fermatresearch
/
sdxl-controlnet-lora
'''Last update: Now supports img2img.''' SDXL Canny controlnet with LoRA support.
- Public
- 839.6K runs
-
L40S
- GitHub
Prediction
fermatresearch/sdxl-controlnet-lora:3bb13fe1IDruzsnmtb7idrwecy2ue5ig6gliStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- shot in the style of sksfer, a woman in alaska
- refine
- base_image_refiner
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar
- refine_steps
- 20
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in alaska", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", { input: { image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", prompt: "shot in the style of sksfer, a woman in alaska", refine: "base_image_refiner", scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", refine_steps: 20, guidance_scale: 7.5, apply_watermark: true, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 40 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", input={ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in alaska", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": True, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fermatresearch/sdxl-controlnet-lora 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": "3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in alaska", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-17T13:48:50.759475Z", "created_at": "2023-10-17T13:48:30.378425Z", "data_removed": false, "error": null, "id": "ruzsnmtb7idrwecy2ue5ig6gli", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in alaska", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }, "logs": "Using seed: 44186\nloading custom weights\nweights already in cache\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: shot in the style of <s0><s1>, a woman in alaska\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:14, 2.72it/s]\n 5%|▌ | 2/40 [00:00<00:13, 2.73it/s]\n 8%|▊ | 3/40 [00:01<00:13, 2.73it/s]\n 10%|█ | 4/40 [00:01<00:13, 2.73it/s]\n 12%|█▎ | 5/40 [00:01<00:12, 2.73it/s]\n 15%|█▌ | 6/40 [00:02<00:12, 2.73it/s]\n 18%|█▊ | 7/40 [00:02<00:12, 2.73it/s]\n 20%|██ | 8/40 [00:02<00:11, 2.73it/s]\n 22%|██▎ | 9/40 [00:03<00:11, 2.73it/s]\n 25%|██▌ | 10/40 [00:03<00:11, 2.72it/s]\n 28%|██▊ | 11/40 [00:04<00:10, 2.72it/s]\n 30%|███ | 12/40 [00:04<00:10, 2.72it/s]\n 32%|███▎ | 13/40 [00:04<00:09, 2.72it/s]\n 35%|███▌ | 14/40 [00:05<00:09, 2.72it/s]\n 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s]\n 40%|████ | 16/40 [00:05<00:08, 2.72it/s]\n 42%|████▎ | 17/40 [00:06<00:08, 2.71it/s]\n 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s]\n 48%|████▊ | 19/40 [00:06<00:07, 2.71it/s]\n 50%|█████ | 20/40 [00:07<00:07, 2.71it/s]\n 52%|█████▎ | 21/40 [00:07<00:06, 2.71it/s]\n 55%|█████▌ | 22/40 [00:08<00:06, 2.72it/s]\n 57%|█████▊ | 23/40 [00:08<00:06, 2.71it/s]\n 60%|██████ | 24/40 [00:08<00:05, 2.71it/s]\n 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s]\n 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s]\n 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s]\n 70%|███████ | 28/40 [00:10<00:04, 2.72it/s]\n 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s]\n 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s]\n 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s]\n 80%|████████ | 32/40 [00:11<00:02, 2.71it/s]\n 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s]\n 85%|████████▌ | 34/40 [00:12<00:02, 2.71it/s]\n 88%|████████▊ | 35/40 [00:12<00:01, 2.71it/s]\n 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s]\n 92%|█████████▎| 37/40 [00:13<00:01, 2.71it/s]\n 95%|█████████▌| 38/40 [00:13<00:00, 2.71it/s]\n 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.72it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.32it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.30it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.28it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.29it/s]", "metrics": { "predict_time": 20.411215, "total_time": 20.38105 }, "output": [ "https://replicate.delivery/pbxt/oDtYIK2lDoaKMtdE4E5ozQSa61BU3gc4aRvGF3xmFpdwCxbE/out-0.png" ], "started_at": "2023-10-17T13:48:30.348260Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ruzsnmtb7idrwecy2ue5ig6gli", "cancel": "https://api.replicate.com/v1/predictions/ruzsnmtb7idrwecy2ue5ig6gli/cancel" }, "version": "a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4" }Generated inUsing seed: 44186 loading custom weights weights already in cache Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: shot in the style of <s0><s1>, a woman in alaska Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:14, 2.72it/s] 5%|▌ | 2/40 [00:00<00:13, 2.73it/s] 8%|▊ | 3/40 [00:01<00:13, 2.73it/s] 10%|█ | 4/40 [00:01<00:13, 2.73it/s] 12%|█▎ | 5/40 [00:01<00:12, 2.73it/s] 15%|█▌ | 6/40 [00:02<00:12, 2.73it/s] 18%|█▊ | 7/40 [00:02<00:12, 2.73it/s] 20%|██ | 8/40 [00:02<00:11, 2.73it/s] 22%|██▎ | 9/40 [00:03<00:11, 2.73it/s] 25%|██▌ | 10/40 [00:03<00:11, 2.72it/s] 28%|██▊ | 11/40 [00:04<00:10, 2.72it/s] 30%|███ | 12/40 [00:04<00:10, 2.72it/s] 32%|███▎ | 13/40 [00:04<00:09, 2.72it/s] 35%|███▌ | 14/40 [00:05<00:09, 2.72it/s] 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s] 40%|████ | 16/40 [00:05<00:08, 2.72it/s] 42%|████▎ | 17/40 [00:06<00:08, 2.71it/s] 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s] 48%|████▊ | 19/40 [00:06<00:07, 2.71it/s] 50%|█████ | 20/40 [00:07<00:07, 2.71it/s] 52%|█████▎ | 21/40 [00:07<00:06, 2.71it/s] 55%|█████▌ | 22/40 [00:08<00:06, 2.72it/s] 57%|█████▊ | 23/40 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[00:00<00:00, 4.28it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.28it/s] 100%|██████████| 6/6 [00:01<00:00, 4.28it/s] 100%|██████████| 6/6 [00:01<00:00, 4.29it/s]Prediction
fermatresearch/sdxl-controlnet-lora:3bb13fe1IDh7masntbsemlwoq32zeozgh4cmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- shot in the style of sksfer, a woman in paris, tower eiffel in te background
- refine
- base_image_refiner
- scheduler
- K_EULER
- lora_scale
- 0.97
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar
- refine_steps
- 20
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in paris, tower eiffel in te background", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.97, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", { input: { image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", prompt: "shot in the style of sksfer, a woman in paris, tower eiffel in te background", refine: "base_image_refiner", scheduler: "K_EULER", lora_scale: 0.97, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", refine_steps: 20, guidance_scale: 7.5, apply_watermark: true, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 40 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", input={ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in paris, tower eiffel in te background", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.97, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": True, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fermatresearch/sdxl-controlnet-lora 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": "3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in paris, tower eiffel in te background", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.97, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-17T14:34:56.602199Z", "created_at": "2023-10-17T14:34:36.986661Z", "data_removed": false, "error": null, "id": "h7masntbsemlwoq32zeozgh4cm", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in paris, tower eiffel in te background", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.97, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }, "logs": "Using seed: 50731\nloading custom weights\nweights already in cache\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: shot in the style of <s0><s1>, a woman in paris, tower eiffel in te background\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:14, 2.73it/s]\n 5%|▌ | 2/40 [00:00<00:13, 2.72it/s]\n 8%|▊ | 3/40 [00:01<00:13, 2.71it/s]\n 10%|█ | 4/40 [00:01<00:13, 2.71it/s]\n 12%|█▎ | 5/40 [00:01<00:12, 2.71it/s]\n 15%|█▌ | 6/40 [00:02<00:12, 2.70it/s]\n 18%|█▊ | 7/40 [00:02<00:12, 2.70it/s]\n 20%|██ | 8/40 [00:02<00:11, 2.70it/s]\n 22%|██▎ | 9/40 [00:03<00:11, 2.70it/s]\n 25%|██▌ | 10/40 [00:03<00:11, 2.70it/s]\n 28%|██▊ | 11/40 [00:04<00:10, 2.71it/s]\n 30%|███ | 12/40 [00:04<00:10, 2.71it/s]\n 32%|███▎ | 13/40 [00:04<00:09, 2.71it/s]\n 35%|███▌ | 14/40 [00:05<00:09, 2.71it/s]\n 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s]\n 40%|████ | 16/40 [00:05<00:08, 2.71it/s]\n 42%|████▎ | 17/40 [00:06<00:08, 2.71it/s]\n 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s]\n 48%|████▊ | 19/40 [00:07<00:07, 2.71it/s]\n 50%|█████ | 20/40 [00:07<00:07, 2.71it/s]\n 52%|█████▎ | 21/40 [00:07<00:07, 2.71it/s]\n 55%|█████▌ | 22/40 [00:08<00:06, 2.71it/s]\n 57%|█████▊ | 23/40 [00:08<00:06, 2.71it/s]\n 60%|██████ | 24/40 [00:08<00:05, 2.71it/s]\n 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s]\n 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s]\n 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s]\n 70%|███████ | 28/40 [00:10<00:04, 2.71it/s]\n 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s]\n 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s]\n 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s]\n 80%|████████ | 32/40 [00:11<00:02, 2.71it/s]\n 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s]\n 85%|████████▌ | 34/40 [00:12<00:02, 2.71it/s]\n 88%|████████▊ | 35/40 [00:12<00:01, 2.71it/s]\n 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s]\n 92%|█████████▎| 37/40 [00:13<00:01, 2.71it/s]\n 95%|█████████▌| 38/40 [00:14<00:00, 2.71it/s]\n 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.32it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.29it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.27it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.27it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]", "metrics": { "predict_time": 19.655256, "total_time": 19.615538 }, "output": [ "https://replicate.delivery/pbxt/HniOoHgF6FZeDat536rBTnSIVdEK9EW8j1nvoVSzDTRIbi3IA/out-0.png" ], "started_at": "2023-10-17T14:34:36.946943Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/h7masntbsemlwoq32zeozgh4cm", "cancel": "https://api.replicate.com/v1/predictions/h7masntbsemlwoq32zeozgh4cm/cancel" }, "version": "a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4" }Generated inUsing seed: 50731 loading custom weights weights already in cache Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: shot in the style of <s0><s1>, a woman in paris, tower eiffel in te background Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:14, 2.73it/s] 5%|▌ | 2/40 [00:00<00:13, 2.72it/s] 8%|▊ | 3/40 [00:01<00:13, 2.71it/s] 10%|█ | 4/40 [00:01<00:13, 2.71it/s] 12%|█▎ | 5/40 [00:01<00:12, 2.71it/s] 15%|█▌ | 6/40 [00:02<00:12, 2.70it/s] 18%|█▊ | 7/40 [00:02<00:12, 2.70it/s] 20%|██ | 8/40 [00:02<00:11, 2.70it/s] 22%|██▎ | 9/40 [00:03<00:11, 2.70it/s] 25%|██▌ | 10/40 [00:03<00:11, 2.70it/s] 28%|██▊ | 11/40 [00:04<00:10, 2.71it/s] 30%|███ | 12/40 [00:04<00:10, 2.71it/s] 32%|███▎ | 13/40 [00:04<00:09, 2.71it/s] 35%|███▌ | 14/40 [00:05<00:09, 2.71it/s] 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s] 40%|████ | 16/40 [00:05<00:08, 2.71it/s] 42%|████▎ | 17/40 [00:06<00:08, 2.71it/s] 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s] 48%|████▊ | 19/40 [00:07<00:07, 2.71it/s] 50%|█████ | 20/40 [00:07<00:07, 2.71it/s] 52%|█████▎ | 21/40 [00:07<00:07, 2.71it/s] 55%|█████▌ | 22/40 [00:08<00:06, 2.71it/s] 57%|█████▊ | 23/40 [00:08<00:06, 2.71it/s] 60%|██████ | 24/40 [00:08<00:05, 2.71it/s] 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s] 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s] 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s] 70%|███████ | 28/40 [00:10<00:04, 2.71it/s] 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s] 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s] 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s] 80%|████████ | 32/40 [00:11<00:02, 2.71it/s] 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s] 85%|████████▌ | 34/40 [00:12<00:02, 2.71it/s] 88%|████████▊ | 35/40 [00:12<00:01, 2.71it/s] 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s] 92%|█████████▎| 37/40 [00:13<00:01, 2.71it/s] 95%|█████████▌| 38/40 [00:14<00:00, 2.71it/s] 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s] 100%|██████████| 40/40 [00:14<00:00, 2.71it/s] 100%|██████████| 40/40 [00:14<00:00, 2.71it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.32it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s] 50%|█████ | 3/6 [00:00<00:00, 4.29it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.27it/s] 100%|██████████| 6/6 [00:01<00:00, 4.27it/s] 100%|██████████| 6/6 [00:01<00:00, 4.28it/s]Prediction
fermatresearch/sdxl-controlnet-lora:3bb13fe1IDxkeyantbqyugkwwppiw6fhdmqaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- shot in the style of sksfer, a woman
- refine
- base_image_refiner
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar
- refine_steps
- 20
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", { input: { image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", prompt: "shot in the style of sksfer, a woman", refine: "base_image_refiner", scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar", refine_steps: 20, guidance_scale: 7.5, apply_watermark: true, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 40 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", input={ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": True, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fermatresearch/sdxl-controlnet-lora 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": "3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-20T09:02:11.421761Z", "created_at": "2023-10-20T08:58:06.318006Z", "data_removed": false, "error": null, "id": "xkeyantbqyugkwwppiw6fhdmqa", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }, "logs": "Using seed: 51567\nloading custom weights\nweights not in cache\nEnsuring enough disk space...\nFree disk space: 1572713168896\nDownloading weights: https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar\ndownloading https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar\nb'Downloaded 186 MB bytes in 1.063s (175 MB/s)\\nExtracted 186 MB in 0.077s (2.4 GB/s)\\n'\nDownloaded weights in 1.4501347541809082 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: shot in the style of <s0><s1>, a woman\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1468: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights`\ndeprecate(\n 2%|▎ | 1/40 [00:00<00:36, 1.07it/s]\n 5%|▌ | 2/40 [00:01<00:22, 1.67it/s]\n 8%|▊ | 3/40 [00:01<00:18, 2.03it/s]\n 10%|█ | 4/40 [00:02<00:15, 2.26it/s]\n 12%|█▎ | 5/40 [00:02<00:14, 2.41it/s]\n 15%|█▌ | 6/40 [00:02<00:13, 2.51it/s]\n 18%|█▊ | 7/40 [00:03<00:12, 2.58it/s]\n 20%|██ | 8/40 [00:03<00:12, 2.63it/s]\n 22%|██▎ | 9/40 [00:03<00:11, 2.66it/s]\n 25%|██▌ | 10/40 [00:04<00:11, 2.68it/s]\n 28%|██▊ | 11/40 [00:04<00:10, 2.69it/s]\n 30%|███ | 12/40 [00:04<00:10, 2.70it/s]\n 32%|███▎ | 13/40 [00:05<00:09, 2.71it/s]\n 35%|███▌ | 14/40 [00:05<00:09, 2.71it/s]\n 38%|███▊ | 15/40 [00:06<00:09, 2.71it/s]\n 40%|████ | 16/40 [00:06<00:08, 