mirror of
https://github.com/Wan-Video/Wan2.1.git
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1443 lines
66 KiB
Python
1443 lines
66 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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#
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# Modified from diffusers==0.29.2
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#
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# ==============================================================================
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import inspect
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from typing import Any, Callable, Dict, List, Optional, Union, Tuple
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import torch
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import torch.distributed as dist
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import numpy as np
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from dataclasses import dataclass
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from packaging import version
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from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
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from diffusers.configuration_utils import FrozenDict
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.utils import BaseOutput
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from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
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from diffusers.models import AutoencoderKL
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from diffusers.models.lora import adjust_lora_scale_text_encoder
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from diffusers.schedulers import KarrasDiffusionSchedulers
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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deprecate,
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logging,
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replace_example_docstring,
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scale_lora_layers,
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unscale_lora_layers,
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)
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.utils import BaseOutput
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from ...constants import PRECISION_TO_TYPE
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from ...vae.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
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from ...text_encoder import TextEncoder
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from ...modules import HYVideoDiffusionTransformer
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from mmgp import offload
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from ...utils.data_utils import black_image
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from einops import rearrange
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EXAMPLE_DOC_STRING = """"""
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def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
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"""
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Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
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Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
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"""
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std_text = noise_pred_text.std(
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dim=list(range(1, noise_pred_text.ndim)), keepdim=True
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)
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std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
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# rescale the results from guidance (fixes overexposure)
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noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
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# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
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noise_cfg = (
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guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
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)
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return noise_cfg
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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timesteps: Optional[List[int]] = None,
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sigmas: Optional[List[float]] = None,
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**kwargs,
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):
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"""
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Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
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custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
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Args:
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scheduler (`SchedulerMixin`):
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The scheduler to get timesteps from.
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
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must be `None`.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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timesteps (`List[int]`, *optional*):
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Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
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`num_inference_steps` and `sigmas` must be `None`.
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sigmas (`List[float]`, *optional*):
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Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
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`num_inference_steps` and `timesteps` must be `None`.
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Returns:
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`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
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second element is the number of inference steps.
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"""
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if timesteps is not None and sigmas is not None:
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raise ValueError(
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"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
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)
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(
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inspect.signature(scheduler.set_timesteps).parameters.keys()
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)
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if not accepts_timesteps:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" timestep schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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elif sigmas is not None:
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accept_sigmas = "sigmas" in set(
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inspect.signature(scheduler.set_timesteps).parameters.keys()
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)
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if not accept_sigmas:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" sigmas schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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else:
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scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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return timesteps, num_inference_steps
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@dataclass
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class HunyuanVideoPipelineOutput(BaseOutput):
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videos: Union[torch.Tensor, np.ndarray]
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class HunyuanVideoPipeline(DiffusionPipeline):
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r"""
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Pipeline for text-to-video generation using HunyuanVideo.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
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text_encoder ([`TextEncoder`]):
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Frozen text-encoder.
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text_encoder_2 ([`TextEncoder`]):
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Frozen text-encoder_2.
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transformer ([`HYVideoDiffusionTransformer`]):
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A `HYVideoDiffusionTransformer` to denoise the encoded video latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents.
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"""
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model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
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_optional_components = ["text_encoder_2"]
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_exclude_from_cpu_offload = ["transformer"]
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_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: TextEncoder,
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transformer: HYVideoDiffusionTransformer,
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scheduler: KarrasDiffusionSchedulers,
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text_encoder_2: Optional[TextEncoder] = None,
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progress_bar_config: Dict[str, Any] = None,
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args=None,
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):
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super().__init__()
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# ==========================================================================================
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if progress_bar_config is None:
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progress_bar_config = {}
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if not hasattr(self, "_progress_bar_config"):
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self._progress_bar_config = {}
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self._progress_bar_config.update(progress_bar_config)
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self.args = args
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# ==========================================================================================
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if (
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hasattr(scheduler.config, "steps_offset")
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and scheduler.config.steps_offset != 1
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):
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
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f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
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"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
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" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
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" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
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" file"
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)
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deprecate(
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"steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
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)
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new_config = dict(scheduler.config)
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new_config["steps_offset"] = 1
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scheduler._internal_dict = FrozenDict(new_config)
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if (
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hasattr(scheduler.config, "clip_sample")
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and scheduler.config.clip_sample is True
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):
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
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" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
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" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
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" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
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" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
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)
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deprecate(
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"clip_sample not set", "1.0.0", deprecation_message, standard_warn=False
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)
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new_config = dict(scheduler.config)
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new_config["clip_sample"] = False
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scheduler._internal_dict = FrozenDict(new_config)
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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transformer=transformer,
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scheduler=scheduler,
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text_encoder_2=text_encoder_2,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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self.noise_pertub = 0
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def encode_prompt(
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self,
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prompt,
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name,
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device,
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num_videos_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=None,
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pixel_value_llava: Optional[torch.Tensor] = None,
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uncond_pixel_value_llava: Optional[torch.Tensor] = None,
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prompt_embeds: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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negative_attention_mask: Optional[torch.Tensor] = None,
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lora_scale: Optional[float] = None,
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clip_skip: Optional[int] = None,
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text_encoder: Optional[TextEncoder] = None,
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data_type: Optional[str] = "image",
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semantic_images=None
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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num_videos_per_prompt (`int`):
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number of videos that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the video generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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pixel_value_llava (`torch.Tensor`, *optional*):
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The image tensor for llava.
