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--- |
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language: |
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- en |
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license: other |
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license_name: flux-1-dev-non-commercial-license |
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license_link: LICENSE.md |
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tags: |
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- text-to-image |
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- image-generation |
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- flux |
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--- |
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`black-forest-labs/FLUX.1-dev` quantized to INT8 using Optimum Quanto. |
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```shell |
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pip install diffusers optimum-quanto |
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``` |
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```python |
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import json |
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import torch |
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import diffusers |
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import transformers |
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from optimum.quanto import requantize |
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from safetensors.torch import load_file |
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from huggingface_hub import hf_hub_download |
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def load_quanto_transformer(repo_path): |
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with open(hf_hub_download(repo_path, "transformer/quantization_map.json"), "r") as f: |
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quantization_map = json.load(f) |
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with torch.device("meta"): |
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transformer = diffusers.FluxTransformer2DModel.from_config(hf_hub_download(repo_path, "transformer/config.json")).to(torch.bfloat16) |
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state_dict = load_file(hf_hub_download(repo_path, "transformer/diffusion_pytorch_model.safetensors")) |
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requantize(transformer, state_dict, quantization_map, device=torch.device("cuda")) |
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return transformer |
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def load_quanto_text_encoder_2(repo_path): |
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with open(hf_hub_download(repo_path, "text_encoder_2/quantization_map.json"), "r") as f: |
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quantization_map = json.load(f) |
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with open(hf_hub_download(repo_path, "text_encoder_2/config.json")) as f: |
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t5_config = transformers.T5Config(**json.load(f)) |
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with torch.device("meta"): |
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text_encoder_2 = transformers.T5EncoderModel(t5_config).to(torch.bfloat16) |
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state_dict = load_file(hf_hub_download(repo_path, "text_encoder_2/model.safetensors")) |
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requantize(text_encoder_2, state_dict, quantization_map, device=torch.device("cuda")) |
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return text_encoder_2 |
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pipe = diffusers.AutoPipelineForText2Image.from_pretrained("Disty0/FLUX.1-dev-qint8", transformer=None, text_encoder_2=None, torch_dtype=torch.bfloat16) |
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pipe.transformer = load_quanto_transformer("Disty0/FLUX.1-dev-qint8") |
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pipe.text_encoder_2 = load_quanto_text_encoder_2("Disty0/FLUX.1-dev-qint8") |
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pipe = pipe.to("cuda", dtype=torch.bfloat16) |
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prompt = "A cat holding a sign that says hello world" |
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image = pipe( |
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prompt, |
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height=1024, |
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width=1024, |
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guidance_scale=3.5, |
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num_inference_steps=50, |
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max_sequence_length=512, |
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generator=torch.Generator("cpu").manual_seed(0) |
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).images[0] |
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image.save("flux-dev.png") |
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``` |
