4.33it/s] 41%|████ | 11/27 [00:02<00:03, 4.33it/s] 44%|████▍ | 12/27 [00:02<00:03, 4.33it/s] 48%|████▊ | 13/27 [00:03<00:03, 4.32it/s] 52%|█████▏ | 14/27 [00:03<00:03, 4.33it/s] 56%|█████▌ | 15/27 [00:03<00:02, 4.33it/s] 59%|█████▉ | 16/27 [00:03<00:02, 4.33it/s] 63%|██████▎ | 17/27 [00:03<00:02, 4.33it/s] 67%|██████▋ | 18/27 [00:04<00:02, 4.32it/s] 70%|███████ | 19/27 [00:04<00:01, 4.33it/s] 74%|███████▍ | 20/27 [00:04<00:01, 4.32it/s] 78%|███████▊ | 21/27 [00:04<00:01, 4.33it/s] 81%|████████▏ | 22/27 [00:05<00:01, 4.32it/s] 85%|████████▌ | 23/27 [00:05<00:00, 4.33it/s] 89%|████████▉ | 24/27 [00:05<00:00, 4.32it/s] 93%|█████████▎| 25/27 [00:05<00:00, 4.33it/s] 96%|█████████▋| 26/27 [00:06<00:00, 4.32it/s] 100%|██████████| 27/27 [00:06<00:00, 4.32it/s] 100%|██████████| 27/27 [00:06<00:00, 4.31it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.17it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.35it/s] 100%|██████████| 3/3 [00:00<00:00, 5.42it/s] 100%|██████████| 3/3 [00:00<00:00, 5.38it/s]
Prediction
fofr/sdxl-black-light:0b682d57IDw5gegadbk7atet3smenylpmhc4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 1152
- prompt
- A dark neon cityscape landscape photo in the style of TOK, green
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.9
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 768, "height": 1152, "prompt": "A dark neon cityscape landscape photo in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", { input: { width: 768, height: 1152, prompt: "A dark neon cityscape landscape photo in the style of TOK, green", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.9, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", input={ "width": 768, "height": 1152, "prompt": "A dark neon cityscape landscape photo in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.9, "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-black-light 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": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", "input": { "width": 768, "height": 1152, "prompt": "A dark neon cityscape landscape photo in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-29T10:07:44.970084Z", "created_at": "2023-10-29T10:07:33.650728Z", "data_removed": false, "error": null, "id": "w5gegadbk7atet3smenylpmhc4", "input": { "width": 768, "height": 1152, "prompt": "A dark neon cityscape landscape photo in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 3840\nEnsuring enough disk space...\nFree disk space: 2347568848896\nDownloading weights: https://pbxt.replicate.delivery/w93EAHCLgoL1IRX2edgMuGed88a311e2EtIuPDz6oFjZ87ljA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.230s (810 MB/s)\\nExtracted 186 MB in 0.073s (2.5 GB/s)\\n'\nDownloaded weights in 0.47434139251708984 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A dark neon cityscape landscape photo in the style of <s0><s1>, green\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:06, 4.06it/s]\n 7%|▋ | 2/27 [00:00<00:05, 4.19it/s]\n 11%|█ | 3/27 [00:00<00:05, 4.23it/s]\n 15%|█▍ | 4/27 [00:00<00:05, 4.26it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 4.27it/s]\n 22%|██▏ | 6/27 [00:01<00:04, 4.27it/s]\n 26%|██▌ | 7/27 [00:01<00:04, 4.28it/s]\n 30%|██▉ | 8/27 [00:01<00:04, 4.28it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 4.27it/s]\n 37%|███▋ | 10/27 [00:02<00:03, 4.28it/s]\n 41%|████ | 11/27 [00:02<00:03, 4.28it/s]\n 44%|████▍ | 12/27 [00:02<00:03, 4.28it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 