1235 lines
57 KiB
Python
1235 lines
57 KiB
Python
# Copyright 2025 The EasyAnimate team 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 inspect
|
|
from typing import Callable, Dict, List, Optional, Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from PIL import Image
|
|
from transformers import (
|
|
BertModel,
|
|
BertTokenizer,
|
|
Qwen2Tokenizer,
|
|
Qwen2VLForConditionalGeneration,
|
|
)
|
|
|
|
from ...callbacks import MultiPipelineCallbacks, PipelineCallback
|
|
from ...image_processor import VaeImageProcessor
|
|
from ...models import AutoencoderKLMagvit, EasyAnimateTransformer3DModel
|
|
from ...pipelines.pipeline_utils import DiffusionPipeline
|
|
from ...schedulers import FlowMatchEulerDiscreteScheduler
|
|
from ...utils import is_torch_xla_available, logging, replace_example_docstring
|
|
from ...utils.torch_utils import randn_tensor
|
|
from ...video_processor import VideoProcessor
|
|
from .pipeline_output import EasyAnimatePipelineOutput
|
|
|
|
|
|
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 EasyAnimateInpaintPipeline
|
|
>>> from diffusers.pipelines.easyanimate.pipeline_easyanimate_inpaint import get_image_to_video_latent
|
|
>>> from diffusers.utils import export_to_video, load_image
|
|
|
|
>>> pipe = EasyAnimateInpaintPipeline.from_pretrained(
|
|
... "alibaba-pai/EasyAnimateV5.1-12b-zh-InP-diffusers", torch_dtype=torch.bfloat16
|
|
... )
|
|
>>> pipe.to("cuda")
|
|
|
|
>>> prompt = "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
|
|
>>> validation_image_start = load_image(
|
|
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
|
|
... )
|
|
|
|
>>> validation_image_end = None
|
|
>>> sample_size = (448, 576)
|
|
>>> num_frames = 49
|
|
>>> input_video, input_video_mask = get_image_to_video_latent(
|
|
... [validation_image_start], validation_image_end, num_frames, sample_size
|
|
... )
|
|
|
|
>>> video = pipe(
|
|
... prompt,
|
|
... num_frames=num_frames,
|
|
... negative_prompt="Twisted body, limb deformities, text subtitles, comics, stillness, ugliness, errors, garbled text.",
|
|
... height=sample_size[0],
|
|
... width=sample_size[1],
|
|
... video=input_video,
|
|
... mask_video=input_video_mask,
|
|
... )
|
|
>>> export_to_video(video.frames[0], "output.mp4", fps=8)
|
|
```
|
|
"""
|
|
|
|
|
|
def preprocess_image(image, sample_size):
|
|
"""
|
|
Preprocess a single image (PIL.Image, numpy.ndarray, or torch.Tensor) to a resized tensor.
|
|
"""
|
|
if isinstance(image, torch.Tensor):
|
|
# If input is a tensor, assume it's in CHW format and resize using interpolation
|
|
image = torch.nn.functional.interpolate(
|
|
image.unsqueeze(0), size=sample_size, mode="bilinear", align_corners=False
|
|
).squeeze(0)
|
|
elif isinstance(image, Image.Image):
|
|
# If input is a PIL image, resize and convert to numpy array
|
|
image = image.resize((sample_size[1], sample_size[0]))
|
|
image = np.array(image)
|
|
elif isinstance(image, np.ndarray):
|
|
# If input is a numpy array, resize using PIL
|
|
image = Image.fromarray(image).resize((sample_size[1], sample_size[0]))
|
|
image = np.array(image)
|
|
else:
|
|
raise ValueError("Unsupported input type. Expected PIL.Image, numpy.ndarray, or torch.Tensor.")
|
|
|
|
# Convert to tensor if not already
|
|
if not isinstance(image, torch.Tensor):
|
|
image = torch.from_numpy(image).permute(2, 0, 1).float() / 255.0 # HWC -> CHW, normalize to [0, 1]
|
|
|
|
return image
|
|
|
|
|
|
def get_image_to_video_latent(validation_image_start, validation_image_end, num_frames, sample_size):
|
|
"""
|
|
Generate latent representations for video from start and end images. Inputs can be PIL.Image, numpy.ndarray, or
|
|
torch.Tensor.