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uncond_pixel_value_llava (`torch.Tensor`, *optional*):
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The image tensor for llava. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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attention_mask (`torch.Tensor`, *optional*):
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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negative_attention_mask (`torch.Tensor`, *optional*):
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lora_scale (`float`, *optional*):
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A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
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clip_skip (`int`, *optional*):
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Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
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the output of the pre-final layer will be used for computing the prompt embeddings.
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text_encoder (TextEncoder, *optional*):
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data_type (`str`, *optional*):
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"""
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if text_encoder is None:
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text_encoder = self.text_encoder
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# set lora scale so that monkey patched LoRA
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# function of text encoder can correctly access it
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if lora_scale is not None and isinstance(self, LoraLoaderMixin):
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self._lora_scale = lora_scale
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# dynamically adjust the LoRA scale
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if not USE_PEFT_BACKEND:
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adjust_lora_scale_text_encoder(text_encoder.model, lora_scale)
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else:
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scale_lora_layers(text_encoder.model, lora_scale)
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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# textual inversion: process multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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prompt = self.maybe_convert_prompt(prompt, text_encoder.tokenizer)
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text_inputs = text_encoder.text2tokens(prompt, data_type=data_type, name = name)
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if pixel_value_llava is not None:
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text_inputs['pixel_value_llava'] = pixel_value_llava
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text_inputs['attention_mask'] = torch.cat([text_inputs['attention_mask'], torch.ones((1, 575 * len(pixel_value_llava))).to(text_inputs['attention_mask'])], dim=1)
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if clip_skip is None:
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prompt_outputs = text_encoder.encode(
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text_inputs, data_type=data_type, semantic_images=semantic_images, device=device
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)
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prompt_embeds = prompt_outputs.hidden_state
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else:
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prompt_outputs = text_encoder.encode(
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text_inputs,
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output_hidden_states=True,
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data_type=data_type,
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semantic_images=semantic_images,
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device=device,
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)
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# Access the `hidden_states` first, that contains a tuple of
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# all the hidden states from the encoder layers. Then index into
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# the tuple to access the hidden states from the desired layer.
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prompt_embeds = prompt_outputs.hidden_states_list[-(clip_skip + 1)]
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# We also need to apply the final LayerNorm here to not mess with the
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# representations. The `last_hidden_states` that we typically use for
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# obtaining the final prompt representations passes through the LayerNorm
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# layer.
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prompt_embeds = text_encoder.model.text_model.final_layer_norm(
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prompt_embeds
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)
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attention_mask = prompt_outputs.attention_mask
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if attention_mask is not None:
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attention_mask = attention_mask.to(device)
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bs_embed, seq_len = attention_mask.shape
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attention_mask = attention_mask.repeat(1, num_videos_per_prompt)
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attention_mask = attention_mask.view(
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bs_embed * num_videos_per_prompt, seq_len
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)
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if text_encoder is not None:
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prompt_embeds_dtype = text_encoder.dtype
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elif self.transformer is not None:
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prompt_embeds_dtype = self.transformer.dtype
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else:
