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restore original LPW names

This commit is contained in:
Sean Sube 2023-02-18 18:54:24 -06:00
parent 4d93c13431
commit 2b83f942af
Signed by: ssube
GPG Key ID: 3EED7B957D362AF1
2 changed files with 13 additions and 13 deletions
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4
api/onnx_web/chain/blend_inpaint.py
View File

@ -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,

22
api/onnx_web/diffusion/lpw_stable_diffusion_onnx.py
View File

@ -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,