|
|
"""
|
|
input_video = None
|
|
input_video_mask = None
|
|
|
|
if validation_image_start is not None:
|
|
# Preprocess the starting image(s)
|
|
if isinstance(validation_image_start, list):
|
|
image_start = [preprocess_image(img, sample_size) for img in validation_image_start]
|
|
else:
|
|
image_start = preprocess_image(validation_image_start, sample_size)
|
|
|
|
# Create video tensor from the starting image(s)
|
|
if isinstance(image_start, list):
|
|
start_video = torch.cat(
|
|
[img.unsqueeze(1).unsqueeze(0) for img in image_start],
|
|
dim=2,
|
|
)
|
|
input_video = torch.tile(start_video[:, :, :1], [1, 1, num_frames, 1, 1])
|
|
input_video[:, :, : len(image_start)] = start_video
|
|
else:
|
|
input_video = torch.tile(
|
|
image_start.unsqueeze(1).unsqueeze(0),
|
|
[1, 1, num_frames, 1, 1],
|
|
)
|
|
|
|
# Normalize input video (already normalized in preprocess_image)
|
|
|
|
# Create mask for the input video
|
|
input_video_mask = torch.zeros_like(input_video[:, :1])
|
|
if isinstance(image_start, list):
|
|
input_video_mask[:, :, len(image_start) :] = 255
|
|
else:
|
|
input_video_mask[:, :, 1:] = 255
|
|
|
|
# Handle ending image(s) if provided
|
|
if validation_image_end is not None:
|
|
if isinstance(validation_image_end, list):
|
|
image_end = [preprocess_image(img, sample_size) for img in validation_image_end]
|
|
end_video = torch.cat(
|
|
[img.unsqueeze(1).unsqueeze(0) for img in image_end],
|
|
dim=2,
|
|
)
|
|
input_video[:, :, -len(end_video) :] = end_video
|
|
input_video_mask[:, :, -len(image_end) :] = 0
|
|
else:
|
|
image_end = preprocess_image(validation_image_end, sample_size)
|
|
input_video[:, :, -1:] = image_end.unsqueeze(1).unsqueeze(0)
|
|
input_video_mask[:, :, -1:] = 0
|
|
|
|
elif validation_image_start is None:
|
|
# If no starting image is provided, initialize empty tensors
|
|
input_video = torch.zeros([1, 3, num_frames, sample_size[0], sample_size[1]])
|
|
input_video_mask = torch.ones([1, 1, num_frames, sample_size[0], sample_size[1]]) * 255
|
|
|
|
return input_video, input_video_mask
|
|
|
|
|
|
# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
|
|
def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
|
|
tw = tgt_width
|
|
th = tgt_height
|
|
h, w = src
|
|
r = h / w
|
|
if r > (th / tw):
|
|
resize_height = th
|
|
resize_width = int(round(th / h * w))
|
|
else:
|
|
resize_width = tw
|
|
resize_height = int(round(tw / w * h))
|
|
|
|
crop_top = int(round((th - resize_height) / 2.0))
|
|
crop_left = int(round((tw - resize_width) / 2.0))
|
|
|
|
return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
|
|
|
|
|
|
# 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
|
|
|
|
|
|
# Resize mask information in magvit
|
|
def resize_mask(mask, latent, process_first_frame_only=True):
|
|
latent_size = latent.size()
|
|
|
|
if process_first_frame_only:
|
|
target_size = list(latent_size[2:])
|
|
target_size[0] = 1
|
|
first_frame_resized = F.interpolate(
|
|
mask[:, :, 0:1, :, :], size=target_size, mode="trilinear", align_corners=False
|
|
)
|
|
|
|
target_size = list(latent_size[2:])
|
|
target_size[0] = target_size[0] - 1
|
|
if target_size[0] != 0:
|
|
remaining_frames_resized = F.interpolate(
|
|
mask[:, :, 1:, :, :], size=target_size, mode="trilinear", align_corners=False
|
|
)
|
|
resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2)
|
|
else:
|
|
resized_mask = first_frame_resized
|
|
else:
|
|
target_size = list(latent_size[2:])
|
|
resized_mask = F.interpolate(mask, size=target_size, mode="trilinear", align_corners=False)
|
|
return resized_mask
|
|
|
|
|
|
## Add noise to reference video
|
|
def add_noise_to_reference_video(image, ratio=None, generator=None):
|
|
if ratio is None:
|
|
sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device)
|
|
sigma = torch.exp(sigma).to(image.dtype)
|
|
else:
|
|
sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
|
|
|
|
if generator is not None:
|
|
image_noise = (
|
|
torch.randn(image.size(), generator=generator, dtype=image.dtype, device=image.device)
|
|
* sigma[:, None, None, None, None]
|
|
)
|
|
else:
|
|
image_noise = torch.randn_like(image) * sigma[:, None, None, None, None]
|
|
image_noise = torch.where(image == -1, torch.zeros_like(image), image_noise)
|
|
image = image + image_noise
|
|
return image
|
|
|
|
|
|
# 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 EasyAnimateInpaintPipeline(DiffusionPipeline):
|
|
r"""
|
|
Pipeline for text-to-video generation using EasyAnimate.
