1078 lines
54 KiB
Python
1078 lines
54 KiB
Python
# Copyright 2025 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 Any, Callable, Dict, List, Optional, Union
|
|
|
|
import torch
|
|
from packaging import version
|
|
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
|
|
|
|
from ...configuration_utils import FrozenDict
|
|
from ...image_processor import PipelineImageInput, VaeImageProcessor
|
|
from ...loaders import FromSingleFileMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
|
|
from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
|
|
from ...models.lora import adjust_lora_scale_text_encoder
|
|
from ...schedulers import KarrasDiffusionSchedulers
|
|
from ...utils import (
|
|
USE_PEFT_BACKEND,
|
|
deprecate,
|
|
is_torch_xla_available,
|
|
logging,
|
|
replace_example_docstring,
|
|
scale_lora_layers,
|
|
unscale_lora_layers,
|
|
)
|
|
from ...utils.torch_utils import randn_tensor
|
|
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
|
|
from ..stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
|
|
from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
|
|
from .pag_utils import PAGMixin
|
|
|
|
|
|
if is_torch_xla_available():
|
|
import torch_xla.core.xla_model as xm
|
|
|
|
XLA_AVAILABLE = True
|
|
else:
|
|
XLA_AVAILABLE = False
|
|
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
EXAMPLE_DOC_STRING = """
|
|
Examples:
|
|
```py
|
|
>>> import torch
|
|
>>> from diffusers import AutoPipelineForText2Image
|
|
|
|
>>> pipe = AutoPipelineForText2Image.from_pretrained(
|
|
... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True
|
|
... )
|
|
>>> pipe = pipe.to("cuda")
|
|
|
|
>>> prompt = "a photo of an astronaut riding a horse on mars"
|
|
>>> image = pipe(prompt, pag_scale=0.3).images[0]
|
|
```
|
|
"""
|
|
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
|
|
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
|
|
r"""
|
|
Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
|
|
Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://huggingface.co/papers/2305.08891).
|
|
|
|
Args:
|
|
noise_cfg (`torch.Tensor`):
|
|
The predicted noise tensor for the guided diffusion process.
|
|
noise_pred_text (`torch.Tensor`):
|
|
The predicted noise tensor for the text-guided diffusion process.
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
A rescale factor applied to the noise predictions.
|
|
|
|
Returns:
|
|
noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
|
|
"""
|
|
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
|
|
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
|
|
# rescale the results from guidance (fixes overexposure)
|
|
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
|
|
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
|
|
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
|
|
return noise_cfg
|
|
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
|
|
def retrieve_timesteps(
|
|
scheduler,
|
|
num_inference_steps: Optional[int] = None,
|
|
device: Optional[Union[str, torch.device]] = None,
|
|
timesteps: Optional[List[int]] = None,
|
|
sigmas: Optional[List[float]] = None,
|
|
**kwargs,
|
|
):
|
|
r"""
|
|
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
|
|
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
|
|
|
|
Args:
|
|
scheduler (`SchedulerMixin`):
|
|
The scheduler to get timesteps from.
|
|
num_inference_steps (`int`):
|
|
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
|
|
must be `None`.
|
|
device (`str` or `torch.device`, *optional*):
|
|
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
|
|
`num_inference_steps` and `sigmas` must be `None`.
|
|
sigmas (`List[float]`, *optional*):
|
|
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
|
|
`num_inference_steps` and `timesteps` must be `None`.
|
|
|
|
Returns:
|
|
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
|
|
second element is the number of inference steps.
|
|
"""
|
|
if timesteps is not None and sigmas is not None:
|
|
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
|
|
if timesteps is not None:
|
|
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
|
|
if not accepts_timesteps:
|
|
raise ValueError(
|
|
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
|
|
f" timestep schedules. Please check whether you are using the correct scheduler."
|
|
)
|
|
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
|
|
timesteps = scheduler.timesteps
|
|
num_inference_steps = len(timesteps)
|
|
elif sigmas is not None:
|
|
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
|
|
if not accept_sigmas:
|
|
raise ValueError(
|
|
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
|
|
f" sigmas schedules. Please check whether you are using the correct scheduler."
|
|
)
|
|
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
|
|
timesteps = scheduler.timesteps
|
|
num_inference_steps = len(timesteps)
|
|
else:
|
|
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
|
|
timesteps = scheduler.timesteps
|
|
return timesteps, num_inference_steps
|
|
|
|
|
|
class StableDiffusionPAGPipeline(
|
|
DiffusionPipeline,
|
|
StableDiffusionMixin,
|
|
TextualInversionLoaderMixin,
|
|
StableDiffusionLoraLoaderMixin,
|
|
IPAdapterMixin,
|
|
FromSingleFileMixin,
|
|
PAGMixin,
|
|
):
|
|
r"""
|
|
Pipeline for text-to-image generation using Stable Diffusion.
|
|
|
|
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
|
|
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
|
|
|
|
The pipeline also inherits the following loading methods:
|
|
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
|
|
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
|
|
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
|
|
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
|
|
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
|
|
|
|
Args:
|
|
vae ([`AutoencoderKL`]):
|
|
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
|
|
text_encoder ([`~transformers.CLIPTextModel`]):
|
|
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
|
|
tokenizer ([`~transformers.CLIPTokenizer`]):
|
|
A `CLIPTokenizer` to tokenize text.
|
|
unet ([`UNet2DConditionModel`]):
|
|
A `UNet2DConditionModel` 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 more details
|
|
about a model's potential harms.
|
|
feature_extractor ([`~transformers.CLIPImageProcessor`]):
|
|
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
|
|
"""
|
|
|
|
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
|
|
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
|
|
_exclude_from_cpu_offload = ["safety_checker"]
|
|
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
|
|
|
|
def __init__(
|
|
self,
|
|
vae: AutoencoderKL,
|
|
text_encoder: CLIPTextModel,
|
|
tokenizer: CLIPTokenizer,
|
|
unet: UNet2DConditionModel,
|
|
scheduler: KarrasDiffusionSchedulers,
|
|
safety_checker: StableDiffusionSafetyChecker,
|
|
feature_extractor: CLIPImageProcessor,
|
|
image_encoder: CLIPVisionModelWithProjection = None,
|
|
requires_safety_checker: bool = True,
|
|
pag_applied_layers: Union[str, List[str]] = "mid",
|
|
):
|
|
super().__init__()
|
|
|
|
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 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 scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
|
|
deprecation_message = (
|
|
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
|
|
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
|
|
" config accordingly as not setting `clip_sample` 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 `scheduler/scheduler_config.json` file"
|
|
)
|
|
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
|
|
new_config = dict(scheduler.config)
|
|
new_config["clip_sample"] = False
|
|
scheduler._internal_dict = FrozenDict(new_config)
|
|
|
|
if safety_checker is None and requires_safety_checker:
|
|
logger.warning(
|
|
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 = (
|
|
unet is not None
|
|
and 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 = (
|
|
unet is not None and 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 your 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,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
unet=unet,
|
|
scheduler=scheduler,
|
|
safety_checker=safety_checker,
|
|
feature_extractor=feature_extractor,
|
|
image_encoder=image_encoder,
|
|
)
|
|
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
|
|
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
|
self.register_to_config(requires_safety_checker=requires_safety_checker)
|
|
|
|
self.set_pag_applied_layers(pag_applied_layers)
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
|
|
def encode_prompt(
|
|
self,
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
lora_scale: Optional[float] = None,
|
|
clip_skip: Optional[int] = None,
|
|
):
|
|
r"""
|
|
Encodes the prompt into text encoder hidden states.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
prompt to be encoded
|
|
device: (`torch.device`):
|
|
torch device
|
|
num_images_per_prompt (`int`):
|
|
number of images that should be generated per prompt
|
|
do_classifier_free_guidance (`bool`):
|
|
whether to use classifier free guidance or not
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
less than `1`).
