Community pipeline img2img inpainting (#1114)
* adds image to image inpainting with `PIL.Image.Image` inputs the base implementation claims to support `torch.Tensor` but seems it would also fail in this case. * `make style` and `make quality` * updates community examples readme Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Alex McKinney
GitHub
parent
72eae64d67
commit
bcdb3d594c
|
|
@ -17,8 +17,8 @@ If a community doesn't work as expected, please open an issue and ping the autho
|
|||
| Wild Card Stable Diffusion | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values | [Wildcard Stable Diffusion](#wildcard-stable-diffusion) | - | [Shyam Sudhakaran](https://github.com/shyamsn97) |
|
||||
| Composable Stable Diffusion| Stable Diffusion Pipeline that supports prompts that contain "|" in prompts (as an AND condition) and weights (separated by "|" as well) to positively / negatively weight prompts. | [Composable Stable Diffusion](#composable-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) |
|
||||
| Seed Resizing Stable Diffusion| Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation. | [Seed Resizing](#seed-resizing) | - | [Mark Rich](https://github.com/MarkRich) |
|
||||
|
||||
| Imagic Stable Diffusion | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image| [Imagic Stable Diffusion](#imagic-stable-diffusion) | - | [Mark Rich](https://github.com/MarkRich) |
|
||||
| Image to Image Inpainting Stable Diffusion | Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting| [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) | - | [Alex McKinney](https://github.com/vvvm23) |
|
||||
|
||||
|
||||
To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
|
||||
|
|
@ -501,3 +501,37 @@ res = pipe_compare(
|
|||
image = res.images[0]
|
||||
image.save('./seed_resize/seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height))
|
||||
```
|
||||
|
||||
### Image to Image Inpainting Stable Diffusion
|
||||
|
||||
Similar to the standard stable diffusion inpainting example, except with the addition of an `inner_image` argument.
|
||||
|
||||
`image`, `inner_image`, and `mask` should have the same dimensions. `inner_image` should have an alpha (transparency) channel.
|
||||
|
||||
The aim is to overlay two images, then mask out the boundary between `image` and `inner_image` to allow stable diffusion to make the connection more seamless.
|
||||
For example, this could be used to place a logo on a shirt and make it blend seamlessly.
|
||||
|
||||
```python
|
||||
import PIL
|
||||
import torch
|
||||
|
||||
from diffusers import StableDiffusionInpaintPipeline
|
||||
|
||||
image_path = "./path-to-image.png"
|
||||
inner_image_path = "./path-to-inner-image.png"
|
||||
mask_path = "./path-to-mask.png"
|
||||
|
||||
init_image = PIL.Image.open(image_path).convert("RGB").resize((512, 512))
|
||||
inner_image = PIL.Image.open(inner_image_path).convert("RGBA").resize((512, 512))
|
||||
mask_image = PIL.Image.open(mask_path).convert("RGB").resize((512, 512))
|
||||
|
||||
pipe = StableDiffusionInpaintPipeline.from_pretrained(
|
||||
"runwayml/stable-diffusion-inpainting",
|
||||
revision="fp16",
|
||||
torch_dtype=torch.float16,
|
||||
)
|
||||
pipe = pipe.to("cuda")
|
||||
|
||||
prompt = "Your prompt here!"
|
||||
image = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]
|
||||
```
|
||||
|
|
@ -0,0 +1,463 @@
|
|||
import inspect
|
||||
from typing import Callable, List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
import PIL
|
||||
from diffusers.configuration_utils import FrozenDict
|
||||
from diffusers.models import AutoencoderKL, UNet2DConditionModel
|
||||
from diffusers.pipeline_utils import DiffusionPipeline
|
||||
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
|
||||
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
|
||||
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
|
||||
from diffusers.utils import deprecate, logging
|
||||
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
def prepare_mask_and_masked_image(image, mask):
|
||||
image = np.array(image.convert("RGB"))
|
||||
image = image[None].transpose(0, 3, 1, 2)
|
||||
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
|
||||
|
||||
mask = np.array(mask.convert("L"))
|
||||
mask = mask.astype(np.float32) / 255.0
|
||||
mask = mask[None, None]
|
||||
mask[mask < 0.5] = 0
|
||||
mask[mask >= 0.5] = 1
|
||||
mask = torch.from_numpy(mask)
|
||||
|
||||
masked_image = image * (mask < 0.5)
|
||||
|
||||
return mask, masked_image
|
||||
|
||||
|
||||
def check_size(image, height, width):
|
||||
if isinstance(image, PIL.Image.Image):
|
||||
w, h = image.size
|
||||
elif isinstance(image, torch.Tensor):
|
||||
*_, h, w = image.shape
|
||||
|
||||
if h != height or w != width:
|
||||
raise ValueError(f"Image size should be {height}x{width}, but got {h}x{w}")
|
||||
|
||||
|
||||
def overlay_inner_image(image, inner_image, paste_offset: Tuple[int] = (0, 0)):
|
||||
inner_image = inner_image.convert("RGBA")
|
||||
image = image.convert("RGB")
|
||||
|
||||
image.paste(inner_image, paste_offset, inner_image)
|
||||
image = image.convert("RGB")
|
||||
|
||||
return image
|
||||
|
||||
|
||||
class ImageToImageInpaintingPipeline(DiffusionPipeline):
|
||||
r"""
|
||||
Pipeline for text-guided image-to-image inpainting using Stable Diffusion. *This is an experimental feature*.
|
||||
|
||||
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
||||
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
||||
|
||||
Args:
|
||||
vae ([`AutoencoderKL`]):
|
||||
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
||||
text_encoder ([`CLIPTextModel`]):
|
||||
Frozen text-encoder. Stable Diffusion uses the text portion of
|
||||
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
||||
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
||||
tokenizer (`CLIPTokenizer`):
|
||||
Tokenizer of class
|
||||
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
||||
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
||||
scheduler ([`SchedulerMixin`]):
|
||||
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
|
||||
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
||||
safety_checker ([`StableDiffusionSafetyChecker`]):
|
||||
Classification module that estimates whether generated images could be considered offensive or harmful.
|
||||
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
|
||||
feature_extractor ([`CLIPFeatureExtractor`]):
|
||||
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vae: AutoencoderKL,
|
||||
text_encoder: CLIPTextModel,
|
||||
tokenizer: CLIPTokenizer,
|
||||
unet: UNet2DConditionModel,
|
||||
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
|
||||
safety_checker: StableDiffusionSafetyChecker,
|
||||
feature_extractor: CLIPFeatureExtractor,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
|
||||
deprecation_message = (
|
||||
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
|
||||
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
|
||||
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
|
||||
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
|
||||
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
|
||||
" file"
|
||||
)
|
||||
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
|
||||
new_config = dict(scheduler.config)
|
||||
new_config["steps_offset"] = 1
|
||||
scheduler._internal_dict = FrozenDict(new_config)
|
||||
|
||||
if safety_checker is None:
|
||||
logger.warn(
|
||||
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
|
||||
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
|
||||
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
|
||||
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
|
||||
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
|
||||
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
|
||||
)
|
||||
|
||||
self.register_modules(
|
||||
vae=vae,
|
||||
text_encoder=text_encoder,
|
||||
tokenizer=tokenizer,
|
||||
unet=unet,
|
||||
scheduler=scheduler,
|
||||
safety_checker=safety_checker,
|
||||
feature_extractor=feature_extractor,
|
||||
)
|
||||
|
||||
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
|
||||
r"""
|
||||
Enable sliced attention computation.
|
||||
|
||||
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
|
||||
in several steps. This is useful to save some memory in exchange for a small speed decrease.
|
||||
|
||||
Args:
|
||||
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
|
||||
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
|
||||
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
|
||||
`attention_head_dim` must be a multiple of `slice_size`.
|
||||
"""
|
||||
if slice_size == "auto":
|
||||
# half the attention head size is usually a good trade-off between
|
||||
# speed and memory
|
||||
slice_size = self.unet.config.attention_head_dim // 2
|
||||
self.unet.set_attention_slice(slice_size)
|
||||
|
||||
def disable_attention_slicing(self):
|
||||
r"""
|
||||
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
|
||||
back to computing attention in one step.
|
||||
"""
|
||||
# set slice_size = `None` to disable `attention slicing`
|
||||
self.enable_attention_slicing(None)
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(
|
||||
self,
|
||||
prompt: Union[str, List[str]],
|
||||
image: Union[torch.FloatTensor, PIL.Image.Image],
|
||||
inner_image: Union[torch.FloatTensor, PIL.Image.Image],
|
||||
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
|
||||
height: int = 512,
|
||||
width: int = 512,
|
||||
num_inference_steps: int = 50,
|
||||
guidance_scale: float = 7.5,
|
||||
negative_prompt: Optional[Union[str, List[str]]] = None,
|
||||
num_images_per_prompt: Optional[int] = 1,
|
||||
eta: float = 0.0,
|
||||
generator: Optional[torch.Generator] = None,
|
||||
latents: Optional[torch.FloatTensor] = None,
|
||||
output_type: Optional[str] = "pil",
|
||||
return_dict: bool = True,
|
||||
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
||||
callback_steps: Optional[int] = 1,
|
||||
**kwargs,
|
||||
):
|
||||
r"""
|
||||
Function invoked when calling the pipeline for generation.