2.72it/s]\n 42%|████▎ | 17/40 [00:06<00:08, 2.72it/s]\n 45%|████▌ | 18/40 [00:07<00:08, 2.72it/s]\n 48%|████▊ | 19/40 [00:07<00:07, 2.72it/s]\n 50%|█████ | 20/40 [00:07<00:07, 2.72it/s]\n 52%|█████▎ | 21/40 [00:08<00:06, 2.72it/s]\n 55%|█████▌ | 22/40 [00:08<00:06, 2.72it/s]\n 57%|█████▊ | 23/40 [00:09<00:06, 2.72it/s]\n 60%|██████ | 24/40 [00:09<00:05, 2.72it/s]\n 62%|██████▎ | 25/40 [00:09<00:05, 2.72it/s]\n 65%|██████▌ | 26/40 [00:10<00:05, 2.71it/s]\n 68%|██████▊ | 27/40 [00:10<00:04, 2.71it/s]\n 70%|███████ | 28/40 [00:10<00:04, 2.71it/s]\n 72%|███████▎ | 29/40 [00:11<00:04, 2.71it/s]\n 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s]\n 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s]\n 80%|████████ | 32/40 [00:12<00:02, 2.71it/s]\n 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s]\n 85%|████████▌ | 34/40 [00:13<00:02, 2.71it/s]\n 88%|████████▊ | 35/40 [00:13<00:01, 2.71it/s]\n 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s]\n 92%|█████████▎| 37/40 [00:14<00:01, 2.71it/s]\n 95%|█████████▌| 38/40 [00:14<00:00, 2.71it/s]\n 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:15<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:15<00:00, 2.62it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.15it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.22it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.24it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.26it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.26it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.27it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.25it/s]", "metrics": { "predict_time": 22.523399, "total_time": 245.103755 }, "output": [ "https://replicate.delivery/pbxt/PT6jEHZpzlbiCdfOtAlcHP4SojSosoPX4p81c0vWheQSQffGB/out-0.png" ], "started_at": "2023-10-20T09:01:48.898362Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xkeyantbqyugkwwppiw6fhdmqa", "cancel": "https://api.replicate.com/v1/predictions/xkeyantbqyugkwwppiw6fhdmqa/cancel" }, "version": "a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4" }Generated inUsing seed: 51567 loading custom weights weights not in cache Ensuring enough disk space... Free disk space: 1572713168896 Downloading weights: https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar downloading https://pbxt.replicate.delivery/RPXHgYYxlbIvOFJ67PFceIrRKbGWTC5kRS1zTUibWvuLt53IA/trained_model.tar b'Downloaded 186 MB bytes in 1.063s (175 MB/s)\nExtracted 186 MB in 0.077s (2.4 GB/s)\n' Downloaded weights in 1.4501347541809082 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: shot in the style of <s0><s1>, a woman Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 txt2img mode 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1468: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights` deprecate( 2%|▎ | 1/40 [00:00<00:36, 1.07it/s] 5%|▌ | 2/40 [00:01<00:22, 1.67it/s] 8%|▊ | 3/40 [00:01<00:18, 2.03it/s] 10%|█ | 4/40 [00:02<00:15, 2.26it/s] 12%|█▎ | 5/40 [00:02<00:14, 2.41it/s] 15%|█▌ | 6/40 [00:02<00:13, 2.51it/s] 18%|█▊ | 7/40 [00:03<00:12, 2.58it/s] 20%|██ | 8/40 [00:03<00:12, 2.63it/s] 22%|██▎ | 9/40 [00:03<00:11, 2.66it/s] 25%|██▌ | 10/40 [00:04<00:11, 2.68it/s] 28%|██▊ | 11/40 [00:04<00:10, 2.69it/s] 30%|███ | 12/40 [00:04<00:10, 2.70it/s] 32%|███▎ | 13/40 [00:05<00:09, 2.71it/s] 35%|███▌ | 14/40 [00:05<00:09, 2.71it/s] 38%|███▊ | 15/40 [00:06<00:09, 2.71it/s] 40%|████ | 16/40 [00:06<00:08, 2.72it/s] 42%|████▎ | 17/40 [00:06<00:08, 2.72it/s] 45%|████▌ | 18/40 [00:07<00:08, 2.72it/s] 48%|████▊ | 19/40 [00:07<00:07, 2.72it/s] 50%|█████ | 20/40 [00:07<00:07, 2.72it/s] 52%|█████▎ | 21/40 [00:08<00:06, 2.72it/s] 55%|█████▌ | 22/40 [00:08<00:06, 2.72it/s] 57%|█████▊ | 23/40 [00:09<00:06, 2.72it/s] 60%|██████ | 24/40 [00:09<00:05, 2.72it/s] 62%|██████▎ | 25/40 [00:09<00:05, 2.72it/s] 65%|██████▌ | 26/40 [00:10<00:05, 2.71it/s] 68%|██████▊ | 27/40 [00:10<00:04, 2.71it/s] 70%|███████ | 28/40 [00:10<00:04, 2.71it/s] 72%|███████▎ | 29/40 [00:11<00:04, 2.71it/s] 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s] 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s] 80%|████████ | 32/40 [00:12<00:02, 2.71it/s] 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s] 85%|████████▌ | 34/40 [00:13<00:02, 2.71it/s] 88%|████████▊ | 35/40 [00:13<00:01, 2.71it/s] 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s] 92%|█████████▎| 37/40 [00:14<00:01, 2.71it/s] 95%|█████████▌| 38/40 [00:14<00:00, 2.71it/s] 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s] 100%|██████████| 40/40 [00:15<00:00, 2.71it/s] 100%|██████████| 40/40 [00:15<00:00, 2.62it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.15it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.22it/s] 50%|█████ | 3/6 [00:00<00:00, 4.24it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.26it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.26it/s] 100%|██████████| 6/6 [00:01<00:00, 4.27it/s] 100%|██████████| 6/6 [00:01<00:00, 4.25it/s]Prediction
fermatresearch/sdxl-controlnet-lora:3bb13fe1IDnfobo4lbzipkb2d5y4ufmk5geaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- shot in the style of sksfer, a woman
