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alexgenovese /test-endpoint:cacd901f
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=alexgenovese/test-endpoint
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import 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 alexgenovese/test-endpoint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"alexgenovese/test-endpoint:cacd901fa63f464ab00d47dcca090658defb4d3d75ff047f564151f854d23764",
{
input: {
seed: null,
width: 1024,
height: 1024,
prompt: "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3",
scheduler: "PNDM",
base_model: "FFusion/FFusionXL-BASE",
num_outputs: 1,
guidance_scale: 7.5,
apply_watermark: true,
negative_prompt: "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting",
num_inference_steps: 50
}
}
);
// 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 replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun alexgenovese/test-endpoint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"alexgenovese/test-endpoint:cacd901fa63f464ab00d47dcca090658defb4d3d75ff047f564151f854d23764",
input={
"seed": null,
"width": 1024,
"height": 1024,
"prompt": "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3",
"scheduler": "PNDM",
"base_model": "FFusion/FFusionXL-BASE",
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": True,
"negative_prompt": "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting",
"num_inference_steps": 50
}
)
print(output)To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run alexgenovese/test-endpoint 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": "alexgenovese/test-endpoint:cacd901fa63f464ab00d47dcca090658defb4d3d75ff047f564151f854d23764",
"input": {
"seed": null,
"width": 1024,
"height": 1024,
"prompt": "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3",
"scheduler": "PNDM",
"base_model": "FFusion/FFusionXL-BASE",
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"negative_prompt": "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting",
"num_inference_steps": 50
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:
brew install cogIf 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/alexgenovese/test-endpoint@sha256:cacd901fa63f464ab00d47dcca090658defb4d3d75ff047f564151f854d23764 \
-i 'seed=null' \
-i 'width=1024' \
-i 'height=1024' \
-i 'prompt="A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3"' \
-i 'scheduler="PNDM"' \
-i 'base_model="FFusion/FFusionXL-BASE"' \
-i 'num_outputs=1' \
-i 'guidance_scale=7.5' \
-i 'apply_watermark=true' \
-i 'negative_prompt="worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting"' \
-i 'num_inference_steps=50'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/alexgenovese/test-endpoint@sha256:cacd901fa63f464ab00d47dcca090658defb4d3d75ff047f564151f854d23764curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": null, "width": 1024, "height": 1024, "prompt": "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3", "scheduler": "PNDM", "base_model": "FFusion/FFusionXL-BASE", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "negative_prompt": "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting", "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
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Output
{
"completed_at": "2023-09-22T20:02:12.199470Z",
"created_at": "2023-09-22T20:01:59.458620Z",
"data_removed": false,
"error": null,
"id": "wwhxk6rbuksj2qucmd3swphcdm",
"input": {
"seed": null,
"width": 1024,
"height": 1024,
"prompt": "A realistic fashion photography of an elegant 30 years old blonde woman in an hotel with one red bag. ((full shot)) 8k uhd, dslr, soft lighting, high quality, Fujifilm XT3",
"scheduler": "PNDM",
"base_model": "FFusion/FFusionXL-BASE",
"num_outputs": 1,
"guidance_scale": 7.5,
"apply_watermark": true,
"negative_prompt": "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting",
"num_inference_steps": 50
},
"logs": "Using seed: 7649\nLoading sdxl pipeline...\nA mixture of fp16 and non-fp16 filenames will be loaded.\nLoaded fp16 filenames:\n[text_encoder/model.fp16.safetensors, vae_0_9/diffusion_pytorch_model.fp16.safetensors, text_encoder_2/model.fp16.safetensors, vae_1_0/diffusion_pytorch_model.fp16.safetensors, unet/diffusion_pytorch_model.fp16.safetensors, vae/diffusion_pytorch_model.fp16.safetensors]\nLoaded non-fp16 filenames:\n[model.safetensors\nIf this behavior is not expected, please check your folder structure.\nLoading pipeline components...: 0%| | 0/7 [00:00<?, ?it/s]\nLoading pipeline components...: 14%|█▍ | 1/7 [00:00<00:00, 6.66it/s]\nLoading pipeline components...: 43%|████▎ | 3/7 [00:00<00:00, 8.77it/s]\nLoading pipeline components...: 71%|███████▏ | 5/7 [00:00<00:00, 11.31it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:02<00:00, 2.29it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:02<00:00, 3.04it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.73it/s]\n 4%|▍ | 2/50 [00:00<00:08, 5.60it/s]\n 6%|▌ | 3/50 [00:00<00:07, 6.66it/s]\n 8%|▊ | 4/50 [00:00<00:06, 7.27it/s]\n 10%|█ | 5/50 [00:00<00:05, 7.67it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 7.97it/s]\n 14%|█▍ | 7/50 [00:00<00:05, 8.17it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 8.31it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.40it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.46it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.51it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.54it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.56it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 8.47it/s]\n 30%|███ | 15/50 [00:01<00:04, 8.51it/s]\n 32%|███▏ | 16/50 [00:02<00:03, 8.54it/s]\n 34%|███▍ | 17/50 [00:02<00:03, 8.56it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 8.58it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 8.59it/s]\n 40%|████ | 20/50 [00:02<00:03, 8.60it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 8.52it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 8.44it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 8.48it/s]\n 48%|████▊ | 24/50 [00:02<00:03, 8.52it/s]\n 50%|█████ | 25/50 [00:03<00:02, 8.55it/s]\n 52%|█████▏ | 26/50 [00:03<00:02, 8.57it/s]\n 54%|█████▍ | 27/50 [00:03<00:03, 7.53it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 7.76it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 8.00it/s]\n 60%|██████ | 30/50 [00:03<00:02, 8.17it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 8.21it/s]\n 64%|██████▍ | 32/50 [00:03<00:02, 8.32it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 8.41it/s]\n 68%|██████▊ | 34/50 [00:04<00:01, 8.40it/s]\n 70%|███████ | 35/50 [00:04<00:01, 8.46it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 8.51it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.54it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.56it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.56it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.57it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.58it/s]\n 84%|████████▍ | 42/50 [00:05<00:00, 8.59it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 8.59it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.60it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.52it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.36it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.42it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.42it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.47it/s]\n100%|██████████| 50/50 [00:06<00:00, 8.51it/s]\n100%|██████████| 50/50 [00:06<00:00, 8.25it/s]",
"metrics": {
"predict_time": 12.821431,
"total_time": 12.74085
},
"output": [
"https://replicate.delivery/pbxt/YRnIHam8EYa0Bhe902tLRSmcrPweduCEuKJ9PkjkCfYGm0NjA/out-0.png"
],
"started_at": "2023-09-22T20:01:59.378039Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/wwhxk6rbuksj2qucmd3swphcdm",
"cancel": "https://api.replicate.com/v1/predictions/wwhxk6rbuksj2qucmd3swphcdm/cancel"
},
"version": "cacd901fa63f464ab00d47dcca090658defb4d3d75ff047f564151f854d23764"
}Generated in
Using seed: 7649
Loading sdxl pipeline...
A mixture of fp16 and non-fp16 filenames will be loaded.
Loaded fp16 filenames:
[text_encoder/model.fp16.safetensors, vae_0_9/diffusion_pytorch_model.fp16.safetensors, text_encoder_2/model.fp16.safetensors, vae_1_0/diffusion_pytorch_model.fp16.safetensors, unet/diffusion_pytorch_model.fp16.safetensors, vae/diffusion_pytorch_model.fp16.safetensors]
Loaded non-fp16 filenames:
[model.safetensors
If this behavior is not expected, please check your folder structure.
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