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prompt_embeds_dtype = prompt_embeds.dtype
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prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
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if prompt_embeds.ndim == 2:
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bs_embed, _ = prompt_embeds.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt)
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prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, -1)
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else:
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bs_embed, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(
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bs_embed * num_videos_per_prompt, seq_len, -1
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)
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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# textual inversion: process multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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uncond_tokens = self.maybe_convert_prompt(
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uncond_tokens, text_encoder.tokenizer
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)
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# max_length = prompt_embeds.shape[1]
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uncond_input = text_encoder.text2tokens(uncond_tokens, data_type=data_type, name = name)
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if semantic_images is not None:
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uncond_image = [black_image(img.size[0], img.size[1]) for img in semantic_images]
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else:
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uncond_image = None
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if uncond_pixel_value_llava is not None:
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uncond_input['pixel_value_llava'] = uncond_pixel_value_llava
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uncond_input['attention_mask'] = torch.cat([uncond_input['attention_mask'], torch.ones((1, 575 * len(uncond_pixel_value_llava))).to(uncond_input['attention_mask'])], dim=1)
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negative_prompt_outputs = text_encoder.encode(
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uncond_input, data_type=data_type, semantic_images=uncond_image, device=device
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)
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negative_prompt_embeds = negative_prompt_outputs.hidden_state
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negative_attention_mask = negative_prompt_outputs.attention_mask
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if negative_attention_mask is not None:
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negative_attention_mask = negative_attention_mask.to(device)
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_, seq_len = negative_attention_mask.shape
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negative_attention_mask = negative_attention_mask.repeat(
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1, num_videos_per_prompt
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)
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negative_attention_mask = negative_attention_mask.view(
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batch_size * num_videos_per_prompt, seq_len
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)
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|
|
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
|
|
)
|
|
|
|
if negative_prompt_embeds.ndim == 2:
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(
|
|
1, num_videos_per_prompt
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(
|
|
batch_size * num_videos_per_prompt, -1
|
|
)
|
|
else:
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(
|
|
1, num_videos_per_prompt, 1
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(
|
|
batch_size * num_videos_per_prompt, seq_len, -1
|
|
)
|
|
|
|
if text_encoder is not None:
|
|
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
|
|
# Retrieve the original scale by scaling back the LoRA layers
|
|
unscale_lora_layers(text_encoder.model, lora_scale)
|
|
|
|
return (
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
attention_mask,
|
|
negative_attention_mask,
|
|
)
|
|
|
|
def decode_latents(self, latents, enable_tiling=True):
|
|
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
|
|
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
|
|
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
if enable_tiling:
|
|
self.vae.enable_tiling()
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
else:
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
image = (image / 2 + 0.5).clamp(0, 1)
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
|
if image.ndim == 4:
|
|
image = image.cpu().permute(0, 2, 3, 1).float()
|
|
else:
|
|
image = image.cpu().float()
|
|
return image
|
|
|
|
def prepare_extra_func_kwargs(self, func, kwargs):
|
|
# 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]
|
|
extra_step_kwargs = {}
|
|
|
|
for k, v in kwargs.items():
|
|
accepts = k in set(inspect.signature(func).parameters.keys())
|
|
if accepts:
|
|
extra_step_kwargs[k] = v
|
|
return extra_step_kwargs
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
video_length,
|
|
callback_steps,
|
|
pixel_value_llava=None,
|
|
uncond_pixel_value_llava=None,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
vae_ver="88-4c-sd",
|
|
):
|
|
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 video_length is not None:
|
|
if "884" in vae_ver:
|
|
if video_length != 1 and (video_length - 1) % 4 != 0:
|
|
raise ValueError(
|
|
f"`video_length` has to be 1 or a multiple of 4 but is {video_length}."
|
|
)
|
|
elif "888" in vae_ver:
|
|
if video_length != 1 and (video_length - 1) % 8 != 0:
|
|
raise ValueError(
|
|
f"`video_length` has to be 1 or a multiple of 8 but is {video_length}."
|
|
)
|
|
|
|
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 pixel_value_llava is not None and uncond_pixel_value_llava is not None:
|
|
if len(pixel_value_llava) != len(uncond_pixel_value_llava):
|
|
raise ValueError(
|
|
"`pixel_value_llava` and `uncond_pixel_value_llava` must have the same length when passed directly, but"
|
|
