Readme
This model doesn't have a readme.
DPO-SDXL Canny controlnet with LoRA support.
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run batouresearch/dpo-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(
"batouresearch/dpo-sdxl-controlnet-lora:5779933f36e3ecc23c51c18de01caff6aa54ea9e147ef3edf0b2924191595216",
{
input: {
image: "https://replicate.delivery/pbxt/K589OVTpTQjio99XGHopletMbpSrpgXDvT8VJahdVeHOAOgk/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp",
prompt: "shot in the style of sksfer, a woman wearing an organic shaped hat in alaska",
refine: "no_refiner",
scheduler: "K_EULER",
lora_scale: 0.95,
num_outputs: 1,
lora_weights: "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
refine_steps: 10,
guidance_scale: 7.5,
apply_watermark: true,
condition_scale: 0.5,
negative_prompt: "",
num_inference_steps: 50
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run batouresearch/dpo-sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"batouresearch/dpo-sdxl-controlnet-lora:5779933f36e3ecc23c51c18de01caff6aa54ea9e147ef3edf0b2924191595216",
input={
"image": "https://replicate.delivery/pbxt/K589OVTpTQjio99XGHopletMbpSrpgXDvT8VJahdVeHOAOgk/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp",
"prompt": "shot in the style of sksfer, a woman wearing an organic shaped hat in alaska",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.95,
"num_outputs": 1,
"lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
"refine_steps": 10,
"guidance_scale": 7.5,
"apply_watermark": True,
"condition_scale": 0.5,
"negative_prompt": "",
"num_inference_steps": 50
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run batouresearch/dpo-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": "5779933f36e3ecc23c51c18de01caff6aa54ea9e147ef3edf0b2924191595216",
"input": {
"image": "https://replicate.delivery/pbxt/K589OVTpTQjio99XGHopletMbpSrpgXDvT8VJahdVeHOAOgk/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp",
"prompt": "shot in the style of sksfer, a woman wearing an organic shaped hat in alaska",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.95,
"num_outputs": 1,
"lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
"refine_steps": 10,
"guidance_scale": 7.5,
"apply_watermark": true,
"condition_scale": 0.5,
"negative_prompt": "",
"num_inference_steps": 50
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
brew install cog
If you don’t have Homebrew, there are other installation options available.
Pull and run batouresearch/dpo-sdxl-controlnet-lora using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/batouresearch/dpo-sdxl-controlnet-lora@sha256:5779933f36e3ecc23c51c18de01caff6aa54ea9e147ef3edf0b2924191595216 \
-i 'image="https://replicate.delivery/pbxt/K589OVTpTQjio99XGHopletMbpSrpgXDvT8VJahdVeHOAOgk/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp"' \
-i 'prompt="shot in the style of sksfer, a woman wearing an organic shaped hat in alaska"' \
-i 'refine="no_refiner"' \
-i 'scheduler="K_EULER"' \
-i 'lora_scale=0.95' \
-i 'num_outputs=1' \
-i 'lora_weights="https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar"' \
-i 'refine_steps=10' \
-i 'guidance_scale=7.5' \
-i 'apply_watermark=true' \
-i 'condition_scale=0.5' \
-i 'negative_prompt=""' \
-i 'num_inference_steps=50'
To learn more, take a look at the Cog documentation.
Pull and run batouresearch/dpo-sdxl-controlnet-lora using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/batouresearch/dpo-sdxl-controlnet-lora@sha256:5779933f36e3ecc23c51c18de01caff6aa54ea9e147ef3edf0b2924191595216
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/K589OVTpTQjio99XGHopletMbpSrpgXDvT8VJahdVeHOAOgk/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp", "prompt": "shot in the style of sksfer, a woman wearing an organic shaped hat in alaska", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.95, "num_outputs": 1, "lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar", "refine_steps": 10, "guidance_scale": 7.5, "apply_watermark": true, "condition_scale": 0.5, "negative_prompt": "", "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
Add a payment method to run this model.
