Instructions to use SamuelTallet/FLUX.1-Fill-dev-SDNQ-4bit-dynamic-hadamard256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SamuelTallet/FLUX.1-Fill-dev-SDNQ-4bit-dynamic-hadamard256 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("SamuelTallet/FLUX.1-Fill-dev-SDNQ-4bit-dynamic-hadamard256", 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
This is FLUX.1 Fill [dev] optimized using SDNQ with UINT4 dynamic quantization and Hadamard Rotation (Group size: 256).
Sample
| Input | Mask | Output |
|---|---|---|
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Prompt:
a white paper cup
Seed: 0
Usage
Install Torch, Diffusers (Git), SDNQ 0.2.0+ and Triton.
Use CFG 30 and 50 steps.
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Model tree for SamuelTallet/FLUX.1-Fill-dev-SDNQ-4bit-dynamic-hadamard256
Base model
black-forest-labs/FLUX.1-Fill-dev