f" got: `pixel_value_llava` {len(pixel_value_llava)} != `uncond_pixel_value_llava`"
|
|
f" {len(uncond_pixel_value_llava)}."
|
|
)
|
|
|
|
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}."
|
|
)
|
|
|
|
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.to(device), num_inference_steps - t_start
|
|
|
|
|
|
def prepare_latents(
|
|
self,
|
|
batch_size,
|
|
num_channels_latents,
|
|
num_inference_steps,
|
|
height,
|
|
width,
|
|
video_length,
|
|
dtype,
|
|
device,
|
|
timesteps,
|
|
generator,
|
|
latents=None,
|
|
denoise_strength=1.0,
|
|
img_latents=None,
|
|
i2v_mode=False,
|
|
i2v_condition_type=None,
|
|
i2v_stability=True,
|
|
):
|
|
if i2v_mode and i2v_condition_type == "latent_concat":
|
|
num_channels_latents = (num_channels_latents - 1) // 2
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
video_length,
|
|
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 i2v_mode and i2v_stability:
|
|
if img_latents.shape[2] == 1:
|
|
img_latents = img_latents.repeat(1, 1, video_length, 1, 1)
|
|
x0 = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
x1 = img_latents
|
|
|
|
t = torch.tensor([0.999]).to(device=device)
|
|
latents = x0 * t + x1 * (1 - t)
|
|
latents = latents.to(dtype=dtype)
|
|
|
|
if denoise_strength == 0:
|
|
if latents is None:
|
|
latents = randn_tensor(
|
|
shape, generator=generator, device=device, dtype=dtype
|
|
)
|
|
else:
|
|
latents = latents.to(device)
|
|
original_latents = None
|
|
noise = None
|
|
timesteps = timesteps
|
|
else:
|
|
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, denoise_strength, device)
|
|
|
|
if latents is None:
|
|
latents = noise
|
|
original_latents = None
|
|
else:
|
|
latents = latents.to(device)
|
|
latent_timestep = timesteps[:1]
|
|
frames_needed = noise.shape[2]
|
|
current_frames = latents.shape[2]
|
|
|
|
if frames_needed > current_frames:
|
|
repeat_factor = frames_needed - current_frames
|
|
additional_frame = torch.randn((latents.size(0), latents.size(1),repeat_factor, latents.size(3), latents.size(4)), dtype=latents.dtype, device=latents.device)
|
|
latents = torch.cat((additional_frame, latents), dim=2)
|
|
self.additional_frames = repeat_factor
|
|
elif frames_needed < current_frames:
|
|
latents = latents[:, :, :frames_needed, :, :]
|
|
|
|
original_latents = latents.clone()
|
|
latents = latents * (1 - latent_timestep / 1000) + latent_timestep / 1000 * noise
|
|
print(f'debug:latent_timestep={latent_timestep}, latents-size={latents.shape}')
|
|
|
|
# Check existence to make it compatible with FlowMatchEulerDiscreteScheduler
|
|
if hasattr(self.scheduler, "init_noise_sigma"):
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents, original_latents, noise, timesteps
|
|
|
|
# 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://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
# return self._guidance_scale > 1 and self.transformer.config.time_cond_proj_dim is None
|
|
return self._guidance_scale > 1
|
|
|
|
@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]],
|
|
height: int,
|
|
width: int,
|
|
video_length: int,
|
|
name: Union[str, List[str]] = None,
|
|
data_type: str = "video",
|
|
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,
|
|
pixel_value_ref=None,
|
|
# ref_latents: Optional[torch.Tensor] = None,
|
|
# uncond_ref_latents: Optional[torch.Tensor] = None,
|
|
pixel_value_llava: Optional[torch.Tensor] = None,
|
|
uncond_pixel_value_llava: Optional[torch.Tensor] = None,
|
|
bg_latents: Optional[torch.Tensor] = None,
|
|
audio_prompts: Optional[torch.Tensor] = None,
|
|
ip_cfg_scale: float = 0.0,
|
|
audio_strength: float = 1.0,
|
|
use_deepcache: int = 1,
|
|
num_videos_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,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_attention_mask: Optional[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[
|
|
Union[
|
|
Callable[[int, int, Dict], None],
|
|
PipelineCallback,
|
|
MultiPipelineCallbacks,
|
|
]
|
|
] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
freqs_cis: Tuple[torch.Tensor, torch.Tensor] = None,
|
|
vae_ver: str = "88-4c-sd",
|
|
enable_tiling: bool = False,
|
|
n_tokens: Optional[int] = None,
|
|
video_val_flag: bool=False,
|
|
denoise_strength: float = 1.0,
|
|
mask = None,
|
|
embedded_guidance_scale: Optional[float] = None,
|
|
i2v_mode: bool = False,
|
|
i2v_condition_type: str = None,
|
|
i2v_stability: bool = True,
|
|
img_latents: Optional[torch.Tensor] = None,
|
|
semantic_images=None,
|
|
joint_pass = False,
|
|
cfg_star_rescale = False,
|
|
callback = None,
|
|
**kwargs,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
height (`int`):
|
|
The height in pixels of the generated image.
|
|
width (`int`):
|
|
The width in pixels of the generated image.
|
|
video_length (`int`):
|
|
The number of frames in the generated video.
|
|
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`).
|
|
ref_latents (`torch.Tensor`, *optional*):
|
|
The image tensor for time-concat.
|
|
uncond_ref_latents (`torch.Tensor`, *optional*):
|
|
The image tensor for time-concat. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
less than `1`).
|
|
pixel_value_llava (`torch.Tensor`, *optional*):
|
|
The image tensor for llava.
|
|
uncond_pixel_value_llava (`torch.Tensor`, *optional*):
|
|
The image tensor for llava. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
less than `1`).
|
|
num_videos_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://arxiv.org/abs/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.
|
|
|
|
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 [`HunyuanVideoPipelineOutput`] 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://arxiv.org/pdf/2305.08891.pdf). 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.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~HunyuanVideoPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`HunyuanVideoPipelineOutput`] 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.
|
|
"""
|
|
callback_steps = kwargs.pop("callback_steps", None)
|
|
|
|
# if callback is not None:
|
|
# deprecate(
|
|
# "callback",
|
|
# "1.0.0",
|
|
# "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
|
|
# )
|
|
# if callback_steps is not None:
|
|
# deprecate(
|
|
# "callback_steps",
|
|
# "1.0.0",
|
|
# "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
|
|
# )
|
|
|
|
|
|
if self._interrupt:
|
|
return [None]
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
|
|
|
if pixel_value_ref != None:
|
|
pixel_value_ref = pixel_value_ref * 2 - 1.