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
|
provided, text embeddings will be generated from `prompt` input argument.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
|
argument.
|
|
lora_scale (`float`, *optional*):
|
|
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
|
clip_skip (`int`, *optional*):
|
|
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
|
the output of the pre-final layer will be used for computing the prompt embeddings.
|
|
"""
|
|
# set lora scale so that monkey patched LoRA
|
|
# function of text encoder can correctly access it
|
|
if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
|
|
self._lora_scale = lora_scale
|
|
|
|
# dynamically adjust the LoRA scale
|
|
if not USE_PEFT_BACKEND:
|
|
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
|
|
else:
|
|
scale_lora_layers(self.text_encoder, lora_scale)
|
|
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
if prompt_embeds is None:
|
|
# textual inversion: process multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
|
|
|
text_inputs = self.tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
|
|
|
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
|
text_input_ids, untruncated_ids
|
|
):
|
|
removed_text = self.tokenizer.batch_decode(
|
|
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
|
)
|
|
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}"
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = text_inputs.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
if clip_skip is None:
|
|
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
|
|
prompt_embeds = prompt_embeds[0]
|
|
else:
|
|
prompt_embeds = self.text_encoder(
|
|
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
|
|
)
|
|
# Access the `hidden_states` first, that contains a tuple of
|
|
# all the hidden states from the encoder layers. Then index into
|
|
# the tuple to access the hidden states from the desired layer.
|
|
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
|
|
# We also need to apply the final LayerNorm here to not mess with the
|
|
# representations. The `last_hidden_states` that we typically use for
|
|
# obtaining the final prompt representations passes through the LayerNorm
|
|
# layer.
|
|
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
|
|
|
|
if self.text_encoder is not None:
|
|
prompt_embeds_dtype = self.text_encoder.dtype
|
|
elif self.unet is not None:
|
|
prompt_embeds_dtype = self.unet.dtype
|
|
else:
|
|
prompt_embeds_dtype = prompt_embeds.dtype
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
bs_embed, seq_len, _ = prompt_embeds.shape
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
uncond_tokens: List[str]
|
|
if negative_prompt is None:
|
|
uncond_tokens = [""] * batch_size
|
|
elif prompt is not None and 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
|
|
|
|
# textual inversion: process multi-vector tokens if necessary
|
|
if isinstance(self, TextualInversionLoaderMixin):
|
|
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
|
|
|
|
max_length = prompt_embeds.shape[1]
|
|
uncond_input = self.tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
|
attention_mask = uncond_input.attention_mask.to(device)
|
|
else:
|
|
attention_mask = None
|
|
|
|
negative_prompt_embeds = self.text_encoder(
|
|
uncond_input.input_ids.to(device),
|
|
attention_mask=attention_mask,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds[0]
|
|
|
|
if do_classifier_free_guidance:
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
|
|
|
if self.text_encoder is not None:
|
|
if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
|
|
# Retrieve the original scale by scaling back the LoRA layers
|
|
unscale_lora_layers(self.text_encoder, lora_scale)
|
|
|
|
return prompt_embeds, negative_prompt_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
|
|
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
|
|
dtype = next(self.image_encoder.parameters()).dtype
|
|
|
|
if not isinstance(image, torch.Tensor):
|
|
image = self.feature_extractor(image, return_tensors="pt").pixel_values
|
|
|
|
image = image.to(device=device, dtype=dtype)
|
|
if output_hidden_states:
|
|
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
|
|
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
|
|
uncond_image_enc_hidden_states = self.image_encoder(
|
|
torch.zeros_like(image), output_hidden_states=True
|
|
).hidden_states[-2]
|
|
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
|
|
num_images_per_prompt, dim=0
|
|
)
|
|
return image_enc_hidden_states, uncond_image_enc_hidden_states
|
|
else:
|
|
image_embeds = self.image_encoder(image).image_embeds
|
|
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
|
uncond_image_embeds = torch.zeros_like(image_embeds)
|
|
|
|
return image_embeds, uncond_image_embeds
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
|
|
def prepare_ip_adapter_image_embeds(
|
|
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
|
|
):
|
|
image_embeds = []
|
|
if do_classifier_free_guidance:
|
|
negative_image_embeds = []
|
|
if ip_adapter_image_embeds is None:
|
|
if not isinstance(ip_adapter_image, list):
|
|
ip_adapter_image = [ip_adapter_image]
|
|
|
|
if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
|
|
raise ValueError(
|
|
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
|
|
)
|
|
|
|
for single_ip_adapter_image, image_proj_layer in zip(
|
|
