pnyompen/sdxl-controlnet-lora-small
sdxl-controlnet-lora-small
SDXL Canny controlnet with LoRA support.
Prediction
pnyompen/sdxl-controlnet-lora-small:0a0d2136ID9e7sq1d8ndrgj0cg8eqsa578s4StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- image
- prompt
- shot in the style of sksfer, a woman in alaska
- img2img
- strength
- 0.8
- scheduler
- K_EULER
- lora_scale
- 0.95
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar
- guidance_scale
- 7.5
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 40
- auto_generate_caption
{ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in alaska", "img2img": false, "strength": 0.8, "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40, "auto_generate_caption": false }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run pnyompen/sdxl-controlnet-lora-small using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", { input: { image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", prompt: "shot in the style of sksfer, a woman in alaska", img2img: false, strength: 0.8, scheduler: "K_EULER", lora_scale: 0.95, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", guidance_scale: 7.5, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 40, auto_generate_caption: false } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun pnyompen/sdxl-controlnet-lora-small using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", input={ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in alaska", "img2img": False, "strength": 0.8, "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40, "auto_generate_caption": False } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run pnyompen/sdxl-controlnet-lora-small 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": "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in alaska", "img2img": false, "strength": 0.8, "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40, "auto_generate_caption": false } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-23T06:02:03.981923Z", "created_at": "2024-06-23T06:01:43.851000Z", "data_removed": false, "error": null, "id": "9e7sq1d8ndrgj0cg8eqsa578s4", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman in alaska", "img2img": false, "strength": 0.8, "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 40, "auto_generate_caption": false }, "logs": "Using seed: 47765\nloading custom weights\nweights not in cache\nEnsuring enough disk space...\nFree disk space: 1634558648320\nDownloading weights: https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar\ndownloading https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar\n2024-06-23T06:01:44Z | INFO | [ Initiating ] chunk_size=150M dest=./weights-cache/931be18428e37365 url=https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar\n2024-06-23T06:01:46Z | INFO | [ Complete ] dest=./weights-cache/931be18428e37365 size=\"186 MB\" total_elapsed=2.434s url=https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar\nb''\nDownloaded weights in 2.5596249103546143 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\nPrompt: shot in the style of <s0><s1>, a woman in alaska\ntext2img mode\n 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.19/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, 2.74it/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, 2.74it/s]\n 18%|█▊ | 7/40 [00:02<00:12, 2.74it/s]\n 20%|██ | 8/40 [00:02<00:11, 2.74it/s]\n 22%|██▎ | 9/40 [00:03<00:11, 2.74it/s]\n 25%|██▌ | 10/40 [00:03<00:10, 2.74it/s]\n 28%|██▊ | 11/40 [00:04<00:10, 2.73it/s]\n 30%|███ | 12/40 [00:04<00:10, 2.73it/s]\n 32%|███▎ | 13/40 [00:04<00:09, 2.73it/s]\n 35%|███▌ | 14/40 [00:05<00:09, 2.73it/s]\n 38%|███▊ | 15/40 [00:05<00:09, 2.73it/s]\n 40%|████ | 16/40 [00:05<00:08, 2.73it/s]\n 42%|████▎ | 17/40 [00:06<00:08, 2.73it/s]\n 45%|████▌ | 18/40 [00:06<00:08, 2.73it/s]\n 48%|████▊ | 19/40 [00:06<00:07, 2.73it/s]\n 50%|█████ | 20/40 [00:07<00:07, 2.73it/s]\n 52%|█████▎ | 21/40 [00:07<00:06, 2.73it/s]\n 55%|█████▌ | 22/40 [00:08<00:06, 2.73it/s]\n 57%|█████▊ | 23/40 [00:08<00:06, 2.73it/s]\n 60%|██████ | 24/40 [00:08<00:05, 2.73it/s]\n 62%|██████▎ | 25/40 [00:09<00:05, 2.73it/s]\n 65%|██████▌ | 26/40 [00:09<00:05, 2.73it/s]\n 68%|██████▊ | 27/40 [00:09<00:04, 2.73it/s]\n 70%|███████ | 28/40 [00:10<00:04, 2.73it/s]\n 72%|███████▎ | 29/40 [00:10<00:04, 2.73it/s]\n 75%|███████▌ | 30/40 [00:10<00:03, 2.73it/s]\n 78%|███████▊ | 31/40 [00:11<00:03, 2.72it/s]\n 80%|████████ | 32/40 [00:11<00:02, 2.72it/s]\n 82%|████████▎ | 33/40 [00:12<00:02, 2.72it/s]\n 85%|████████▌ | 34/40 [00:12<00:02, 2.72it/s]\n 88%|████████▊ | 35/40 [00:12<00:01, 2.72it/s]\n 90%|█████████ | 36/40 [00:13<00:01, 2.72it/s]\n 92%|█████████▎| 37/40 [00:13<00:01, 2.72it/s]\n 95%|█████████▌| 38/40 [00:13<00:00, 2.72it/s]\n 98%|█████████▊| 39/40 [00:14<00:00, 2.72it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.72it/s]\n100%|██████████| 40/40 [00:14<00:00, 2.73it/s]", "metrics": { "predict_time": 20.08523615, "total_time": 20.130923 }, "output": [ "https://replicate.delivery/pbxt/PtfoygW38dw4dqfWutkOcYUDelpveL0oVb6BKoFvDVioFbFMB/out-0.png" ], "started_at": "2024-06-23T06:01:43.896686Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/9e7sq1d8ndrgj0cg8eqsa578s4", "cancel": "https://api.replicate.com/v1/predictions/9e7sq1d8ndrgj0cg8eqsa578s4/cancel" }, "version": "0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616" }Generated inUsing seed: 47765 loading custom weights weights not in cache Ensuring enough disk space... Free disk space: 1634558648320 Downloading weights: https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar downloading https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar 2024-06-23T06:01:44Z | INFO | [ Initiating ] chunk_size=150M dest=./weights-cache/931be18428e37365 url=https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar 2024-06-23T06:01:46Z | INFO | [ Complete ] dest=./weights-cache/931be18428e37365 size="186 MB" total_elapsed=2.434s url=https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar b'' Downloaded weights in 2.5596249103546143 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 Prompt: shot in the style of <s0><s1>, a woman in alaska text2img mode 0%| | 0/40 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.19/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, 2.74it/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, 2.74it/s] 18%|█▊ | 7/40 [00:02<00:12, 2.74it/s] 20%|██ | 8/40 [00:02<00:11, 2.74it/s] 22%|██▎ | 9/40 [00:03<00:11, 2.74it/s] 25%|██▌ | 10/40 [00:03<00:10, 2.74it/s] 28%|██▊ | 11/40 [00:04<00:10, 2.73it/s] 30%|███ | 12/40 [00:04<00:10, 2.73it/s] 32%|███▎ | 13/40 [00:04<00:09, 2.73it/s] 35%|███▌ | 14/40 [00:05<00:09, 2.73it/s] 38%|███▊ | 15/40 [00:05<00:09, 2.73it/s] 40%|████ | 16/40 [00:05<00:08, 2.73it/s] 42%|████▎ | 17/40 [00:06<00:08, 2.73it/s] 45%|████▌ | 18/40 [00:06<00:08, 2.73it/s] 48%|████▊ | 19/40 [00:06<00:07, 2.73it/s] 50%|█████ | 20/40 [00:07<00:07, 2.73it/s] 52%|█████▎ | 21/40 [00:07<00:06, 2.73it/s] 55%|█████▌ | 22/40 [00:08<00:06, 2.73it/s] 57%|█████▊ | 23/40 [00:08<00:06, 2.73it/s] 60%|██████ | 24/40 [00:08<00:05, 2.73it/s] 62%|██████▎ | 25/40 [00:09<00:05, 2.73it/s] 65%|██████▌ | 26/40 [00:09<00:05, 2.73it/s] 68%|██████▊ | 27/40 [00:09<00:04, 2.73it/s] 70%|███████ | 28/40 [00:10<00:04, 2.73it/s] 72%|███████▎ | 29/40 [00:10<00:04, 2.73it/s] 75%|███████▌ | 30/40 [00:10<00:03, 2.73it/s] 78%|███████▊ | 31/40 [00:11<00:03, 2.72it/s] 80%|████████ | 32/40 [00:11<00:02, 2.72it/s] 82%|████████▎ | 33/40 [00:12<00:02, 2.72it/s] 85%|████████▌ | 34/40 [00:12<00:02, 2.72it/s] 88%|████████▊ | 35/40 [00:12<00:01, 2.72it/s] 90%|█████████ | 36/40 [00:13<00:01, 2.72it/s] 92%|█████████▎| 37/40 [00:13<00:01, 2.72it/s] 95%|█████████▌| 38/40 [00:13<00:00, 2.72it/s] 98%|█████████▊| 39/40 [00:14<00:00, 2.72it/s] 100%|██████████| 40/40 [00:14<00:00, 2.72it/s] 100%|██████████| 40/40 [00:14<00:00, 2.73it/s]Prediction
pnyompen/sdxl-controlnet-lora-small:0a0d2136IDcfcwjpnq4nrgj0cg8esag941agStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- image
- prompt
