Instructions to use shangguanyanyan/flux1-fill-dev-custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shangguanyanyan/flux1-fill-dev-custom with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import FluxFillPipeline from diffusers.utils import load_image image = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup.png") mask = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup_mask.png") # switch to "mps" for apple devices pipe = FluxFillPipeline.from_pretrained("shangguanyanyan/flux1-fill-dev-custom", dtype=torch.bfloat16, device_map="cuda") image = pipe( prompt="a white paper cup", image=image, mask_image=mask, height=1632, width=1232, guidance_scale=30, num_inference_steps=50, max_sequence_length=512, generator=torch.Generator("cpu").manual_seed(0) ).images[0] image.save(f"flux-fill-dev.png") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
FLUX.1-Fill-dev Custom Inference Endpoint
This repository is set up to deploy a custom Hugging Face Inference Endpoint using the StableDiffusionInstructPix2PixPipeline, enabling image-to-image transformation with prompt guidance.
How to Use
Send a POST request with base64-encoded image and your prompt to get an enhanced version of the image.
Input example:
{
"inputs": {
"prompt": "高清艺术风格头像",
"image": "<base64-encoded-image>",
"steps": 30,
"guidance_scale": 7.5,
"image_guidance_scale": 1.5
}
}
- Downloads last month
- 81
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support