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
|
|
):
|
|
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
|
|
single_image_embeds, single_negative_image_embeds = self.encode_image(
|
|
single_ip_adapter_image, device, 1, output_hidden_state
|
|
)
|
|
|
|
image_embeds.append(single_image_embeds[None, :])
|
|
if do_classifier_free_guidance:
|
|
negative_image_embeds.append(single_negative_image_embeds[None, :])
|
|
else:
|
|
for single_image_embeds in ip_adapter_image_embeds:
|
|
if do_classifier_free_guidance:
|
|
single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
|
|
negative_image_embeds.append(single_negative_image_embeds)
|
|
image_embeds.append(single_image_embeds)
|
|
|
|
ip_adapter_image_embeds = []
|
|
for i, single_image_embeds in enumerate(image_embeds):
|
|
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
|
|
if do_classifier_free_guidance:
|
|
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
|
|
single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
|
|
|
|
single_image_embeds = single_image_embeds.to(device=device)
|
|
ip_adapter_image_embeds.append(single_image_embeds)
|
|
|
|
return ip_adapter_image_embeds
|
|
|
|
# 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.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://huggingface.co/papers/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.check_inputs
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
callback_steps,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
ip_adapter_image=None,
|
|
ip_adapter_image_embeds=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
):
|
|
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 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)}."
|
|
)
|
|
if callback_on_step_end_tensor_inputs is not None and not all(
|
|
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
|
):
|
|
raise ValueError(
|
|
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
|
|
)
|
|
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
|
|
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
|
|
raise ValueError(
|
|
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
|
|
)
|
|
|
|
if ip_adapter_image_embeds is not None:
|
|
if not isinstance(ip_adapter_image_embeds, list):
|
|
raise ValueError(
|
|
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
|
|
)
|
|
elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
|
|
raise ValueError(
|
|
f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
|
|
)
|
|
|
|
# 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,
|
|
int(height) // self.vae_scale_factor,
|
|
int(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
|
|
|
|
# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
|
|
def get_guidance_scale_embedding(
|
|
self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
|
|
) -> torch.Tensor:
|
|
"""
|
|
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
|
|
|
|
Args:
|
|
w (`torch.Tensor`):
|
|
Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
|
|
embedding_dim (`int`, *optional*, defaults to 512):
|
|
Dimension of the embeddings to generate.
|
|
dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
|
|
Data type of the generated embeddings.
|
|
|
|
Returns:
|
|
`torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
|
|
"""
|
|
assert len(w.shape) == 1
|
|
w = w * 1000.0
|
|
|
|
half_dim = embedding_dim // 2
|
|
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
|
|
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
|
|
emb = w.to(dtype)[:, None] * emb[None, :]
|
|
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
|
|
if embedding_dim % 2 == 1: # zero pad
|
|
emb = torch.nn.functional.pad(emb, (0, 1))
|
|
assert emb.shape == (w.shape[0], embedding_dim)
|
|
return emb
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def guidance_rescale(self):
|
|
return self._guidance_rescale
|
|
|
|
@property
|
|
def clip_skip(self):
|
|
return self._clip_skip
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
|
|
|
|
@property
|
|
def cross_attention_kwargs(self):
|
|
return self._cross_attention_kwargs
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
num_inference_steps: int = 50,
|
|
timesteps: List[int] = None,
|
|
sigmas: List[float] = None,
|
|
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[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
ip_adapter_image: Optional[PipelineImageInput] = None,
|
|
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
guidance_rescale: float = 0.0,
|
|
clip_skip: Optional[int] = None,
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
pag_scale: float = 3.0,
|
|
pag_adaptive_scale: float = 0.0,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
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.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
|
|
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
|
|
passed will be used. Must be in descending order.
|
|
sigmas (`List[float]`, *optional*):
|
|
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
|
|
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
|
|
will be used.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
|
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only
|
|
applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
|
generation deterministic.
|
|
latents (`torch.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 is generated by sampling using the supplied random `generator`.
