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goodguy1963 /good-sdxl-models-plus-loras:4ea2fb4e
Input
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variable:export 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 goodguy1963/good-sdxl-models-plus-loras using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"goodguy1963/good-sdxl-models-plus-loras:4ea2fb4e250cb8749d0375f010823c2c2b69f3b7d14c6a03a7ed7298213fb4f9",
{
input: {
width: 768,
height: 768,
sampler: "DPMSolverMultistep",
model_name: "realvisxl_v50_sg161222",
lora_weight: 0.8,
guidance_scale: 7.5,
lora_selection: "none",
scheduler_type: "default",
negative_prompt: "worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art, mutated, deformed, distorted, disfigured, body horror, watermark, text, meme, bad proportions, cropped head, out of frame, cut off, ugly, duplicate, mutilated, mutation, disgusting, bad anatomy, bad hands, three hands, three legs, bad feet, three feet",
prompt_strength: 0.8,
num_inference_steps: 30
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run goodguy1963/good-sdxl-models-plus-loras using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"goodguy1963/good-sdxl-models-plus-loras:4ea2fb4e250cb8749d0375f010823c2c2b69f3b7d14c6a03a7ed7298213fb4f9",
input={
"width": 768,
"height": 768,
"sampler": "DPMSolverMultistep",
"model_name": "realvisxl_v50_sg161222",
"lora_weight": 0.8,
"guidance_scale": 7.5,
"lora_selection": "none",
"scheduler_type": "default",
"negative_prompt": "worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art, mutated, deformed, distorted, disfigured, body horror, watermark, text, meme, bad proportions, cropped head, out of frame, cut off, ugly, duplicate, mutilated, mutation, disgusting, bad anatomy, bad hands, three hands, three legs, bad feet, three feet",
"prompt_strength": 0.8,
"num_inference_steps": 30
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run goodguy1963/good-sdxl-models-plus-loras 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": "4ea2fb4e250cb8749d0375f010823c2c2b69f3b7d14c6a03a7ed7298213fb4f9",
"input": {
"width": 768,
"height": 768,
"sampler": "DPMSolverMultistep",
"model_name": "realvisxl_v50_sg161222",
"lora_weight": 0.8,
"guidance_scale": 7.5,
"lora_selection": "none",
"scheduler_type": "default",
"negative_prompt": "worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art, mutated, deformed, distorted, disfigured, body horror, watermark, text, meme, bad proportions, cropped head, out of frame, cut off, ugly, duplicate, mutilated, mutation, disgusting, bad anatomy, bad hands, three hands, three legs, bad feet, three feet",
"prompt_strength": 0.8,
"num_inference_steps": 30
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/goodguy1963/good-sdxl-models-plus-loras@sha256:4ea2fb4e250cb8749d0375f010823c2c2b69f3b7d14c6a03a7ed7298213fb4f9 \
-i 'width=768' \
-i 'height=768' \
-i 'sampler="DPMSolverMultistep"' \
-i 'model_name="realvisxl_v50_sg161222"' \
-i 'lora_weight=0.8' \
-i 'guidance_scale=7.5' \
-i 'lora_selection="none"' \
-i 'scheduler_type="default"' \
-i 'negative_prompt="worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art, mutated, deformed, distorted, disfigured, body horror, watermark, text, meme, bad proportions, cropped head, out of frame, cut off, ugly, duplicate, mutilated, mutation, disgusting, bad anatomy, bad hands, three hands, three legs, bad feet, three feet"' \
-i 'prompt_strength=0.8' \
-i 'num_inference_steps=30'
To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/goodguy1963/good-sdxl-models-plus-loras@sha256:4ea2fb4e250cb8749d0375f010823c2c2b69f3b7d14c6a03a7ed7298213fb4f9
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 768, "height": 768, "sampler": "DPMSolverMultistep", "model_name": "realvisxl_v50_sg161222", "lora_weight": 0.8, "guidance_scale": 7.5, "lora_selection": "none", "scheduler_type": "default", "negative_prompt": "worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art, mutated, deformed, distorted, disfigured, body horror, watermark, text, meme, bad proportions, cropped head, out of frame, cut off, ugly, duplicate, mutilated, mutation, disgusting, bad anatomy, bad hands, three hands, three legs, bad feet, three feet", "prompt_strength": 0.8, "num_inference_steps": 30 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
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Output
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