Merge pull request #7234 from brkirch/fix-full-previews
Fix full previews and--no-half-vae to work correctly with --upcast-sampling
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commit
645f4e7ef8
@ -172,7 +172,7 @@ class StableDiffusionProcessing:
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midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
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midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_unet) if devices.unet_needs_upcast else source_image))
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_vae) if devices.unet_needs_upcast else source_image))
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conditioning_image = conditioning_image.float() if devices.unet_needs_upcast else conditioning_image
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conditioning = torch.nn.functional.interpolate(
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self.sd_model.depth_model(midas_in),
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@ -217,7 +217,7 @@ class StableDiffusionProcessing:
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)
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# Encode the new masked image using first stage of network.
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_unet) if devices.unet_needs_upcast else conditioning_image))
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_vae) if devices.unet_needs_upcast else conditioning_image))
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# Create the concatenated conditioning tensor to be fed to `c_concat`
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conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
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@ -417,7 +417,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
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def decode_first_stage(model, x):
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with devices.autocast(disable=x.dtype == devices.dtype_vae):
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x = model.decode_first_stage(x)
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x = model.decode_first_stage(x.to(devices.dtype_vae) if devices.unet_needs_upcast else x)
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return x
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@ -1001,7 +1001,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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image = torch.from_numpy(batch_images)
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image = 2. * image - 1.
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image = image.to(device=shared.device, dtype=devices.dtype_unet if devices.unet_needs_upcast else None)
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image = image.to(device=shared.device, dtype=devices.dtype_vae if devices.unet_needs_upcast else None)
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self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
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@ -5,7 +5,7 @@ class CondFunc:
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self = super(CondFunc, cls).__new__(cls)
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if isinstance(orig_func, str):
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func_path = orig_func.split('.')
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for i in range(len(func_path)-2, -1, -1):
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for i in range(len(func_path)-1, -1, -1):
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try:
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resolved_obj = importlib.import_module('.'.join(func_path[:i]))
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break
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