- refine
- base_image_refiner
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar
- refine_steps
- 20
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", { input: { image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", prompt: "shot in the style of sksfer, a woman", refine: "base_image_refiner", scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar", refine_steps: 20, guidance_scale: 7.5, apply_watermark: true, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 40 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", input={ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": True, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fermatresearch/sdxl-controlnet-lora 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": "3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-20T09:17:12.161874Z", "created_at": "2023-10-20T09:16:49.580032Z", "data_removed": false, "error": null, "id": "nfobo4lbzipkb2d5y4ufmk5gea", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }, "logs": "Using seed: 57790\nloading custom weights\nweights not in cache\nEnsuring enough disk space...\nFree disk space: 1462651285504\nDownloading weights: https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar\ndownloading https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar\nb'Downloaded 186 MB bytes in 1.558s (119 MB/s)\\nExtracted 186 MB in 0.060s (3.1 GB/s)\\n'\nDownloaded weights in 1.97196626663208 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: shot in the style of <s0><s1>, a woman\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1468: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights`\ndeprecate(\n 2%|▎ | 1/40 [00:00<00:14, 2.72it/s]\n 5%|▌ | 2/40 [00:00<00:14, 2.71it/s]\n 8%|▊ | 3/40 [00:01<00:13, 2.71it/s]\n 10%|█ | 4/40 [00:01<00:13, 2.71it/s]\n 12%|█▎ | 5/40 [00:01<00:12, 2.70it/s]\n 15%|█▌ | 6/40 [00:02<00:12, 2.70it/s]\n 18%|█▊ | 7/40 [00:02<00:12, 2.70it/s]\n 20%|██ | 8/40 [00:02<00:11, 2.71it/s]\n 22%|██▎ | 9/40 [00:03<00:11, 2.71it/s]\n 25%|██▌ | 10/40 [00:03<00:11, 2.71it/s]\n 28%|██▊ | 11/40 [00:04<00:10, 2.72it/s]\n 30%|███ | 12/40 [00:04<00:10, 2.71it/s]\n 32%|███▎ | 13/40 [00:04<00:09, 2.72it/s]\n 35%|███▌ | 14/40 [00:05<00:09, 2.72it/s]\n 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s]\n 40%|████ | 16/40 [00:05<00:08, 2.72it/s]\n 42%|████▎ | 17/40 [00:06<00:08, 2.72it/s]\n 45%|████▌ | 18/40 [00:06<00:08, 2.72it/s]\n 48%|████▊ | 19/40 [00:07<00:07, 2.71it/s]\n 50%|█████ | 20/40 [00:07<00:07, 2.71it/s]\n 52%|█████▎ | 21/40 [00:07<00:06, 2.71it/s]\n 55%|█████▌ | 22/40 [00:08<00:06, 2.71it/s]\n 57%|█████▊ | 23/40 [00:08<00:06, 2.71it/s]\n 60%|██████ | 24/40 [00:08<00:05, 2.71it/s]\n 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s]\n 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s]\n 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s]\n 70%|███████ | 28/40 [00:10<00:04, 2.71it/s]\n 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s]\n 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s]\n 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s]\n 80%|████████ | 32/40 [00:11<00:02, 2.71it/s]\n 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s]\n 85%|████████▌ | 34/40 [00:12<00:02, 2.71it/s]\n 88%|████████▊ | 35/40 [00:12<00:01, 2.71it/s]\n 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s]\n 92%|█████████▎| 37/40 [00:13<00:01, 2.71it/s]\n 95%|█████████▌| 38/40 [00:14<00:00, 2.71it/s]\n 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.33it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.29it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.27it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.27it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]", "metrics": { "predict_time": 22.601503, "total_time": 22.581842 }, "output": [ "https://replicate.delivery/pbxt/xqJDSe1pbrVPRatKXkTu3CbJj2TQutaeQPxA5UeKlxOv8efNC/out-0.png" ], "started_at": "2023-10-20T09:16:49.560371Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nfobo4lbzipkb2d5y4ufmk5gea", "cancel": "https://api.replicate.com/v1/predictions/nfobo4lbzipkb2d5y4ufmk5gea/cancel" }, "version": "a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4" }Generated inUsing seed: 57790 loading custom weights weights not in cache Ensuring enough disk space... Free disk space: 1462651285504 Downloading weights: https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar downloading https://pbxt.replicate.delivery/3wwmvGfvB4weYkJMAR2JJNMXu7RPtd8Hc5ONP3IP23fioXfGB/trained_model.tar b'Downloaded 186 MB bytes in 1.558s (119 MB/s)\nExtracted 186 MB in 0.060s (3.1 GB/s)\n' Downloaded weights in 1.97196626663208 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: shot in the style of <s0><s1>, a woman Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 txt2img mode 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1468: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights` deprecate( 2%|▎ | 1/40 [00:00<00:14, 2.72it/s] 5%|▌ | 2/40 [00:00<00:14, 2.71it/s] 8%|▊ | 3/40 [00:01<00:13, 2.71it/s] 10%|█ | 4/40 [00:01<00:13, 2.71it/s] 12%|█▎ | 5/40 [00:01<00:12, 2.70it/s] 15%|█▌ | 6/40 [00:02<00:12, 2.70it/s] 18%|█▊ | 7/40 [00:02<00:12, 2.70it/s] 20%|██ | 8/40 [00:02<00:11, 2.71it/s] 22%|██▎ | 9/40 [00:03<00:11, 2.71it/s] 25%|██▌ | 10/40 [00:03<00:11, 2.71it/s] 28%|██▊ | 11/40 [00:04<00:10, 2.72it/s] 30%|███ | 12/40 [00:04<00:10, 2.71it/s] 32%|███▎ | 13/40 [00:04<00:09, 2.72it/s] 35%|███▌ | 14/40 [00:05<00:09, 2.72it/s] 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s] 40%|████ | 16/40 [00:05<00:08, 2.72it/s] 42%|████▎ | 17/40 [00:06<00:08, 2.72it/s] 45%|████▌ | 18/40 [00:06<00:08, 2.72it/s] 48%|████▊ | 19/40 [00:07<00:07, 2.71it/s] 50%|█████ | 20/40 [00:07<00:07, 2.71it/s] 52%|█████▎ | 21/40 [00:07<00:06, 2.71it/s] 55%|█████▌ | 22/40 [00:08<00:06, 2.71it/s] 57%|█████▊ | 23/40 [00:08<00:06, 2.71it/s] 60%|██████ | 24/40 [00:08<00:05, 2.71it/s] 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s] 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s] 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s] 70%|███████ | 28/40 [00:10<00:04, 2.71it/s] 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s] 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s] 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s] 80%|████████ | 32/40 [00:11<00:02, 2.71it/s] 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s] 85%|████████▌ | 34/40 [00:12<00:02, 2.71it/s] 88%|████████▊ | 35/40 [00:12<00:01, 2.71it/s] 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s] 92%|█████████▎| 37/40 [00:13<00:01, 2.71it/s] 95%|█████████▌| 38/40 [00:14<00:00, 2.71it/s] 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s] 100%|██████████| 40/40 [00:14<00:00, 2.71it/s] 100%|██████████| 40/40 [00:14<00:00, 2.71it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.33it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s] 50%|█████ | 3/6 [00:00<00:00, 4.29it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.27it/s] 100%|██████████| 6/6 [00:01<00:00, 4.27it/s] 100%|██████████| 6/6 [00:01<00:00, 4.28it/s]Prediction
fermatresearch/sdxl-controlnet-lora:3bb13fe1IDol5yxldbqdt6rjq54t6vlbz5xyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- shot in the style of sksfer, a woman
- refine
- base_image_refiner
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar
- refine_steps
- 20
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", { input: { image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", prompt: "shot in the style of sksfer, a woman", refine: "base_image_refiner", scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", refine_steps: 20, guidance_scale: 7.5, apply_watermark: true, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 40 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", input={ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": True, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fermatresearch/sdxl-controlnet-lora 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": "3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-20T09:18:02.032142Z", "created_at": "2023-10-20T09:17:39.923283Z", "data_removed": false, "error": null, "id": "ol5yxldbqdt6rjq54t6vlbz5xy", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }, "logs": "Using seed: 15875\nloading custom weights\nweights not in cache\nEnsuring enough disk space...\nFree disk space: 1462463770624\nDownloading weights: https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar\ndownloading https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar\nb'Downloaded 186 MB bytes in 1.252s (149 MB/s)\\nExtracted 186 MB in 0.068s (2.7 GB/s)\\n'\nDownloaded weights in 1.6658592224121094 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: shot in the style of <s0><s1>, a woman\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1468: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights`\ndeprecate(\n 2%|▎ | 1/40 [00:00<00:14, 2.72it/s]\n 5%|▌ | 2/40 [00:00<00:13, 2.71it/s]\n 8%|▊ | 3/40 [00:01<00:13, 2.71it/s]\n 10%|█ | 4/40 [00:01<00:13, 2.71it/s]\n 12%|█▎ | 5/40 [00:01<00:12, 2.70it/s]\n 15%|█▌ | 6/40 [00:02<00:12, 2.70it/s]\n 18%|█▊ | 7/40 [00:02<00:12, 2.70it/s]\n 20%|██ | 8/40 [00:02<00:11, 2.71it/s]\n 22%|██▎ | 9/40 [00:03<00:11, 2.71it/s]\n 25%|██▌ | 10/40 [00:03<00:11, 2.71it/s]\n 28%|██▊ | 11/40 [00:04<00:10, 2.71it/s]\n 30%|███ | 12/40 [00:04<00:10, 2.71it/s]\n 32%|███▎ | 13/40 [00:04<00:09, 2.71it/s]\n 35%|███▌ | 14/40 [00:05<00:09, 2.71it/s]\n 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s]\n 40%|████ | 16/40 [00:05<00:08, 2.71it/s]\n 42%|████▎ | 17/40 [00:06<00:08, 2.72it/s]\n 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s]\n 48%|████▊ | 19/40 [00:07<00:07, 2.71it/s]\n 50%|█████ | 20/40 [00:07<00:07, 2.70it/s]\n 52%|█████▎ | 21/40 [00:07<00:07, 2.69it/s]\n 55%|█████▌ | 22/40 [00:08<00:06, 2.70it/s]\n 57%|█████▊ | 23/40 [00:08<00:06, 2.70it/s]\n 60%|██████ | 24/40 [00:08<00:05, 2.71it/s]\n 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s]\n 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s]\n 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s]\n 70%|███████ | 28/40 [00:10<00:04, 2.71it/s]\n 