Update README.md

README.md

@@ -109,7 +109,6 @@ def get_masked_image(image, image_mask, width, height):
 image_transforms = transforms.Compose(
     [
         transforms.ToTensor(),
-        # transforms.Normalize([0.5], [0.5]),
     ]
 )
 
@@ -125,7 +124,6 @@ mask_image = mask_image.convert("L")
 
 width, height = init_image.size
 
-
 # Load, init model
 controlnet = ControlNetModel().from_config('briaai/DEV-ControlNetInpaintingFast', torch_dtype=torch.float16)          
 controlnet.controlnet_cond_embedding = ControlNetConditioningEmbedding(
@@ -133,7 +131,7 @@ controlnet.controlnet_cond_embedding = ControlNetConditioningEmbedding(
     conditioning_channels = 5
 )   
     
-# controlnet.load_state_dict(torch.load('briaai/DEV-ControlNetInpaintingFast'))
+controlnet = ControlNetModel().from_pretrained("briaai/DEV-ControlNetInpaintingFast", torch_dtype=torch.float16)
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet.to(dtype=torch.float16), torch_dtype=torch.float16, vae=vae) #force_zeros_for_empty_prompt=False, # vae=vae)
 
@@ -146,6 +144,7 @@ pipe.enable_xformers_memory_efficient_attention()
 
 generator = torch.Generator(device='cuda:0').manual_seed(123456)
 
+
 vae = pipe.vae
 
 masked_image, image_mask, masked_image_to_present = get_masked_image(init_image, mask_image, width, height)
@@ -153,7 +152,6 @@ masked_image_tensor = image_transforms(masked_image)
 masked_image_tensor = (masked_image_tensor - 0.5) / 0.5
 
 masked_image_tensor = masked_image_tensor.unsqueeze(0).to(device="cuda")
-# masked_image_tensor = masked_image_tensor.permute((0,3,1,2))
 control_latents = vae.encode(  
         masked_image_tensor[:, :3, :, :].to(vae.dtype)
     ).latent_dist.sample()   
@@ -185,6 +183,5 @@ gen_img = pipe(negative_prompt=default_negative_prompt, prompt=prompt,
             generator=generator).images[0]
 
 
-gen_img.save("./a_park_bench.png")
 ```