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remote vae support raw/png/jpg

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-02-23 15:01:43 -05:00
parent eee4d1acfe
commit f39ae70eed
4 changed files with 90 additions and 40 deletions
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modules/processing_vae.py
+33 -22
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@@ -230,6 +230,34 @@ def taesd_vae_encode(image):
return encoded
def vae_postprocess(tensor, model, output_type='np'):
images = []
try:
if isinstance(tensor, list) and len(tensor) > 0 and torch.is_tensor(tensor[0]):
tensor = torch.stack(tensor)
if torch.is_tensor(tensor):
if len(tensor.shape) == 3 and tensor.shape[0] == 3:
tensor = tensor.unsqueeze(0)
if hasattr(model, 'video_processor'):
images = model.video_processor.postprocess_video(tensor, output_type='pil')
elif hasattr(model, 'image_processor'):
images = model.image_processor.postprocess(tensor, output_type=output_type)
elif hasattr(model, "vqgan"):
images = tensor.permute(0, 2, 3, 1).cpu().float().numpy()
if output_type == "pil":
images = model.numpy_to_pil(images)
else:
import diffusers
model.image_processor = diffusers.image_processor.VaeImageProcessor()
images = model.image_processor.postprocess(tensor, output_type=output_type)
else:
images = tensor if isinstance(tensor, list) or isinstance(tensor, np.ndarray) else [tensor]
except Exception as e:
shared.log.error(f'VAE postprocess: {e}')
errors.display(e, 'VAE')
return images
def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, height=None, frames=None):
t0 = time.time()
model = model or shared.sd_model
@@ -242,10 +270,10 @@ def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, he
if vae_type == 'Remote':
shared.state.job = 'Remote VAE'
from modules.sd_vae_remote import remote_decode
images = remote_decode(latents=latents, width=width, height=height)
tensors = remote_decode(latents=latents, width=width, height=height)
shared.state.job = prev_job
if images is not None and len(images) > 0:
return images
if tensors is not None and len(tensors) > 0:
return vae_postprocess(tensors, model, output_type)
shared.state.job = 'VAE'
if latents.shape[0] == 0:
@@ -279,30 +307,13 @@ def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, he
if torch.is_tensor(decoded):
decoded = 2.0 * decoded - 1.0 # typical normalized range
if torch.is_tensor(decoded):
if len(decoded.shape) == 3 and decoded.shape[0] == 3:
decoded = decoded.unsqueeze(0)
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()
if output_type == "pil":
imgs = model.numpy_to_pil(imgs)
else:
import diffusers
model.image_processor = diffusers.image_processor.VaeImageProcessor()
imgs = model.image_processor.postprocess(decoded, output_type=output_type)
else:
imgs = decoded if isinstance(decoded, list) or isinstance(decoded, np.ndarray) else [decoded]
images = vae_postprocess(decoded, model, output_type)
shared.state.job = prev_job
if shared.cmd_opts.profile or debug:
t1 = time.time()
shared.log.debug(f'Profile: VAE decode: {t1-t0:.2f}')
devices.torch_gc()
return imgs
return images
def vae_encode(image, model, vae_type='Full'): # pylint: disable=unused-variable