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
|
provided, text embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
|
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
|
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
|
|
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
|
|
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
|
|
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
|
|
contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
|
|
provided, embeddings are computed from the `ip_adapter_image` input argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.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.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
|
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
|
|
using zero terminal SNR.
|
|
clip_skip (`int`, *optional*):
|
|
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
|
the output of the pre-final layer will be used for computing the prompt embeddings.
|
|
callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
|
|
A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
|
|
each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
|
|
DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
|
|
list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
|
|
callback_on_step_end_tensor_inputs (`List`, *optional*):
|
|
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
|
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
|
`._callback_tensor_inputs` attribute of your pipeline class.
|
|
pag_scale (`float`, *optional*, defaults to 3.0):
|
|
The scale factor for the perturbed attention guidance. If it is set to 0.0, the perturbed attention
|
|
guidance will not be used.
|
|
pag_adaptive_scale (`float`, *optional*, defaults to 0.0):
|
|
The adaptive scale factor for the perturbed attention guidance. If it is set to 0.0, `pag_scale` is
|
|
used.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
|
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
|
|
# 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
|
|
# to deal with lora scaling and other possible forward hooks
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
None,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._guidance_rescale = guidance_rescale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
self._interrupt = False
|
|
self._pag_scale = pag_scale
|
|
self._pag_adaptive_scale = pag_adaptive_scale
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._execution_device
|
|
|
|
# 3. Encode input prompt
|
|
lora_scale = (
|
|
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
|
|
)
|
|
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
|
|
# 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
|
|
if self.do_perturbed_attention_guidance:
|
|
prompt_embeds = self._prepare_perturbed_attention_guidance(
|
|
prompt_embeds, negative_prompt_embeds, self.do_classifier_free_guidance
|
|
)
|
|
elif self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
|
|
ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
device,
|
|
batch_size * num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
)
|
|
|
|
for i, image_embeds in enumerate(ip_adapter_image_embeds):
|
|
negative_image_embeds = None
|
|
if self.do_classifier_free_guidance:
|
|
negative_image_embeds, image_embeds = image_embeds.chunk(2)
|
|
if self.do_perturbed_attention_guidance:
|
|
image_embeds = self._prepare_perturbed_attention_guidance(
|
|
image_embeds, negative_image_embeds, self.do_classifier_free_guidance
|
|
)
|
|
|
|
elif self.do_classifier_free_guidance:
|
|
image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
|
|
image_embeds = image_embeds.to(device)
|
|
ip_adapter_image_embeds[i] = image_embeds
|
|
|
|
# 4. Prepare timesteps
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps, sigmas
|
|
)
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.unet.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
prompt_embeds.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)
|
|
|
|
# 6.1 Add image embeds for IP-Adapter
|
|
added_cond_kwargs = (
|
|
{"image_embeds": ip_adapter_image_embeds}
|
|
if (ip_adapter_image is not None or ip_adapter_image_embeds is not None)
|
|
else None
|
|
)
|
|
|
|
# 6.2 Optionally get Guidance Scale Embedding
|
|
timestep_cond = None
|
|
if self.unet.config.time_cond_proj_dim is not None:
|
|
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
|
|
timestep_cond = self.get_guidance_scale_embedding(
|
|
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
|
|
).to(device=device, dtype=latents.dtype)
|
|
|
|
# 7. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
if self.do_perturbed_attention_guidance:
|
|
original_attn_proc = self.unet.attn_processors
|
|
self._set_pag_attn_processor(
|
|
pag_applied_layers=self.pag_applied_layers,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
)
|
|
self._num_timesteps = len(timesteps)
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * (prompt_embeds.shape[0] // latents.shape[0]))
|
|
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=prompt_embeds,
|
|
timestep_cond=timestep_cond,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if self.do_perturbed_attention_guidance:
|
|
noise_pred, noise_pred_text = self._apply_perturbed_attention_guidance(
|
|
noise_pred, self.do_classifier_free_guidance, self.guidance_scale, t, True
|
|
)
|
|
|
|
elif self.do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
|
|
# Based on 3.4. in https://huggingface.co/papers/2305.08891
|
|
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
|
|
|
# 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 XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
if not output_type == "latent":
|
|
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
|
|
0
|
|
]
|
|
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.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)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if self.do_perturbed_attention_guidance:
|
|
self.unet.set_attn_processor(original_attn_proc)
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|