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Use float16 for image processing, force dtype_vae for encoding

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Kubuxu
2023-07-11 22:06:14 +01:00
parent 5d0c01b9e3
commit a966a0d5ad
1 changed files with 5 additions and 5 deletions
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modules/processing.py
+5 -5
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@@ -216,7 +216,7 @@ class StableDiffusionProcessing:
conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
image_conditioning = image_conditioning.to(device = shared.device, dtype = source_image.dtype)
return image_conditioning
def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
@@ -1020,7 +1020,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
image = np.moveaxis(image, 2, 0)
batch_images.append(image)
decoded_samples = torch.from_numpy(np.array(batch_images))
decoded_samples = decoded_samples.to(shared.device)
decoded_samples = decoded_samples.to(device=shared.device, dtype=devices.dtype_vae)
decoded_samples = 2. * decoded_samples - 1.
if shared.opts.sd_vae_sliced_encode and len(decoded_samples) > 1:
samples = torch.stack([
@@ -1149,7 +1149,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
image = torch.from_numpy(batch_images)
image = 2. * image - 1.
image = image.to(shared.device)
image = image.to(device=shared.device, dtype=devices.dtype_vae)
if shared.backend == Backend.ORIGINAL:
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
@@ -1166,8 +1166,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
latmask = latmask[0]
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
self.mask = torch.asarray(1.0 - latmask).to(device=shared.device, dtype=self.sd_model.dtype)
self.nmask = torch.asarray(latmask).to(device=shared.device, dtype=self.sd_model.dtype)
# this needs to be fixed to be done in sample() using actual seeds for batches
if self.inpainting_fill == 2:
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask