parent
969a54e85b
commit
b6752c74e3
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@ -1 +1 @@
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version = '2.0.68'
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version = '2.0.70'
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@ -55,6 +55,7 @@ def worker():
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outpaint_selections = [o.lower() for o in outpaint_selections]
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loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
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loras_user_raw_input = copy.deepcopy(loras)
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raw_style_selections = copy.deepcopy(style_selections)
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@ -207,8 +208,6 @@ def worker():
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inpaint_mask = core.numpy_to_pytorch(inpaint_worker.current_task.mask_ready[None])
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inpaint_mask = torch.nn.functional.avg_pool2d(inpaint_mask, (8, 8))
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inpaint_mask = torch.nn.functional.interpolate(inpaint_mask, (H, W), mode='bilinear')
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width = W * 8
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height = H * 8
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inpaint_worker.current_task.load_latent(latent=inpaint_latent, mask=inpaint_mask)
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progressbar(0, 'VAE inpaint encoding ...')
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@ -223,6 +222,10 @@ def worker():
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inpaint_mask = vae_dict['noise_mask']
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inpaint_worker.current_task.load_inpaint_guidance(latent=inpaint_latent, mask=inpaint_mask, model_path=inpaint_head_model_path)
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B, C, H, W = inpaint_latent.shape
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height, width = inpaint_worker.current_task.image_raw.shape[:2]
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print(f'Final resolution is {str((height, width))}, latent is {str((H * 8, W * 8))}.')
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progressbar(1, 'Initializing ...')
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raw_prompt = prompt
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@ -363,7 +366,7 @@ def worker():
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('Refiner Model', refiner_model_name),
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('Seed', task['task_seed'])
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]
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for n, w in loras:
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for n, w in loras_user_raw_input:
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if n != 'None':
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d.append((f'LoRA [{n}] weight', w))
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log(x, d, single_line_number=3)
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