|
||||
|
||||
Args:
|
||||
prompt (`str` or `List[str]`):
|
||||
The prompt or prompts to guide the image generation.
|
||||
image (`torch.Tensor` or `PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
|
||||
be masked out with `mask_image` and repainted according to `prompt`.
|
||||
inner_image (`torch.Tensor` or `PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch which will be overlayed onto `image`. Non-transparent
|
||||
regions of `inner_image` must fit inside white pixels in `mask_image`. Expects four channels, with
|
||||
the last channel representing the alpha channel, which will be used to blend `inner_image` with
|
||||
`image`. If not provided, it will be forcibly cast to RGBA.
|
||||
mask_image (`PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
|
||||
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):
|
||||
The height in pixels of the generated image.
|
||||
width (`int`, *optional*, defaults to 512):
|
||||
The width in pixels of the generated image.
|
||||
num_inference_steps (`int`, *optional*, defaults to 50):
|
||||
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
||||
expense of slower inference.
|
||||
guidance_scale (`float`, *optional*, defaults to 7.5):
|
||||
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
||||
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
||||
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
||||
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
||||
usually at the expense of lower image quality.
|
||||
negative_prompt (`str` or `List[str]`, *optional*):
|
||||
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
||||
if `guidance_scale` is less than `1`).
|
||||
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
||||
The number of images to generate per prompt.
|
||||
eta (`float`, *optional*, defaults to 0.0):
|
||||
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
||||
[`schedulers.DDIMScheduler`], will be ignored for others.
|
||||
generator (`torch.Generator`, *optional*):
|
||||
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
|
||||
deterministic.
|
||||
latents (`torch.FloatTensor`, *optional*):
|
||||
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
||||
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
||||
tensor will ge generated by sampling using the supplied random `generator`.
|
||||
output_type (`str`, *optional*, defaults to `"pil"`):
|
||||
The output format of the generate image. Choose between
|
||||
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
||||
return_dict (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
||||
plain tuple.
|
||||
callback (`Callable`, *optional*):
|
||||
A function that will be called every `callback_steps` steps during inference. The function will be
|
||||
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
||||
callback_steps (`int`, *optional*, defaults to 1):
|
||||
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
||||
called at every step.
|
||||
|
||||
Returns:
|
||||
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
||||
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
||||
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
||||
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
||||
(nsfw) content, according to the `safety_checker`.
|
||||
"""
|
||||
|
||||
if isinstance(prompt, str):
|
||||
batch_size = 1
|
||||
elif isinstance(prompt, list):
|
||||
batch_size = len(prompt)
|
||||
else:
|
||||
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
||||
|
||||
if height % 8 != 0 or width % 8 != 0:
|
||||
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
||||
|
||||
if (callback_steps is None) or (
|
||||
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
||||
):
|
||||
raise ValueError(
|
||||
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
||||
f" {type(callback_steps)}."
|
||||
)
|
||||
|
||||
# check if input sizes are correct
|
||||
check_size(image, height, width)
|
||||
check_size(inner_image, height, width)
|
||||
check_size(mask_image, height, width)
|
||||
|
||||
# get prompt text embeddings
|
||||
text_inputs = self.tokenizer(
|
||||
prompt,
|
||||
padding="max_length",
|
||||
max_length=self.tokenizer.model_max_length,
|
||||
return_tensors="pt",
|
||||
)
|
||||
text_input_ids = text_inputs.input_ids
|
||||
|
||||
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
|
||||
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
|
||||
logger.warning(
|
||||
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
||||
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
||||
)
|
||||
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
|
||||
text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
|
||||
|
||||
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
||||
bs_embed, seq_len, _ = text_embeddings.shape
|
||||
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
|
||||
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
||||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
||||
# corresponds to doing no classifier free guidance.
|
||||
do_classifier_free_guidance = guidance_scale > 1.0
|
||||
# get unconditional embeddings for classifier free guidance
|
||||
if do_classifier_free_guidance:
|
||||
uncond_tokens: List[str]
|
||||
if negative_prompt is None:
|
||||
uncond_tokens = [""]
|
||||
elif type(prompt) is not type(negative_prompt):
|
||||
raise TypeError(
|
||||
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
||||
f" {type(prompt)}."
|
||||
)
|
||||
elif isinstance(negative_prompt, str):
|
||||
uncond_tokens = [negative_prompt]
|
||||
elif batch_size != len(negative_prompt):
|
||||
raise ValueError(
|
||||
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
||||
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
||||
" the batch size of `prompt`."
