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[StableDiffusionInpaintPipeline] accept tensors for init and mask image #439

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Merged
merged 4 commits into from
Sep 16, 2022
Merged

[StableDiffusionInpaintPipeline] accept tensors for init and mask image #439

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6ae085f
accept tensors
patil-suraj Sep 9, 2022
7cd259d
fix mask handling
patil-suraj Sep 9, 2022
02b6fd2
make device placement cleaner
patil-suraj Sep 9, 2022
5d5e194
update doc for mask image
patil-suraj Sep 16, 2022
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17 changes: 11 additions & 6 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -145,8 +145,9 @@ def __call__(
process. This is the image whose masked region will be inpainted.
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. The mask image will be
converted to a single channel (luminance) before use.
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
strength (`float`, *optional*, defaults to 0.8):
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
is 1, the denoising process will be run on the masked area for the full number of iterations specified
Expand Down Expand Up @@ -202,10 +203,12 @@ def __call__(
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)

# preprocess image
init_image = preprocess_image(init_image).to(self.device)
if not isinstance(init_image, torch.FloatTensor):
init_image = preprocess_image(init_image)
init_image.to(self.device)

# encode the init image into latents and scale the latents
init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
init_latent_dist = self.vae.encode(init_image).latent_dist
init_latents = init_latent_dist.sample(generator=generator)

init_latents = 0.18215 * init_latents
Expand All @@ -215,8 +218,10 @@ def __call__(
init_latents_orig = init_latents

# preprocess mask
mask = preprocess_mask(mask_image).to(self.device)
mask = torch.cat([mask] * batch_size)
if not isinstance(mask_image, torch.FloatTensor):
mask_image = preprocess_mask(mask_image)
mask_image.to(self.device)
mask = torch.cat([mask_image] * batch_size)

# check sizes
if not mask.shape == init_latents.shape:
Expand Down