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Skip VAE with latent upscale

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Disty0
2024-03-31 23:45:26 +03:00
parent ee39345c0c
commit 8d77ede895
1 changed files with 2 additions and 2 deletions
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modules/processing_helpers.py
+2 -2
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@@ -355,7 +355,7 @@ def resize_init_images(p):
return p.width, p.height
def resize_hires(p, latents): # input=latents output=pil
def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler else latent
if not torch.is_tensor(latents):
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')
@@ -363,7 +363,7 @@ def resize_hires(p, latents): # input=latents output=pil
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:
latents = 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"])
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')
resized_images = []
for img in first_pass_images: