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# Copyright 2022 The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import inspect | |
from typing import Callable, List, Optional, Union | |
import numpy as np | |
import paddle | |
import PIL | |
from paddlenlp.transformers import CLIPFeatureExtractor | |
from ...models import AutoencoderKL, UNet2DConditionModel | |
from ...pipeline_utils import DiffusionPipeline | |
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler | |
from ...utils import logging | |
from ..stable_diffusion import StableDiffusionPipelineOutput | |
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
from .image_encoder import PaintByExampleImageEncoder | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
def prepare_mask_and_masked_image(image, mask): | |
""" | |
Prepares a pair (image, mask) to be consumed by the Paint by Example pipeline. This means that those inputs will be | |
converted to ``paddle.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the | |
``image`` and ``1`` for the ``mask``. | |
The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be | |
binarized (``mask > 0.5``) and cast to ``torch.float32`` too. | |
Args: | |
image (Union[np.array, PIL.Image, paddle.Tensor]): The image to inpaint. | |
It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width`` | |
``paddle.Tensor`` or a ``batch x channels x height x width`` ``paddle.Tensor``. | |
mask (_type_): The mask to apply to the image, i.e. regions to inpaint. | |
It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width`` | |
``paddle.Tensor`` or a ``batch x 1 x height x width`` ``paddle.Tensor``. | |
Raises: | |
ValueError: ``paddle.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``paddle.Tensor`` mask | |
should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions. | |
TypeError: ``mask`` is a ``paddle.Tensor`` but ``image`` is not | |
(ot the other way around). | |
Returns: | |
tuple[paddle.Tensor]: The pair (mask, masked_image) as ``paddle.Tensor`` with 4 | |
dimensions: ``batch x channels x height x width``. | |
""" | |
if isinstance(image, paddle.Tensor): | |
if not isinstance(mask, paddle.Tensor): | |
raise TypeError(f"`image` is a paddle.Tensor but `mask` (type: {type(mask)} is not") | |
# Batch single image | |
if image.ndim == 3: | |
assert image.shape[0] == 3, "Image outside a batch should be of shape (3, H, W)" | |
image = image.unsqueeze(0) | |
# Batch and add channel dim for single mask | |
if mask.ndim == 2: | |
mask = mask.unsqueeze(0).unsqueeze(0) | |
# Batch single mask or add channel dim | |
if mask.ndim == 3: | |
# Batched mask | |
if mask.shape[0] == image.shape[0]: | |
mask = mask.unsqueeze(1) | |
else: | |
mask = mask.unsqueeze(0) | |
assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions" | |
assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions" | |
assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size" | |
assert mask.shape[1] == 1, "Mask image must have a single channel" | |
# Check image is in [-1, 1] | |
if image.min() < -1 or image.max() > 1: | |
raise ValueError("Image should be in [-1, 1] range") | |
# Check mask is in [0, 1] | |
if mask.min() < 0 or mask.max() > 1: | |
raise ValueError("Mask should be in [0, 1] range") | |
# paint-by-example inverses the mask | |
mask = 1 - mask | |
# Binarize mask | |
mask[mask < 0.5] = 0 | |
mask[mask >= 0.5] = 1 | |
# Image as float32 | |
image = image.cast(paddle.float32) | |
elif isinstance(mask, paddle.Tensor): | |
raise TypeError(f"`mask` is a paddle.Tensor but `image` (type: {type(image)} is not") | |
else: | |
if isinstance(image, PIL.Image.Image): | |
image = [image] | |
image = np.concatenate([np.array(i.convert("RGB"))[None, :] for i in image], axis=0) | |
image = image.transpose(0, 3, 1, 2) | |
image = paddle.to_tensor(image).cast(paddle.float32) / 127.5 - 1.0 | |
# preprocess mask | |
if isinstance(mask, PIL.Image.Image): | |
mask = [mask] | |
mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0) | |
mask = mask.astype(np.float32) / 255.0 | |
# paint-by-example inverses the mask | |
mask = 1 - mask | |
mask[mask < 0.5] = 0 | |
mask[mask >= 0.5] = 1 | |
mask = paddle.to_tensor(mask) | |
masked_image = image * mask | |
return mask, masked_image | |
class PaintByExamplePipeline(DiffusionPipeline): | |
r""" | |
Pipeline for text-guided 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 latents. 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`. | |
""" | |
_optional_components = ["safety_checker"] | |
def __init__( | |
self, | |
vae: AutoencoderKL, | |
image_encoder: PaintByExampleImageEncoder, | |
unet: UNet2DConditionModel, | |
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], | |
safety_checker: StableDiffusionSafetyChecker, | |
feature_extractor: CLIPFeatureExtractor, | |
requires_safety_checker: bool = False, | |
): | |
super().__init__() | |
self.register_modules( | |
vae=vae, | |
image_encoder=image_encoder, | |
unet=unet, | |
scheduler=scheduler, | |
safety_checker=safety_checker, | |
feature_extractor=feature_extractor, | |
) | |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