- shot in the style of sksfer, a woman in paris, tower eiffel in te background
- img2img
- strength
- 0.8
- scheduler
- KarrasDPM
- lora_scale
- 1
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar
- guidance_scale
- 7.5
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 20
- auto_generate_caption
{ "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", "img2img": false, "strength": 0.8, "scheduler": "KarrasDPM", "lora_scale": 1, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 20, "auto_generate_caption": false }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run pnyompen/sdxl-controlnet-lora-small using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", { 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", img2img: false, strength: 0.8, scheduler: "KarrasDPM", lora_scale: 1, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", guidance_scale: 7.5, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 20, auto_generate_caption: false } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun pnyompen/sdxl-controlnet-lora-small using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", 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", "img2img": False, "strength": 0.8, "scheduler": "KarrasDPM", "lora_scale": 1, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 20, "auto_generate_caption": False } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run pnyompen/sdxl-controlnet-lora-small 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": "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", "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", "img2img": false, "strength": 0.8, "scheduler": "KarrasDPM", "lora_scale": 1, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 20, "auto_generate_caption": false } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-23T06:05:15.807833Z", "created_at": "2024-06-23T06:05:04.165000Z", "data_removed": false, "error": null, "id": "cfcwjpnq4nrgj0cg8esag941ag", "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", "img2img": false, "strength": 0.8, "scheduler": "KarrasDPM", "lora_scale": 1, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 20, "auto_generate_caption": false }, "logs": "Using seed: 37162\nloading custom weights\nweights already in cache\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\nPrompt: shot in the style of <s0><s1>, a woman in paris, tower eiffel in te background\ntext2img mode\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:06, 2.73it/s]\n 10%|█ | 2/20 [00:00<00:06, 2.73it/s]\n 15%|█▌ | 3/20 [00:01<00:06, 2.73it/s]\n 20%|██ | 4/20 [00:01<00:05, 2.73it/s]\n 25%|██▌ | 5/20 [00:01<00:05, 2.73it/s]\n 30%|███ | 6/20 [00:02<00:05, 2.73it/s]\n 35%|███▌ | 7/20 [00:02<00:04, 2.73it/s]\n 40%|████ | 8/20 [00:02<00:04, 2.73it/s]\n 45%|████▌ | 9/20 [00:03<00:04, 2.73it/s]\n 50%|█████ | 10/20 [00:03<00:03, 2.72it/s]\n 55%|█████▌ | 11/20 [00:04<00:03, 2.72it/s]\n 60%|██████ | 12/20 [00:04<00:02, 2.72it/s]\n 65%|██████▌ | 13/20 [00:04<00:02, 2.72it/s]\n 70%|███████ | 14/20 [00:05<00:02, 2.72it/s]\n 75%|███████▌ | 15/20 [00:05<00:01, 2.72it/s]\n 80%|████████ | 16/20 [00:05<00:01, 2.72it/s]\n 85%|████████▌ | 17/20 [00:06<00:01, 2.72it/s]\n 90%|█████████ | 18/20 [00:06<00:00, 2.72it/s]\n 95%|█████████▌| 19/20 [00:06<00:00, 2.72it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.72it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.72it/s]", "metrics": { "predict_time": 11.59882577, "total_time": 11.642833 }, "output": [ "https://replicate.delivery/pbxt/fuzK9RUpCi3Vf0oTwo89AFw7TcMRZ7DFggkzhMXgeXS1otCmA/out-0.png" ], "started_at": "2024-06-23T06:05:04.209008Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cfcwjpnq4nrgj0cg8esag941ag", "cancel": "https://api.replicate.com/v1/predictions/cfcwjpnq4nrgj0cg8esag941ag/cancel" }, "version": "0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616" }Generated inUsing seed: 37162 loading custom weights weights already in cache Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 Prompt: shot in the