|
|
|
|
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.)
|
|
|
|
EasyAnimate uses one text encoder [qwen2 vl](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) in V5.1.
|
|
|
|
Args:
|
|
vae ([`AutoencoderKLMagvit`]):
|
|
Variational Auto-Encoder (VAE) Model to encode and decode video to and from latent representations.
|
|
text_encoder (Optional[`~transformers.Qwen2VLForConditionalGeneration`, `~transformers.BertModel`]):
|
|
EasyAnimate uses [qwen2 vl](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) in V5.1.
|
|
tokenizer (Optional[`~transformers.Qwen2Tokenizer`, `~transformers.BertTokenizer`]):
|
|
A `Qwen2Tokenizer` or `BertTokenizer` to tokenize text.
|
|
transformer ([`EasyAnimateTransformer3DModel`]):
|
|
The EasyAnimate model designed by EasyAnimate Team.
|
|
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
|
|
A scheduler to be used in combination with EasyAnimate to denoise the encoded image latents.
|
|
"""
|
|
|
|
model_cpu_offload_seq = "text_encoder->transformer->vae"
|
|
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
|
|
|
|
def __init__(
|
|
self,
|
|
vae: AutoencoderKLMagvit,
|
|
text_encoder: Union[Qwen2VLForConditionalGeneration, BertModel],
|
|
tokenizer: Union[Qwen2Tokenizer, BertTokenizer],
|
|
transformer: EasyAnimateTransformer3DModel,
|
|
scheduler: FlowMatchEulerDiscreteScheduler,
|
|
):
|
|
super().__init__()
|
|
|
|
self.register_modules(
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
transformer=transformer,
|
|
scheduler=scheduler,
|
|
)
|
|
|
|
self.enable_text_attention_mask = (
|
|
self.transformer.config.enable_text_attention_mask
|
|
if getattr(self, "transformer", None) is not None
|
|
else True
|
|
)
|
|
self.vae_spatial_compression_ratio = (
|
|
self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 8
|
|
)
|
|
self.vae_temporal_compression_ratio = (
|
|
self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 4
|
|
)
|
|
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
|
|
self.mask_processor = VaeImageProcessor(
|
|
vae_scale_factor=self.vae_spatial_compression_ratio,
|
|
do_normalize=False,
|
|
do_binarize=True,
|
|
do_convert_grayscale=True,
|
|
)
|
|
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
|
|
|
|
# Copied from diffusers.pipelines.easyanimate.pipeline_easyanimate.EasyAnimatePipeline.encode_prompt
|
|
def encode_prompt(
|
|
self,
|
|
prompt: Union[str, List[str]],
|
|
num_images_per_prompt: int = 1,
|
|
do_classifier_free_guidance: bool = True,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
device: Optional[torch.device] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
max_sequence_length: int = 256,
|
|
):
|
|
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
|
|
dtype (`torch.dtype`):
|
|
torch dtype
|
|
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.
|
|
prompt_attention_mask (`torch.Tensor`, *optional*):
|
|
Attention mask for the prompt. Required when `prompt_embeds` is passed directly.
|
|
negative_prompt_attention_mask (`torch.Tensor`, *optional*):
|
|
Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly.
|
|
max_sequence_length (`int`, *optional*): maximum sequence length to use for the prompt.
