Readme
Implementation of SDXL Image blending via Compels weighted prompt blending
Inspired by the ComfyUI ReVision tutorial here
Changelog
10/11/23 - Updated Cog to use pget to download weights
SDXL Image Blending
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 lucataco/sdxl-img-blend using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"lucataco/sdxl-img-blend:192db23e4232f6a722111ce49776c055d6b2952a7d2feed0e50c6b96a8af1227",
{
input: {
seed: 1310845407,
width: 1024,
height: 1024,
image1: "https://replicate.delivery/pbxt/Jy9xv0Ljh6HfSknClD6j9NOuH6spysZUOEoK1ZVZ0Ne5Ht8K/mermaid.png",
image2: "https://replicate.delivery/pbxt/Jy9xuAeWhd4Ij1XR9zvWtoGSmgRCzDOHVdpgD4qfHZ4efv7h/flowers.png",
scheduler: "K_EULER",
strength1: 1,
strength2: 1,
guidance_scale: 7.5,
num_inference_steps: 25
}
}
);
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 lucataco/sdxl-img-blend using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"lucataco/sdxl-img-blend:192db23e4232f6a722111ce49776c055d6b2952a7d2feed0e50c6b96a8af1227",
input={
"seed": 1310845407,
"width": 1024,
"height": 1024,
"image1": "https://replicate.delivery/pbxt/Jy9xv0Ljh6HfSknClD6j9NOuH6spysZUOEoK1ZVZ0Ne5Ht8K/mermaid.png",
"image2": "https://replicate.delivery/pbxt/Jy9xuAeWhd4Ij1XR9zvWtoGSmgRCzDOHVdpgD4qfHZ4efv7h/flowers.png",
"scheduler": "K_EULER",
"strength1": 1,
"strength2": 1,
"guidance_scale": 7.5,
"num_inference_steps": 25
}
)
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 lucataco/sdxl-img-blend 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": "192db23e4232f6a722111ce49776c055d6b2952a7d2feed0e50c6b96a8af1227",
"input": {
"seed": 1310845407,
"width": 1024,
"height": 1024,
"image1": "https://replicate.delivery/pbxt/Jy9xv0Ljh6HfSknClD6j9NOuH6spysZUOEoK1ZVZ0Ne5Ht8K/mermaid.png",
"image2": "https://replicate.delivery/pbxt/Jy9xuAeWhd4Ij1XR9zvWtoGSmgRCzDOHVdpgD4qfHZ4efv7h/flowers.png",
"scheduler": "K_EULER",
"strength1": 1,
"strength2": 1,
"guidance_scale": 7.5,
"num_inference_steps": 25
}
}' \
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 lucataco/sdxl-img-blend using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/lucataco/sdxl-img-blend@sha256:192db23e4232f6a722111ce49776c055d6b2952a7d2feed0e50c6b96a8af1227 \
-i 'seed=1310845407' \
-i 'width=1024' \
-i 'height=1024' \
-i 'image1="https://replicate.delivery/pbxt/Jy9xv0Ljh6HfSknClD6j9NOuH6spysZUOEoK1ZVZ0Ne5Ht8K/mermaid.png"' \
-i 'image2="https://replicate.delivery/pbxt/Jy9xuAeWhd4Ij1XR9zvWtoGSmgRCzDOHVdpgD4qfHZ4efv7h/flowers.png"' \
-i 'scheduler="K_EULER"' \
-i 'strength1=1' \
-i 'strength2=1' \
-i 'guidance_scale=7.5' \
-i 'num_inference_steps=25'
To learn more, take a look at the Cog documentation.
Pull and run lucataco/sdxl-img-blend 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/lucataco/sdxl-img-blend@sha256:192db23e4232f6a722111ce49776c055d6b2952a7d2feed0e50c6b96a8af1227
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 1310845407, "width": 1024, "height": 1024, "image1": "https://replicate.delivery/pbxt/Jy9xv0Ljh6HfSknClD6j9NOuH6spysZUOEoK1ZVZ0Ne5Ht8K/mermaid.png", "image2": "https://replicate.delivery/pbxt/Jy9xuAeWhd4Ij1XR9zvWtoGSmgRCzDOHVdpgD4qfHZ4efv7h/flowers.png", "scheduler": "K_EULER", "strength1": 1, "strength2": 1, "guidance_scale": 7.5, "num_inference_steps": 25 } }' \ http://localhost:5000/predictions
Add a payment method to run this model.
