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fix state interrupted checks

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-09-14 13:03:03 -04:00
parent e54065c3b3
commit 0f7d2e95ca
5 changed files with 100 additions and 90 deletions
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modules/processing_helpers.py
+21 -16
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@@ -298,28 +298,33 @@ def resize_init_images(p):
def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler else latent
jobid = shared.state.begin('Resize')
if not torch.is_tensor(latents):
shared.log.warning('Hires: input is not tensor')
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)
shared.state.end(jobid)
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')
shared.state.end(jobid)
return decoded
return latents
jobid = shared.state.begin('Resize')
if p.hr_upscaler.lower().startswith('latent'):
if isinstance(latents, list):
try:
for i in range(len(latents)):
if not torch.is_tensor(latents[i]):
shared.log.warning(f'Hires: input[{i}]={type(latents[i])} not tensor')
latents[i] = processing_vae.vae_encode(image=latents[i], model=shared.sd_model, vae_type=p.vae_type)
latents = torch.cat(latents, dim=0)
except Exception as e:
shared.log.error(f'Hires: prepare latents: {e}')
resized = latents
elif not torch.is_tensor(latents):
shared.log.warning(f'Hires: input={type(latents)} not tensor')
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)
shared.state.end(jobid)
return resized
else:
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)
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()
shared.state.end(jobid)
return resized