Each run costs approximately $0.059. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2023-12-20T13:16:36.944207Z",
"created_at": "2023-12-20T13:16:15.278155Z",
"data_removed": false,
"error": null,
"id": "rwgw7ttbrlod5vyfcaqok2owgy",
"input": {
"image": "https://replicate.delivery/pbxt/K589OVTpTQjio99XGHopletMbpSrpgXDvT8VJahdVeHOAOgk/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp",
"prompt": "shot in the style of sksfer, a woman wearing an organic shaped hat in alaska",
"refine": "no_refiner",
"scheduler": "K_EULER",
"lora_scale": 0.95,
"num_outputs": 1,
"lora_weights": "https://pbxt.replicate.delivery/mwN3AFyYZyouOB03Uhw8ubKW9rpqMgdtL9zYV9GF2WGDiwbE/trained_model.tar",
"refine_steps": 10,
"guidance_scale": 7.5,
"apply_watermark": true,
"condition_scale": 0.5,
"negative_prompt": "",
"num_inference_steps": 50
},
"logs": "Using seed: 19520\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 wearing an organic shaped hat in alaska\nOriginal width:960, height:1200\nAspect Ratio: 0.80\nnew_width:896, new_height:1152\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:17, 2.80it/s]\n 4%|▍ | 2/50 [00:00<00:17, 2.80it/s]\n 6%|▌ | 3/50 [00:01<00:16, 2.80it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.80it/s]\n 10%|█ | 5/50 [00:01<00:16, 2.80it/s]\n 12%|█▏ | 6/50 [00:02<00:15, 2.80it/s]\n 14%|█▍ | 7/50 [00:02<00:15, 2.80it/s]\n 16%|█▌ | 8/50 [00:02<00:15, 2.80it/s]\n 18%|█▊ | 9/50 [00:03<00:14, 2.80it/s]\n 20%|██ | 10/50 [00:03<00:14, 2.80it/s]\n 22%|██▏ | 11/50 [00:03<00:13, 2.80it/s]\n 24%|██▍ | 12/50 [00:04<00:13, 2.80it/s]\n 26%|██▌ | 13/50 [00:04<00:13, 2.80it/s]\n 28%|██▊ | 14/50 [00:05<00:12, 2.80it/s]\n 30%|███ | 15/50 [00:05<00:12, 2.80it/s]\n 32%|███▏ | 16/50 [00:05<00:12, 2.80it/s]\n 34%|███▍ | 17/50 [00:06<00:11, 2.80it/s]\n 36%|███▌ | 18/50 [00:06<00:11, 2.79it/s]\n 38%|███▊ | 19/50 [00:06<00:11, 2.79it/s]\n 40%|████ | 20/50 [00:07<00:10, 2.79it/s]\n 42%|████▏ | 21/50 [00:07<00:10, 2.79it/s]\n 44%|████▍ | 22/50 [00:07<00:10, 2.79it/s]\n 46%|████▌ | 23/50 [00:08<00:09, 2.79it/s]\n 48%|████▊ | 24/50 [00:08<00:09, 2.79it/s]\n 50%|█████ | 25/50 [00:08<00:08, 2.79it/s]\n 52%|█████▏ | 26/50 [00:09<00:08, 2.79it/s]\n 54%|█████▍ | 27/50 [00:09<00:08, 2.79it/s]\n 56%|█████▌ | 28/50 [00:10<00:07, 2.79it/s]\n 58%|█████▊ | 29/50 [00:10<00:07, 2.79it/s]\n 60%|██████ | 30/50 [00:10<00:07, 2.79it/s]\n 62%|██████▏ | 31/50 [00:11<00:06, 2.79it/s]\n 64%|██████▍ | 32/50 [00:11<00:06, 2.79it/s]\n 66%|██████▌ | 33/50 [00:11<00:06, 2.79it/s]\n 68%|██████▊ | 34/50 [00:12<00:05, 2.79it/s]\n 70%|███████ | 35/50 [00:12<00:05, 2.79it/s]\n 72%|███████▏ | 36/50 [00:12<00:05, 2.79it/s]\n 74%|███████▍ | 37/50 [00:13<00:04, 2.79it/s]\n 76%|███████▌ | 38/50 [00:13<00:04, 2.78it/s]\n 78%|███████▊ | 39/50 [00:13<00:03, 2.78it/s]\n 80%|████████ | 40/50 [00:14<00:03, 2.78it/s]\n 82%|████████▏ | 41/50 [00:14<00:03, 2.78it/s]\n 84%|████████▍ | 42/50 [00:15<00:02, 2.78it/s]\n 86%|████████▌ | 43/50 [00:15<00:02, 2.78it/s]\n 88%|████████▊ | 44/50 [00:15<00:02, 2.78it/s]\n 90%|█████████ | 45/50 [00:16<00:01, 2.78it/s]\n 92%|█████████▏| 46/50 [00:16<00:01, 2.78it/s]\n 94%|█████████▍| 47/50 [00:16<00:01, 2.79it/s]\n 96%|█████████▌| 48/50 [00:17<00:00, 2.78it/s]\n 98%|█████████▊| 49/50 [00:17<00:00, 2.78it/s]\n100%|██████████| 50/50 [00:17<00:00, 2.79it/s]\n100%|██████████| 50/50 [00:17<00:00, 2.79it/s]",
