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hao-ai-lab-FastVideo/opensora/sample/pipeline_opensora.py
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Peiyuan Zhang 7c5b0a6ccd Remove all npu code
2024-10-24 21:35:27 +00:00

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# Copyright 2024 PixArt-Sigma Authors and 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 html
import inspect
import re
import urllib.parse as ul
from typing import Callable, List, Optional, Tuple, Union
import math
import torch
from transformers import T5EncoderModel, T5Tokenizer
from diffusers.models import AutoencoderKL, Transformer2DModel
from diffusers.schedulers import DPMSolverMultistepScheduler
from diffusers.utils import (
BACKENDS_MAPPING,
deprecate,
is_bs4_available,
is_ftfy_available,
logging,
replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import PixArtSigmaPipeline
>>> # You can replace the checkpoint id with "PixArt-alpha/PixArt-Sigma-XL-2-512-MS" too.
>>> pipe = PixArtSigmaPipeline.from_pretrained(
... "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", torch_dtype=torch.float16
... )
>>> # Enable memory optimizations.
>>> # pipe.enable_model_cpu_offload()
>>> prompt = "A small cactus with a happy face in the Sahara desert."
>>> image = pipe(prompt).images[0]
```
"""
# 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,
**kwargs,
):
"""
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 support arbitrary spacing between timesteps. If `None`, then the default
timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
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:
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)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
class OpenSoraPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using PixArt-Sigma.
"""
bad_punct_regex = re.compile(
r"["
+ "#®•©™&@·º½¾¿¡§~"
+ r"\)"
+ r"\("
+ r"\]"
+ r"\["
+ r"\}"
+ r"\{"
+ r"\|"
+ "\\"
+ r"\/"
+ r"\*"
+ r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder"]
model_cpu_offload_seq = "text_encoder->transformer->vae"
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
vae: AutoencoderKL,
transformer: Transformer2DModel,
scheduler: DPMSolverMultistepScheduler,
):
super().__init__()
self.register_modules(
tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
)
# self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
# Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
negative_prompt: str = "",
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_attention_mask: Optional[torch.FloatTensor] = None,
negative_prompt_attention_mask: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
max_sequence_length: int = 120,
**kwargs,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt 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`). For
PixArt-Alpha, this should be "".
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *optional*):
Pre-generated negative text embeddings. For PixArt-Alpha, it's should be the embeddings of the ""
string.
clean_caption (`bool`, defaults to `False`):
If `True`, the function will preprocess and clean the provided caption before encoding.
max_sequence_length (`int`, defaults to 120): Maximum sequence length to use for the prompt.
"""
if "mask_feature" in kwargs:
deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)
if device is None:
device = getattr(self, '_execution_device', None) or getattr(self, 'device', None) or torch.device('cuda')
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]
# See Section 3.1. of the paper.
max_length = max_sequence_length
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
add_special_tokens=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[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
prompt_attention_mask = text_inputs.attention_mask
prompt_attention_mask = prompt_attention_mask.to(device)
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)
prompt_embeds = prompt_embeds[0]
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.transformer is not None:
dtype = self.transformer.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask 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)
prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1)
prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens = [negative_prompt] * batch_size
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
negative_prompt_attention_mask = uncond_input.attention_mask
negative_prompt_attention_mask = negative_prompt_attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device), attention_mask=negative_prompt_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=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)
negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1)
negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)
else:
negative_prompt_embeds = None
negative_prompt_attention_mask = None
return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
# Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.check_inputs
def check_inputs(
self,
prompt,
num_frames,
height,
width,
negative_prompt,
callback_steps,
prompt_embeds=None,
negative_prompt_embeds=None,
prompt_attention_mask=None,
negative_prompt_attention_mask=None,
):
if num_frames <= 0:
raise ValueError(f"`num_frames` have to be positive but is {num_frames}.")
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)}."
)
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 prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
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 prompt_attention_mask is None:
raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
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 prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
raise ValueError(
"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
f" {negative_prompt_attention_mask.shape}."