|
|
"""
|
|
dtype = dtype or self.text_encoder.dtype
|
|
device = device or self.text_encoder.device
|
|
|
|
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:
|
|
if isinstance(prompt, str):
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": prompt}],
|
|
}
|
|
]
|
|
else:
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": _prompt}],
|
|
}
|
|
for _prompt in prompt
|
|
]
|
|
text = [
|
|
self.tokenizer.apply_chat_template([m], tokenize=False, add_generation_prompt=True) for m in messages
|
|
]
|
|
|
|
text_inputs = self.tokenizer(
|
|
text=text,
|
|
padding="max_length",
|
|
max_length=max_sequence_length,
|
|
truncation=True,
|
|
return_attention_mask=True,
|
|
padding_side="right",
|
|
return_tensors="pt",
|
|
)
|
|
text_inputs = text_inputs.to(self.text_encoder.device)
|
|
|
|
text_input_ids = text_inputs.input_ids
|
|
prompt_attention_mask = text_inputs.attention_mask
|
|
if self.enable_text_attention_mask:
|
|
# Inference: Generation of the output
|
|
prompt_embeds = self.text_encoder(
|
|
input_ids=text_input_ids, attention_mask=prompt_attention_mask, output_hidden_states=True
|
|
).hidden_states[-2]
|
|
else:
|
|
raise ValueError("LLM needs attention_mask")
|
|
prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=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)
|
|
prompt_attention_mask = prompt_attention_mask.to(device=device)
|
|
|
|
# get unconditional embeddings for classifier free guidance
|
|
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
|
if negative_prompt is not None and isinstance(negative_prompt, str):
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": negative_prompt}],
|
|
}
|
|
]
|
|
else:
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": _negative_prompt}],
|
|
}
|
|
for _negative_prompt in negative_prompt
|
|
]
|
|
text = [
|
|
self.tokenizer.apply_chat_template([m], tokenize=False, add_generation_prompt=True) for m in messages
|
|
]
|
|
|
|
text_inputs = self.tokenizer(
|
|
text=text,
|
|
padding="max_length",
|
|
max_length=max_sequence_length,
|
|
truncation=True,
|
|
return_attention_mask=True,
|
|
padding_side="right",
|
|
return_tensors="pt",
|
|
)
|
|
text_inputs = text_inputs.to(self.text_encoder.device)
|
|
|
|
text_input_ids = text_inputs.input_ids
|
|
negative_prompt_attention_mask = text_inputs.attention_mask
|
|
if self.enable_text_attention_mask:
|
|
# Inference: Generation of the output
|
|
negative_prompt_embeds = self.text_encoder(
|
|
input_ids=text_input_ids,
|
|
attention_mask=negative_prompt_attention_mask,
|
|
output_hidden_states=True,
|
|
).hidden_states[-2]
|
|
else:
|
|
raise ValueError("LLM needs attention_mask")
|
|
negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)
|
|
|
|
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.to(device=device)
|
|
|
|
return prompt_embeds, negative_prompt_embeds, prompt_attention_mask, 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://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
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
prompt_attention_mask=None,
|
|
negative_prompt_attention_mask=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
):
|
|
if height % 16 != 0 or width % 16 != 0:
|
|
raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
|
|
|
|
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 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 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 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}."
|
|
)
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
|
|
def get_timesteps(self, num_inference_steps, strength, device):
|
|
# get the original timestep using init_timestep
|
|
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
|
|
|
|
t_start = max(num_inference_steps - init_timestep, 0)
|
|
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
|
|
if hasattr(self.scheduler, "set_begin_index"):
|
|
self.scheduler.set_begin_index(t_start * self.scheduler.order)
|
|
|
|
return timesteps, num_inference_steps - t_start
|
|
|
|
def prepare_mask_latents(
|
|
self,
|
|
mask,
|
|
masked_image,
|
|
batch_size,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
do_classifier_free_guidance,
|
|
noise_aug_strength,
|
|
):
|
|
# resize the mask to latents shape as we concatenate the mask to the latents
|
|
# we do that before converting to dtype to avoid breaking in case we're using cpu_offload
|
|
# and half precision
|
|
if mask is not None:
|
|
mask = mask.to(device=device, dtype=dtype)
|
|
new_mask = []
|
|
bs = 1
|
|
for i in range(0, mask.shape[0], bs):
|
|
mask_bs = mask[i : i + bs]
|
|
mask_bs = self.vae.encode(mask_bs)[0]
|
|
mask_bs = mask_bs.mode()
|
|
new_mask.append(mask_bs)
|
|
mask = torch.cat(new_mask, dim=0)
|
|
mask = mask * self.vae.config.scaling_factor
|
|
|
|
if masked_image is not None:
|
|
