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refactor to unify latent, resize and model based upscalers

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-01-18 11:49:43 -05:00
parent facfe2be6b
commit ae9b40c688
17 changed files with 223 additions and 169 deletions
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modules/processing_helpers.py
+9 -10
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@@ -409,20 +409,19 @@ def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler
shared.log.warning('Hires: input is not tensor')
first_pass_images = processing_vae.vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
return first_pass_images
latent_upscaler = shared.latent_upscale_modes.get(p.hr_upscaler, None)
# shared.log.info(f'Hires: upscaler={p.hr_upscaler} width={p.hr_upscale_to_x} height={p.hr_upscale_to_y} images={latents.shape[0]}')
if latent_upscaler is not None:
return torch.nn.functional.interpolate(latents, size=(p.hr_upscale_to_y // 8, p.hr_upscale_to_x // 8), mode=latent_upscaler["mode"], antialias=latent_upscaler["antialias"])
first_pass_images = processing_vae.vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
if p.hr_upscale_to_x == 0 or (p.hr_upscale_to_y == 0 and hasattr(p, 'init_hr')):
if (p.hr_upscale_to_x == 0 or p.hr_upscale_to_y == 0) and hasattr(p, 'init_hr'):
shared.log.error('Hires: missing upscaling dimensions')
return first_pass_images
if p.hr_upscaler.lower().startswith('latent'):
resized_image = images.resize_image(p.hr_resize_mode, latents, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
return resized_image
first_pass_images = processing_vae.vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
resized_images = []
for img in first_pass_images:
if latent_upscaler is None:
resized_image = images.resize_image(p.hr_resize_mode, img, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
else:
resized_image = img
resized_image = images.resize_image(p.hr_resize_mode, img, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
resized_images.append(resized_image)
devices.torch_gc()
return resized_images