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edenartlab /eden-sd-pipelines:961e5735
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 edenartlab/eden-sd-pipelines using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"edenartlab/eden-sd-pipelines:961e5735a1702fda9fb20649a7ff13e2d09481000b265dea27eac3fca1d1daba",
{
input: {
fps: 12,
loop: true,
mode: "generate",
seed: 13,
steps: 60,
width: 512,
height: 512,
n_film: 0,
smooth: false,
stream: false,
sampler: "euler",
uc_text: "poorly drawn face, ugly, tiling, out of frame, extra limbs, disfigured, deformed body, blurry, blurred, watermark, text, grainy, signature, cut off, draft",
n_frames: 50,
n_samples: 1,
upscale_f: 1,
checkpoint: "dreamlike-art/dreamlike-photoreal-2.0",
lora_scale: 0.8,
stream_every: 1,
guidance_scale: 10,
scale_modulation: 0,
init_image_strength: 0,
interpolation_init_images_power: 2.5,
interpolation_init_images_min_strength: 0.2
}
}
);
// 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.
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 edenartlab/eden-sd-pipelines using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"edenartlab/eden-sd-pipelines:961e5735a1702fda9fb20649a7ff13e2d09481000b265dea27eac3fca1d1daba",
input={
"fps": 12,
"loop": True,
"mode": "generate",
"seed": 13,
"steps": 60,
"width": 512,
"height": 512,
"n_film": 0,
"smooth": False,
"stream": False,
"sampler": "euler",
"uc_text": "poorly drawn face, ugly, tiling, out of frame, extra limbs, disfigured, deformed body, blurry, blurred, watermark, text, grainy, signature, cut off, draft",
"n_frames": 50,
"n_samples": 1,
"upscale_f": 1,
"checkpoint": "dreamlike-art/dreamlike-photoreal-2.0",
"lora_scale": 0.8,
"stream_every": 1,
"guidance_scale": 10,
"scale_modulation": 0,
"init_image_strength": 0,
"interpolation_init_images_power": 2.5,
"interpolation_init_images_min_strength": 0.2
}
)
# The edenartlab/eden-sd-pipelines model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
# https://replicate.com/edenartlab/eden-sd-pipelines/api#output-schema
print(item)
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 edenartlab/eden-sd-pipelines 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": "961e5735a1702fda9fb20649a7ff13e2d09481000b265dea27eac3fca1d1daba",
"input": {
"fps": 12,
"loop": true,
"mode": "generate",
"seed": 13,
"steps": 60,
"width": 512,
"height": 512,
"n_film": 0,
"smooth": false,
"stream": false,
"sampler": "euler",
"uc_text": "poorly drawn face, ugly, tiling, out of frame, extra limbs, disfigured, deformed body, blurry, blurred, watermark, text, grainy, signature, cut off, draft",
"n_frames": 50,
"n_samples": 1,
"upscale_f": 1,
"checkpoint": "dreamlike-art/dreamlike-photoreal-2.0",
"lora_scale": 0.8,
"stream_every": 1,
"guidance_scale": 10,
"scale_modulation": 0,
"init_image_strength": 0,
"interpolation_init_images_power": 2.5,
"interpolation_init_images_min_strength": 0.2
}
}' \
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/abraham-ai/eden-sd-pipelines@sha256:961e5735a1702fda9fb20649a7ff13e2d09481000b265dea27eac3fca1d1daba \
-i 'fps=12' \
-i 'loop=true' \
-i 'mode="generate"' \
-i 'seed=13' \
-i 'steps=60' \
-i 'width=512' \
-i 'height=512' \
-i 'n_film=0' \
-i 'smooth=false' \
-i 'stream=false' \
-i 'sampler="euler"' \
-i 'uc_text="poorly drawn face, ugly, tiling, out of frame, extra limbs, disfigured, deformed body, blurry, blurred, watermark, text, grainy, signature, cut off, draft"' \
-i 'n_frames=50' \
-i 'n_samples=1' \
-i 'upscale_f=1' \
-i 'checkpoint="dreamlike-art/dreamlike-photoreal-2.0"' \
-i 'lora_scale=0.8' \
-i 'stream_every=1' \
-i 'guidance_scale=10' \
-i 'scale_modulation=0' \
-i 'init_image_strength=0' \
-i 'interpolation_init_images_power=2.5' \
-i 'interpolation_init_images_min_strength=0.2'
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/abraham-ai/eden-sd-pipelines@sha256:961e5735a1702fda9fb20649a7ff13e2d09481000b265dea27eac3fca1d1daba
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "fps": 12, "loop": true, "mode": "generate", "seed": 13, "steps": 60, "width": 512, "height": 512, "n_film": 0, "smooth": false, "stream": false, "sampler": "euler", "uc_text": "poorly drawn face, ugly, tiling, out of frame, extra limbs, disfigured, deformed body, blurry, blurred, watermark, text, grainy, signature, cut off, draft", "n_frames": 50, "n_samples": 1, "upscale_f": 1, "checkpoint": "dreamlike-art/dreamlike-photoreal-2.0", "lora_scale": 0.8, "stream_every": 1, "guidance_scale": 10, "scale_modulation": 0, "init_image_strength": 0, "interpolation_init_images_power": 2.5, "interpolation_init_images_min_strength": 0.2 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
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Output
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