AI/diffusers
1
0
Fork 0
mirror of https://github.com/huggingface/diffusers.git synced 2026-01-27 17:22:53 +03:00
Code Activity

add padding_mask_crop to all inpaint pipelines (#6360)

Browse Source 
* add padding_mask_crop
---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
This commit is contained in:
Vinh H. Pham
2024-01-22 12:42:22 +07:00
committed by GitHub
parent e2773c6255
commit 8e7bbfbe5a
4 changed files with 154 additions and 14 deletions
Download Patch File Download Diff File Expand all files Collapse all files

57
src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py
View File

@@ -683,9 +683,11 @@ class StableDiffusionControlNetInpaintPipeline(
self,
prompt,
image,
mask_image,
height,
width,
callback_steps,
output_type,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
@@ -693,6 +695,7 @@ class StableDiffusionControlNetInpaintPipeline(
control_guidance_start=0.0,
control_guidance_end=1.0,
callback_on_step_end_tensor_inputs=None,
padding_mask_crop=None,
):
if height is not None and height % 8 != 0 or width is not None and width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
@@ -736,6 +739,19 @@ class StableDiffusionControlNetInpaintPipeline(
f" {negative_prompt_embeds.shape}."
)
if padding_mask_crop is not None:
if not isinstance(image, PIL.Image.Image):
raise ValueError(
f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
)
if not isinstance(mask_image, PIL.Image.Image):
raise ValueError(
f"The mask image should be a PIL image when inpainting mask crop, but is of type"
f" {type(mask_image)}."
)
if output_type != "pil":
raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
# `prompt` needs more sophisticated handling when there are multiple
# conditionings.
if isinstance(self.controlnet, MultiControlNetModel):
@@ -862,7 +878,6 @@ class StableDiffusionControlNetInpaintPipeline(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
)
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
def prepare_control_image(
self,
image,
@@ -872,10 +887,14 @@ class StableDiffusionControlNetInpaintPipeline(
num_images_per_prompt,
device,
dtype,
crops_coords,
resize_mode,
do_classifier_free_guidance=False,
guess_mode=False,
):
image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
image = self.control_image_processor.preprocess(
image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
).to(dtype=torch.float32)
image_batch_size = image.shape[0]
if image_batch_size == 1:
@@ -1074,6 +1093,7 @@ class StableDiffusionControlNetInpaintPipeline(
control_image: PipelineImageInput = None,
height: Optional[int] = None,
width: Optional[int] = None,
padding_mask_crop: Optional[int] = None,
strength: float = 1.0,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
@@ -1130,6 +1150,12 @@ class StableDiffusionControlNetInpaintPipeline(
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
padding_mask_crop (`int`, *optional*, defaults to `None`):
The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If
`padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and
contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on
the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large
and contain information inreleant for inpainging, such as background.
strength (`float`, *optional*, defaults to 1.0):
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
@@ -1240,9 +1266,11 @@ class StableDiffusionControlNetInpaintPipeline(
self.check_inputs(
prompt,
control_image,
mask_image,
height,
width,
callback_steps,
output_type,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
@@ -1250,6 +1278,7 @@ class StableDiffusionControlNetInpaintPipeline(
control_guidance_start,
control_guidance_end,
callback_on_step_end_tensor_inputs,
padding_mask_crop,
)
self._guidance_scale = guidance_scale
@@ -1264,6 +1293,14 @@ class StableDiffusionControlNetInpaintPipeline(
else:
batch_size = prompt_embeds.shape[0]
if padding_mask_crop is not None:
height, width = self.image_processor.get_default_height_width(image, height, width)
crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
resize_mode = "fill"
else:
crops_coords = None
resize_mode = "default"
device = self._execution_device
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
@@ -1315,6 +1352,8 @@ class StableDiffusionControlNetInpaintPipeline(
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
crops_coords=crops_coords,
resize_mode=resize_mode,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
@@ -1330,6 +1369,8 @@ class StableDiffusionControlNetInpaintPipeline(
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
crops_coords=crops_coords,
resize_mode=resize_mode,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
@@ -1341,10 +1382,15 @@ class StableDiffusionControlNetInpaintPipeline(
assert False
# 4.1 Preprocess mask and image - resizes image and mask w.r.t height and width
init_image = self.image_processor.preprocess(image, height=height, width=width)
original_image = image
init_image = self.image_processor.preprocess(
image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
)
init_image = init_image.to(dtype=torch.float32)
mask = self.mask_processor.preprocess(mask_image, height=height, width=width)
mask = self.mask_processor.preprocess(
mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
)
masked_image = init_image * (mask < 0.5)
_, _, height, width = init_image.shape
@@ -1534,6 +1580,9 @@ class StableDiffusionControlNetInpaintPipeline(
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if padding_mask_crop is not None:
image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]
# Offload all models
self.maybe_free_model_hooks()

