Delete main.py

main.py

@@ -1,50 +0,0 @@
-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")