Update gguf_loader.py

gguf_loader.py

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