|
|
pixel_value_ref_for_vae = rearrange(pixel_value_ref,"b c h w -> b c 1 h w")
|
|
|
|
ref_latents = self.vae.encode(pixel_value_ref_for_vae.clone()).latent_dist.sample()
|
|
uncond_ref_latents = self.vae.encode(torch.ones_like(pixel_value_ref_for_vae)).latent_dist.sample()
|
|
ref_latents.mul_(self.vae.config.scaling_factor)
|
|
uncond_ref_latents.mul_(self.vae.config.scaling_factor)
|
|
else:
|
|
ref_latents = None
|
|
uncond_ref_latents = None
|
|
|
|
|
|
# 0. Default height and width to unet
|
|
# height = height or self.transformer.config.sample_size * self.vae_scale_factor
|
|
# width = width or self.transformer.config.sample_size * self.vae_scale_factor
|
|
# to deal with lora scaling and other possible forward hooks
|
|
trans = self.transformer
|
|
if trans.enable_cache == "tea":
|
|
teacache_multiplier = trans.cache_multiplier
|
|
trans.accumulated_rel_l1_distance = 0
|
|
trans.rel_l1_thresh = 0.1 if teacache_multiplier < 2 else 0.15
|
|
elif trans.enable_cache == "mag":
|
|
trans.compute_magcache_threshold(trans.cache_start_step, num_inference_steps, trans.cache_multiplier)
|
|
trans.accumulated_err, trans.accumulated_steps, trans.accumulated_ratio = 0, 0, 1.0
|
|
else:
|
|
trans.enable_cache == None
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
video_length,
|
|
callback_steps,
|
|
negative_prompt,
|
|
pixel_value_llava,
|
|
uncond_pixel_value_llava,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
callback_on_step_end_tensor_inputs,
|
|
vae_ver=vae_ver,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._guidance_rescale = guidance_rescale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
|
|
# 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 = torch.device(f"cuda:{dist.get_rank()}") if dist.is_initialized() else 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,
|
|
prompt_mask,
|
|
negative_prompt_mask,
|
|
) = self.encode_prompt(
|
|
prompt,
|
|
name,
|
|
device,
|
|
num_videos_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
pixel_value_llava=pixel_value_llava,
|
|
uncond_pixel_value_llava=uncond_pixel_value_llava,
|
|
prompt_embeds=prompt_embeds,
|
|
attention_mask=attention_mask,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
negative_attention_mask=negative_attention_mask,
|
|
lora_scale=lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
data_type=data_type,
|
|
semantic_images=semantic_images
|
|
)
|
|
if self.text_encoder_2 is not None:
|
|
(
|
|
prompt_embeds_2,
|
|
negative_prompt_embeds_2,
|
|
prompt_mask_2,
|
|
negative_prompt_mask_2,
|
|
) = self.encode_prompt(
|
|
prompt,
|
|
name,
|
|
device,
|
|
num_videos_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=None,
|
|
attention_mask=None,
|
|
negative_prompt_embeds=None,
|
|
negative_attention_mask=None,
|
|
lora_scale=lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
text_encoder=self.text_encoder_2,
|
|
data_type=data_type,
|
|
)
|
|
else:
|
|
prompt_embeds_2 = None
|
|
negative_prompt_embeds_2 = None
|
|
prompt_mask_2 = None
|
|
negative_prompt_mask_2 = None
|
|
|
|
# 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_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
if prompt_mask is not None:
|
|
prompt_mask = torch.cat([negative_prompt_mask, prompt_mask])
|
|
if prompt_embeds_2 is not None:
|
|
prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2])
|
|
if prompt_mask_2 is not None:
|
|
prompt_mask_2 = torch.cat([negative_prompt_mask_2, prompt_mask_2])
|
|
|
|
if self.do_classifier_free_guidance:
|
|
if ref_latents is not None:
|
|
ref_latents = torch.cat([ref_latents, ref_latents], dim=0)
|
|
if prompt_mask[0].sum() > 575:
|
|
prompt_mask[0] = torch.cat([torch.ones((1, prompt_mask[0].sum() - 575)).to(prompt_mask),
|
|
torch.zeros((1, prompt_mask.shape[1] - prompt_mask[0].sum() + 575)).to(prompt_mask)], dim=1)
|
|
|
|
if bg_latents is not None:
|
|
bg_latents = torch.cat([bg_latents, bg_latents], dim=0)
|
|
|
|
if audio_prompts is not None:
|
|
audio_prompts = torch.cat([torch.zeros_like(audio_prompts), audio_prompts], dim=0)
|
|
|
|
