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flux hires and refiner workflows

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Vladimir Mandic
2024-09-08 10:48:22 -04:00
parent 787b321d57
commit 51e8567eec
7 changed files with 55 additions and 23 deletions
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modules/processing_helpers.py
+3 -3
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@@ -383,13 +383,13 @@ 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, full_quality=p.full_quality, output_type='pil')
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')
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'):
shared.log.error('Hires: missing upscaling dimensions')
return first_pass_images
@@ -531,7 +531,7 @@ def save_intermediate(p, latents, suffix):
for i in range(len(latents)):
from modules.processing import create_infotext
info=create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, [], iteration=p.iteration, position_in_batch=i)
decoded = processing_vae.vae_decode(latents=latents, model=shared.sd_model, output_type='pil', full_quality=p.full_quality)
decoded = processing_vae.vae_decode(latents=latents, model=shared.sd_model, output_type='pil', full_quality=p.full_quality, width=p.width, height=p.height)
for j in range(len(decoded)):
images.save_image(decoded[j], path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix=suffix)