56
src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
View File

@@ -557,9 +557,11 @@ class StableDiffusionXLControlNetInpaintPipeline(
prompt,
prompt_2,
image,
mask_image,
strength,
num_inference_steps,
callback_steps,
output_type,
negative_prompt=None,
negative_prompt_2=None,
prompt_embeds=None,
@@ -570,6 +572,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
control_guidance_start=0.0,
control_guidance_end=1.0,
callback_on_step_end_tensor_inputs=None,
padding_mask_crop=None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
@@ -632,6 +635,19 @@ class StableDiffusionXLControlNetInpaintPipeline(
f" {negative_prompt_embeds.shape}."
)
if padding_mask_crop is not None:
if not isinstance(image, PIL.Image.Image):
raise ValueError(
f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
)
if not isinstance(mask_image, PIL.Image.Image):
raise ValueError(
f"The mask image should be a PIL image when inpainting mask crop, but is of type"
f" {type(mask_image)}."
)
if output_type != "pil":
raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
@@ -745,10 +761,14 @@ class StableDiffusionXLControlNetInpaintPipeline(
num_images_per_prompt,
device,
dtype,
crops_coords,
resize_mode,
do_classifier_free_guidance=False,
guess_mode=False,
):
image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
image = self.control_image_processor.preprocess(
image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
).to(dtype=torch.float32)
image_batch_size = image.shape[0]
if image_batch_size == 1:
@@ -1066,6 +1086,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
] = None,
height: Optional[int] = None,
width: Optional[int] = None,
padding_mask_crop: Optional[int] = None,
strength: float = 0.9999,
num_inference_steps: int = 50,
denoising_start: Optional[float] = None,
@@ -1121,6 +1142,12 @@ class StableDiffusionXLControlNetInpaintPipeline(
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
padding_mask_crop (`int`, *optional*, defaults to `None`):
The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If
`padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and
contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on
the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large
and contain information inreleant for inpainging, such as background.
strength (`float`, *optional*, defaults to 0.9999):
Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
@@ -1290,9 +1317,11 @@ class StableDiffusionXLControlNetInpaintPipeline(
prompt,
prompt_2,
control_image,
mask_image,
strength,
num_inference_steps,
callback_steps,
output_type,
negative_prompt,
negative_prompt_2,
prompt_embeds,
@@ -1303,6 +1332,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
control_guidance_start,
control_guidance_end,
callback_on_step_end_tensor_inputs,
padding_mask_crop,
)
self._guidance_scale = guidance_scale
@@ -1370,7 +1400,18 @@ class StableDiffusionXLControlNetInpaintPipeline(
# 5. Preprocess mask and image - resizes image and mask w.r.t height and width
# 5.1 Prepare init image
init_image = self.image_processor.preprocess(image, height=height, width=width)
if padding_mask_crop is not None:
height, width = self.image_processor.get_default_height_width(image, height, width)
crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
resize_mode = "fill"
else:
crops_coords = None
resize_mode = "default"
original_image = image
init_image = self.image_processor.preprocess(
image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
)
init_image = init_image.to(dtype=torch.float32)
# 5.2 Prepare control images
@@ -1383,6 +1424,8 @@ class StableDiffusionXLControlNetInpaintPipeline(
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
crops_coords=crops_coords,
resize_mode=resize_mode,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
@@ -1398,6 +1441,8 @@ class StableDiffusionXLControlNetInpaintPipeline(
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=controlnet.dtype,
crops_coords=crops_coords,
resize_mode=resize_mode,
do_classifier_free_guidance=self.do_classifier_free_guidance,
guess_mode=guess_mode,
)
@@ -1409,7 +1454,9 @@ class StableDiffusionXLControlNetInpaintPipeline(
raise ValueError(f"{controlnet.__class__} is not supported.")
# 5.3 Prepare mask
mask = self.mask_processor.preprocess(mask_image, height=height, width=width)
mask = self.mask_processor.preprocess(
mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
)
masked_image = init_image * (mask < 0.5)
_, _, height, width = init_image.shape
@@ -1684,6 +1731,9 @@ class StableDiffusionXLControlNetInpaintPipeline(
image = self.image_processor.postprocess(image, output_type=output_type)
if padding_mask_crop is not None:
image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]
# Offload all models
self.maybe_free_model_hooks()