if ip_cfg_scale>0:
|
|
prompt_embeds = torch.cat([prompt_embeds, prompt_embeds[1:]])
|
|
prompt_embeds_2 = torch.cat([prompt_embeds_2, prompt_embeds_2[1:]])
|
|
prompt_mask = torch.cat([prompt_mask, prompt_mask[1:]], dim=0)
|
|
ref_latents = torch.cat([uncond_ref_latents, uncond_ref_latents, ref_latents[1:]], dim=0)
|
|
|
|
|
|
# 4. Prepare timesteps
|
|
extra_set_timesteps_kwargs = self.prepare_extra_func_kwargs(
|
|
self.scheduler.set_timesteps, {"n_tokens": n_tokens}
|
|
)
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler,
|
|
num_inference_steps,
|
|
device,
|
|
timesteps,
|
|
sigmas,
|
|
**extra_set_timesteps_kwargs,
|
|
)
|
|
|
|
if "884" in vae_ver:
|
|
video_length = (video_length - 1) // 4 + 1
|
|
elif "888" in vae_ver:
|
|
video_length = (video_length - 1) // 8 + 1
|
|
else:
|
|
video_length = video_length
|
|
|
|
if self.transformer.mixed_precision:
|
|
latent_dtype = torch.float32
|
|
else:
|
|
latent_dtype = torch.bfloat16
|
|
if prompt_embeds != None:
|
|
prompt_embeds = prompt_embeds.to(torch.bfloat16)
|
|
if prompt_embeds_2 != None:
|
|
prompt_embeds_2 = prompt_embeds_2.to(torch.bfloat16)
|
|
# if prompt_mask != None:
|
|
# prompt_mask = prompt_mask.to(torch.bfloat16)
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.transformer.config.in_channels
|
|
latents, original_latents, noise, timesteps = self.prepare_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
num_channels_latents,
|
|
num_inference_steps,
|
|
height,
|
|
width,
|
|
video_length,
|
|
latent_dtype, #prompt_embeds.dtype,
|
|
device,
|
|
timesteps,
|
|
generator,
|
|
latents,
|
|
denoise_strength,
|
|
img_latents=img_latents,
|
|
i2v_mode=i2v_mode,
|
|
i2v_condition_type=i2v_condition_type,
|
|
i2v_stability=i2v_stability
|
|
)
|
|
|
|
if i2v_mode and i2v_condition_type == "latent_concat":
|
|
if img_latents.shape[2] == 1:
|
|
img_latents_concat = img_latents.repeat(1, 1, video_length, 1, 1)
|
|
else:
|
|
img_latents_concat = img_latents
|
|
img_latents_concat[:, :, 1:, ...] = 0
|
|
|
|
i2v_mask = torch.zeros(video_length)
|
|
i2v_mask[0] = 1
|
|
|
|
mask_concat = torch.ones(img_latents_concat.shape[0], 1, img_latents_concat.shape[2], img_latents_concat.shape[3],
|
|
img_latents_concat.shape[4]).to(device=img_latents.device)
|
|
mask_concat[:, :, 1:, ...] = 0
|
|
|
|
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_func_kwargs(
|
|
self.scheduler.step,
|
|
{"generator": generator, "eta": eta},
|
|
)
|
|
|
|
vae_precision = "fp16" # torch.float16
|
|
precision = "bf16" # torch.bfloat16
|
|
|
|
disable_autocast = True
|
|
|
|
target_dtype = PRECISION_TO_TYPE[precision]
|
|
autocast_enabled = target_dtype != torch.float32 and not disable_autocast
|
|
vae_dtype = self.vae._model_dtype # PRECISION_TO_TYPE[vae_precision]
|
|
vae_autocast_enabled = vae_dtype != torch.float32 and not disable_autocast
|
|
|
|
# 7. Denoising loop
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
self._num_timesteps = len(timesteps)
|
|
start_scale = ip_cfg_scale # 3.0
|
|
end_scale = 1.0
|
|
step_scale = (start_scale - end_scale) / (self._num_timesteps - 1 + 1e-3)
|
|
|
|
# print('sigmas used in generation:', self.scheduler.sigmas)
|
|
# print('inference timesteps used in generation:', timesteps)
|
|
|
|
|
|
# 8. Mask latents
|
|
mask_latents = None
|
|
if mask is not None:
|
|
target_video_length = mask.shape[0]
|
|
target_height = mask.shape[1]
|
|
target_width = mask.shape[2]
|
|
|
|
mask_length = (target_video_length - 1) // 4 + 1
|
|
mask_height = target_height // 8
|
|
mask_width = target_width // 8
|
|
|
|
mask = mask[...,0:1]
|
|
mask = mask.unsqueeze(0)
|
|
mask = rearrange(mask, "b t h w c -> b c t h w")
|
|
|
|
mask_latents = torch.nn.functional.interpolate(mask, size=(mask_length, mask_height, mask_width))
|
|
mask_latents = mask_latents.to(device)
|
|
|
|
if mask_latents is not None:
|
|
mask_latents_model_input = (
|
|
torch.cat([mask_latents] * 2)
|
|
if self.do_classifier_free_guidance
|
|
else mask_latents
|
|
)
|
|
print(f'maskinfo, mask={mask.shape}, mask_latents_model_input={mask_latents_model_input.shape} ')