self.register_to_config(requires_safety_checker=requires_safety_checker) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker | |
def run_safety_checker(self, image, dtype): | |
if self.safety_checker is not None: | |
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pd") | |
image, has_nsfw_concept = self.safety_checker( | |
images=image, clip_input=safety_checker_input.pixel_values.cast(dtype) | |
) | |
else: | |
has_nsfw_concept = None | |
return image, has_nsfw_concept | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs | |
def prepare_extra_step_kwargs(self, generator, eta): | |
# 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 | |
# check if the scheduler accepts generator | |
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
if accepts_generator: | |
extra_step_kwargs["generator"] = generator | |
return extra_step_kwargs | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents | |
def decode_latents(self, latents): | |
latents = 1 / 0.18215 * latents | |
image = self.vae.decode(latents).sample | |
image = (image / 2 + 0.5).clip(0, 1) | |
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 | |
image = image.transpose([0, 2, 3, 1]).cast("float32").numpy() | |
return image | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs | |
def check_inputs(self, image, height, width, callback_steps): | |
if ( | |
not isinstance(image, paddle.Tensor) | |
and not isinstance(image, PIL.Image.Image) | |
and not isinstance(image, list) | |
): | |
raise ValueError( | |
"`image` has to be of type `paddle.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" | |
f" {type(image)}" | |
) | |
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)}." | |
) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents | |
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None): | |
shape = [batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor] | |
if isinstance(generator, list) and len(generator) != batch_size: | |
raise ValueError( | |
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" | |
f" size of {batch_size}. Make sure the batch size matches the length of the generators." | |
) | |
if latents is None: | |
if isinstance(generator, list): | |
shape = [ | |
1, | |
] + shape[1:] | |
latents = [paddle.randn(shape, generator=generator[i], dtype=dtype) for i in range(batch_size)] | |
latents = paddle.concat(latents, axis=0) | |
else: | |
latents = paddle.randn(shape, generator=generator, dtype=dtype) | |
else: | |
if latents.shape != shape: | |
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") | |
# scale the initial noise by the standard deviation required by the scheduler | |
latents = latents * self.scheduler.init_noise_sigma | |
return latents | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline.prepare_mask_latents | |
def prepare_mask_latents( | |
self, mask, masked_image, batch_size, height, width, dtype, generator, do_classifier_free_guidance | |
): | |
# resize the mask to latents shape as we concatenate the mask to the latents | |
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload | |
# and half precision | |
mask = paddle.nn.functional.interpolate( | |
mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) | |
) | |
mask = mask.cast(dtype) | |
masked_image = masked_image.cast(dtype) | |
# encode the mask image into latents space so we can concatenate it to the latents | |
if isinstance(generator, list): | |
masked_image_latents = [ | |
self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i]) | |
for i in range(batch_size) | |
] | |
masked_image_latents = paddle.concat(masked_image_latents, axis=0) | |
else: | |
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 | |
if mask.shape[0] < batch_size: | |
if not batch_size % mask.shape[0] == 0: | |
raise ValueError( | |
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" | |
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" | |
" of masks that you pass is divisible by the total requested batch size." | |
) | |
mask = mask.tile([batch_size // mask.shape[0], 1, 1, 1]) | |
if masked_image_latents.shape[0] < batch_size: | |
if not batch_size % masked_image_latents.shape[0] == 0: | |
raise ValueError( | |
"The passed images and the required batch size don't match. Images are supposed to be duplicated" | |
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." | |
" Make sure the number of images that you pass is divisible by the total requested batch size." | |
) | |
masked_image_latents = masked_image_latents.tile([batch_size // masked_image_latents.shape[0], 1, 1, 1]) | |
mask = paddle.concat([mask] * 2) if do_classifier_free_guidance else mask | |
masked_image_latents = ( | |
paddle.concat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents | |
) | |
# aligning device to prevent device errors when concating it with the latent model input | |
masked_image_latents = masked_image_latents.cast(dtype) | |
return mask, masked_image_latents | |
def _encode_image(self, image, num_images_per_prompt, do_classifier_free_guidance): | |
# dtype = self.image_encoder.dtype | |
if not isinstance(image, paddle.Tensor): | |