9
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
View File

@@ -642,6 +642,7 @@ class StableDiffusionInpaintPipeline(
width,
strength,
callback_steps,
output_type,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
@@ -693,11 +694,6 @@ class StableDiffusionInpaintPipeline(
f" {negative_prompt_embeds.shape}."
)
if padding_mask_crop is not None:
if self.unet.config.in_channels != 4:
raise ValueError(
f"The UNet should have 4 input channels for inpainting mask crop, but has"
f" {self.unet.config.in_channels} input channels."
)
if not isinstance(image, PIL.Image.Image):
raise ValueError(
f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
@@ -707,6 +703,8 @@ class StableDiffusionInpaintPipeline(
f"The mask image should be a PIL image when inpainting mask crop, but is of type"
f" {type(mask_image)}."
)
if output_type != "pil":
raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
def prepare_latents(
self,
@@ -1166,6 +1164,7 @@ class StableDiffusionInpaintPipeline(
width,
strength,
callback_steps,
output_type,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,

46
src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
View File

@@ -744,15 +744,19 @@ class StableDiffusionXLInpaintPipeline(
self,
prompt,
prompt_2,
image,
mask_image,
height,
width,
strength,
callback_steps,
output_type,
negative_prompt=None,
negative_prompt_2=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
padding_mask_crop=None,
):
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
@@ -810,6 +814,18 @@ class StableDiffusionXLInpaintPipeline(
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if padding_mask_crop is not None:
if not isinstance(image, PIL.Image.Image):
raise ValueError(
f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
)
if not isinstance(mask_image, PIL.Image.Image):
raise ValueError(
f"The mask image should be a PIL image when inpainting mask crop, but is of type"
f" {type(mask_image)}."
)
if output_type != "pil":
raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
def prepare_latents(
self,
@@ -1225,6 +1241,7 @@ class StableDiffusionXLInpaintPipeline(
masked_image_latents: torch.FloatTensor = None,
height: Optional[int] = None,
width: Optional[int] = None,
padding_mask_crop: Optional[int] = None,
strength: float = 0.9999,
num_inference_steps: int = 50,
timesteps: List[int] = None,
@@ -1287,6 +1304,12 @@ class StableDiffusionXLInpaintPipeline(
Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
and checkpoints that are not specifically fine-tuned on low resolutions.
padding_mask_crop (`int`, *optional*, defaults to `None`):
The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If
`padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and
contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on
the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large
and contain information inreleant for inpainging, such as background.
strength (`float`, *optional*, defaults to 0.9999):
Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
@@ -1449,15 +1472,19 @@ class StableDiffusionXLInpaintPipeline(
self.check_inputs(
prompt,
prompt_2,
image,
mask_image,
height,
width,
strength,
callback_steps,
output_type,
negative_prompt,
negative_prompt_2,
prompt_embeds,
negative_prompt_embeds,
callback_on_step_end_tensor_inputs,
padding_mask_crop,
)
self._guidance_scale = guidance_scale
@@ -1527,10 +1554,22 @@ class StableDiffusionXLInpaintPipeline(
is_strength_max = strength == 1.0
# 5. Preprocess mask and image
init_image = self.image_processor.preprocess(image, height=height, width=width)
if padding_mask_crop is not None:
crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
resize_mode = "fill"
else:
crops_coords = None
resize_mode = "default"
original_image = image
init_image = self.image_processor.preprocess(
image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
)
init_image = init_image.to(dtype=torch.float32)
mask = self.mask_processor.preprocess(mask_image, height=height, width=width)
mask = self.mask_processor.preprocess(
mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
)
if masked_image_latents is not None:
masked_image = masked_image_latents
@@ -1791,6 +1830,9 @@ class StableDiffusionXLInpaintPipeline(
image = self.image_processor.postprocess(image, output_type=output_type)
if padding_mask_crop is not None:
image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]
# Offload all models
self.maybe_free_model_hooks()