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s]\n 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s]\n 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s]\n 80%|████████ | 32/40 [00:11<00:02, 2.71it/s]\n 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s]\n 85%|████████▌ | 34/40 [00:12<00:02, 2.71it/s]\n 88%|████████▊ | 35/40 [00:12<00:01, 2.71it/s]\n 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s]\n 92%|█████████▎| 37/40 [00:13<00:01, 2.71it/s]\n 95%|█████████▌| 38/40 [00:14<00:00, 2.71it/s]\n 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.32it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.29it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.27it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.28it/s]", "metrics": { "predict_time": 22.128027, "total_time": 22.108859 }, "output": [ "https://replicate.delivery/pbxt/TgqCZMNv1RJAGNzSLZrFUbbeAyADrvs2dxhIaKStmhTkvfvRA/out-0.png" ], "started_at": "2023-10-20T09:17:39.904115Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ol5yxldbqdt6rjq54t6vlbz5xy", "cancel": "https://api.replicate.com/v1/predictions/ol5yxldbqdt6rjq54t6vlbz5xy/cancel" }, "version": "a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4" }Generated inUsing seed: 15875 loading custom weights weights not in cache Ensuring enough disk space... Free disk space: 1462463770624 Downloading weights: https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar downloading https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar b'Downloaded 186 MB bytes in 1.252s (149 MB/s)\nExtracted 186 MB in 0.068s (2.7 GB/s)\n' Downloaded weights in 1.6658592224121094 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: shot in the style of <s0><s1>, a woman Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 txt2img mode 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/diffusers/models/attention_processor.py:1468: FutureWarning: `LoRAAttnProcessor2_0` is deprecated and will be removed in version 0.26.0. Make sure use AttnProcessor2_0 instead by settingLoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using `LoraLoaderMixin.load_lora_weights` deprecate( 2%|▎ | 1/40 [00:00<00:14, 2.72it/s] 5%|▌ | 2/40 [00:00<00:13, 2.71it/s] 8%|▊ | 3/40 [00:01<00:13, 2.71it/s] 10%|█ | 4/40 [00:01<00:13, 2.71it/s] 12%|█▎ | 5/40 [00:01<00:12, 2.70it/s] 15%|█▌ | 6/40 [00:02<00:12, 2.70it/s] 18%|█▊ | 7/40 [00:02<00:12, 2.70it/s] 20%|██ | 8/40 [00:02<00:11, 2.71it/s] 22%|██▎ | 9/40 [00:03<00:11, 2.71it/s] 25%|██▌ | 10/40 [00:03<00:11, 2.71it/s] 28%|██▊ | 11/40 [00:04<00:10, 2.71it/s] 30%|███ | 12/40 [00:04<00:10, 2.71it/s] 32%|███▎ | 13/40 [00:04<00:09, 2.71it/s] 35%|███▌ | 14/40 [00:05<00:09, 2.71it/s] 38%|███▊ | 15/40 [00:05<00:09, 2.72it/s] 40%|████ | 16/40 [00:05<00:08, 2.71it/s] 42%|████▎ | 17/40 [00:06<00:08, 2.72it/s] 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s] 48%|████▊ | 19/40 [00:07<00:07, 2.71it/s] 50%|█████ | 20/40 [00:07<00:07, 2.70it/s] 52%|█████▎ | 21/40 [00:07<00:07, 2.69it/s] 55%|█████▌ | 22/40 [00:08<00:06, 2.70it/s] 57%|█████▊ | 23/40 [00:08<00:06, 2.70it/s] 60%|██████ | 24/40 [00:08<00:05, 2.71it/s] 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s] 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s] 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s] 70%|███████ | 28/40 [00:10<00:04, 2.71it/s] 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s] 75%|███████▌ | 30/40 [00:11<00:03, 2.71it/s] 78%|███████▊ | 31/40 [00:11<00:03, 2.71it/s] 80%|████████ | 32/40 [00:11<00:02, 2.71it/s] 82%|████████▎ | 33/40 [00:12<00:02, 2.71it/s] 85%|████████▌ | 34/40 [00:12<00:02, 2.71it/s] 88%|████████▊ | 35/40 [00:12<00:01, 2.71it/s] 90%|█████████ | 36/40 [00:13<00:01, 2.71it/s] 92%|█████████▎| 37/40 [00:13<00:01, 2.71it/s] 95%|█████████▌| 38/40 [00:14<00:00, 2.71it/s] 98%|█████████▊| 39/40 [00:14<00:00, 2.71it/s] 100%|██████████| 40/40 [00:14<00:00, 2.71it/s] 100%|██████████| 40/40 [00:14<00:00, 2.71it/s] 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [00:00<00:01, 4.32it/s] 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s] 50%|█████ | 3/6 [00:00<00:00, 4.29it/s] 67%|██████▋ | 4/6 [00:00<00:00, 4.28it/s] 83%|████████▎ | 5/6 [00:01<00:00, 4.27it/s] 100%|██████████| 6/6 [00:01<00:00, 4.28it/s] 100%|██████████| 6/6 [00:01<00:00, 4.28it/s]Prediction
fermatresearch/sdxl-controlnet-lora:3bb13fe1IDlzuvxgtbxqumopcfiby2pvdqfmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- null
- image
- prompt
- shot in the style of sksfer, a woman, blue sky
- refine
- base_image_refiner
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar
- refine_steps
- 20
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 40
