Create main.py

main.py

@@ -0,0 +1,50 @@
+import torch
+from diffusers.utils import load_image, check_min_version
+from controlnet_flux import FluxControlNetModel
+from transformer_flux import FluxTransformer2DModel
+from pipeline_flux_controlnet_inpaint import FluxControlNetInpaintingPipeline
+
+check_min_version("0.30.2")
+
+# Set image path , mask path and prompt
+image_path='https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha/resolve/main/images/bucket.png',
+mask_path='https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha/resolve/main/images/bucket_mask.jpeg',
+prompt='a person wearing a white shoe, carrying a white bucket with text "FLUX" on it'
+
+# Build pipeline
+controlnet = FluxControlNetModel.from_pretrained("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", torch_dtype=torch.bfloat16)
+transformer = FluxTransformer2DModel.from_pretrained(
+        "black-forest-labs/FLUX.1-dev", subfolder='transformer', torch_dytpe=torch.bfloat16
+    )
+pipe = FluxControlNetInpaintingPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    controlnet=controlnet,
+    transformer=transformer,
+    torch_dtype=torch.bfloat16
+).to("cuda")
+pipe.transformer.to(torch.bfloat16)
+pipe.controlnet.to(torch.bfloat16)
+
+# Load image and mask
+size = (768, 768)
+image = load_image(image_path).convert("RGB").resize(size)
+mask = load_image(mask_path).convert("RGB").resize(size)
+generator = torch.Generator(device="cuda").manual_seed(24)
+
+# Inpaint
+result = pipe(
+    prompt=prompt,
+    height=size[1],
+    width=size[0],
+    control_image=image,
+    control_mask=mask,
+    num_inference_steps=28,
+    generator=generator,
+    controlnet_conditioning_scale=0.9,
+    guidance_scale=3.5,
+    negative_prompt="",
+    true_guidance_scale=3.5
+).images[0]
+
+result.save('flux_inpaint.png')
+print("Successfully inpaint image")