style of <s0><s1>, a woman in paris, tower eiffel in te background text2img mode 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:06, 2.73it/s] 10%|█ | 2/20 [00:00<00:06, 2.73it/s] 15%|█▌ | 3/20 [00:01<00:06, 2.73it/s] 20%|██ | 4/20 [00:01<00:05, 2.73it/s] 25%|██▌ | 5/20 [00:01<00:05, 2.73it/s] 30%|███ | 6/20 [00:02<00:05, 2.73it/s] 35%|███▌ | 7/20 [00:02<00:04, 2.73it/s] 40%|████ | 8/20 [00:02<00:04, 2.73it/s] 45%|████▌ | 9/20 [00:03<00:04, 2.73it/s] 50%|█████ | 10/20 [00:03<00:03, 2.72it/s] 55%|█████▌ | 11/20 [00:04<00:03, 2.72it/s] 60%|██████ | 12/20 [00:04<00:02, 2.72it/s] 65%|██████▌ | 13/20 [00:04<00:02, 2.72it/s] 70%|███████ | 14/20 [00:05<00:02, 2.72it/s] 75%|███████▌ | 15/20 [00:05<00:01, 2.72it/s] 80%|████████ | 16/20 [00:05<00:01, 2.72it/s] 85%|████████▌ | 17/20 [00:06<00:01, 2.72it/s] 90%|█████████ | 18/20 [00:06<00:00, 2.72it/s] 95%|█████████▌| 19/20 [00:06<00:00, 2.72it/s] 100%|██████████| 20/20 [00:07<00:00, 2.72it/s] 100%|██████████| 20/20 [00:07<00:00, 2.72it/s]Prediction
pnyompen/sdxl-controlnet-lora-small:0a0d2136IDn7h4pmsykxrgp0cg8estvdw0h0StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- image
- prompt
- shot in the style of sksfer, a woman
- img2img
- strength
- 0.8
- scheduler
- KarrasDPM
- lora_scale
- 1
- num_outputs
- 1
- lora_weights
- https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar
- guidance_scale
- 7.5
- condition_scale
- 0.5
- negative_prompt
- num_inference_steps
- 20
- auto_generate_caption
{ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "img2img": false, "strength": 0.8, "scheduler": "KarrasDPM", "lora_scale": 1, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 20, "auto_generate_caption": false }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run pnyompen/sdxl-controlnet-lora-small using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", { input: { image: "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", prompt: "shot in the style of sksfer, a woman", img2img: false, strength: 0.8, scheduler: "KarrasDPM", lora_scale: 1, num_outputs: 1, lora_weights: "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", guidance_scale: 7.5, condition_scale: 0.5, negative_prompt: "", num_inference_steps: 20, auto_generate_caption: false } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun pnyompen/sdxl-controlnet-lora-small using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", input={ "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "img2img": False, "strength": 0.8, "scheduler": "KarrasDPM", "lora_scale": 1, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 20, "auto_generate_caption": False } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run pnyompen/sdxl-controlnet-lora-small 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": "pnyompen/sdxl-controlnet-lora-small:0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "img2img": false, "strength": 0.8, "scheduler": "KarrasDPM", "lora_scale": 1, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 20, "auto_generate_caption": false } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-23T06:05:52.988385Z", "created_at": "2024-06-23T06:05:38.847000Z", "data_removed": false, "error": null, "id": "n7h4pmsykxrgp0cg8estvdw0h0", "input": { "image": "https://replicate.delivery/pbxt/JiOTMCHj4oGrTTf8Pg2r7vyI8YdXc5jL2IDyC2SfhuggjYe6/out-0%20%281%29.png", "prompt": "shot in the style of sksfer, a woman", "img2img": false, "strength": 0.8, "scheduler": "KarrasDPM", "lora_scale": 1, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar", "guidance_scale": 7.5, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 20, "auto_generate_caption": false }, "logs": "Using seed: 64988\nloading custom weights\nweights not in cache\nEnsuring enough disk space...