Image-to-Image
Diffusers
Safetensors
Transformers
GGUF
English
Flux2Pipeline
image-generation
image-editing
text-to-image
flux2
flux
quantization
hqq
optimization
quantized
2bit
8-bit precision
Instructions to use AlekseyCalvin/FLUX2_dev_2bit_hqq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlekseyCalvin/FLUX2_dev_2bit_hqq 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("AlekseyCalvin/FLUX2_dev_2bit_hqq", 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] - Transformers
How to use AlekseyCalvin/FLUX2_dev_2bit_hqq with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-image" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-image", model="AlekseyCalvin/FLUX2_dev_2bit_hqq")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlekseyCalvin/FLUX2_dev_2bit_hqq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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