{ "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman, blue sky", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", { input: { image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", prompt: "shot in the style of sksfer, a woman, blue sky", refine: "base_image_refiner", scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar", refine_steps: 20, guidance_scale: 7.5, apply_watermark: true, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 40 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-controlnet-lora:3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", input={ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman, blue sky", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": True, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fermatresearch/sdxl-controlnet-lora 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": "3bb13fe1c33c35987b33792b01b71ed6529d03f165d1c2416375859f09ca9fef", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman, blue sky", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-10-20T09:21:42.203146Z", "created_at": "2023-10-20T09:21:21.779714Z", "data_removed": false, "error": null, "id": "lzuvxgtbxqumopcfiby2pvdqfm", "input": { "seed": null, "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman, blue sky", "refine": "base_image_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/QmdRkthSZmqTEBMgVi17OqpdKkafyLTS6TGmzTF5Qbo9d13IA/trained_model.tar", "refine_steps": 20, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40 }, "logs": "Using seed: 60592\nloading custom weights\nweights already in cache\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: shot in the style of <s0><s1>, a woman, blue sky\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:14, 2.71it/s]\n 5%|▌ | 2/40 [00:00<00:14, 2.71it/s]\n 8%|▊ | 3/40 [00:01<00:13, 2.71it/s]\n 10%|█ | 4/40 [00:01<00:13, 2.71it/s]\n 12%|█▎ | 5/40 [00:01<00:12, 2.71it/s]\n 15%|█▌ | 6/40 [00:02<00:12, 2.71it/s]\n 18%|█▊ | 7/40 [00:02<00:12, 2.71it/s]\n 20%|██ | 8/40 [00:02<00:11, 2.71it/s]\n 22%|██▎ | 9/40 [00:03<00:11, 2.71it/s]\n 25%|██▌ | 10/40 [00:03<00:11, 2.71it/s]\n 28%|██▊ | 11/40 [00:04<00:10, 2.71it/s]\n 30%|███ | 12/40 [00:04<00:10, 2.71it/s]\n 32%|███▎ | 13/40 [00:04<00:09, 2.71it/s]\n 35%|███▌ | 14/40 [00:05<00:09, 2.71it/s]\n 38%|███▊ | 15/40 [00:05<00:09, 2.71it/s]\n 40%|████ | 16/40 [00:05<00:08, 2.71it/s]\n 42%|████▎ | 17/40 [00:06<00:08, 2.71it/s]\n 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s]\n 48%|████▊ | 19/40 [00:07<00:07, 2.71it/s]\n 50%|█████ | 20/40 [00:07<00:07, 2.71it/s]\n 52%|█████▎ | 21/40 [00:07<00:07, 2.71it/s]\n 55%|█████▌ | 22/40 [00:08<00:06, 2.71it/s]\n 57%|█████▊ | 23/40 [00:08<00:06, 2.71it/s]\n 60%|██████ | 24/40 [00:08<00:05, 2.71it/s]\n 62%|██████▎ | 25/40 [00:09<00:05, 2.71it/s]\n 65%|██████▌ | 26/40 [00:09<00:05, 2.71it/s]\n 68%|██████▊ | 27/40 [00:09<00:04, 2.71it/s]\n 70%|███████ | 28/40 [00:10<00:04, 2.71it/s]\n 72%|███████▎ | 29/40 [00:10<00:04, 2.71it/s]\n 75%|███████▌ | 30/40 [00:11<00:03, 2.70it/s]\n 78%|███████▊ | 31/40 [00:11<00:03, 2.70it/s]\n 80%|████████ | 32/40 [00:11<00:02, 2.70it/s]\n 82%|████████▎ | 33/40 [00:12<00:02, 2.70it/s]\n 85%|████████▌ | 34/40 [00:12<00:02, 2.70it/s]\n 88%|████████▊ | 35/40 [00:12<00:01, 2.70it/s]\n 90%|█████████ | 36/40 [00:13<00:01, 2.70it/s]\n 92%|█████████▎| 37/40 [00:13<00:01, 2.70it/s]\n 95%|█████████▌| 38/40 [00:14<00:00, 2.70it/s]\n 98%|█████████▊| 39/40 [00:14<00:00, 2.70it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.70it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.71it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n 17%|█▋ | 1/6 [00:00<00:01, 4.31it/s]\n 33%|███▎ | 2/6 [00:00<00:00, 4.30it/s]\n 50%|█████ | 3/6 [00:00<00:00, 4.29it/s]\n 67%|██████▋ | 4/6 [00:00<00:00, 4.26it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 4.26it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.27it/s]\n100%|██████████| 6/6 [00:01<00:00, 4.27it/s]", "metrics": { "predict_time": 20.398097, "total_time": 20.423432 }, "output": [ "https://replicate.delivery/pbxt/esqC43QNvDzLXqkIl0zAVRLFk53F0NzMJwZ20B9C504SxfvRA/out-0.png" ], "started_at": "2023-10-20T09:21:21.805049Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lzuvxgtbxqumopcfiby2pvdqfm", "cancel": "https://api.replicate.com/v1/predictions/lzuvxgtbxqumopcfiby2pvdqfm/cancel" }, "version": "a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4" }Generated inUsing seed: 60592 loading custom weights weights already in cache Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: shot in the style of <s0><s1>, a woman, blue sky Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:14, 2.71it/s] 5%|▌ | 2/40 [00:00<00:14, 2.71it/s] 8%|▊ | 3/40 [00:01<00:13, 2.71it/s] 10%|█ | 4/40 [00:01<00:13, 2.71it/s] 12%|█▎ | 5/40 [00:01<00:12, 2.71it/s] 15%|█▌ | 6/40 [00:02<00:12, 2.71it/s] 18%|█▊ | 7/40 [00:02<00:12, 2.71it/s] 20%|██ | 8/40 [00:02<00:11, 2.71it/s] 22%|██▎ | 9/40 [00:03<00:11, 2.71it/s] 25%|██▌ | 10/40 [00:03<00:11, 2.71it/s] 28%|██▊ | 11/40 [00:04<00:10, 2.71it/s] 30%|███ | 12/40 [00:04<00:10, 2.71it/s] 32%|███▎ | 13/40 [00:04<00:09, 2.71it/s] 35%|███▌ | 14/40 [00:05<00:09, 2.71it/s] 38%|███▊ | 15/40 [00:05<00:09, 2.71it/s] 40%|████ | 16/40 [00:05<00:08, 2.71it/s] 42%|████▎ | 17/40 [00:06<00:08, 2.71it/s] 45%|████▌ | 18/40 [00:06<00:08, 2.71it/s] 48%|████▊ | 19/40 [00:07<00:07, 2.71it/s] 50%|█████ | 20/40 [00:07<00:07, 2.71it/s] 52%|█████▎ | 21/40 [00:07<00:07, 2.71it/s] 55%|█████▌ | 22/40 [00:08<00:06, 2.71it/s] 57%|█████▊ | 23/40 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