4.28it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 4.28it/s]\n 56%|█████▌ | 15/27 [00:03<00:02, 4.28it/s]\n 59%|█████▉ | 16/27 [00:03<00:02, 4.28it/s]\n 63%|██████▎ | 17/27 [00:03<00:02, 4.28it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 4.28it/s]\n 70%|███████ | 19/27 [00:04<00:01, 4.28it/s]\n 74%|███████▍ | 20/27 [00:04<00:01, 4.29it/s]\n 78%|███████▊ | 21/27 [00:04<00:01, 4.30it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 4.30it/s]\n 85%|████████▌ | 23/27 [00:05<00:00, 4.30it/s]\n 89%|████████▉ | 24/27 [00:05<00:00, 4.30it/s]\n 93%|█████████▎| 25/27 [00:05<00:00, 4.30it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 4.30it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.30it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.28it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.14it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.31it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.38it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.34it/s]", "metrics": { "predict_time": 9.450975, "total_time": 11.319356 }, "output": [ "https://pbxt.replicate.delivery/CMvrEfSw601MDK6ePJLP5lpYRCHWeuot154FkPbhqHGhH8ljA/out-0.png" ], "started_at": "2023-10-29T10:07:35.519109Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w5gegadbk7atet3smenylpmhc4", "cancel": "https://api.replicate.com/v1/predictions/w5gegadbk7atet3smenylpmhc4/cancel" }, "version": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95" }
Generated inUsing seed: 3840 Ensuring enough disk space... Free disk space: 2347568848896 Downloading weights: https://pbxt.replicate.delivery/w93EAHCLgoL1IRX2edgMuGed88a311e2EtIuPDz6oFjZ87ljA/trained_model.tar b'Downloaded 186 MB bytes in 0.230s (810 MB/s)\nExtracted 186 MB in 0.073s (2.5 GB/s)\n' Downloaded weights in 0.47434139251708984 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A dark neon cityscape landscape photo in the style of <s0><s1>, green txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:06, 4.06it/s] 7%|▋ | 2/27 [00:00<00:05, 4.19it/s] 11%|█ | 3/27 [00:00<00:05, 4.23it/s] 15%|█▍ | 4/27 [00:00<00:05, 4.26it/s] 19%|█▊ | 5/27 [00:01<00:05, 4.27it/s] 22%|██▏ | 6/27 [00:01<00:04, 4.27it/s] 26%|██▌ | 7/27 [00:01<00:04, 4.28it/s] 30%|██▉ | 8/27 [00:01<00:04, 4.28it/s] 33%|███▎ | 9/27 [00:02<00:04, 4.27it/s] 37%|███▋ | 10/27 [00:02<00:03, 4.28it/s] 41%|████ | 11/27 [00:02<00:03, 4.28it/s] 44%|████▍ | 12/27 [00:02<00:03, 4.28it/s] 48%|████▊ | 13/27 [00:03<00:03, 4.28it/s] 52%|█████▏ | 14/27 [00:03<00:03, 4.28it/s] 56%|█████▌ | 15/27 [00:03<00:02, 4.28it/s] 59%|█████▉ | 16/27 [00:03<00:02, 4.28it/s] 63%|██████▎ | 17/27 [00:03<00:02, 4.28it/s] 67%|██████▋ | 18/27 [00:04<00:02, 4.28it/s] 70%|███████ | 19/27 [00:04<00:01, 4.28it/s] 74%|███████▍ | 20/27 [00:04<00:01, 4.29it/s] 78%|███████▊ | 21/27 [00:04<00:01, 4.30it/s] 81%|████████▏ | 22/27 [00:05<00:01, 4.30it/s] 85%|████████▌ | 23/27 [00:05<00:00, 4.30it/s] 89%|████████▉ | 24/27 [00:05<00:00, 4.30it/s] 93%|█████████▎| 25/27 [00:05<00:00, 4.30it/s] 96%|█████████▋| 26/27 [00:06<00:00, 4.30it/s] 100%|██████████| 27/27 [00:06<00:00, 4.30it/s] 100%|██████████| 27/27 [00:06<00:00, 4.28it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.14it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.31it/s] 100%|██████████| 3/3 [00:00<00:00, 5.38it/s] 100%|██████████| 3/3 [00:00<00:00, 5.34it/s]
Prediction
fofr/sdxl-black-light:0b682d57IDxgnhml3b2y6thsxd2ejyqye5zuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1152
- height
- 768
- prompt