Each run costs approximately $0.38. Alternatively, try out our featured models for free.
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{
"completed_at": "2023-12-11T20:47:05.564287Z",
"created_at": "2023-12-11T20:38:56.028260Z",
"data_removed": false,
"error": null,
"id": "argibx3bllmi3rtifjjbvasvie",
"input": {
"seed": 1310845407,
"width": 1024,
"height": 1024,
"image1": "https://replicate.delivery/pbxt/Jy9xv0Ljh6HfSknClD6j9NOuH6spysZUOEoK1ZVZ0Ne5Ht8K/mermaid.png",
"image2": "https://replicate.delivery/pbxt/Jy9xuAeWhd4Ij1XR9zvWtoGSmgRCzDOHVdpgD4qfHZ4efv7h/flowers.png",
"scheduler": "K_EULER",
"strength1": 1,
"strength2": 1,
"guidance_scale": 7.5,
"num_inference_steps": 25
},
"logs": "Using seed: 1310845407\n 0%| | 0/55 [00:00<?, ?it/s]\n 49%|████▉ | 27/55 [00:00<00:00, 263.05it/s]\n 98%|█████████▊| 54/55 [00:00<00:00, 237.14it/s]\n100%|██████████| 55/55 [00:00<00:00, 242.50it/s]\nFlavor chain: 0%| | 0/32 [00:00<?, ?it/s]\nFlavor chain: 3%|▎ | 1/32 [00:03<01:43, 3.34s/it]\nFlavor chain: 6%|▋ | 2/32 [00:06<01:41, 3.37s/it]\nFlavor chain: 9%|▉ | 3/32 [00:10<01:38, 3.39s/it]\nFlavor chain: 12%|█▎ | 4/32 [00:13<01:35, 3.41s/it]\nFlavor chain: 16%|█▌ | 5/32 [00:17<01:32, 3.42s/it]\nFlavor chain: 19%|█▉ | 6/32 [00:20<01:29, 3.44s/it]\nFlavor chain: 22%|██▏ | 7/32 [00:24<01:26, 3.46s/it]\nFlavor chain: 25%|██▌ | 8/32 [00:27<01:23, 3.48s/it]\nFlavor chain: 28%|██▊ | 9/32 [00:31<01:20, 3.49s/it]\nFlavor chain: 31%|███▏ | 10/32 [00:34<01:17, 3.52s/it]\nFlavor chain: 34%|███▍ | 11/32 [00:38<01:14, 3.54s/it]\nFlavor chain: 38%|███▊ | 12/32 [00:41<01:11, 3.56s/it]\nFlavor chain: 41%|████ | 13/32 [00:45<01:07, 3.57s/it]\nFlavor chain: 44%|████▍ | 14/32 [00:49<01:04, 3.59s/it]\nFlavor chain: 47%|████▋ | 15/32 [00:52<01:01, 3.61s/it]\nFlavor chain: 50%|█████ | 16/32 [00:56<00:57, 3.62s/it]\nFlavor chain: 50%|█████ | 16/32 [01:00<01:00, 3.75s/it]\n 0%| | 0/55 [00:00<?, ?it/s]\n 35%|███▍ | 19/55 [00:00<00:00, 181.72it/s]\n 73%|███████▎ | 40/55 [00:00<00:00, 196.63it/s]\n100%|██████████| 55/55 [00:00<00:00, 210.04it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n100%|██████████| 6/6 [00:00<00:00, 237.45it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 58%|█████▊ | 29/50 [00:00<00:00, 279.39it/s]\n100%|██████████| 50/50 [00:00<00:00, 165.04it/s]\n 0%| | 0/55 [00:00<?, ?it/s]\n 53%|█████▎ | 29/55 [00:00<00:00, 282.04it/s]\n100%|██████████| 55/55 [00:00<00:00, 255.30it/s]\nFlavor chain: 0%| | 0/32 [00:00<?, ?it/s]\nFlavor chain: 3%|▎ | 1/32 [00:03<01:44, 3.36s/it]\nFlavor chain: 6%|▋ | 2/32 [00:06<01:41, 3.38s/it]\nFlavor chain: 9%|▉ | 3/32 [00:10<01:38, 3.40s/it]\nFlavor chain: 12%|█▎ | 4/32 [00:13<01:35, 3.42s/it]\nFlavor chain: 16%|█▌ | 5/32 [00:17<01:32, 3.43s/it]\nFlavor chain: 19%|█▉ | 6/32 [00:20<01:29, 3.45s/it]\nFlavor chain: 22%|██▏ | 7/32 [00:24<01:26, 3.47s/it]\nFlavor chain: 25%|██▌ | 8/32 [00:27<01:23, 3.48s/it]\nFlavor chain: 25%|██▌ | 8/32 [00:31<01:33, 3.89s/it]\n 0%| | 0/55 [00:00<?, ?it/s]\n 42%|████▏ | 23/55 [00:00<00:00, 225.56it/s]\n 84%|████████▎ | 46/55 [00:00<00:00, 218.01it/s]\n100%|██████████| 55/55 [00:00<00:00, 215.54it/s]\n 0%| | 0/6 [00:00<?, ?it/s]\n100%|██████████| 6/6 [00:00<00:00, 237.27it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 50%|█████ | 25/50 [00:00<00:00, 248.05it/s]\n100%|██████████| 50/50 [00:00<00:00, 261.35it/s]\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:05, 4.68it/s]\n 8%|▊ | 2/25 [00:00<00:04, 4.80it/s]\n 12%|█▏ | 3/25 [00:00<00:04, 4.84it/s]\n 16%|█▌ | 4/25 [00:00<00:04, 4.85it/s]\n 20%|██ | 5/25 [00:01<00:04, 4.84it/s]\n 24%|██▍ | 6/25 [00:01<00:03, 4.83it/s]\n 28%|██▊ | 7/25 [00:01<00:03, 4.84it/s]\n 32%|███▏ | 8/25 [00:01<00:03, 4.85it/s]\n 36%|███▌ | 9/25 [00:01<00:03, 4.85it/s]\n 40%|████ | 10/25 [00:02<00:03, 4.83it/s]\n 44%|████▍ | 11/25 [00:02<00:02, 4.82it/s]\n 48%|████▊ | 12/25 [00:02<00:02, 4.84it/s]\n 52%|█████▏ | 13/25 [00:02<00:02, 4.84it/s]\n 56%|█████▌ | 14/25 [00:02<00:02, 4.84it/s]\n 60%|██████ | 15/25 [00:03<00:02, 4.84it/s]\n 64%|██████▍ | 16/25 [00:03<00:01, 4.83it/s]\n 68%|██████▊ | 17/25 [00:03<00:01, 4.84it/s]\n 72%|███████▏ | 18/25 [00:03<00:01, 4.84it/s]\n 76%|███████▌ | 19/25 [00:03<00:01, 4.77it/s]\n 80%|████████ | 20/25 [00:04<00:01, 4.78it/s]\n 84%|████████▍ | 21/25 [00:04<00:00, 4.79it/s]\n 88%|████████▊ | 22/25 [00:04<00:00, 4.81it/s]\n 92%|█████████▏| 23/25 [00:04<00:00, 4.82it/s]\n 96%|█████████▌| 24/25 [00:04<00:00, 4.82it/s]\n100%|██████████| 25/25 [00:05<00:00, 4.82it/s]\n100%|██████████| 25/25 [00:05<00:00, 4.82it/s]",
"metrics": {
"predict_time": 104.240489,
"total_time": 489.536027
},
"output": "https://replicate.delivery/pbxt/OgH33MrXOuL4DlSPBG2OTaDWFTey5t1rqCrdpV1A9EWkOpAJA/output.png",
"started_at": "2023-12-11T20:45:21.323798Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/argibx3bllmi3rtifjjbvasvie",
"cancel": "https://api.replicate.com/v1/predictions/argibx3bllmi3rtifjjbvasvie/cancel"
},
"version": "192db23e4232f6a722111ce49776c055d6b2952a7d2feed0e50c6b96a8af1227"
}
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This model costs approximately $0.38 to run on Replicate, or 2 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 7 minutes.
Implementation of SDXL Image blending via Compels weighted prompt blending
Inspired by the ComfyUI ReVision tutorial here
10/11/23 - Updated Cog to use pget to download weights
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
Choose a file from your machine
Hint: you can also drag files onto the input
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