|
|
|
|
|
|
if callback != None:
|
|
callback(-1, None, True)
|
|
|
|
load_latent = True
|
|
load_latent = False
|
|
|
|
multi_passes_free_guidance = not joint_pass
|
|
if load_latent:
|
|
timesteps = []
|
|
|
|
latent_items = 2 if self.do_classifier_free_guidance else 1
|
|
if ip_cfg_scale>0:
|
|
latent_items += 1
|
|
|
|
if self.transformer.enable_cache:
|
|
self.transformer.previous_residual = [None] * latent_items
|
|
|
|
# if is_progress_bar:
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
offload.set_step_no_for_lora(self.transformer, i)
|
|
if self.interrupt:
|
|
continue
|
|
if i2v_mode and i2v_condition_type == "token_replace":
|
|
latents = torch.concat([img_latents, latents[:, :, 1:, :, :]], dim=2)
|
|
|
|
# expand the latents if we are doing classifier free guidance
|
|
if i2v_mode and i2v_condition_type == "latent_concat":
|
|
latent_model_input = torch.concat([latents, img_latents_concat, mask_concat], dim=1)
|
|
else:
|
|
latent_model_input = latents
|
|
|
|
latent_model_input = torch.cat([latent_model_input] * latent_items) if latent_items > 1 else latent_model_input
|
|
|
|
latent_model_input = self.scheduler.scale_model_input(
|
|
latent_model_input, t
|
|
)
|
|
|
|
if mask_latents is not None:
|
|
original_latents_noise = original_latents * (1 - t / 1000.0) + t / 1000.0 * noise
|
|
original_latent_noise_model_input = (
|
|
torch.cat([original_latents_noise] * 2)
|
|
if self.do_classifier_free_guidance
|
|
else original_latents_noise
|
|
)
|
|
original_latent_noise_model_input = self.scheduler.scale_model_input(original_latent_noise_model_input, t)
|
|
latent_model_input = mask_latents_model_input * latent_model_input + (1 - mask_latents_model_input) * original_latent_noise_model_input
|
|
|
|
t_expand = t.repeat(latent_model_input.shape[0])
|
|
guidance_expand = (
|
|
torch.tensor(
|
|
[embedded_guidance_scale] * latent_model_input.shape[0],
|
|
dtype=torch.float32,
|
|
device=device,
|
|
).to(latent_dtype)
|
|
* 1000.0
|
|
if embedded_guidance_scale is not None
|
|
else None
|
|
)
|
|
|
|
# predict the noise residual
|
|
with torch.autocast(
|
|
device_type="cuda", dtype=target_dtype, enabled=autocast_enabled
|
|
):
|
|
|
|
if self.do_classifier_free_guidance and multi_passes_free_guidance:
|
|
for j in range(len(latent_model_input)):
|
|
ret = self.transformer( # For an input image (129, 192, 336) (1, 256, 256)
|
|
latent_model_input[j].unsqueeze(0), # [2, 16, 33, 24, 42]
|
|
t_expand[j].unsqueeze(0), # [2]
|
|
text_states=prompt_embeds[j].unsqueeze(0), # [2, 256, 4096]
|
|
text_mask=prompt_mask[j].unsqueeze(0), # [2, 256]
|
|
text_states_2=prompt_embeds_2[j].unsqueeze(0), # [2, 768]
|
|
ref_latents=ref_latents[j].unsqueeze(0),
|
|
freqs_cos=freqs_cis[0], # [seqlen, head_dim]
|
|
freqs_sin=freqs_cis[1], # [seqlen, head_dim]
|
|
guidance=guidance_expand,
|
|
pipeline=self,
|
|
x_id=j,
|
|
step_no=i,
|
|
bg_latents=bg_latents[j].unsqueeze(0) if bg_latents!=None else None,
|
|
audio_prompts=audio_prompts[j].unsqueeze(0) if audio_prompts!=None else None,
|
|
audio_strength=audio_strength,
|
|
callback = callback,
|
|
)
|
|
if self._interrupt:
|
|
return [None]
|
|
if j==0:
|
|
noise_pred_uncond= ret[0]
|
|
elif j==1:
|
|
noise_pred_text= ret[0]
|
|
else:
|
|
noise_pred_ip = ret[0]
|
|
ret = None
|
|
else:
|
|
# if self.do_classifier_free_guidance:
|
|
# noise_pred_uncond = self.transformer(latent_model_input[:1], t_expand[:1], ref_latents=ref_latents[:1], text_states=prompt_embeds[:1], text_mask=prompt_mask[:1], text_states_2=prompt_embeds_2[:1], freqs_cos=freqs_cis[0],freqs_sin=freqs_cis[1], guidance=guidance_expand,return_dict=True)['x']
|
|
# noise_pred_text = self.transformer(latent_model_input[1:], t_expand[1:], ref_latents=ref_latents[1:], text_states=prompt_embeds[1:], text_mask=prompt_mask[1:], text_states_2=prompt_embeds_2[1:], freqs_cos=freqs_cis[0],freqs_sin=freqs_cis[1], guidance=guidance_expand,return_dict=True)['x']
|
|
# noise_pred = torch.cat([noise_pred_uncond, noise_pred_text], dim=0)
|
|
# else:
|
|