- A closeup neon portrait photo in the style of TOK, green
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.9
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", { input: { width: 1152, height: 768, prompt: "A closeup neon portrait photo in the style of TOK, green", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.9, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", input={ "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.9, "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-black-light 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": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", "input": { "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-29T10:08:56.297728Z", "created_at": "2023-10-29T10:08:44.944328Z", "data_removed": false, "error": null, "id": "xgnhml3b2y6thsxd2ejyqye5zu", "input": { "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 56039\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A closeup neon portrait photo in the style of <s0><s1>, green\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:06, 4.01it/s]\n 7%|▋ | 2/27 [00:00<00:05, 4.17it/s]\n 11%|█ | 3/27 [00:00<00:05, 4.23it/s]\n 15%|█▍ | 4/27 [00:00<00:05, 4.25it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 4.26it/s]\n 22%|██▏ | 6/27 [00:01<00:04, 4.27it/s]\n 26%|██▌ | 7/27 [00:01<00:04, 4.27it/s]\n 30%|██▉ | 8/27 [00:01<00:04, 4.27it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 4.27it/s]\n 37%|███▋ | 10/27 [00:02<00:03, 4.26it/s]\n 41%|████ | 11/27 [00:02<00:03, 4.27it/s]\n 44%|████▍ | 12/27 [00:02<00:03, 4.27it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 4.27it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 4.27it/s]\n 56%|█████▌ | 15/27 [00:03<00:02, 4.27it/s]\n 59%|█████▉ | 16/27 [00:03<00:02, 4.27it/s]\n 63%|██████▎ | 17/27 [00:03<00:02, 4.27it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 4.27it/s]\n 70%|███████ | 19/27 [00:04<00:01, 4.26it/s]\n 74%|███████▍ | 20/27 [00:04<00:01, 4.26it/s]\n 78%|███████▊ | 21/27 [00:04<00:01, 4.26it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 4.26it/s]\n 85%|████████▌ | 23/27 [00:05<00:00, 4.26it/s]\n 89%|████████▉ | 24/27 [00:05<00:00, 4.26it/s]\n 93%|█████████▎| 25/27 [00:05<00:00, 4.26it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 4.25it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.26it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.26it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.09it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.29it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.31it/s]", "metrics": { "predict_time": 9.536119, "total_time": 11.3534 }, "output": [ "https://pbxt.replicate.delivery/qV5K3NPB3XJID1ZMCMzLrgkiKlfGrv5SIP1q4wMSFLxbCfyRA/out-0.png" ], "started_at": "2023-10-29T10:08:46.761609Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xgnhml3b2y6thsxd2ejyqye5zu", "cancel": "https://api.replicate.com/v1/predictions/xgnhml3b2y6thsxd2ejyqye5zu/cancel" }, "version": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95" }
