Image-to-Image
Diffusers
Safetensors
Transformers
GGUF
English
Flux2Pipeline
image-generation
image-editing
text-to-image
flux2
flux
quantization
hqq
optimization
quantized
2bit
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 from transformers import pipeline pipe = pipeline("image-to-image", model="AlekseyCalvin/FLUX2_dev_2bit_hqq")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlekseyCalvin/FLUX2_dev_2bit_hqq", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update text_encoder/config.json
Browse files- text_encoder/config.json +1 -0
text_encoder/config.json
CHANGED
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@@ -48,6 +48,7 @@
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"zero_quant_params": null
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},
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"quant_method": "hqq",
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"skip_modules": [
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"lm_head"
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]
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"zero_quant_params": null
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},
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"quant_method": "hqq",
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+
"format": "hqq_2bit_packed",
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"skip_modules": [
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"lm_head"
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]
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