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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_vae.py
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@@ -162,6 +162,7 @@ def full_vae_decode(latents, model):
def full_vae_encode(image, model):
t0 = time.time()
if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False) and hasattr(model, 'unet'):
log_debug('Moving to CPU: model=UNet')
unet_device = model.unet.device
@@ -170,9 +171,25 @@ def full_vae_encode(image, model):
sd_models.move_model(model.vae, devices.device)
vae_name = sd_vae.loaded_vae_file if sd_vae.loaded_vae_file is not None else "default"
log_debug(f'Encode vae="{vae_name}" dtype={model.vae.dtype} upcast={model.vae.config.get("force_upcast", None)}')
upcast = (model.vae.dtype == torch.float16) and (getattr(model.vae.config, 'force_upcast', False) or shared.opts.no_half_vae)
if upcast:
if hasattr(model, 'upcast_vae'): # this is done by diffusers automatically if output_type != 'latent'
model.upcast_vae()
else: # manual upcast and we restore it later
model.vae.orig_dtype = model.vae.dtype
model.vae = model.vae.to(dtype=torch.float32)
encoded = model.vae.encode(image.to(model.vae.device, model.vae.dtype)).latent_dist.sample()
if hasattr(model.vae, "orig_dtype"):
model.vae = model.vae.to(dtype=model.vae.orig_dtype)
del model.vae.orig_dtype
if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False) and hasattr(model, 'unet'):
sd_models.move_model(model.unet, unet_device)
t1 = time.time()
shared.log.debug(f'Encode: vae="{vae_name}" upcast={upcast} slicing={getattr(model.vae, "use_slicing", None)} tiling={getattr(model.vae, "use_tiling", None)} latents={encoded.shape}:{encoded.device}:{encoded.dtype} time={t1-t0:.3f}')
return encoded