INNER CODE UNIT · Python

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Shilin-LU/TF-ICON · scripts/app.py:75

        w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
        w = h = 512
        image = image.resize((w, h), resample=PIL.Image.LANCZOS)
        
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    
    if pad or seg_map:
        return 2. * image - 1., new_w, new_h, padded_segmentation_map
    
    return 2. * image - 1., w, h 


def load_model_and_get_prompt_embedding(model, scale, device, prompts, inv=False):
           
    if inv:
        inv_emb = model.get_learned_conditioning(prompts, inv)

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