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fix hires with latent upscale

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-03-16 13:25:26 -04:00
parent 281afcde1a
commit a4b26e7ddb
3 changed files with 14 additions and 11 deletions
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modules/processing_helpers.py
+11 -11
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@@ -401,24 +401,24 @@ def resize_init_images(p):
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, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
return first_pass_images
decoded = processing_vae.vae_decode(latents=latents, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
return decoded
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
return decoded
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
resized = 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
first_pass_images = processing_vae.vae_decode(latents=latents, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
resized_images = []
for img in first_pass_images:
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)
decoded = processing_vae.vae_decode(latents=latents, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
resized = []
for image in decoded:
resize = images.resize_image(p.hr_resize_mode, image, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
resized.append(resize)
devices.torch_gc()
return resized_images
return resized
def fix_prompts(p, prompts, negative_prompts, prompts_2, negative_prompts_2):