Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
Flux2Pipeline
image-generation
image-editing
flux
8-bit precision
Instructions to use OzzyGT/FLUX2_dev_sdnq_dynamic_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/FLUX2_dev_sdnq_dynamic_4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OzzyGT/FLUX2_dev_sdnq_dynamic_4bit", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Diffusion Single File
How to use OzzyGT/FLUX2_dev_sdnq_dynamic_4bit with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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- diffusion-single-file
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pipeline_tag: image-to-image
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library_name: diffusers
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- diffusion-single-file
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pipeline_tag: image-to-image
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library_name: diffusers
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---
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# Flux-2.dev SDNQ Dynamic INT4
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This is an int4 quantized version of [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev) using [SDNQ](https://github.com/Disty0/sdnq) (SD.Next Quantization) with the dynamic option and Hadamard Rotation.
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## Usage
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You can find ready-to-use scripts in the [diffusers-recipes](https://github.com/asomoza/diffusers-recipes/blob/main/models/flux2_dev/README.md) repository.
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