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# Copyright 2023 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 | |
import math | |
import warnings | |
from typing import Any, Callable, Dict, List, Optional, Union | |
import PIL | |
import torch | |
import torchvision.transforms.functional as TF | |
from diffusers.configuration_utils import ConfigMixin, FrozenDict, register_to_config | |
from diffusers.image_processor import VaeImageProcessor | |
from diffusers.models import AutoencoderKL, UNet2DConditionModel | |
from diffusers.models.modeling_utils import ModelMixin | |
from diffusers.pipelines.pipeline_utils import DiffusionPipeline | |
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput | |
from diffusers.pipelines.stable_diffusion.safety_checker import ( | |
StableDiffusionSafetyChecker, | |
) | |
from diffusers.schedulers import KarrasDiffusionSchedulers | |
from diffusers.utils import deprecate, is_accelerate_available, logging | |
from diffusers.utils.torch_utils import randn_tensor | |
from packaging import version | |
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
class CLIPCameraProjection(ModelMixin, ConfigMixin): | |
""" | |
A Projection layer for CLIP embedding and camera embedding. | |
Parameters: | |
embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `clip_embed` | |
additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the | |
projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings + | |
additional_embeddings`. | |
""" | |
def __init__(self, embedding_dim: int = 768, additional_embeddings: int = 4): | |
super().__init__() | |
self.embedding_dim = embedding_dim | |
self.additional_embeddings = additional_embeddings | |
self.input_dim = self.embedding_dim + self.additional_embeddings | |
self.output_dim = self.embedding_dim | |
self.proj = torch.nn.Linear(self.input_dim, self.output_dim) | |
def forward( | |
self, | |
embedding: torch.FloatTensor, | |
): | |
""" | |
The [`PriorTransformer`] forward method. | |
Args: | |
hidden_states (`torch.FloatTensor` of shape `(batch_size, input_dim)`): | |
The currently input embeddings. | |
Returns: | |
The output embedding projection (`torch.FloatTensor` of shape `(batch_size, output_dim)`). | |
""" | |
proj_embedding = self.proj(embedding) | |
return proj_embedding | |
class Zero123Pipeline(DiffusionPipeline): | |
r""" | |
Pipeline to generate variations from an input image using Stable Diffusion. | |
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. | |
image_encoder ([`CLIPVisionModelWithProjection`]): | |
Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of | |
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection), | |
specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. | |
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 ([`CLIPImageProcessor`]): | |
Model that extracts features from generated images to be used as inputs for the `safety_checker`. | |
""" | |
# TODO: feature_extractor is required to encode images (if they are in PIL format), | |
# we should give a descriptive message if the pipeline doesn't have one. | |
_optional_components = ["safety_checker"] | |
def __init__( | |
self, | |
vae: AutoencoderKL, | |
image_encoder: CLIPVisionModelWithProjection, | |
unet: UNet2DConditionModel, | |
scheduler: KarrasDiffusionSchedulers, | |
safety_checker: StableDiffusionSafetyChecker, | |
feature_extractor: CLIPImageProcessor, | |
clip_camera_projection: CLIPCameraProjection, | |
requires_safety_checker: bool = True, | |
): | |
super().__init__() | |
if safety_checker is None and requires_safety_checker: | |
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 ." | |
) | |
if safety_checker is not None and feature_extractor is None: | |
raise ValueError( | |
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" | |
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." | |
) | |
is_unet_version_less_0_9_0 = hasattr( | |
unet.config, "_diffusers_version" | |
) and version.parse( | |
version.parse(unet.config._diffusers_version).base_version | |
) < version.parse( | |
"0.9.0.dev0" | |
) | |
is_unet_sample_size_less_64 = ( | |
hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 | |
) | |
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: | |
deprecation_message = ( | |
"The configuration file of the unet has set the default `sample_size` to smaller than" | |
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" | |
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" | |
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" | |
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" | |
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" | |
" in the config might lead 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 `unet/config.json` file" | |
) | |
deprecate( | |
"sample_size<64", "1.0.0", deprecation_message, standard_warn=False | |
) | |
new_config = dict(unet.config) | |
new_config["sample_size"] = 64 | |
unet._internal_dict = FrozenDict(new_config) | |
self.register_modules( | |
vae=vae, | |
image_encoder=image_encoder, | |
unet=unet, | |
scheduler=scheduler, | |
safety_checker=safety_checker, | |
feature_extractor=feature_extractor, | |
clip_camera_projection=clip_camera_projection, | |
) | |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | |
self.register_to_config(requires_safety_checker=requires_safety_checker) | |
def enable_sequential_cpu_offload(self, gpu_id=0): | |
r""" | |
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, | |
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a | |
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. | |
""" | |
if is_accelerate_available(): | |
from accelerate import cpu_offload | |
else: | |
raise ImportError("Please install accelerate via `pip install accelerate`") | |
device = torch.device(f"cuda:{gpu_id}") | |
for cpu_offloaded_model in [ | |
self.unet, | |
self.image_encoder, | |
self.vae, | |
self.safety_checker, | |
]: | |
if cpu_offloaded_model is not None: | |
cpu_offload(cpu_offloaded_model, device) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device | |
def _execution_device(self): | |
r""" | |
Returns the device on which the pipeline's models will be executed. After calling | |
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module | |
hooks. | |
""" | |
if not hasattr(self.unet, "_hf_hook"): | |
return self.device | |
for module in self.unet.modules(): | |
if ( | |
hasattr(module, "_hf_hook") | |
and hasattr(module._hf_hook, "execution_device") | |
and module._hf_hook.execution_device is not None | |
): | |
return torch.device(module._hf_hook.execution_device) | |
return self.device | |
def _encode_image( | |
self, | |
image, | |
elevation, | |
azimuth, | |
distance, | |
device, | |
num_images_per_prompt, | |
do_classifier_free_guidance, | |
clip_image_embeddings=None, | |
image_camera_embeddings=None, | |
): | |
dtype = next(self.image_encoder.parameters()).dtype | |
if image_camera_embeddings is None: | |
if image is None: | |
assert clip_image_embeddings is not None | |
image_embeddings = clip_image_embeddings.to(device=device, dtype=dtype) | |
else: | |
if not isinstance(image, torch.Tensor): | |
image = self.feature_extractor( | |
images=image, return_tensors="pt" | |
).pixel_values | |
image = image.to(device=device, dtype=dtype) | |
image_embeddings = self.image_encoder(image).image_embeds | |
image_embeddings = image_embeddings.unsqueeze(1) | |
bs_embed, seq_len, _ = image_embeddings.shape | |
if isinstance(elevation, float): | |
elevation = torch.as_tensor( | |
[elevation] * bs_embed, dtype=dtype, device=device | |
) | |
if isinstance(azimuth, float): | |
azimuth = torch.as_tensor( | |
[azimuth] * bs_embed, dtype=dtype, device=device | |
) | |
if isinstance(distance, float): | |
distance = torch.as_tensor( | |
[distance] * bs_embed, dtype=dtype, device=device | |
) | |
camera_embeddings = torch.stack( | |
[ | |
torch.deg2rad(elevation), | |
torch.sin(torch.deg2rad(azimuth)), | |
torch.cos(torch.deg2rad(azimuth)), | |
distance, | |
], | |
dim=-1, | |
)[:, None, :] | |
image_embeddings = torch.cat([image_embeddings, camera_embeddings], dim=-1) | |
# project (image, camera) embeddings to the same dimension as clip embeddings | |
image_embeddings = self.clip_camera_projection(image_embeddings) | |