Generated inUsing seed: 56039 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A closeup neon portrait photo in the style of <s0><s1>, green txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:06, 4.01it/s] 7%|▋ | 2/27 [00:00<00:05, 4.17it/s] 11%|█ | 3/27 [00:00<00:05, 4.23it/s] 15%|█▍ | 4/27 [00:00<00:05, 4.25it/s] 19%|█▊ | 5/27 [00:01<00:05, 4.26it/s] 22%|██▏ | 6/27 [00:01<00:04, 4.27it/s] 26%|██▌ | 7/27 [00:01<00:04, 4.27it/s] 30%|██▉ | 8/27 [00:01<00:04, 4.27it/s] 33%|███▎ | 9/27 [00:02<00:04, 4.27it/s] 37%|███▋ | 10/27 [00:02<00:03, 4.26it/s] 41%|████ | 11/27 [00:02<00:03, 4.27it/s] 44%|████▍ | 12/27 [00:02<00:03, 4.27it/s] 48%|████▊ | 13/27 [00:03<00:03, 4.27it/s] 52%|█████▏ | 14/27 [00:03<00:03, 4.27it/s] 56%|█████▌ | 15/27 [00:03<00:02, 4.27it/s] 59%|█████▉ | 16/27 [00:03<00:02, 4.27it/s] 63%|██████▎ | 17/27 [00:03<00:02, 4.27it/s] 67%|██████▋ | 18/27 [00:04<00:02, 4.27it/s] 70%|███████ | 19/27 [00:04<00:01, 4.26it/s] 74%|███████▍ | 20/27 [00:04<00:01, 4.26it/s] 78%|███████▊ | 21/27 [00:04<00:01, 4.26it/s] 81%|████████▏ | 22/27 [00:05<00:01, 4.26it/s] 85%|████████▌ | 23/27 [00:05<00:00, 4.26it/s] 89%|████████▉ | 24/27 [00:05<00:00, 4.26it/s] 93%|█████████▎| 25/27 [00:05<00:00, 4.26it/s] 96%|█████████▋| 26/27 [00:06<00:00, 4.25it/s] 100%|██████████| 27/27 [00:06<00:00, 4.26it/s] 100%|██████████| 27/27 [00:06<00:00, 4.26it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.09it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.29it/s] 100%|██████████| 3/3 [00:00<00:00, 5.35it/s] 100%|██████████| 3/3 [00:00<00:00, 5.31it/s]
Prediction
fofr/sdxl-black-light:0b682d57IDlt4j6i3b6fgr7jvpp3d5g6jeeuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1152
- height
- 768
- prompt
- A closeup neon portrait photo of a spooky monster in the style of TOK, green
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.9
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky monster in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", { input: { width: 1152, height: 768, prompt: "A closeup neon portrait photo of a spooky monster in the style of TOK, green", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.9, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", input={ "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky monster in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.9, "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-black-light 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": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", "input": { "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky monster in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-29T10:09:30.892824Z", "created_at": "2023-10-29T10:09:22.030191Z", "data_removed": false, "error": null, "id": "lt4j6i3b6fgr7jvpp3d5g6jeeu", "input": { "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky monster in the style of TOK, green", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 11733\nEnsuring enough disk space...\nFree disk space: 1807255982080\nDownloading weights: https://pbxt.replicate.delivery/w93EAHCLgoL1IRX2edgMuGed88a311e2EtIuPDz6oFjZ87ljA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.269s (692 MB/s)\\nExtracted 186 MB in 0.069s (2.7 GB/s)\\n'\nDownloaded weights in 0.449491024017334 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A closeup neon portrait photo of a spooky monster in the style of <s0><s1>, green\ntxt2img mode\n 0%| | 0/21 [00:00<?, ?it/s]\n 5%|▍ | 1/21 [00:00<00:04, 4.01it/s]\n 10%|▉ | 2/21 [00:00<00:04, 4.17it/s]\n 14%|█▍ | 3/21 [00:00<00:04, 4.21it/s]\n 19%|█▉ | 4/21 [00:00<00:04, 4.24it/s]\n 24%|██▍ | 5/21 [00:01<00:03, 4.24it/s]\n 29%|██▊ | 6/21 [00:01<00:03, 4.25it/s]\n 33%|███▎ | 7/21 [00:01<00:03, 4.25it/s]\n 38%|███▊ | 8/21 [00:01<00:03, 4.26it/s]\n 43%|████▎ | 9/21 [00:02<00:02, 4.26it/s]\n 48%|████▊ | 10/21 [00:02<00:02, 4.26it/s]\n 52%|█████▏ | 11/21 [00:02<00:02, 4.26it/s]\n 57%|█████▋ | 12/21 [00:02<00:02, 4.26it/s]\n 62%|██████▏ | 13/21 [00:03<00:01, 4.26it/s]\n 67%|██████▋ | 14/21 [00:03<00:01, 4.26it/s]\n 71%|███████▏ | 15/21 [00:03<00:01, 4.26it/s]\n 76%|███████▌ | 16/21 [00:03<00:01, 4.26it/s]\n 81%|████████ | 17/21 [00:04<00:00, 4.26it/s]\n 86%|████████▌ | 18/21 [00:04<00:00, 4.26it/s]\n 90%|█████████ | 19/21 [00:04<00:00, 4.26it/s]\n 95%|█████████▌| 20/21 [00:04<00:00, 4.26it/s]\n100%|██████████| 21/21 [00:04<00:00, 4.26it/s]\n100%|██████████| 21/21 [00:04<00:00, 4.25it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.06it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.26it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.34it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.29it/s]", "metrics": { "predict_time": 8.040941, "total_time": 8.862633 }, "output": [ "https://pbxt.replicate.delivery/dSiWTqxsbf2IKyZOwBfskefhXjdOW3eUZdBPHI4DTf9hWhvcE/out-0.png" ], "started_at": "2023-10-29T10:09:22.851883Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lt4j6i3b6fgr7jvpp3d5g6jeeu", "cancel": "https://api.replicate.com/v1/predictions/lt4j6i3b6fgr7jvpp3d5g6jeeu/cancel" }, "version": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95" }
Generated inUsing seed: 11733 Ensuring enough disk space... Free disk space: 1807255982080 Downloading weights: https://pbxt.replicate.delivery/w93EAHCLgoL1IRX2edgMuGed88a311e2EtIuPDz6oFjZ87ljA/trained_model.tar b'Downloaded 186 MB bytes in 0.269s (692 MB/s)\nExtracted 186 MB in 0.069s (2.7 GB/s)\n' Downloaded weights in 0.449491024017334 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A closeup neon portrait photo of a spooky monster in the style of <s0><s1>, green txt2img mode 0%| | 0/21 [00:00<?, ?it/s] 5%|▍ | 1/21 [00:00<00:04, 4.01it/s] 10%|▉ | 2/21 [00:00<00:04, 4.17it/s] 14%|█▍ | 3/21 [00:00<00:04, 4.21it/s] 19%|█▉ | 4/21 [00:00<00:04, 4.24it/s] 24%|██▍ | 5/21 [00:01<00:03, 4.24it/s] 29%|██▊ | 6/21 [00:01<00:03, 4.25it/s] 33%|███▎ | 7/21 [00:01<00:03, 4.25it/s] 38%|███▊ | 8/21 [00:01<00:03, 4.26it/s] 43%|████▎ | 9/21 [00:02<00:02, 4.26it/s] 48%|████▊ | 10/21 [00:02<00:02, 4.26it/s] 52%|█████▏ | 11/21 [00:02<00:02, 4.26it/s] 57%|█████▋ | 12/21 [00:02<00:02, 4.26it/s] 62%|██████▏ | 13/21 [00:03<00:01, 4.26it/s] 67%|██████▋ | 14/21 [00:03<00:01, 4.26it/s] 71%|███████▏ | 15/21 [00:03<00:01, 4.26it/s] 76%|███████▌ | 16/21 [00:03<00:01, 4.26it/s] 81%|████████ | 17/21 [00:04<00:00, 4.26it/s] 86%|████████▌ | 18/21 [00:04<00:00, 4.26it/s] 90%|█████████ | 19/21 [00:04<00:00, 4.26it/s] 95%|█████████▌| 20/21 [00:04<00:00, 4.26it/s] 100%|██████████| 21/21 [00:04<00:00, 4.26it/s] 100%|██████████| 21/21 [00:04<00:00, 4.25it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.06it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.26it/s] 100%|██████████| 3/3 [00:00<00:00, 5.34it/s] 100%|██████████| 3/3 [00:00<00:00, 5.29it/s]
Prediction
fofr/sdxl-black-light:0b682d57IDwbqigrdblto6ryz76aib22xnl4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1152
- height
- 768
- prompt
- A closeup neon portrait photo of a spooky skeleton in the style of TOK
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.9