\nFree disk space: 1575584018432\nDownloading weights: https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar\ndownloading https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar\n2024-06-23T06:05:39Z | INFO | [ Initiating ] chunk_size=150M dest=./weights-cache/6e964691c70b02c0 url=https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar\n2024-06-23T06:05:41Z | INFO | [ Complete ] dest=./weights-cache/6e964691c70b02c0 size=\"186 MB\" total_elapsed=2.574s url=https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar\nb''\nDownloaded weights in 2.6818389892578125 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nOriginal width:1024, height:1024\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\nPrompt: shot in the style of <s0><s1>, a woman\ntext2img mode\n 0%| | 0/20 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.19/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 5%|▌ | 1/20 [00:00<00:06, 2.73it/s]\n 10%|█ | 2/20 [00:00<00:06, 2.73it/s]\n 15%|█▌ | 3/20 [00:01<00:06, 2.73it/s]\n 20%|██ | 4/20 [00:01<00:05, 2.73it/s]\n 25%|██▌ | 5/20 [00:01<00:05, 2.73it/s]\n 30%|███ | 6/20 [00:02<00:05, 2.73it/s]\n 35%|███▌ | 7/20 [00:02<00:04, 2.73it/s]\n 40%|████ | 8/20 [00:02<00:04, 2.72it/s]\n 45%|████▌ | 9/20 [00:03<00:04, 2.72it/s]\n 50%|█████ | 10/20 [00:03<00:03, 2.71it/s]\n 55%|█████▌ | 11/20 [00:04<00:03, 2.71it/s]\n 60%|██████ | 12/20 [00:04<00:02, 2.71it/s]\n 65%|██████▌ | 13/20 [00:04<00:02, 2.71it/s]\n 70%|███████ | 14/20 [00:05<00:02, 2.71it/s]\n 75%|███████▌ | 15/20 [00:05<00:01, 2.71it/s]\n 80%|████████ | 16/20 [00:05<00:01, 2.71it/s]\n 85%|████████▌ | 17/20 [00:06<00:01, 2.71it/s]\n 90%|█████████ | 18/20 [00:06<00:00, 2.71it/s]\n 95%|█████████▌| 19/20 [00:06<00:00, 2.71it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.71it/s]\n100%|██████████| 20/20 [00:07<00:00, 2.72it/s]", "metrics": { "predict_time": 14.097555783, "total_time": 14.141385 }, "output": [ "https://replicate.delivery/pbxt/VLN4lScjfnXsDyfEtAf1tYt5n66pywKK8mSic2makqFeTbFMB/out-0.png" ], "started_at": "2024-06-23T06:05:38.890829Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/n7h4pmsykxrgp0cg8estvdw0h0", "cancel": "https://api.replicate.com/v1/predictions/n7h4pmsykxrgp0cg8estvdw0h0/cancel" }, "version": "0a0d21366b464cc0d6618f0a8da2ecbd22290423faa800a97e733be748a68616" }Generated inUsing seed: 64988 loading custom weights weights not in cache Ensuring enough disk space... Free disk space: 1575584018432 Downloading weights: https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar downloading https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar 2024-06-23T06:05:39Z | INFO | [ Initiating ] chunk_size=150M dest=./weights-cache/6e964691c70b02c0 url=https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar 2024-06-23T06:05:41Z | INFO | [ Complete ] dest=./weights-cache/6e964691c70b02c0 size="186 MB" total_elapsed=2.574s url=https://pbxt.replicate.delivery/lCD5GVbdy17LO5fq9Kf3yOpRmIkJ1UIgU4mHrnfPqm2TkYfGB/trained_model.tar b'' Downloaded weights in 2.6818389892578125 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Original width:1024, height:1024 Aspect Ratio: 1.00 new_width:1024, new_height:1024 Prompt: shot in the style of <s0><s1>, a woman text2img mode 0%| | 0/20 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.19/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( 5%|▌ | 1/20 [00:00<00:06, 2.73it/s] 10%|█ | 2/20 [00:00<00:06, 2.73it/s] 15%|█▌ | 3/20 [00:01<00:06, 2.73it/s] 20%|██ | 4/20 [00:01<00:05, 2.73it/s] 25%|██▌ | 5/20 [00:01<00:05, 2.73it/s] 30%|███ | 6/20 [00:02<00:05, 2.73it/s] 35%|███▌ | 7/20 [00:02<00:04, 2.73it/s] 40%|████ | 8/20 [00:02<00:04, 2.72it/s] 45%|████▌ | 9/20 [00:03<00:04, 2.72it/s] 50%|█████ | 10/20 [00:03<00:03, 2.71it/s] 55%|█████▌ | 11/20 [00:04<00:03, 2.71it/s] 60%|██████ | 12/20 [00:04<00:02, 2.71it/s] 65%|██████▌ | 13/20 [00:04<00:02, 2.71it/s] 70%|███████ | 14/20 [00:05<00:02, 2.71it/s] 75%|███████▌ | 15/20 [00:05<00:01, 2.71it/s] 80%|████████ | 16/20 [00:05<00:01, 2.71it/s] 85%|████████▌ | 17/20 [00:06<00:01, 2.71it/s] 90%|█████████ | 18/20 [00:06<00:00, 2.71it/s] 95%|█████████▌| 19/20 [00:06<00:00, 2.71it/s] 100%|██████████| 20/20 [00:07<00:00, 2.71it/s] 100%|██████████| 20/20 [00:07<00:00, 2.72it/s]
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