else: | |
image_embeddings = image_camera_embeddings.to(device=device, dtype=dtype) | |
bs_embed, seq_len, _ = image_embeddings.shape | |
# duplicate image embeddings for each generation per prompt, using mps friendly method | |
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1) | |
image_embeddings = image_embeddings.view( | |
bs_embed * num_images_per_prompt, seq_len, -1 | |
) | |
if do_classifier_free_guidance: | |
negative_prompt_embeds = torch.zeros_like(image_embeddings) | |
# 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 = torch.cat([negative_prompt_embeds, image_embeddings]) | |
return image_embeddings | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker | |
def run_safety_checker(self, image, device, dtype): | |
if self.safety_checker is None: | |
has_nsfw_concept = None | |
else: | |
if torch.is_tensor(image): | |
feature_extractor_input = self.image_processor.postprocess( | |
image, output_type="pil" | |
) | |
else: | |
feature_extractor_input = self.image_processor.numpy_to_pil(image) | |
safety_checker_input = self.feature_extractor( | |
feature_extractor_input, return_tensors="pt" | |
).to(device) | |
image, has_nsfw_concept = self.safety_checker( | |
images=image, clip_input=safety_checker_input.pixel_values.to(dtype) | |
) | |
return image, has_nsfw_concept | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents | |
def decode_latents(self, latents): | |
warnings.warn( | |
"The decode_latents method is deprecated and will be removed in a future version. Please" | |
" use VaeImageProcessor instead", | |
FutureWarning, | |
) | |
latents = 1 / self.vae.config.scaling_factor * latents | |
image = self.vae.decode(latents, return_dict=False)[0] | |
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() | |
return image | |
# 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 | |
def check_inputs(self, image, height, width, callback_steps): | |
# TODO: check image size or adjust image size to (height, width) | |
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, | |
device, | |
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: | |
latents = randn_tensor( | |
shape, generator=generator, device=device, dtype=dtype | |
) | |
else: | |
latents = latents.to(device) | |
# scale the initial noise by the standard deviation required by the scheduler | |
latents = latents * self.scheduler.init_noise_sigma | |
return latents | |
def _get_latent_model_input( | |
self, | |
latents: torch.FloatTensor, | |
image: Optional[ | |
Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor] | |
], | |
num_images_per_prompt: int, | |
do_classifier_free_guidance: bool, | |
image_latents: Optional[torch.FloatTensor] = None, | |
): | |
if isinstance(image, PIL.Image.Image): | |
image_pt = TF.to_tensor(image).unsqueeze(0).to(latents) | |
elif isinstance(image, list): | |
image_pt = torch.stack([TF.to_tensor(img) for img in image], dim=0).to( | |
latents | |
) | |
elif isinstance(image, torch.Tensor): | |
image_pt = image | |
else: | |
image_pt = None | |
if image_pt is None: | |
assert image_latents is not None | |
image_pt = image_latents.repeat_interleave(num_images_per_prompt, dim=0) | |
else: | |
image_pt = image_pt * 2.0 - 1.0 # scale to [-1, 1] | |
# FIXME: encoded latents should be multiplied with self.vae.config.scaling_factor | |
# but zero123 was not trained this way | |
image_pt = self.vae.encode(image_pt).latent_dist.mode() | |
image_pt = image_pt.repeat_interleave(num_images_per_prompt, dim=0) | |
if do_classifier_free_guidance: | |
latent_model_input = torch.cat( | |
[ | |
torch.cat([latents, latents], dim=0), | |
torch.cat([torch.zeros_like(image_pt), image_pt], dim=0), | |
], | |
dim=1, | |
) | |
else: | |
latent_model_input = torch.cat([latents, image_pt], dim=1) | |
return latent_model_input | |
def __call__( | |
self, | |
image: Optional[ | |
Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor] | |
] = None, | |
elevation: Optional[Union[float, torch.FloatTensor]] = None, | |
azimuth: Optional[Union[float, torch.FloatTensor]] = None, | |
distance: Optional[Union[float, torch.FloatTensor]] = None, | |
height: Optional[int] = None, | |
width: Optional[int] = None, | |
num_inference_steps: int = 50, | |
guidance_scale: float = 3.0, | |
num_images_per_prompt: int = 1, | |
eta: float = 0.0, | |
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
latents: Optional[torch.FloatTensor] = None, | |
clip_image_embeddings: Optional[torch.FloatTensor] = None, | |
image_camera_embeddings: Optional[torch.FloatTensor] = None, | |
image_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: int = 1, | |
cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
): | |
r""" | |
Function invoked when calling the pipeline for generation. | |
Args: | |
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): | |
The image or images to guide the image generation. If you provide a tensor, it needs to comply with the | |
configuration of | |
[this](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json) | |
`CLIPImageProcessor` | |
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 (`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`. | |
""" | |
# 0. Default height and width to unet | |
height = height or self.unet.config.sample_size * self.vae_scale_factor | |
width = width or self.unet.config.sample_size * self.vae_scale_factor | |
# 1. Check inputs. Raise error if not correct | |
# TODO: check input elevation, azimuth, and distance | |
# TODO: check image, clip_image_embeddings, image_latents | |
self.check_inputs(image, height, width, callback_steps) | |
# 2. Define call parameters | |
if isinstance(image, PIL.Image.Image): | |
batch_size = 1 | |
elif isinstance(image, list): | |
batch_size = len(image) | |
elif isinstance(image, torch.Tensor): | |
batch_size = image.shape[0] | |
else: | |
assert image_latents is not None | |
assert ( | |
clip_image_embeddings is not None or image_camera_embeddings is not None | |
) | |
batch_size = image_latents.shape[0] | |
device = self._execution_device | |
# 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 | |
# 3. Encode input image | |
if isinstance(image, PIL.Image.Image) or isinstance(image, list): | |
pil_image = image | |
elif isinstance(image, torch.Tensor): | |
pil_image = [TF.to_pil_image(image[i]) for i in range(image.shape[0])] | |
else: | |
pil_image = None | |
image_embeddings = self._encode_image( | |
pil_image, | |
elevation, | |
azimuth, | |
distance, | |
device, | |
num_images_per_prompt, | |
do_classifier_free_guidance, | |
clip_image_embeddings, | |
image_camera_embeddings, | |
) | |
# 4. Prepare timesteps | |
self.scheduler.set_timesteps(num_inference_steps, device=device) | |
timesteps = self.scheduler.timesteps | |
# 5. Prepare latent variables | |
# num_channels_latents = self.unet.config.in_channels | |
num_channels_latents = 4 # FIXME: hard-coded | |
latents = self.prepare_latents( | |
batch_size * num_images_per_prompt, | |
num_channels_latents, | |
height, | |
width, | |
image_embeddings.dtype, | |
device, | |
generator, | |
latents, | |
) | |
# 6. 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) | |
# 7. 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 = self._get_latent_model_input( | |
latents, | |
image, | |
num_images_per_prompt, | |
do_classifier_free_guidance, | |
image_latents, | |
) | |
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=image_embeddings, | |
cross_attention_kwargs=cross_attention_kwargs, | |
).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) | |
if not output_type == "latent": | |
image = self.vae.decode( | |
latents / self.vae.config.scaling_factor, return_dict=False | |
)[0] | |
image, has_nsfw_concept = self.run_safety_checker( | |
image, device, image_embeddings.dtype | |
) | |
else: | |
image = latents | |
has_nsfw_concept = None | |
if has_nsfw_concept is None: | |
do_denormalize = [True] * image.shape[0] | |
else: | |
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] | |
image = self.image_processor.postprocess( | |
image, output_type=output_type, do_denormalize=do_denormalize | |
) | |
if not return_dict: | |
return (image, has_nsfw_concept) | |
return StableDiffusionPipelineOutput( | |
images=image, nsfw_content_detected=has_nsfw_concept | |
) |