ret = self.transformer( # For an input image (129, 192, 336) (1, 256, 256)
|
|
latent_model_input, # [2, 16, 33, 24, 42]
|
|
t_expand, # [2]
|
|
text_states=prompt_embeds, # [2, 256, 4096]
|
|
text_mask=prompt_mask, # [2, 256]
|
|
text_states_2=prompt_embeds_2, # [2, 768]
|
|
ref_latents=ref_latents,
|
|
freqs_cos=freqs_cis[0], # [seqlen, head_dim]
|
|
freqs_sin=freqs_cis[1], # [seqlen, head_dim]
|
|
guidance=guidance_expand,
|
|
pipeline=self,
|
|
step_no=i,
|
|
bg_latents=bg_latents,
|
|
audio_prompts=audio_prompts,
|
|
audio_strength=audio_strength,
|
|
callback = callback,
|
|
)
|
|
if self._interrupt:
|
|
return [None]
|
|
if self.do_classifier_free_guidance :
|
|
if ip_cfg_scale > 0:
|
|
noise_pred_uncond, noise_pred_text, noise_pred_ip = ret
|
|
else:
|
|
noise_pred_uncond, noise_pred_text = noise_pred = ret
|
|
else:
|
|
noise_pred = ret[0]
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
if cfg_star_rescale:
|
|
batch_size = 1
|
|
positive_flat = noise_pred_text.view(batch_size, -1)
|
|
negative_flat = noise_pred_uncond.view(batch_size, -1)
|
|
dot_product = torch.sum(
|
|
positive_flat * negative_flat, dim=1, keepdim=True
|
|
)
|
|
squared_norm = torch.sum(negative_flat**2, dim=1, keepdim=True) + 1e-8
|
|
positive_flat, negative_flat = None, None
|
|
alpha = dot_product / squared_norm
|
|
noise_pred_uncond *= alpha
|
|
|
|
if ip_cfg_scale > 0:
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) + start_scale * (noise_pred_ip-noise_pred_text)
|
|
start_scale -= step_scale
|
|
if i==0:
|
|
print(f'i={i}, noise_pred shape={noise_pred.shape}')
|
|
else:
|
|
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://arxiv.org/pdf/2305.08891.pdf
|
|
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
|
|
if i2v_mode and i2v_condition_type == "token_replace":
|
|
noise_pred = noise_pred.unsqueeze(0)
|
|
latents = self.scheduler.step(
|
|
noise_pred[:, :, 1:, :, :], t, latents[:, :, 1:, :, :], **extra_step_kwargs, return_dict=False
|
|
)[0]
|
|
latents = torch.concat(
|
|
[img_latents, latents], dim=2
|
|
)
|
|
else:
|
|
latents = self.scheduler.step(
|
|
noise_pred, t, latents, **extra_step_kwargs, return_dict=False
|
|
)[0]
|
|
|
|
|
|
noise_pred_uncond, noise_pred_text, noise_pred, noise_pred_ip, ret = None, None, None, None, None
|
|
|
|
if callback is not None:
|
|
callback(i, latents.squeeze(0), False)
|
|
|
|
if self.interrupt:
|
|
return [None]
|
|
|
|
# if load_latent:
|
|
# latents = torch.load("latent.pt")
|
|
# else:
|
|
# torch.save(latents, "latent.pt")
|
|
|
|
|
|
if mask_latents is not None:
|
|
latents = mask_latents * latents + (1 - mask_latents) * original_latents
|
|
|
|
if not output_type == "latent":
|
|
expand_temporal_dim = False
|
|
if len(latents.shape) == 4:
|
|
if isinstance(self.vae, AutoencoderKLCausal3D):
|
|
latents = latents.unsqueeze(2)
|
|
expand_temporal_dim = True
|
|
elif len(latents.shape) == 5:
|
|
pass
|
|
else:
|
|
raise ValueError(
|
|
f"Only support latents with shape (b, c, h, w) or (b, c, f, h, w), but got {latents.shape}."
|
|
)
|
|
|
|
if (
|
|
hasattr(self.vae.config, "shift_factor")
|
|
and self.vae.config.shift_factor
|
|
):
|
|
latents = (
|
|
latents / self.vae.config.scaling_factor
|
|
+ self.vae.config.shift_factor
|
|
)
|
|
else:
|
|
latents = latents / self.vae.config.scaling_factor
|
|
|
|
with torch.autocast(
|
|
device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled
|
|
):
|
|
if enable_tiling:
|
|
self.vae.enable_tiling()
|
|
image = self.vae.decode(
|
|
latents, return_dict=False, generator=generator
|
|
)[0]
|
|
else:
|
|
image = self.vae.decode(
|
|
latents, return_dict=False, generator=generator
|
|
)[0]
|
|
|
|
if expand_temporal_dim or image.shape[2] == 1:
|
|
image = image.squeeze(2)
|
|
|
|
else:
|
|
image = latents
|
|
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
|
image = image.cpu().float()
|
|
|
|
if i2v_mode and i2v_condition_type == "latent_concat":
|
|
image = image[:, :, 4:, :, :]
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return image
|
|
|
|
return HunyuanVideoPipelineOutput(videos=image)
|