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update offloading codepaths for non-balanced

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-08-11 22:09:12 +02:00
parent 5123bfd96b
commit f92ef90d3d
6 changed files with 31 additions and 73 deletions
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modules/processing_vae.py
+1 -19
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@@ -43,10 +43,6 @@ def full_vqgan_decode(latents, model):
devices.torch_gc(force=True)
shared.mem_mon.reset()
base_device = None
if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False):
base_device = sd_models.move_base(model, devices.cpu)
if shared.opts.diffusers_offload_mode == "balanced":
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
elif shared.opts.diffusers_offload_mode != "sequential":
@@ -76,8 +72,6 @@ def full_vqgan_decode(latents, model):
if shared.opts.diffusers_offload_mode == "balanced":
shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
elif shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False) and base_device is not None:
sd_models.move_base(model, base_device)
t1 = time.time()
if debug:
log_debug(f'VAE memory: {shared.mem_mon.read()}')
@@ -97,10 +91,7 @@ def full_vae_decode(latents, model):
devices.torch_gc(force=True)
shared.mem_mon.reset()
base_device = None
if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False):
base_device = sd_models.move_base(model, devices.cpu)
elif shared.opts.diffusers_offload_mode != "sequential":
if shared.opts.diffusers_offload_mode != "sequential":
sd_models.move_model(model.vae, devices.device)
sd_models.set_vae_options(model, vae=None, op='decode')
@@ -169,9 +160,6 @@ def full_vae_decode(latents, model):
model.vae.apply(sd_models_utils.convert_to_faketensors)
devices.torch_gc(force=True)
elif shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False) and base_device is not None:
sd_models.move_base(model, base_device)
t1 = time.time()
if debug:
log_debug(f'VAE memory: {shared.mem_mon.read()}')
@@ -183,10 +171,6 @@ 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
sd_models.move_model(model.unet, devices.cpu)
if shared.opts.diffusers_offload_mode != "sequential" and hasattr(model, 'vae'):
sd_models.move_model(model.vae, devices.device)
if getattr(model.vae, 'sdnext_ondemand', False):
@@ -209,8 +193,6 @@ def full_vae_encode(image, model):
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()
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