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Update gguf_loader.py
Browse files- gguf_loader.py +45 -1
gguf_loader.py
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@@ -2,6 +2,8 @@ import torch
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import logging
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from pathlib import Path
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from typing import Optional, Union, Dict, Any
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class GGUFUNetLoader:
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"""
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@@ -141,4 +143,46 @@ class GGUFUNetLoader:
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result = base_weight.clone()
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for patch in patches:
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result += patch
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return result
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import logging
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from pathlib import Path
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from typing import Optional, Union, Dict, Any
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# Set up logging
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logging.basicConfig(level=logging.INFO)
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class GGUFUNetLoader:
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"""
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result = base_weight.clone()
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for patch in patches:
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result += patch
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return result
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def main():
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# Initialize the loader
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loader = GGUFUNetLoader()
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# Specify model path
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model_path = Path("path/to/your/model.gguf")
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ckpt_path = (
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"https://huggingface.co/city96/flux.1-lite-8B-alpha-gguf/flux.1-lite-8B-alpha-Q3_K_S.gguf"
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)
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transformer = FluxTransformer2DModel.from_single_file(
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ckpt_path,
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quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
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torch_dtype=torch.bfloat16,
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)
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pipe = FluxPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-dev",
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transformer=transformer,
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torch_dtype=torch.bfloat16,
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)
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# https://huggingface.co/martintomov/Hyper-FLUX.1-dev-gguf/resolve/main/hyper-flux-16step-Q3_K_M.gguf
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#pipe = FluxPipeline.from_pretrained("flux1-schnell-Q3_K_S.gguf")
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pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"))
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pipe.fuse_lora(lora_scale=0.125)
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pipe.enable_model_cpu_offload()
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prompt = "A cat holding a sign that says hello world"
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image = pipe(prompt, generator=torch.manual_seed(0)).images[0]
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image.save("flux-gguf.png")
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# Optional configuration for model loading
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config = {
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"attention_slicing": "auto",
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"channels_last": True
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}
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if __name__ == "__main__":
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main()
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