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky skeleton in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", { input: { width: 1152, height: 768, prompt: "A closeup neon portrait photo of a spooky skeleton in the style of TOK", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.9, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", input={ "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky skeleton in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.9, "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-black-light 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": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", "input": { "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky skeleton in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-29T10:12:33.321274Z", "created_at": "2023-10-29T10:12:22.833504Z", "data_removed": false, "error": null, "id": "wbqigrdblto6ryz76aib22xnl4", "input": { "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky skeleton in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 44495\nEnsuring enough disk space...\nFree disk space: 1851849293824\nDownloading weights: https://pbxt.replicate.delivery/w93EAHCLgoL1IRX2edgMuGed88a311e2EtIuPDz6oFjZ87ljA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.432s (431 MB/s)\\nExtracted 186 MB in 0.063s (2.9 GB/s)\\n'\nDownloaded weights in 0.6079506874084473 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A closeup neon portrait photo of a spooky skeleton in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:06, 4.05it/s]\n 7%|▋ | 2/27 [00:00<00:05, 4.20it/s]\n 11%|█ | 3/27 [00:00<00:05, 4.25it/s]\n 15%|█▍ | 4/27 [00:00<00:05, 4.28it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 4.29it/s]\n 22%|██▏ | 6/27 [00:01<00:04, 4.30it/s]\n 26%|██▌ | 7/27 [00:01<00:04, 4.30it/s]\n 30%|██▉ | 8/27 [00:01<00:04, 4.30it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 4.31it/s]\n 37%|███▋ | 10/27 [00:02<00:03, 4.31it/s]\n 41%|████ | 11/27 [00:02<00:03, 4.31it/s]\n 44%|████▍ | 12/27 [00:02<00:03, 4.31it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 4.31it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 4.31it/s]\n 56%|█████▌ | 15/27 [00:03<00:02, 4.31it/s]\n 59%|█████▉ | 16/27 [00:03<00:02, 4.31it/s]\n 63%|██████▎ | 17/27 [00:03<00:02, 4.30it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 4.30it/s]\n 70%|███████ | 19/27 [00:04<00:01, 4.30it/s]\n 74%|███████▍ | 20/27 [00:04<00:01, 4.30it/s]\n 78%|███████▊ | 21/27 [00:04<00:01, 4.30it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 4.30it/s]\n 85%|████████▌ | 23/27 [00:05<00:00, 4.30it/s]\n 89%|████████▉ | 24/27 [00:05<00:00, 4.29it/s]\n 93%|█████████▎| 25/27 [00:05<00:00, 4.29it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 4.29it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.29it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.29it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.17it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.36it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.41it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.38it/s]", "metrics": { "predict_time": 10.101312, "total_time": 10.48777 }, "output": [ "https://pbxt.replicate.delivery/hbLNDuXFSPZLGRR5oUwQjpPXldpIuOycNnxVBVZjyfaIEfyRA/out-0.png" ], "started_at": "2023-10-29T10:12:23.219962Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wbqigrdblto6ryz76aib22xnl4", "cancel": "https://api.replicate.com/v1/predictions/wbqigrdblto6ryz76aib22xnl4/cancel" }, "version": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95" }
