Image-to-Image
Diffusers
Safetensors
Diffusion Single File
English
Flux2KleinPipeline
text-to-image
image-editing
flux
Instructions to use YuCollection/FLUX.2-klein-4B-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use YuCollection/FLUX.2-klein-4B-Diffusers 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("YuCollection/FLUX.2-klein-4B-Diffusers", 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 YuCollection/FLUX.2-klein-4B-Diffusers 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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# FLUX.2 [klein]
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> **Note:** This repository is an **archived mirror** and is **not** the original upstream source.
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> The original model, weights, and documentation are developed and maintained by **Black Forest Labs**.
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> This mirror is provided solely for convenience and preservation purposes.
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> The model weights hosted here are **unmodified**. Minor editorial changes may have been made to this README (e.g., formatting or clarification) and do **not** alter the model, its behavior, or its licensing.
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> The model is released under the **Apache License, Version 2.0**, which permits use, reproduction, modification, and redistribution, including for commercial purposes, subject to the terms of the license.
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> **Cooperation Notice:**
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> If the original rights holder or upstream maintainers of this model request changes, clarification, or removal of this mirror, the repository maintainer is willing to cooperate in good faith.
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> Any such requests
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The **FLUX.2 [klein]** model family represents Black Forest Labs’ fastest image models to date. FLUX.2 [klein] unifies image generation and editing within a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in under one second**. It is designed for real-time and latency-critical applications and runs on consumer hardware with as little as **~13GB VRAM**.
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# FLUX.2 [klein]
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> **Note:** This repository is an **archived mirror** and is **not** the original upstream source.
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> The original model, weights, and documentation are developed and maintained by **Black Forest Labs**.
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>
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> This mirror is provided solely for convenience and preservation purposes.
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> The model weights hosted here are **unmodified**. Minor editorial changes may have been made to this README (e.g., formatting or clarification) and do **not** alter the model, its behavior, or its licensing.
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> The model is released under the **Apache License, Version 2.0**, which permits use, reproduction, modification, and redistribution, including for commercial purposes, subject to the terms of the license.
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> A copy of the Apache License, Version 2.0 is included in this repository.
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> **Cooperation Notice:**
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> If the original rights holder or upstream maintainers of this model request changes, clarification, or removal of this mirror, the repository maintainer is willing to cooperate in good faith, where reasonable and consistent with the Apache License, Version 2.0.
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> Any such requests may be raised via the **Discussions** section of this repository so they can be addressed transparently and appropriately.
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> *This repository does not claim any affiliation with or endorsement by Black Forest Labs.*
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The **FLUX.2 [klein]** model family represents Black Forest Labs’ fastest image models to date. FLUX.2 [klein] unifies image generation and editing within a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in under one second**. It is designed for real-time and latency-critical applications and runs on consumer hardware with as little as **~13GB VRAM**.
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