)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warning("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warning("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
# caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# &quot;
caption = re.sub(r"&quot;?", "", caption)
# &amp
caption = re.sub(r"&amp", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
(math.ceil((int(num_frames) - 1) / self.vae.vae_scale_factor[0]) + 1) if int(num_frames) % 2 == 1 else math.ceil(int(num_frames) / self.vae.vae_scale_factor[0]),
math.ceil(int(height) / self.vae.vae_scale_factor[1]),
math.ceil(int(width) / self.vae.vae_scale_factor[2]),
)
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
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
negative_prompt: str = "",
num_inference_steps: int = 20,
timesteps: List[int] = None,
guidance_scale: float = 4.5,
num_images_per_prompt: Optional[int] = 1,
num_frames: Optional[int] = None,
height: Optional[int] = None,
width: Optional[int] = None,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_attention_mask: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_attention_mask: 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,
clean_caption: bool = True,
use_resolution_binning: bool = True,
max_sequence_length: int = 300,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
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`).
num_inference_steps (`int`, *optional*, defaults to 100):
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. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 4.5):
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.
height (`int`, *optional*, defaults to self.unet.config.sample_size):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size):
The width in pixels of the generated image.
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` or `List[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`.
prompt_embeds (`torch.FloatTensor`, *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.
prompt_attention_mask (`torch.FloatTensor`, *optional*): Pre-generated attention mask for text embeddings.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
negative_prompt_attention_mask (`torch.FloatTensor`, *optional*):
Pre-generated attention mask for negative text embeddings.
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.IFPipelineOutput`] 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.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
use_resolution_binning (`bool` defaults to `True`):
If set to `True`, the requested height and width are first mapped to the closest resolutions using
`ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to
the requested resolution. Useful for generating non-square images.
max_sequence_length (`int` defaults to 120): Maximum sequence length to use with the `prompt`.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
# 1. Check inputs. Raise error if not correct
num_frames = num_frames or self.transformer.config.sample_size_t * self.vae.vae_scale_factor[0]
height = height or self.transformer.config.sample_size[0] * self.vae.vae_scale_factor[1]
width = width or self.transformer.config.sample_size[1] * self.vae.vae_scale_factor[2]
self.check_inputs(
prompt,
num_frames,
height,
width,
negative_prompt,
callback_steps,
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
)
# 2. Default height and width to transformer
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]
# import ipdb;ipdb.set_trace()
device = getattr(self, '_execution_device', None) or getattr(self, 'device', None) or torch.device('cuda')
# 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 prompt
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt,
do_classifier_free_guidance,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
prompt_attention_mask=prompt_attention_mask,
negative_prompt_attention_mask=negative_prompt_attention_mask,
clean_caption=clean_caption,
max_sequence_length=max_sequence_length,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
# 5. Prepare latents.
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
latent_channels,
num_frames,
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 Prepare micro-conditions.
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
# 7. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
current_timestep = t
if not torch.is_tensor(current_timestep):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
elif len(current_timestep.shape) == 0:
current_timestep = current_timestep[None].to(latent_model_input.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
current_timestep = current_timestep.expand(latent_model_input.shape[0])
# import ipdb;ipdb.set_trace()
if prompt_embeds.ndim == 3:
prompt_embeds = prompt_embeds.unsqueeze(1) # b l d -> b 1 l d
if prompt_attention_mask.ndim == 2:
prompt_attention_mask = prompt_attention_mask.unsqueeze(1) # b l -> b 1 l
# prepare attention_mask.
# b c t h w -> b t h w
attention_mask = torch.ones_like(latent_model_input)[:, 0]
# predict noise model_output
noise_pred = self.transformer(
latent_model_input,
attention_mask=attention_mask,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
timestep=current_timestep,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# 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)
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
noise_pred = noise_pred.chunk(2, dim=1)[0]
else:
noise_pred = noise_pred
# compute previous image: x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# print(f'latents_{i}_{t}', torch.max(latents), torch.min(latents), torch.mean(latents), torch.std(latents))
# 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:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
# import ipdb;ipdb.set_trace()
# latents = latents.squeeze(2)
if not output_type == "latent":
# b t h w c
image = self.decode_latents(latents)
image = image[:, :num_frames, :height, :width]
else:
image = latents
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
def decode_latents(self, latents):
# print(f'before vae decode', torch.max(latents).item(), torch.min(latents).item(), torch.mean(latents).item(), torch.std(latents).item())
video = self.vae.decode(latents.to(self.vae.vae.dtype))
# print(f'after vae decode', torch.max(video).item(), torch.min(video).item(), torch.mean(video).item(), torch.std(video).item())
video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(dtype=torch.uint8).cpu().permute(0, 1, 3, 4, 2).contiguous() # b t h w c
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
return video
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