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update vae upscale logic

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Vladimir Mandic
2024-09-22 14:14:21 -04:00
parent fdb2ea8260
commit 2d0ad9ae61
1 changed files with 3 additions and 6 deletions
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modules/processing_vae.py
+3 -6
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@@ -52,19 +52,16 @@ def full_vae_decode(latents, model):
elif shared.opts.diffusers_offload_mode != "sequential":
sd_models.move_model(model.vae, devices.device)
upcast = (model.vae.dtype == torch.float16) and getattr(model.vae.config, 'force_upcast', False)
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()
model.vae = model.vae.to(dtype=torch.float32)
latents = latents.to(torch.float32)
if getattr(model.vae, "post_quant_conv", None) is not None:
latents = latents.to(next(iter(model.vae.post_quant_conv.parameters())).dtype)
elif shared.opts.no_half_vae:
latents = latents.to(torch.float32)
else:
latents = latents.to(model.vae.device)
if getattr(model.vae, "post_quant_conv", None) is not None:
latents = latents.to(next(iter(model.vae.post_quant_conv.parameters())).dtype)
# normalize latents
latents_mean = model.vae.config.get("latents_mean", None)