image = self.feature_extractor(images=image, return_tensors="pd").pixel_values | |
# image = image.cast(dtype) | |
image_embeddings = self.image_encoder(image) | |
# duplicate image embeddings for each generation per prompt, using mps friendly method | |
bs_embed, seq_len, _ = image_embeddings.shape | |
image_embeddings = image_embeddings.tile([1, num_images_per_prompt, 1]) | |
image_embeddings = image_embeddings.reshape([bs_embed * num_images_per_prompt, seq_len, -1]) | |
if do_classifier_free_guidance: | |
uncond_embeddings = self.image_encoder.uncond_vector | |
uncond_embeddings = uncond_embeddings.tile([1, image_embeddings.shape[0], 1]) | |
uncond_embeddings = uncond_embeddings.reshape([bs_embed * num_images_per_prompt, 1, -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 | |
image_embeddings = paddle.concat([uncond_embeddings, image_embeddings]) | |
return image_embeddings | |
def __call__( | |
self, | |
example_image: Union[paddle.Tensor, PIL.Image.Image], | |
image: Union[paddle.Tensor, PIL.Image.Image], | |
mask_image: Union[paddle.Tensor, PIL.Image.Image], | |
height: Optional[int] = None, | |
width: Optional[int] = None, | |
num_inference_steps: int = 50, | |
guidance_scale: float = 5.0, | |
num_images_per_prompt: Optional[int] = 1, | |
eta: float = 0.0, | |
generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None, | |
latents: Optional[paddle.Tensor] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None, | |
callback_steps: Optional[int] = 1, | |
): | |
r""" | |
Function invoked when calling the pipeline for generation. | |
Args: | |
example_image (`paddle.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]`): | |
The exemplar image to guide the image generation. | |
image (`paddle.Tensor` or `PIL.Image.Image` or `List[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`. | |
mask_image (`paddle.Tensor` or `PIL.Image.Image` or `List[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 self.unet.config.sample_size * self.vae_scale_factor): | |
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. | |
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. | |
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*): | |
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) | |
to make generation deterministic. | |
latents (`paddle.Tensor`, *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: paddle.Tensor)`. | |
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`. | |
""" | |
# 1. Define call parameters | |
if isinstance(image, PIL.Image.Image): | |
batch_size = 1 | |
elif isinstance(image, list): | |
batch_size = len(image) | |
else: | |
batch_size = image.shape[0] | |
# 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 | |
# 2. Preprocess mask and image | |
mask, masked_image = prepare_mask_and_masked_image(image, mask_image) | |
height, width = masked_image.shape[-2:] | |
# 3. Check inputs | |
self.check_inputs(example_image, height, width, callback_steps) | |
# 4. Encode input image | |
image_embeddings = self._encode_image(example_image, num_images_per_prompt, do_classifier_free_guidance) | |
# 5. set timesteps | |
self.scheduler.set_timesteps(num_inference_steps) | |
timesteps = self.scheduler.timesteps | |
# 6. Prepare latent variables | |
num_channels_latents = self.vae.config.latent_channels | |
latents = self.prepare_latents( | |
batch_size * num_images_per_prompt, | |
num_channels_latents, | |
height, | |
width, | |
image_embeddings.dtype, | |
generator, | |
latents, | |
) | |
# 7. Prepare mask latent variables | |
mask, masked_image_latents = self.prepare_mask_latents( | |
mask, | |
masked_image, | |
batch_size * num_images_per_prompt, | |
height, | |
width, | |
image_embeddings.dtype, | |
generator, | |
do_classifier_free_guidance, | |
) | |
# 8. Check that sizes of mask, masked image and latents match | |
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." | |
) | |
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline | |
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
# 10. Denoising loop | |
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order | |
with self.progress_bar(total=num_inference_steps) as progress_bar: | |
for i, t in enumerate(timesteps): | |
# expand the latents if we are doing classifier free guidance | |
latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents | |
# concat latents, mask, masked_image_latents in the channel dimension | |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
latent_model_input = paddle.concat([latent_model_input, masked_image_latents, mask], axis=1) | |
# predict the noise residual | |
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_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 i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): | |
progress_bar.update() | |
if callback is not None and i % callback_steps == 0: | |
callback(i, t, latents) | |
# 11. Post-processing | |
image = self.decode_latents(latents) | |
# 12. Run safety checker | |
image, has_nsfw_concept = self.run_safety_checker(image, image_embeddings.dtype) | |
# 13. Convert to PIL | |
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) | |