|
||||
)
|
||||
else:
|
||||
uncond_tokens = negative_prompt
|
||||
|
||||
max_length = text_input_ids.shape[-1]
|
||||
uncond_input = self.tokenizer(
|
||||
uncond_tokens,
|
||||
padding="max_length",
|
||||
max_length=max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
|
||||
|
||||
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
||||
seq_len = uncond_embeddings.shape[1]
|
||||
uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
|
||||
uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
# For classifier free guidance, we need to do two forward passes.
|
||||
# Here we concatenate the unconditional and text embeddings into a single batch
|
||||
# to avoid doing two forward passes
|
||||
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
|
||||
|
||||
# get the initial random noise unless the user supplied it
|
||||
# Unlike in other pipelines, latents need to be generated in the target device
|
||||
# for 1-to-1 results reproducibility with the CompVis implementation.
|
||||
# However this currently doesn't work in `mps`.
|
||||
num_channels_latents = self.vae.config.latent_channels
|
||||
latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)
|
||||
latents_dtype = text_embeddings.dtype
|
||||
if latents is None:
|
||||
if self.device.type == "mps":
|
||||
# randn does not exist on mps
|
||||
latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
|
||||
self.device
|
||||
)
|
||||
else:
|
||||
latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
|
||||
else:
|
||||
if latents.shape != latents_shape:
|
||||
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
|
||||
latents = latents.to(self.device)
|
||||
|
||||
# overlay the inner image
|
||||
image = overlay_inner_image(image, inner_image)
|
||||
|
||||
# prepare mask and masked_image
|
||||
mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
|
||||
mask = mask.to(device=self.device, dtype=text_embeddings.dtype)
|
||||
masked_image = masked_image.to(device=self.device, dtype=text_embeddings.dtype)
|
||||
|
||||
# resize the mask to latents shape as we concatenate the mask to the latents
|
||||
mask = torch.nn.functional.interpolate(mask, size=(height // 8, width // 8))
|
||||
|
||||
# encode the mask image into latents space so we can concatenate it to the latents
|
||||
masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
|
||||
masked_image_latents = 0.18215 * masked_image_latents
|
||||
|
||||
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
|
||||
mask = mask.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
|
||||
masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 1, 1, 1)
|
||||
|
||||
mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
|
||||
masked_image_latents = (
|
||||
torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
|
||||
)
|
||||
|
||||
num_channels_mask = mask.shape[1]
|
||||
num_channels_masked_image = masked_image_latents.shape[1]
|
||||
|
||||
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
|
||||
raise ValueError(
|
||||
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
||||
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
||||
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
|
||||
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
|
||||
" `pipeline.unet` or your `mask_image` or `image` input."
|
||||
)
|
||||
|
||||
# set timesteps
|
||||
self.scheduler.set_timesteps(num_inference_steps)
|
||||
|
||||
# Some schedulers like PNDM have timesteps as arrays
|
||||
# It's more optimized to move all timesteps to correct device beforehand
|
||||
timesteps_tensor = self.scheduler.timesteps.to(self.device)
|
||||
|
||||
# scale the initial noise by the standard deviation required by the scheduler
|
||||
latents = latents * self.scheduler.init_noise_sigma
|
||||
|
||||
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
||||
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
||||
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
||||
# and should be between [0, 1]
|
||||
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
extra_step_kwargs = {}
|
||||
if accepts_eta:
|
||||
extra_step_kwargs["eta"] = eta
|
||||
|
||||
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
|
||||
# concat latents, mask, masked_image_latents in the channel dimension
|
||||
latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
|
||||
|
||||
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
||||
|
||||
# predict the noise residual
|
||||
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
|
||||
|
||||
# perform guidance
|
||||
if do_classifier_free_guidance:
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
||||
|
||||
# call the callback, if provided
|
||||
if callback is not None and i % callback_steps == 0:
|
||||
callback(i, t, latents)
|
||||
|
||||
latents = 1 / 0.18215 * latents
|
||||
image = self.vae.decode(latents).sample
|
||||
|
||||
image = (image / 2 + 0.5).clamp(0, 1)
|
||||
|
||||
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
||||
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
||||
|
||||
if self.safety_checker is not None:
|
||||
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
|
||||
self.device
|
||||
)
|
||||
image, has_nsfw_concept = self.safety_checker(
|
||||
images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
|
||||
)
|
||||
else:
|
||||
has_nsfw_concept = None
|
||||
|
||||
if output_type == "pil":
|
||||
image = self.numpy_to_pil(image)
|
||||
|
||||
if not return_dict:
|
||||
return (image, has_nsfw_concept)
|
||||
|
||||
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
||||
Loading…
Reference in New Issue