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fix many inpaint bugs (#731)

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fix many inpaint bugs
This commit is contained in:
lllyasviel
2023-10-18 06:22:08 -07:00
committed by GitHub
parent d2c8f16082
commit 9660daff94
6 changed files with 131 additions and 96 deletions
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modules/async_worker.py
+27 -28
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@@ -400,43 +400,42 @@ def worker():
pipeline.final_unet.model.diffusion_model.in_inpaint = True
# print(f'Inpaint task: {str((height, width))}')
# outputs.append(['results', inpaint_worker.current_task.visualize_mask_processing()])
# return
progressbar(13, 'VAE encoding ...')
inpaint_pixels = core.numpy_to_pytorch(inpaint_worker.current_task.image_ready)
initial_latent = core.encode_vae(vae=pipeline.final_vae, pixels=inpaint_pixels)
inpaint_latent = initial_latent['samples']
B, C, H, W = inpaint_latent.shape
inpaint_mask = core.numpy_to_pytorch(inpaint_worker.current_task.mask_ready[None])
inpaint_mask = torch.nn.functional.avg_pool2d(inpaint_mask, (8, 8))
inpaint_mask = torch.nn.functional.interpolate(inpaint_mask, (H, W), mode='bilinear')
progressbar(13, 'VAE Inpaint encoding ...')
latent_after_swap = None
inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask)
latent_inpaint, latent_mask = core.encode_vae_inpaint(
mask=inpaint_pixel_mask,
vae=pipeline.final_vae,
pixels=inpaint_pixel_image)
latent_swap = None
if pipeline.final_refiner_vae is not None:
progressbar(13, 'VAE SD15 encoding ...')
latent_after_swap = core.encode_vae(vae=pipeline.final_refiner_vae, pixels=inpaint_pixels)['samples']
progressbar(13, 'VAE Inpaint SD15 encoding ...')
latent_swap = core.encode_vae(
vae=pipeline.final_refiner_vae,
pixels=inpaint_pixel_fill)['samples']
inpaint_worker.current_task.load_latent(latent=inpaint_latent, mask=inpaint_mask,
latent_after_swap=latent_after_swap)
progressbar(13, 'VAE encoding ...')
latent_fill = core.encode_vae(
vae=pipeline.final_vae,
pixels=inpaint_pixel_fill)['samples']
progressbar(13, 'VAE inpaint encoding ...')
inpaint_worker.current_task.load_latent(latent_fill=latent_fill,
latent_inpaint=latent_inpaint,
latent_mask=latent_mask,
latent_swap=latent_swap,
inpaint_head_model_path=inpaint_head_model_path)
inpaint_mask = (inpaint_worker.current_task.mask_ready > 0).astype(np.float32)
inpaint_mask = torch.tensor(inpaint_mask).float()
vae_dict = core.encode_vae_inpaint(
mask=inpaint_mask, vae=pipeline.final_vae, pixels=inpaint_pixels)
inpaint_latent = vae_dict['samples']
inpaint_mask = vae_dict['noise_mask']
inpaint_worker.current_task.load_inpaint_guidance(latent=inpaint_latent, mask=inpaint_mask,
model_path=inpaint_head_model_path)
B, C, H, W = inpaint_latent.shape
final_height, final_width = inpaint_worker.current_task.image_raw.shape[:2]
B, C, H, W = latent_fill.shape
height, width = H * 8, W * 8
final_height, final_width = inpaint_worker.current_task.image.shape[:2]
initial_latent = {'samples': latent_fill}
print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.')
if 'cn' in goals: