mirror of https://github.com/vladmandic/automatic
774 lines
36 KiB
Python
774 lines
36 KiB
Python
# Copyright 2024 The HunyuanVideo Team and 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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import inspect
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import numpy as np
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import torch
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from transformers import CLIPTextModel, CLIPTokenizer, LlamaModel, LlamaTokenizerFast
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from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
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from diffusers.loaders import HunyuanVideoLoraLoaderMixin
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from diffusers.models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel
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from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
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from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.video_processor import VideoProcessor
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.hunyuan_video.pipeline_output import HunyuanVideoPipelineOutput
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```python
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>>> import torch
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>>> from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
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>>> from diffusers.utils import export_to_video
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>>> model_id = "hunyuanvideo-community/HunyuanVideo"
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>>> transformer = HunyuanVideoTransformer3DModel.from_pretrained(
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... model_id, subfolder="transformer", torch_dtype=torch.bfloat16
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... )
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>>> pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.float16)
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>>> pipe.vae.enable_tiling()
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>>> pipe.to("cuda")
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>>> output = pipe(
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... prompt="A cat walks on the grass, realistic",
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... height=320,
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... width=512,
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... num_frames=61,
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... num_inference_steps=30,
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... ).frames[0]
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>>> export_to_video(output, "output.mp4", fps=15)
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```
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"""
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@torch.cuda.amp.autocast(dtype=torch.float32)
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def optimized_scale(positive_flat, negative_flat):
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# Calculate dot production
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dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
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# Squared norm of uncondition
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squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
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# st_star = v_cond^T * v_uncond / ||v_uncond||^2
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st_star = dot_product / squared_norm
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return st_star
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DEFAULT_PROMPT_TEMPLATE = {
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"template": (
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"<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
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"1. The main content and theme of the video."
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"2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
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"3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
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"4. background environment, light, style and atmosphere."
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"5. camera angles, movements, and transitions used in the video:<|eot_id|>"
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"<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
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),
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"crop_start": 95,
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}
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
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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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r"""
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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("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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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(inspect.signature(scheduler.set_timesteps).parameters.keys())
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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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class HunyuanVideoCFGZeroPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
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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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text_encoder ([`LlamaModel`]):
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[Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
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tokenizer (`LlamaTokenizer`):
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Tokenizer from [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
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transformer ([`HunyuanVideoTransformer3DModel`]):
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Conditional Transformer to denoise the encoded image latents.
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scheduler ([`FlowMatchEulerDiscreteScheduler`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
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vae ([`AutoencoderKLHunyuanVideo`]):
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Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
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text_encoder_2 ([`CLIPTextModel`]):
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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tokenizer_2 (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
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"""
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model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
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_callback_tensor_inputs = ["latents", "prompt_embeds"]
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def __init__(
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self,
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text_encoder: LlamaModel,
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tokenizer: LlamaTokenizerFast,
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transformer: HunyuanVideoTransformer3DModel,
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vae: AutoencoderKLHunyuanVideo,
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scheduler: FlowMatchEulerDiscreteScheduler,
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text_encoder_2: CLIPTextModel,
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tokenizer_2: CLIPTokenizer,
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):
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super().__init__()
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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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tokenizer=tokenizer,
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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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tokenizer_2=tokenizer_2,
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)
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self.vae_scale_factor_temporal = self.vae.temporal_compression_ratio if getattr(self, "vae", None) else 4
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self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio if getattr(self, "vae", None) else 8
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self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
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def _get_llama_prompt_embeds(
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self,
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prompt: Union[str, List[str]],
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prompt_template: Dict[str, Any],
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num_videos_per_prompt: int = 1,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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max_sequence_length: int = 256,
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num_hidden_layers_to_skip: int = 2,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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device = device or self._execution_device
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dtype = dtype or self.text_encoder.dtype
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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prompt = [prompt_template["template"].format(p) for p in prompt]
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crop_start = prompt_template.get("crop_start", None)
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if crop_start is None:
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prompt_template_input = self.tokenizer(
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prompt_template["template"],
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padding="max_length",
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return_tensors="pt",
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return_length=False,
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return_overflowing_tokens=False,
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return_attention_mask=False,
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)
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crop_start = prompt_template_input["input_ids"].shape[-1]
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# Remove <|eot_id|> token and placeholder {}
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crop_start -= 2
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max_sequence_length += crop_start
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text_inputs = self.tokenizer(
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prompt,
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max_length=max_sequence_length,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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return_length=False,
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return_overflowing_tokens=False,