masked_image = masked_image.to(device=device, dtype=dtype)
|
|
if self.transformer.config.add_noise_in_inpaint_model:
|
|
masked_image = add_noise_to_reference_video(
|
|
masked_image, ratio=noise_aug_strength, generator=generator
|
|
)
|
|
new_mask_pixel_values = []
|
|
bs = 1
|
|
for i in range(0, masked_image.shape[0], bs):
|
|
mask_pixel_values_bs = masked_image[i : i + bs]
|
|
mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0]
|
|
mask_pixel_values_bs = mask_pixel_values_bs.mode()
|
|
new_mask_pixel_values.append(mask_pixel_values_bs)
|
|
masked_image_latents = torch.cat(new_mask_pixel_values, dim=0)
|
|
masked_image_latents = masked_image_latents * self.vae.config.scaling_factor
|
|
|
|
# aligning device to prevent device errors when concating it with the latent model input
|
|
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
|
|
else:
|
|
masked_image_latents = None
|
|
|
|
return mask, masked_image_latents
|
|
|
|
def prepare_latents(
|
|
self,
|
|
batch_size,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
num_frames,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents=None,
|
|
video=None,
|
|
timestep=None,
|
|
is_strength_max=True,
|
|
return_noise=False,
|
|
return_video_latents=False,
|
|
):
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
(num_frames - 1) // self.vae_temporal_compression_ratio + 1,
|
|
height // self.vae_spatial_compression_ratio,
|
|
width // self.vae_spatial_compression_ratio,
|
|
)
|
|
|
|
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 return_video_latents or (latents is None and not is_strength_max):
|
|
video = video.to(device=device, dtype=dtype)
|
|
bs = 1
|
|
new_video = []
|
|
for i in range(0, video.shape[0], bs):
|
|
video_bs = video[i : i + bs]
|
|
video_bs = self.vae.encode(video_bs)[0]
|
|
video_bs = video_bs.sample()
|
|
new_video.append(video_bs)
|
|
video = torch.cat(new_video, dim=0)
|
|
video = video * self.vae.config.scaling_factor
|
|
|
|
video_latents = video.repeat(batch_size // video.shape[0], 1, 1, 1, 1)
|
|
video_latents = video_latents.to(device=device, dtype=dtype)
|
|
|
|
if latents is None:
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
# if strength is 1. then initialise the latents to noise, else initial to image + noise
|
|
if isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
|
|
latents = noise if is_strength_max else self.scheduler.scale_noise(video_latents, timestep, noise)
|
|
else:
|
|
latents = noise if is_strength_max else self.scheduler.add_noise(video_latents, noise, timestep)
|
|
# if pure noise then scale the initial latents by the Scheduler's init sigma
|
|
if hasattr(self.scheduler, "init_noise_sigma"):
|
|
latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
|
|
else:
|
|
if hasattr(self.scheduler, "init_noise_sigma"):
|
|
noise = latents.to(device)
|
|
latents = noise * self.scheduler.init_noise_sigma
|
|
else:
|
|
latents = latents.to(device)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
outputs = (latents,)
|
|
|
|
if return_noise:
|
|
outputs += (noise,)
|
|
|
|
if return_video_latents:
|
|
outputs += (video_latents,)
|
|
|
|
return outputs
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def guidance_rescale(self):
|
|
return self._guidance_rescale
|
|
|
|
# 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
|
|
|
|
@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,
|
|
num_frames: Optional[int] = 49,
|
|
video: Union[torch.FloatTensor] = None,
|
|
mask_video: Union[torch.FloatTensor] = None,
|
|
masked_video_latents: Union[torch.FloatTensor] = None,
|
|
height: Optional[int] = 512,
|
|
width: Optional[int] = 512,
|
|
num_inference_steps: Optional[int] = 50,
|
|
guidance_scale: Optional[float] = 5.0,
|
|
negative_prompt: Optional[Union[str, List[str]]] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
eta: Optional[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,
|
|
prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
callback_on_step_end: Optional[
|
|
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
|
|
] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
guidance_rescale: float = 0.0,
|
|
strength: float = 1.0,
|
|
noise_aug_strength: float = 0.0563,
|
|
timesteps: Optional[List[int]] = None,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation with HunyuanDiT.
|
|
|
|
Examples:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
num_frames (`int`, *optional*):
|
|
Length of the video to be generated in seconds. This parameter influences the number of frames and
|
|
continuity of generated content.
|
|
video (`torch.FloatTensor`, *optional*):
|
|
A tensor representing an input video, which can be modified depending on the prompts provided.
|
|
mask_video (`torch.FloatTensor`, *optional*):
|
|
A tensor to specify areas of the video to be masked (omitted from generation).
|
|
masked_video_latents (`torch.FloatTensor`, *optional*):
|
|
Latents from masked portions of the video, utilized during image generation.
|
|
height (`int`, *optional*):
|
|
The height in pixels of the generated image or video frames.
|
|
width (`int`, *optional*):
|
|
The width in pixels of the generated image or video frames.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image but slower
|
|
inference time. This parameter is modulated by `strength`.
|
|
guidance_scale (`float`, *optional*, defaults to 5.0):
|
|
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 effective when `guidance_scale > 1`.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide what to exclude in image generation. If not defined, you need to provide
|
|
`negative_prompt_embeds`. This parameter is 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):
|
|
A parameter defined in the [DDIM](https://huggingface.co/papers/2010.02502) paper. Only applies to the
|
|
[`~schedulers.DDIMScheduler`] and is ignored in other schedulers. It adjusts noise level during the
|
|
inference process.
|
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) for setting
|
|
random seeds which helps in making generation deterministic.
|
|
latents (`torch.Tensor`, *optional*):
|
|
A pre-computed latent representation which can be used to guide the generation process.
|
|
prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
|
provided, embeddings are generated from the `prompt` input argument.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings, aiding in fine-tuning what should not be represented in the
|
|
outputs. If not provided, embeddings are generated from the `negative_prompt` argument.
|
|
prompt_attention_mask (`torch.Tensor`, *optional*):
|
|
Attention mask guiding the focus of the model on specific parts of the prompt text. Required when using
|
|
`prompt_embeds`.
|
|
negative_prompt_attention_mask (`torch.Tensor`, *optional*):
|
|
Attention mask for the negative prompt, needed when `negative_prompt_embeds` are used.
|
|
output_type (`str`, *optional*, defaults to `"latent"`):
|
|
The output format of the generated image. Choose between `PIL.Image` and `np.array` to define how you
|
|
want the results to be formatted.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
If set to `True`, a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] will be returned;
|
|
otherwise, a tuple containing the generated images and safety flags will be returned.
|
|
callback_on_step_end (`Callable[[int, int, Dict], None]`, `PipelineCallback`, `MultiPipelineCallbacks`,
|
|
*optional*):
|
|
A callback function (or a list of them) that will be executed at the end of each denoising step,
|
|
allowing for custom processing during generation.
|
|
callback_on_step_end_tensor_inputs (`List[str]`, *optional*):
|
|
Specifies which tensor inputs should be included in the callback function. If not defined, all tensor
|
|
inputs will be passed, facilitating enhanced logging or monitoring of the generation process.
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
Rescale parameter for adjusting noise configuration based on guidance rescale. Based on findings from
|
|
[Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://huggingface.co/papers/2305.08891).
|
|
strength (`float`, *optional*, defaults to 1.0):
|
|
Affects the overall styling or quality of the generated output. Values closer to 1 usually provide
|
|
direct adherence to prompts.
|
|
|
|
Examples:
|
|
# Example usage of the function for generating images based on prompts.
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
Returns either a structured output containing generated images and their metadata when `return_dict` is
|
|
`True`, or a simpler tuple, where the first element is a list of generated images and the second
|
|
element indicates if any of them contain "not-safe-for-work" (NSFW) content.
|
|
"""
|
|
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
|
|
|
# 0. default height and width
|
|
height = int(height // 16 * 16)
|
|
width = int(width // 16 * 16)
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
prompt_attention_mask,
|
|
negative_prompt_attention_mask,
|
|
callback_on_step_end_tensor_inputs,
|
|
)
|
|
self._guidance_scale = guidance_scale
|
|
self._guidance_rescale = guidance_rescale
|
|
self._interrupt = False
|
|
|
|
# 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
|
|
if self.text_encoder is not None:
|
|
dtype = self.text_encoder.dtype
|
|
else:
|
|
dtype = self.transformer.dtype
|
|
|
|
# 3. Encode input prompt
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
prompt_attention_mask,
|
|
negative_prompt_attention_mask,
|
|
) = self.encode_prompt(
|
|
prompt=prompt,
|
|
device=device,
|
|
dtype=dtype,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
)
|
|
|
|
# 4. set timesteps
|
|
if isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps, mu=1
|
|
)
|
|
else:
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
|
|
timesteps, num_inference_steps = self.get_timesteps(
|
|
num_inference_steps=num_inference_steps, strength=strength, device=device
|
|
)
|
|
|
|
# at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
|
|
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
|
|
# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
|
|
is_strength_max = strength == 1.0
|
|
|
|
if video is not None:
|
|
batch_size, channels, num_frames, height_video, width_video = video.shape
|
|
init_video = self.image_processor.preprocess(
|
|
video.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height_video, width_video),
|
|
height=height,
|
|
width=width,
|
|
)
|
|
init_video = init_video.to(dtype=torch.float32)
|
|
init_video = init_video.reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
|
|
else:
|
|
init_video = None
|
|
|
|
# Prepare latent variables
|
|
num_channels_latents = self.vae.config.latent_channels
|
|
num_channels_transformer = self.transformer.config.in_channels
|
|
return_image_latents = num_channels_transformer == num_channels_latents
|
|
|
|
# 5. Prepare latents.
|
|
latents_outputs = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
num_frames,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
video=init_video,
|
|
timestep=latent_timestep,
|
|
is_strength_max=is_strength_max,
|
|
return_noise=True,
|
|
return_video_latents=return_image_latents,
|
|
)
|
|
if return_image_latents:
|
|
latents, noise, image_latents = latents_outputs
|
|
else:
|
|
latents, noise = latents_outputs
|
|
|
|
# 6. Prepare inpaint latents if it needs.
|
|
if mask_video is not None:
|
|
if (mask_video == 255).all():
|
|
mask = torch.zeros_like(latents).to(device, dtype)
|
|
# Use zero latents if we want to t2v.
|
|
if self.transformer.config.resize_inpaint_mask_directly:
|
|
mask_latents = torch.zeros_like(latents)[:, :1].to(device, dtype)
|
|
else:
|
|
mask_latents = torch.zeros_like(latents).to(device, dtype)
|
|
masked_video_latents = torch.zeros_like(latents).to(device, dtype)
|
|
|
|
mask_input = torch.cat([mask_latents] * 2) if self.do_classifier_free_guidance else mask_latents
|
|
masked_video_latents_input = (
|
|
torch.cat([masked_video_latents] * 2) if self.do_classifier_free_guidance else masked_video_latents
|
|
)
|
|
inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(dtype)
|
|
else:
|
|
# Prepare mask latent variables
|
|
batch_size, channels, num_frames, height_video, width_video = mask_video.shape
|
|
mask_condition = self.mask_processor.preprocess(
|
|
mask_video.permute(0, 2, 1, 3, 4).reshape(
|
|
batch_size * num_frames, channels, height_video, width_video
|
|
),
|
|
height=height,
|
|
width=width,
|
|
)
|
|
mask_condition = mask_condition.to(dtype=torch.float32)
|
|
mask_condition = mask_condition.reshape(batch_size, num_frames, channels, height, width).permute(
|
|
0, 2, 1, 3, 4
|
|
)
|
|
|
|
if num_channels_transformer != num_channels_latents:
|
|
mask_condition_tile = torch.tile(mask_condition, [1, 3, 1, 1, 1])
|
|
if masked_video_latents is None:
|
|
masked_video = (
|
|
init_video * (mask_condition_tile < 0.5)
|
|
+ torch.ones_like(init_video) * (mask_condition_tile > 0.5) * -1
|
|
)
|
|
else:
|
|
masked_video = masked_video_latents
|
|
|
|
if self.transformer.config.resize_inpaint_mask_directly:
|
|
_, masked_video_latents = self.prepare_mask_latents(
|
|
None,
|
|
masked_video,
|
|
batch_size,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
self.do_classifier_free_guidance,
|
|
noise_aug_strength=noise_aug_strength,
|
|
)
|
|
mask_latents = resize_mask(
|
|
1 - mask_condition, masked_video_latents, self.vae.config.cache_mag_vae
|
|
)
|
|
mask_latents = mask_latents.to(device, dtype) * self.vae.config.scaling_factor
|
|
else:
|
|
mask_latents, masked_video_latents = self.prepare_mask_latents(
|
|
mask_condition_tile,
|
|
masked_video,
|
|
batch_size,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
self.do_classifier_free_guidance,
|
|
noise_aug_strength=noise_aug_strength,
|
|
)
|
|
|
|
mask_input = torch.cat([mask_latents] * 2) if self.do_classifier_free_guidance else mask_latents
|
|
masked_video_latents_input = (
|
|
torch.cat([masked_video_latents] * 2)
|
|
if self.do_classifier_free_guidance
|
|
else masked_video_latents
|
|
)
|
|
inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(dtype)
|
|
else:
|
|
inpaint_latents = None
|
|
|
|
mask = torch.tile(mask_condition, [1, num_channels_latents, 1, 1, 1])
|
|
mask = F.interpolate(mask, size=latents.size()[-3:], mode="trilinear", align_corners=True).to(
|
|
device, dtype
|
|
)
|
|
else:
|
|
if num_channels_transformer != num_channels_latents:
|
|
mask = torch.zeros_like(latents).to(device, dtype)
|
|
if self.transformer.config.resize_inpaint_mask_directly:
|
|