"metrics": {
"predict_time": 21.603646,
"total_time": 21.666052
},
"output": [
"https://replicate.delivery/pbxt/2M0ou11DRJL2PFeYrnBlKSf7xqni9wQHQPtFKq52VKL0sJESA/out-0.png"
],
"started_at": "2023-12-20T13:16:15.340561Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/rwgw7ttbrlod5vyfcaqok2owgy",
"cancel": "https://api.replicate.com/v1/predictions/rwgw7ttbrlod5vyfcaqok2owgy/cancel"
},
"version": "5779933f36e3ecc23c51c18de01caff6aa54ea9e147ef3edf0b2924191595216"
}
Using seed: 19520
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 wearing an organic shaped hat in alaska
Original width:960, height:1200
Aspect Ratio: 0.80
new_width:896, new_height:1152
txt2img mode
0%| | 0/50 [00:00<?, ?it/s]
2%|▏ | 1/50 [00:00<00:17, 2.80it/s]
4%|▍ | 2/50 [00:00<00:17, 2.80it/s]
6%|▌ | 3/50 [00:01<00:16, 2.80it/s]
8%|▊ | 4/50 [00:01<00:16, 2.80it/s]
10%|█ | 5/50 [00:01<00:16, 2.80it/s]
12%|█▏ | 6/50 [00:02<00:15, 2.80it/s]
14%|█▍ | 7/50 [00:02<00:15, 2.80it/s]
16%|█▌ | 8/50 [00:02<00:15, 2.80it/s]
18%|█▊ | 9/50 [00:03<00:14, 2.80it/s]
20%|██ | 10/50 [00:03<00:14, 2.80it/s]
22%|██▏ | 11/50 [00:03<00:13, 2.80it/s]
24%|██▍ | 12/50 [00:04<00:13, 2.80it/s]
26%|██▌ | 13/50 [00:04<00:13, 2.80it/s]
28%|██▊ | 14/50 [00:05<00:12, 2.80it/s]
30%|███ | 15/50 [00:05<00:12, 2.80it/s]
32%|███▏ | 16/50 [00:05<00:12, 2.80it/s]
34%|███▍ | 17/50 [00:06<00:11, 2.80it/s]
36%|███▌ | 18/50 [00:06<00:11, 2.79it/s]
38%|███▊ | 19/50 [00:06<00:11, 2.79it/s]
40%|████ | 20/50 [00:07<00:10, 2.79it/s]
42%|████▏ | 21/50 [00:07<00:10, 2.79it/s]
44%|████▍ | 22/50 [00:07<00:10, 2.79it/s]
46%|████▌ | 23/50 [00:08<00:09, 2.79it/s]
48%|████▊ | 24/50 [00:08<00:09, 2.79it/s]
50%|█████ | 25/50 [00:08<00:08, 2.79it/s]
52%|█████▏ | 26/50 [00:09<00:08, 2.79it/s]
54%|█████▍ | 27/50 [00:09<00:08, 2.79it/s]
56%|█████▌ | 28/50 [00:10<00:07, 2.79it/s]
58%|█████▊ | 29/50 [00:10<00:07, 2.79it/s]
60%|██████ | 30/50 [00:10<00:07, 2.79it/s]
62%|██████▏ | 31/50 [00:11<00:06, 2.79it/s]
64%|██████▍ | 32/50 [00:11<00:06, 2.79it/s]
66%|██████▌ | 33/50 [00:11<00:06, 2.79it/s]
68%|██████▊ | 34/50 [00:12<00:05, 2.79it/s]
70%|███████ | 35/50 [00:12<00:05, 2.79it/s]
72%|███████▏ | 36/50 [00:12<00:05, 2.79it/s]
74%|███████▍ | 37/50 [00:13<00:04, 2.79it/s]
76%|███████▌ | 38/50 [00:13<00:04, 2.78it/s]
78%|███████▊ | 39/50 [00:13<00:03, 2.78it/s]
80%|████████ | 40/50 [00:14<00:03, 2.78it/s]
82%|████████▏ | 41/50 [00:14<00:03, 2.78it/s]
84%|████████▍ | 42/50 [00:15<00:02, 2.78it/s]
86%|████████▌ | 43/50 [00:15<00:02, 2.78it/s]
88%|████████▊ | 44/50 [00:15<00:02, 2.78it/s]
90%|█████████ | 45/50 [00:16<00:01, 2.78it/s]
92%|█████████▏| 46/50 [00:16<00:01, 2.78it/s]
94%|█████████▍| 47/50 [00:16<00:01, 2.79it/s]
96%|█████████▌| 48/50 [00:17<00:00, 2.78it/s]
98%|█████████▊| 49/50 [00:17<00:00, 2.78it/s]
100%|██████████| 50/50 [00:17<00:00, 2.79it/s]
100%|██████████| 50/50 [00:17<00:00, 2.79it/s]
This model costs approximately $0.059 to run on Replicate, or 16 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 61 seconds. The predict time for this model varies significantly based on the inputs.