Generated inUsing seed: 44495 Ensuring enough disk space... Free disk space: 1851849293824 Downloading weights: https://pbxt.replicate.delivery/w93EAHCLgoL1IRX2edgMuGed88a311e2EtIuPDz6oFjZ87ljA/trained_model.tar b'Downloaded 186 MB bytes in 0.432s (431 MB/s)\nExtracted 186 MB in 0.063s (2.9 GB/s)\n' Downloaded weights in 0.6079506874084473 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A closeup neon portrait photo of a spooky skeleton in the style of <s0><s1> txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:06, 4.05it/s] 7%|▋ | 2/27 [00:00<00:05, 4.20it/s] 11%|█ | 3/27 [00:00<00:05, 4.25it/s] 15%|█▍ | 4/27 [00:00<00:05, 4.28it/s] 19%|█▊ | 5/27 [00:01<00:05, 4.29it/s] 22%|██▏ | 6/27 [00:01<00:04, 4.30it/s] 26%|██▌ | 7/27 [00:01<00:04, 4.30it/s] 30%|██▉ | 8/27 [00:01<00:04, 4.30it/s] 33%|███▎ | 9/27 [00:02<00:04, 4.31it/s] 37%|███▋ | 10/27 [00:02<00:03, 4.31it/s] 41%|████ | 11/27 [00:02<00:03, 4.31it/s] 44%|████▍ | 12/27 [00:02<00:03, 4.31it/s] 48%|████▊ | 13/27 [00:03<00:03, 4.31it/s] 52%|█████▏ | 14/27 [00:03<00:03, 4.31it/s] 56%|█████▌ | 15/27 [00:03<00:02, 4.31it/s] 59%|█████▉ | 16/27 [00:03<00:02, 4.31it/s] 63%|██████▎ | 17/27 [00:03<00:02, 4.30it/s] 67%|██████▋ | 18/27 [00:04<00:02, 4.30it/s] 70%|███████ | 19/27 [00:04<00:01, 4.30it/s] 74%|███████▍ | 20/27 [00:04<00:01, 4.30it/s] 78%|███████▊ | 21/27 [00:04<00:01, 4.30it/s] 81%|████████▏ | 22/27 [00:05<00:01, 4.30it/s] 85%|████████▌ | 23/27 [00:05<00:00, 4.30it/s] 89%|████████▉ | 24/27 [00:05<00:00, 4.29it/s] 93%|█████████▎| 25/27 [00:05<00:00, 4.29it/s] 96%|█████████▋| 26/27 [00:06<00:00, 4.29it/s] 100%|██████████| 27/27 [00:06<00:00, 4.29it/s] 100%|██████████| 27/27 [00:06<00:00, 4.29it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.17it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.36it/s] 100%|██████████| 3/3 [00:00<00:00, 5.41it/s] 100%|██████████| 3/3 [00:00<00:00, 5.38it/s]
Prediction
fofr/sdxl-black-light:0b682d57IDwqjx2mdb3gtek465ickddlpv34StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1152
- height
- 768
- prompt
- A closeup neon portrait photo of a spooky man in the style of TOK
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.9
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 30
{ "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky man in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", { input: { width: 1152, height: 768, prompt: "A closeup neon portrait photo of a spooky man in the style of TOK", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.9, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 30 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/sdxl-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-black-light:0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", input={ "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky man in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.9, "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/sdxl-black-light 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": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95", "input": { "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky man in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-29T10:14:31.379929Z", "created_at": "2023-10-29T10:14:17.371377Z", "data_removed": false, "error": null, "id": "wqjx2mdb3gtek465ickddlpv34", "input": { "width": 1152, "height": 768, "prompt": "A closeup neon portrait photo of a spooky man in the style of TOK", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.9, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 30 }, "logs": "Using seed: 13876\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A closeup neon portrait photo of a spooky man in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/27 [00:00<?, ?it/s]\n 4%|▎ | 1/27 [00:00<00:05, 4.35it/s]\n 7%|▋ | 2/27 [00:00<00:05, 4.34it/s]\n 