mirror of
https://github.com/vladmandic/sdnext.git
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79 lines
3.6 KiB
Python
79 lines
3.6 KiB
Python
import os
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import time
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import torch
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from modules import shared, sd_models, devices, timer, errors
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debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
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def hijack_vae_upscale(*args, **kwargs):
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import torch.nn.functional as F
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tensor = shared.sd_model.vae.orig_decode(*args, **kwargs)[0]
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tensor = F.pixel_shuffle(tensor.movedim(2, 1), upscale_factor=2).movedim(1, 2) # vae returns 16-dim latents, we need to pixel shuffle to 4-dim images
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tensor = tensor.unsqueeze(0) # add batch dimension
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return tensor
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def hijack_vae_decode(*args, **kwargs):
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jobid = shared.state.begin('VAE Decode')
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t0 = time.time()
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res = None
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shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, exclude=['vae'])
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try:
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sd_models.move_model(shared.sd_model.vae, devices.device)
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if torch.is_tensor(args[0]):
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latents = args[0].to(device=devices.device, dtype=shared.sd_model.vae.dtype) # upcast to vae dtype
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if hasattr(shared.sd_model.vae, '_asymmetric_upscale_vae'):
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res = hijack_vae_upscale(latents, *args[1:], **kwargs)
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else:
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res = shared.sd_model.vae.orig_decode(latents, *args[1:], **kwargs)
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t1 = time.time()
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try:
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shared.log.debug(f'Decode: vae={shared.sd_model.vae.__class__.__name__} dtype={latents.dtype} latents={list(latents.shape)}:{latents.device} decoded={list(res[0].shape)} slicing={getattr(shared.sd_model.vae, "use_slicing", None)} tiling={getattr(shared.sd_model.vae, "use_tiling", None)} time={t1-t0:.3f}')
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except Exception:
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pass
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else:
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res = shared.sd_model.vae.orig_decode(*args, **kwargs)
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except Exception as e:
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shared.log.error(f'Decode: vae={shared.sd_model.vae.__class__.__name__} {e}')
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errors.display(e, 'vae')
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res = None
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t1 = time.time()
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timer.process.add('vae', t1-t0)
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shared.state.end(jobid)
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return res
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def hijack_vae_encode(*args, **kwargs):
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jobid = shared.state.begin('VAE Encode')
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t0 = time.time()
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res = None
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shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model, exclude=['vae'])
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try:
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sd_models.move_model(shared.sd_model.vae, devices.device)
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if torch.is_tensor(args[0]):
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latents = args[0].to(device=devices.device, dtype=shared.sd_model.vae.dtype) # upcast to vae dtype
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res = shared.sd_model.vae.orig_encode(latents, *args[1:], **kwargs)
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t1 = time.time()
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shared.log.debug(f'Encode: vae={shared.sd_model.vae.__class__.__name__} slicing={getattr(shared.sd_model.vae, "use_slicing", None)} tiling={getattr(shared.sd_model.vae, "use_tiling", None)} latents={list(latents.shape)}:{latents.device}:{latents.dtype} time={t1-t0:.3f}')
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else:
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res = shared.sd_model.vae.orig_encode(*args, **kwargs)
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except Exception as e:
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shared.log.error(f'Encode: vae={shared.sd_model.vae.__class__.__name__} {e}')
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errors.display(e, 'vae')
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res = None
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t1 = time.time()
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timer.process.add('vae', t1-t0)
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shared.state.end(jobid)
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return res
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def init_hijack(pipe):
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if pipe is not None and hasattr(pipe, 'vae') and hasattr(pipe.vae, 'decode') and not hasattr(pipe.vae, 'orig_decode'):
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pipe.vae.orig_decode = pipe.vae.decode
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pipe.vae.decode = hijack_vae_decode
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if pipe is not None and hasattr(pipe, 'vae') and hasattr(pipe.vae, 'encode') and not hasattr(pipe.vae, 'orig_encode'):
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pipe.vae.orig_encode = pipe.vae.encode
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pipe.vae.encode = hijack_vae_encode
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