Update app.py

app.py

@@ -13,9 +13,32 @@ from PIL import Image
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
-pipe.load_lora_weights("blanchon/FluxFillFurniture", weight_name="lora_fill.safetensors")
-pipe.fuse_lora(lora_scale=1.0)
+# pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
+transformer = FluxTransformer2DModel.from_pretrained(
+    "xiaozaa/flux1-fill-dev-diffusers",   ## The official Flux-Fill weights
+    torch_dtype=torch.bfloat16
+)
+print("Start loading LoRA weights")
+state_dict, network_alphas = FluxFillPipeline.lora_state_dict(
+    pretrained_model_name_or_path_or_dict="blanchon/FluxFillFurniture",
+    weight_name="lora_fill.safetensors",
+    return_alphas=True
+)
+is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
+if not is_correct_format:
+    raise ValueError("Invalid LoRA checkpoint.")
+FluxFillPipeline.load_lora_into_transformer(
+    state_dict=state_dict,
+    network_alphas=network_alphas,
+    transformer=transformer,
+)
+pipe = FluxFillPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Fill-dev", 
+    torch_dtype=torch.bfloat16,
+    transformer=transformer,
+).to("cuda")
+# pipe.load_lora_weights("blanchon/FluxFillFurniture", weight_name="lora_fill.safetensors")
+# pipe.fuse_lora(lora_scale=1.0)
 pipe.to("cuda")
 
 def calculate_optimal_dimensions(image: Image.Image):