11%|█ | 3/27 [00:00<00:05, 4.33it/s]\n 15%|█▍ | 4/27 [00:00<00:05, 4.33it/s]\n 19%|█▊ | 5/27 [00:01<00:05, 4.32it/s]\n 22%|██▏ | 6/27 [00:01<00:04, 4.32it/s]\n 26%|██▌ | 7/27 [00:01<00:04, 4.31it/s]\n 30%|██▉ | 8/27 [00:01<00:04, 4.31it/s]\n 33%|███▎ | 9/27 [00:02<00:04, 4.30it/s]\n 37%|███▋ | 10/27 [00:02<00:03, 4.30it/s]\n 41%|████ | 11/27 [00:02<00:03, 4.30it/s]\n 44%|████▍ | 12/27 [00:02<00:03, 4.30it/s]\n 48%|████▊ | 13/27 [00:03<00:03, 4.30it/s]\n 52%|█████▏ | 14/27 [00:03<00:03, 4.30it/s]\n 56%|█████▌ | 15/27 [00:03<00:02, 4.30it/s]\n 59%|█████▉ | 16/27 [00:03<00:02, 4.30it/s]\n 63%|██████▎ | 17/27 [00:03<00:02, 4.30it/s]\n 67%|██████▋ | 18/27 [00:04<00:02, 4.30it/s]\n 70%|███████ | 19/27 [00:04<00:01, 4.30it/s]\n 74%|███████▍ | 20/27 [00:04<00:01, 4.30it/s]\n 78%|███████▊ | 21/27 [00:04<00:01, 4.30it/s]\n 81%|████████▏ | 22/27 [00:05<00:01, 4.30it/s]\n 85%|████████▌ | 23/27 [00:05<00:00, 4.30it/s]\n 89%|████████▉ | 24/27 [00:05<00:00, 4.30it/s]\n 93%|█████████▎| 25/27 [00:05<00:00, 4.30it/s]\n 96%|█████████▋| 26/27 [00:06<00:00, 4.30it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.30it/s]\n100%|██████████| 27/27 [00:06<00:00, 4.30it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.54it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.50it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.50it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.50it/s]", "metrics": { "predict_time": 9.333786, "total_time": 14.008552 }, "output": [ "https://pbxt.replicate.delivery/lzVu4RgbFJpuJtaTnEUUU040sywrfeujzrdCQfI6djQMU8ljA/out-0.png" ], "started_at": "2023-10-29T10:14:22.046143Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wqjx2mdb3gtek465ickddlpv34", "cancel": "https://api.replicate.com/v1/predictions/wqjx2mdb3gtek465ickddlpv34/cancel" }, "version": "0b682d5744e86e988216141edd6a99be821941fd1a49a64786ad47fa48c33a95" }
Generated inUsing seed: 13876 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A closeup neon portrait photo of a spooky man in the style of <s0><s1> txt2img mode 0%| | 0/27 [00:00<?, ?it/s] 4%|▎ | 1/27 [00:00<00:05, 4.35it/s] 7%|▋ | 2/27 [00:00<00:05, 4.34it/s] 11%|█ | 3/27 [00:00<00:05, 4.33it/s] 15%|█▍ | 4/27 [00:00<00:05, 4.33it/s] 19%|█▊ | 5/27 [00:01<00:05, 4.32it/s] 22%|██▏ | 6/27 [00:01<00:04, 4.32it/s] 26%|██▌ | 7/27 [00:01<00:04, 4.31it/s] 30%|██▉ | 8/27 [00:01<00:04, 4.31it/s] 33%|███▎ | 9/27 [00:02<00:04, 4.30it/s] 37%|███▋ | 10/27 [00:02<00:03, 4.30it/s] 41%|████ | 11/27 [00:02<00:03, 4.30it/s] 44%|████▍ | 12/27 [00:02<00:03, 4.30it/s] 48%|████▊ | 13/27 [00:03<00:03, 4.30it/s] 52%|█████▏ | 14/27 [00:03<00:03, 4.30it/s] 56%|█████▌ | 15/27 [00:03<00:02, 4.30it/s] 59%|█████▉ | 16/27 [00:03<00:02, 4.30it/s] 63%|██████▎ | 17/27 [00:03<00:02, 4.30it/s] 67%|██████▋ | 18/27 [00:04<00:02, 4.30it/s] 70%|███████ | 19/27 [00:04<00:01, 4.30it/s] 74%|███████▍ | 20/27 [00:04<00:01, 4.30it/s] 78%|███████▊ | 21/27 [00:04<00:01, 4.30it/s] 81%|████████▏ | 22/27 [00:05<00:01, 4.30it/s] 85%|████████▌ | 23/27 [00:05<00:00, 4.30it/s] 89%|████████▉ | 24/27 [00:05<00:00, 4.30it/s] 93%|█████████▎| 25/27 [00:05<00:00, 4.30it/s] 96%|█████████▋| 26/27 [00:06<00:00, 4.30it/s] 100%|██████████| 27/27 [00:06<00:00, 4.30it/s] 100%|██████████| 27/27 [00:06<00:00, 4.30it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.54it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.50it/s] 100%|██████████| 3/3 [00:00<00:00, 5.50it/s] 100%|██████████| 3/3 [00:00<00:00, 5.50it/s]
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