This model doesn't have a readme.
This model is cold. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
Choose a file from your machine
Hint: you can also drag files onto the input
Using seed: 19520
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 wearing an organic shaped hat in alaska
Original width:960, height:1200
Aspect Ratio: 0.80
new_width:896, new_height:1152
txt2img mode
0%| | 0/50 [00:00<?, ?it/s]
2%|▏ | 1/50 [00:00<00:17, 2.80it/s]
4%|▍ | 2/50 [00:00<00:17, 2.80it/s]
6%|▌ | 3/50 [00:01<00:16, 2.80it/s]
8%|▊ | 4/50 [00:01<00:16, 2.80it/s]
10%|█ | 5/50 [00:01<00:16, 2.80it/s]
12%|█▏ | 6/50 [00:02<00:15, 2.80it/s]
14%|█▍ | 7/50 [00:02<00:15, 2.80it/s]
16%|█▌ | 8/50 [00:02<00:15, 2.80it/s]
18%|█▊ | 9/50 [00:03<00:14, 2.80it/s]
20%|██ | 10/50 [00:03<00:14, 2.80it/s]
22%|██▏ | 11/50 [00:03<00:13, 2.80it/s]
24%|██▍ | 12/50 [00:04<00:13, 2.80it/s]
26%|██▌ | 13/50 [00:04<00:13, 2.80it/s]
28%|██▊ | 14/50 [00:05<00:12, 2.80it/s]
30%|███ | 15/50 [00:05<00:12, 2.80it/s]
32%|███▏ | 16/50 [00:05<00:12, 2.80it/s]
34%|███▍ | 17/50 [00:06<00:11, 2.80it/s]
36%|███▌ | 18/50 [00:06<00:11, 2.79it/s]
38%|███▊ | 19/50 [00:06<00:11, 2.79it/s]
40%|████ | 20/50 [00:07<00:10, 2.79it/s]
42%|████▏ | 21/50 [00:07<00:10, 2.79it/s]
44%|████▍ | 22/50 [00:07<00:10, 2.79it/s]
46%|████▌ | 23/50 [00:08<00:09, 2.79it/s]
48%|████▊ | 24/50 [00:08<00:09, 2.79it/s]
50%|█████ | 25/50 [00:08<00:08, 2.79it/s]
52%|█████▏ | 26/50 [00:09<00:08, 2.79it/s]
54%|█████▍ | 27/50 [00:09<00:08, 2.79it/s]
56%|█████▌ | 28/50 [00:10<00:07, 2.79it/s]
58%|█████▊ | 29/50 [00:10<00:07, 2.79it/s]
60%|██████ | 30/50 [00:10<00:07, 2.79it/s]
62%|██████▏ | 31/50 [00:11<00:06, 2.79it/s]
64%|██████▍ | 32/50 [00:11<00:06, 2.79it/s]
66%|██████▌ | 33/50 [00:11<00:06, 2.79it/s]
68%|██████▊ | 34/50 [00:12<00:05, 2.79it/s]
70%|███████ | 35/50 [00:12<00:05, 2.79it/s]
72%|███████▏ | 36/50 [00:12<00:05, 2.79it/s]
74%|███████▍ | 37/50 [00:13<00:04, 2.79it/s]
76%|███████▌ | 38/50 [00:13<00:04, 2.78it/s]
78%|███████▊ | 39/50 [00:13<00:03, 2.78it/s]
80%|████████ | 40/50 [00:14<00:03, 2.78it/s]
82%|████████▏ | 41/50 [00:14<00:03, 2.78it/s]
84%|████████▍ | 42/50 [00:15<00:02, 2.78it/s]
86%|████████▌ | 43/50 [00:15<00:02, 2.78it/s]
88%|████████▊ | 44/50 [00:15<00:02, 2.78it/s]
90%|█████████ | 45/50 [00:16<00:01, 2.78it/s]
92%|█████████▏| 46/50 [00:16<00:01, 2.78it/s]
94%|█████████▍| 47/50 [00:16<00:01, 2.79it/s]
96%|█████████▌| 48/50 [00:17<00:00, 2.78it/s]
98%|█████████▊| 49/50 [00:17<00:00, 2.78it/s]
100%|██████████| 50/50 [00:17<00:00, 2.79it/s]
100%|██████████| 50/50 [00:17<00:00, 2.79it/s]