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return_attention_mask=True,
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)
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text_input_ids = text_inputs.input_ids.to(device=device)
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prompt_attention_mask = text_inputs.attention_mask.to(device=device)
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prompt_embeds = self.text_encoder(
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input_ids=text_input_ids,
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attention_mask=prompt_attention_mask,
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output_hidden_states=True,
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).hidden_states[-(num_hidden_layers_to_skip + 1)]
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prompt_embeds = prompt_embeds.to(dtype=dtype)
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if crop_start is not None and crop_start > 0:
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prompt_embeds = prompt_embeds[:, crop_start:]
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prompt_attention_mask = prompt_attention_mask[:, crop_start:]
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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_, seq_len, _ = prompt_embeds.shape
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prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
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prompt_attention_mask = prompt_attention_mask.repeat(1, num_videos_per_prompt)
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prompt_attention_mask = prompt_attention_mask.view(batch_size * num_videos_per_prompt, seq_len)
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return prompt_embeds, prompt_attention_mask
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def _get_clip_prompt_embeds(
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self,
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prompt: Union[str, List[str]],
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num_videos_per_prompt: int = 1,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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max_sequence_length: int = 77,
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) -> torch.Tensor:
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device = device or self._execution_device
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dtype = dtype or self.text_encoder_2.dtype
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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text_inputs = self.tokenizer_2(
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prompt,
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {max_sequence_length} tokens: {removed_text}"
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)
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prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False).pooler_output
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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(batch_size * num_videos_per_prompt, -1)
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return prompt_embeds
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def encode_prompt(
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self,
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prompt: Union[str, List[str]],
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prompt_2: Union[str, List[str]] = None,
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prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
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num_videos_per_prompt: int = 1,
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prompt_embeds: Optional[torch.Tensor] = None,
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pooled_prompt_embeds: Optional[torch.Tensor] = None,
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prompt_attention_mask: Optional[torch.Tensor] = None,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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max_sequence_length: int = 256,
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):
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if prompt_embeds is None:
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prompt_embeds, prompt_attention_mask = self._get_llama_prompt_embeds(
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prompt,
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prompt_template,
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num_videos_per_prompt,
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device=device,
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dtype=dtype,
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max_sequence_length=max_sequence_length,
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)
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if pooled_prompt_embeds is None:
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if prompt_2 is None:
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prompt_2 = prompt
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pooled_prompt_embeds = self._get_clip_prompt_embeds(
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prompt,
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num_videos_per_prompt,
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device=device,
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dtype=dtype,
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max_sequence_length=77,
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)
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return prompt_embeds, pooled_prompt_embeds, prompt_attention_mask
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def check_inputs(
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self,
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prompt,
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prompt_2,
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height,
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width,
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prompt_embeds=None,
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callback_on_step_end_tensor_inputs=None,
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prompt_template=None,
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):
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if height % 16 != 0 or width % 16 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
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if callback_on_step_end_tensor_inputs is not None and not all(
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k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
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):
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raise ValueError(
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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]}"
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)
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt_2 is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
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raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
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if prompt_template is not None:
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if not isinstance(prompt_template, dict):
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raise ValueError(f"`prompt_template` has to be of type `dict` but is {type(prompt_template)}")
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if "template" not in prompt_template:
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raise ValueError(
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f"`prompt_template` has to contain a key `template` but only found {prompt_template.keys()}"
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)
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def prepare_latents(
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self,
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batch_size: int,
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num_channels_latents: int = 32,
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height: int = 720,
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width: int = 1280,
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num_frames: int = 129,
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dtype: Optional[torch.dtype] = None,
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device: Optional[torch.device] = None,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if latents is not None:
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return latents.to(device=device, dtype=dtype)
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shape = (
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batch_size,
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num_channels_latents,
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(num_frames - 1) // self.vae_scale_factor_temporal + 1,
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int(height) // self.vae_scale_factor_spatial,
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int(width) // self.vae_scale_factor_spatial,
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)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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return latents
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def enable_vae_slicing(self):
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r"""
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.vae.enable_slicing()
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def disable_vae_slicing(self):
|
|
r"""
|
|
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
|
computing decoding in one step.
|
|
"""
|
|
self.vae.disable_slicing()
|
|
|
|
def enable_vae_tiling(self):
|
|
r"""
|
|
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
|
|
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
|
processing larger images.
|
|
"""
|
|
self.vae.enable_tiling()
|
|
|
|
def disable_vae_tiling(self):
|
|
r"""
|
|
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
|
computing decoding in one step.
|
|
"""
|
|
self.vae.disable_tiling()
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def attention_kwargs(self):
|
|
return self._attention_kwargs
|
|
|
|
@property
|
|
def current_timestep(self):
|
|
return self._current_timestep
|
|
|
|
@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,
|
|
prompt_2: Union[str, List[str]] = None,
|
|
negative_prompt: Union[str, List[str]] = None,
|
|
negative_prompt_2: Union[str, List[str]] = None,
|
|
height: int = 720,
|
|
width: int = 1280,
|
|
num_frames: int = 129,
|
|
num_inference_steps: int = 50,
|
|
sigmas: List[float] = None,
|
|
true_cfg_scale: float = 1.0,
|
|
guidance_scale: float = 6.0,
|
|
num_videos_per_prompt: Optional[int] = 1,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
pooled_prompt_embeds: Optional[torch.Tensor] = None,
|
|
prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
callback_on_step_end: Optional[
|
|
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
|
|
] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
|
|
max_sequence_length: int = 256,
|
|
use_cfg_zero_star: Optional[bool] = False,
|
|
use_zero_init: Optional[bool] = True,
|
|
zero_steps: Optional[int] = 0,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
|
instead.
