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new ltxvideo, offloading, quantization

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-12-23 11:56:51 -05:00
parent b15cf4adae
commit 2794b1abc4
6 changed files with 105 additions and 30 deletions
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modules/processing_vae.py
+9 -3
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@@ -203,7 +203,7 @@ def taesd_vae_encode(image):
return encoded
def vae_decode(latents, model, output_type='np', full_quality=True, width=None, height=None):
def vae_decode(latents, model, output_type='np', full_quality=True, width=None, height=None, frames=None):
t0 = time.time()
model = model or shared.sd_model
if not hasattr(model, 'vae') and hasattr(model, 'pipe'):
@@ -221,7 +221,11 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
shared.log.error('VAE not found in model')
return []
if hasattr(model, "_unpack_latents") and hasattr(model, "vae_scale_factor") and width is not None and height is not None: # FLUX
if hasattr(model, '_unpack_latents') and hasattr(model, 'transformer_spatial_patch_size') and frames is not None: # LTX
latent_num_frames = (frames - 1) // model.vae_temporal_compression_ratio + 1
latents = model._unpack_latents(latents.unsqueeze(0), latent_num_frames, height // 32, width // 32, model.transformer_spatial_patch_size, model.transformer_temporal_patch_size) # pylint: disable=protected-access
latents = model._denormalize_latents(latents, model.vae.latents_mean, model.vae.latents_std, model.vae.config.scaling_factor) # pylint: disable=protected-access
if hasattr(model, '_unpack_latents') and hasattr(model, "vae_scale_factor") and width is not None and height is not None: # FLUX
latents = model._unpack_latents(latents, height, width, model.vae_scale_factor) # pylint: disable=protected-access
if len(latents.shape) == 3: # lost a batch dim in hires
latents = latents.unsqueeze(0)
@@ -238,7 +242,9 @@ def vae_decode(latents, model, output_type='np', full_quality=True, width=None,
decoded = taesd_vae_decode(latents=latents)
if torch.is_tensor(decoded):
if hasattr(model, 'image_processor'):
if hasattr(model, 'video_processor'):
imgs = model.video_processor.postprocess_video(decoded, output_type='pil')
elif hasattr(model, 'image_processor'):
imgs = model.image_processor.postprocess(decoded, output_type=output_type)
elif hasattr(model, "vqgan"):
imgs = decoded.permute(0, 2, 3, 1).cpu().float().numpy()