mask_latents = torch.zeros_like(latents)[:, :1].to(device, dtype)
|
|
else:
|
|
mask_latents = torch.zeros_like(latents).to(device, dtype)
|
|
masked_video_latents = torch.zeros_like(latents).to(device, dtype)
|
|
|
|
mask_input = torch.cat([mask_latents] * 2) if self.do_classifier_free_guidance else mask_latents
|
|
masked_video_latents_input = (
|
|
torch.cat([masked_video_latents] * 2) if self.do_classifier_free_guidance else masked_video_latents
|
|
)
|
|
inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(dtype)
|
|
else:
|
|
mask = torch.zeros_like(init_video[:, :1])
|
|
mask = torch.tile(mask, [1, num_channels_latents, 1, 1, 1])
|
|
mask = F.interpolate(mask, size=latents.size()[-3:], mode="trilinear", align_corners=True).to(
|
|
device, dtype
|
|
)
|
|
|
|
inpaint_latents = None
|
|
|
|
# Check that sizes of mask, masked image and latents match
|
|
if num_channels_transformer != num_channels_latents:
|
|
num_channels_mask = mask_latents.shape[1]
|
|
num_channels_masked_image = masked_video_latents.shape[1]
|
|
if (
|
|
num_channels_latents + num_channels_mask + num_channels_masked_image
|
|
!= self.transformer.config.in_channels
|
|
):
|
|
raise ValueError(
|
|
f"Incorrect configuration settings! The config of `pipeline.transformer`: {self.transformer.config} expects"
|
|
f" {self.transformer.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
|
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
|
|
f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of"
|
|
" `pipeline.transformer` or your `mask_image` or `image` input."
|
|
)
|
|
|
|
# 7. 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)
|
|
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask])
|
|
|
|
# To latents.device
|
|
prompt_embeds = prompt_embeds.to(device=device)
|
|
prompt_attention_mask = prompt_attention_mask.to(device=device)
|
|
|
|
# 8. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
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] * 2) if self.do_classifier_free_guidance else latents
|
|
if hasattr(self.scheduler, "scale_model_input"):
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# expand scalar t to 1-D tensor to match the 1st dim of latent_model_input
|
|
t_expand = torch.tensor([t] * latent_model_input.shape[0], device=device).to(
|
|
dtype=latent_model_input.dtype
|
|
)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.transformer(
|
|
latent_model_input,
|
|
t_expand,
|
|
encoder_hidden_states=prompt_embeds,
|
|
inpaint_latents=inpaint_latents,
|
|
return_dict=False,
|
|
)[0]
|
|
if noise_pred.size()[1] != self.vae.config.latent_channels:
|
|
noise_pred, _ = noise_pred.chunk(2, dim=1)
|
|
|
|
# perform guidance
|
|
if self.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)
|
|
|
|
if self.do_classifier_free_guidance and 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=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 num_channels_transformer == num_channels_latents:
|
|
init_latents_proper = image_latents
|
|
init_mask = mask
|
|
if i < len(timesteps) - 1:
|
|
noise_timestep = timesteps[i + 1]
|
|
if isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
|
|
init_latents_proper = self.scheduler.scale_noise(
|
|
init_latents_proper, torch.tensor([noise_timestep], noise)
|
|
)
|
|
else:
|
|
init_latents_proper = self.scheduler.add_noise(
|
|
init_latents_proper, noise, torch.tensor([noise_timestep])
|
|
)
|
|
|
|
latents = (1 - init_mask) * init_latents_proper + init_mask * latents
|
|
|
|
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)
|
|
|
|
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":
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
video = self.vae.decode(latents, return_dict=False)[0]
|
|
video = self.video_processor.postprocess_video(video=video, output_type=output_type)
|
|
else:
|
|
video = latents
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (video,)
|
|
|
|
return EasyAnimatePipelineOutput(frames=video)
|