# 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`.
"""
@register_to_config
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)
@property
# 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
@torch.no_grad()
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
)