|
|
prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
|
will be used instead.
|
|
negative_prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
|
|
not greater than `1`).
|
|
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
|
|
`text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
|
|
height (`int`, defaults to `720`):
|
|
The height in pixels of the generated image.
|
|
width (`int`, defaults to `1280`):
|
|
The width in pixels of the generated image.
|
|
num_frames (`int`, defaults to `129`):
|
|
The number of frames in the generated video.
|
|
num_inference_steps (`int`, defaults to `50`):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
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.
|
|
true_cfg_scale (`float`, *optional*, defaults to 1.0):
|
|
When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
|
|
guidance_scale (`float`, defaults to `6.0`):
|
|
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
|
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
|
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
|
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
|
usually at the expense of lower image quality. Note that the only available HunyuanVideo model is
|
|
CFG-distilled, which means that traditional guidance between unconditional and conditional latent is
|
|
not applied.
|
|
num_videos_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
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.
|
|
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
|
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *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.
|
|
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, pooled negative_prompt_embeds will be generated from `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.
|
|
attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
|
`self.processor` in
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
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.
|
|
"""
|
|
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
prompt_2,
|
|
height,
|
|
width,
|
|
prompt_embeds,
|
|
callback_on_step_end_tensor_inputs,
|
|
prompt_template,
|
|
)
|
|
|
|
has_neg_prompt = negative_prompt is not None or (
|
|
negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
|
|
)
|
|
do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._attention_kwargs = attention_kwargs
|
|
self._current_timestep = None
|
|
self._interrupt = False
|
|
|
|
device = self._execution_device
|
|
|
|
# 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]
|
|
|
|
# 3. Encode input prompt
|
|
transformer_dtype = self.transformer.dtype
|
|
prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = self.encode_prompt(
|
|
prompt=prompt,
|
|
prompt_2=prompt_2,
|
|
prompt_template=prompt_template,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
device=device,
|
|
max_sequence_length=max_sequence_length,
|
|
)
|
|
prompt_embeds = prompt_embeds.to(transformer_dtype)
|
|
prompt_attention_mask = prompt_attention_mask.to(transformer_dtype)
|
|
pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype)
|
|
|
|
if do_true_cfg:
|
|
negative_prompt_embeds, negative_pooled_prompt_embeds, negative_prompt_attention_mask = self.encode_prompt(
|
|
prompt=negative_prompt,
|
|
prompt_2=negative_prompt_2,
|
|
prompt_template=prompt_template,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
prompt_attention_mask=negative_prompt_attention_mask,
|
|
device=device,
|
|
max_sequence_length=max_sequence_length,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
|
|
negative_prompt_attention_mask = negative_prompt_attention_mask.to(transformer_dtype)
|
|
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(transformer_dtype)
|
|
|
|
# 4. Prepare timesteps
|
|
sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
|
|
|
|
# 5. Prepare latent variables
|
|
num_channels_latents = self.transformer.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
num_frames,
|
|
torch.float32,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 6. Prepare guidance condition
|
|
guidance = torch.tensor([guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device) * 1000.0
|
|
|
|
# 7. 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
|
|
|
|
self._current_timestep = t
|
|
latent_model_input = latents.to(transformer_dtype)
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latents.shape[0]).to(latents.dtype)
|
|
|
|
noise_pred = self.transformer(
|
|
hidden_states=latent_model_input,
|
|
timestep=timestep,
|
|
encoder_hidden_states=prompt_embeds,
|
|
encoder_attention_mask=prompt_attention_mask,
|
|
pooled_projections=pooled_prompt_embeds,
|
|
guidance=guidance,
|
|
attention_kwargs=attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
if do_true_cfg:
|
|
neg_noise_pred = self.transformer(
|
|
hidden_states=latent_model_input,
|
|
timestep=timestep,
|
|
encoder_hidden_states=negative_prompt_embeds,
|
|
encoder_attention_mask=negative_prompt_attention_mask,
|
|
pooled_projections=negative_pooled_prompt_embeds,
|
|
guidance=guidance,
|
|
attention_kwargs=attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
|
|
else:
|
|
if (i <= zero_steps) and use_zero_init:
|
|
noise_pred = noise_pred*0.
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
self._current_timestep = None
|
|
|
|
if not output_type == "latent":
|
|
latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
|
|
video = self.vae.decode(latents, return_dict=False)[0]
|
|
video = self.video_processor.postprocess_video(video, output_type=output_type)
|
|
else:
|
|
video = latents
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (video,)
|
|
|
|
return HunyuanVideoPipelineOutput(frames=video)
|