SkalskiP commited on
Commit
a7c5aa1
·
1 Parent(s): c602aa4
Files changed (1) hide show
  1. app.py +20 -20
app.py CHANGED
@@ -33,8 +33,8 @@ if torch.cuda.get_device_properties(0).major >= 8:
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  FLORENCE_MODEL, FLORENCE_PROCESSOR = load_florence_model(device=DEVICE)
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  SAM_IMAGE_MODEL = load_sam_image_model(device=DEVICE)
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- # FLUX_INPAINTING_PIPELINE = FluxInpaintPipeline.from_pretrained(
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- # "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
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  def resize_image_dimensions(
@@ -63,7 +63,7 @@ def is_image_empty(image: Image.Image) -> bool:
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  return all(pixel == 0 for pixel in pixels)
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- @spaces.GPU()
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  @torch.inference_mode()
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  @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
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  def process(
@@ -128,23 +128,23 @@ def process(
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  mask = mask.resize((width, height), Image.LANCZOS)
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  mask = mask.filter(ImageFilter.GaussianBlur(radius=10))
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- return image, mask
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-
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- # if randomize_seed_checkbox:
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- # seed_slicer = random.randint(0, MAX_SEED)
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- # generator = torch.Generator().manual_seed(seed_slicer)
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- # result = FLUX_INPAINTING_PIPELINE(
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- # prompt=inpainting_prompt_text,
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- # image=image,
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- # mask_image=mask,
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- # width=width,
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- # height=height,
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- # strength=strength_slider,
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- # generator=generator,
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- # num_inference_steps=num_inference_steps_slider
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- # ).images[0]
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- # print('INFERENCE DONE')
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- # return result, mask
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  with gr.Blocks() as demo:
 
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  FLORENCE_MODEL, FLORENCE_PROCESSOR = load_florence_model(device=DEVICE)
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  SAM_IMAGE_MODEL = load_sam_image_model(device=DEVICE)
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+ FLUX_INPAINTING_PIPELINE = FluxInpaintPipeline.from_pretrained(
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+ "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
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  def resize_image_dimensions(
 
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  return all(pixel == 0 for pixel in pixels)
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+ @spaces.GPU(duration=150)
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  @torch.inference_mode()
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  @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
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  def process(
 
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  mask = mask.resize((width, height), Image.LANCZOS)
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  mask = mask.filter(ImageFilter.GaussianBlur(radius=10))
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+ # return image, mask
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+
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+ if randomize_seed_checkbox:
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+ seed_slicer = random.randint(0, MAX_SEED)
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+ generator = torch.Generator().manual_seed(seed_slicer)
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+ result = FLUX_INPAINTING_PIPELINE(
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+ prompt=inpainting_prompt_text,
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+ image=image,
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+ mask_image=mask,
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+ width=width,
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+ height=height,
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+ strength=strength_slider,
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+ generator=generator,
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+ num_inference_steps=num_inference_steps_slider
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+ ).images[0]
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+ print('INFERENCE DONE')
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+ return result, mask
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  with gr.Blocks() as demo: