restore original LPW names
This commit is contained in:
parent
4d93c13431
commit
2b83f942af
|
@ -85,7 +85,7 @@ def blend_inpaint(
|
|||
height=size.height,
|
||||
image=tile_source,
|
||||
latents=latents,
|
||||
mask=tile_mask,
|
||||
mask_image=tile_mask,
|
||||
negative_prompt=params.negative_prompt,
|
||||
num_inference_steps=params.steps,
|
||||
width=size.width,
|
||||
|
@ -100,7 +100,7 @@ def blend_inpaint(
|
|||
height=size.height,
|
||||
image=tile_source,
|
||||
latents=latents,
|
||||
mask=mask,
|
||||
mask_image=mask,
|
||||
negative_prompt=params.negative_prompt,
|
||||
num_inference_steps=params.steps,
|
||||
width=size.width,
|
||||
|
|
|
@ -657,7 +657,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
|
|||
prompt: Union[str, List[str]],
|
||||
negative_prompt: Optional[Union[str, List[str]]] = None,
|
||||
image: Union[np.ndarray, PIL.Image.Image] = None,
|
||||
mask: Union[np.ndarray, PIL.Image.Image] = None,
|
||||
mask_image: Union[np.ndarray, PIL.Image.Image] = None,
|
||||
height: int = 512,
|
||||
width: int = 512,
|
||||
num_inference_steps: int = 50,
|
||||
|
@ -687,9 +687,9 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
|
|||
image (`np.ndarray` or `PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch, that will be used as the starting point for the
|
||||
process.
|
||||
mask (`np.ndarray` or `PIL.Image.Image`):
|
||||
mask_image (`np.ndarray` or `PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
|
||||
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask` is a
|
||||
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)`.
|
||||
height (`int`, *optional*, defaults to 512):
|
||||
|
@ -782,10 +782,10 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
|
|||
image = preprocess_image(image)
|
||||
if image is not None:
|
||||
image = image.astype(dtype)
|
||||
if isinstance(mask, PIL.Image.Image):
|
||||
mask = preprocess_mask(mask, self.vae_scale_factor)
|
||||
if mask is not None:
|
||||
mask = mask.astype(dtype)
|
||||
if isinstance(mask_image, PIL.Image.Image):
|
||||
mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
|
||||
if mask_image is not None:
|
||||
mask = mask_image.astype(dtype)
|
||||
mask = np.concatenate([mask] * batch_size * num_images_per_prompt)
|
||||
else:
|
||||
mask = None
|
||||
|
@ -1057,7 +1057,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
|
|||
def inpaint(
|
||||
self,
|
||||
image: Union[np.ndarray, PIL.Image.Image],
|
||||
mask: Union[np.ndarray, PIL.Image.Image],
|
||||
mask_image: Union[np.ndarray, PIL.Image.Image],
|
||||
prompt: Union[str, List[str]],
|
||||
negative_prompt: Optional[Union[str, List[str]]] = None,
|
||||
strength: float = 0.8,
|
||||
|
@ -1079,9 +1079,9 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
|
|||
image (`np.ndarray` or `PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch, that will be used as the starting point for the
|
||||
process. This is the image whose masked region will be inpainted.
|
||||
mask (`np.ndarray` or `PIL.Image.Image`):
|
||||
mask_image (`np.ndarray` or `PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
|
||||
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask` is a
|
||||
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)`.
|
||||
prompt (`str` or `List[str]`):
|
||||
|
@ -1136,7 +1136,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
|
|||
prompt=prompt,
|
||||
negative_prompt=negative_prompt,
|
||||
image=image,
|
||||
mask=mask,
|
||||
mask_image=mask_image,
|
||||
num_inference_steps=num_inference_steps,
|
||||
guidance_scale=guidance_scale,
|
||||
strength=strength,
|
||||
|
|
Loading…
Reference in New Issue