#
CogVideoX

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer from Tsinghua University & ZhipuAI, by Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Xiaotao Gu, Yuxuan Zhang, Weihan Wang, Yean Cheng, Ting Liu, Bin Xu, Yuxiao Dong, Jie Tang.

The abstract from the paper is:

We introduce CogVideoX, a large-scale diffusion transformer model designed for generating videos based on text prompts. To efficently model video data, we propose to levearge a 3D Variational Autoencoder (VAE) to compresses videos along both spatial and temporal dimensions. To improve the text-video alignment, we propose an expert transformer with the expert adaptive LayerNorm to facilitate the deep fusion between the two modalities. By employing a progressive training technique, CogVideoX is adept at producing coherent, long-duration videos characterized by significant motion. In addition, we develop an effectively text-video data processing pipeline that includes various data preprocessing strategies and a video captioning method. It significantly helps enhance the performance of CogVideoX, improving both generation quality and semantic alignment. Results show that CogVideoX demonstrates state-of-the-art performance across both multiple machine metrics and human evaluations. The model weight of CogVideoX-2B is publicly available at https://github.com/THUDM/CogVideo.

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

This pipeline was contributed by zRzRzRzRzRzRzR. The original codebase can be found here. The original weights can be found under hf.co/THUDM.

There are two models available that can be used with the CogVideoX pipeline:

- `THUDM/CogVideoX-2b`
- `THUDM/CogVideoX-5b`

##
Inference

Use `torch.compile` to reduce the inference latency.

First, load the pipeline:

    import torch
    from diffusers import CogVideoXPipeline
    from diffusers.utils import export_to_video

    pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b").to("cuda")

Then change the memory layout of the pipelines `transformer` component to `torch.channels_last`:

    pipe.transformer.to(memory_format=torch.channels_last)

Finally, compile the components and run inference:

    pipe.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True)

    # CogVideoX works well with long and well-described prompts
    prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
    video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]

The benchmark results on an 80GB A100 machine are:

    Without torch.compile(): Average inference time: 96.89 seconds.
    With torch.compile(): Average inference time: 76.27 seconds.

###
Memory optimization

CogVideoX-2b requires about 19 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to this script.

- `pipe.enable_model_cpu_offload()`:
  - Without enabling cpu offloading, memory usage is `33 GB`
  - With enabling cpu offloading, memory usage is `19 GB`
- `pipe.enable_sequential_cpu_offload()`:
  - Similar to `enable_model_cpu_offload` but can significantly reduce memory usage at the cost of slow inference
  - When enabled, memory usage is under `4 GB`
- `pipe.vae.enable_tiling()`:
  - With enabling cpu offloading and tiling, memory usage is `11 GB`
- `pipe.vae.enable_slicing()`

###
Quantized inference

torchao and optimum-quanto can be used to quantize the text encoder, transformer and VAE modules to lower the memory requirements. This makes it possible to run the model on a free-tier T4 Colab or lower VRAM GPUs!

It is also worth noting that torchao quantization is fully compatible with torch.compile, which allows for much faster inference speed. Additionally, models can be serialized and stored in a quantized datatype to save disk space with torchao. Find examples and benchmarks in the gists below.

- torchao
- quanto

##
CogVideoXPipeline

###
class diffusers.CogVideoXPipeline

< source >

( tokenizer: T5Tokenizer text_encoder: T5EncoderModel vae: AutoencoderKLCogVideoX transformer: CogVideoXTransformer3DModel scheduler: Union )

Parameters

- vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
- text_encoder (`T5EncoderModel`) — Frozen text-encoder. CogVideoX uses T5; specifically the t5-v1_1-xxl variant.
- tokenizer (`T5Tokenizer`) — Tokenizer of class T5Tokenizer.
- transformer (CogVideoXTransformer3DModel) — A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
- scheduler (SchedulerMixin) — A scheduler to be used in combination with `transformer` to denoise the encoded video latents.

Pipeline for text-to-video generation using CogVideoX.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

####
__call__

< source >

( prompt: Union = None negative_prompt: Union = None height: int = 480 width: int = 720 num_frames: int = 49 num_inference_steps: int = 50 timesteps: Optional = None guidance_scale: float = 6 use_dynamic_cfg: bool = False num_videos_per_prompt: int = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None output_type: str = 'pil' return_dict: bool = True callback_on_step_end: Union = None callback_on_step_end_tensor_inputs: List = ['latents'] max_sequence_length: int = 226 ) →
CogVideoXPipelineOutput or `tuple`

Parameters

- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image. This is set to 1024 by default for the best results.
- width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image. This is set to 1024 by default for the best results.
- num_frames (`int`, defaults to `48`) — Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that needs to be satisfied is that of divisibility mentioned above.
- 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.
- guidance_scale (`float`, optional, defaults to 7.0) — Guidance scale as defined in Classifier-Free Diffusion Guidance. `guidance_scale` is defined as `w` of equation 2. of Imagen Paper. Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
- num_videos_per_prompt (`int`, optional, defaults to 1) — The number of videos to generate per prompt.
- generator (`torch.Generator` or `List[torch.Generator]`, optional) — One or a list of torch generator(s) to make generation deterministic.
- latents (`torch.FloatTensor`, optional) — Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`.
- prompt_embeds (`torch.FloatTensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- 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.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generate image. Choose between PIL: `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput` instead of a plain tuple.
- callback_on_step_end (`Callable`, optional) — A function that calls at the end of each denoising steps during the inference. The function is called 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.
- max_sequence_length (`int`, defaults to `226`) — Maximum sequence length in encoded prompt. Must be consistent with `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.

Returns

CogVideoXPipelineOutput or `tuple`

CogVideoXPipelineOutput if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images.

Function invoked when calling the pipeline for generation.

Examples:

    >>> import torch
    >>> from diffusers import CogVideoXPipeline
    >>> from diffusers.utils import export_to_video

    >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
    >>> pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
    >>> prompt = (
    ...     "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
    ...     "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
    ...     "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
    ...     "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
    ...     "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
    ...     "atmosphere of this unique musical performance."
    ... )
    >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
    >>> export_to_video(video, "output.mp4", fps=8)

####
encode_prompt

< source >

( prompt: Union negative_prompt: Union = None do_classifier_free_guidance: bool = True num_videos_per_prompt: int = 1 prompt_embeds: Optional = None negative_prompt_embeds: Optional = None max_sequence_length: int = 226 device: Optional = None dtype: Optional = None )

Parameters

- prompt (`str` or `List[str]`, optional) — prompt to be encoded
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- do_classifier_free_guidance (`bool`, optional, defaults to `True`) — Whether to use classifier free guidance or not.
- num_videos_per_prompt (`int`, optional, defaults to 1) — Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
- prompt_embeds (`torch.Tensor`, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.Tensor`, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. device — (`torch.device`, optional): torch device dtype — (`torch.dtype`, optional): torch dtype

Encodes the prompt into text encoder hidden states.

####
fuse_qkv_projections

< source >

( )

Enables fused QKV projections.

####
unfuse_qkv_projections

< source >

( )

Disable QKV projection fusion if enabled.

##
CogVideoXPipelineOutput

###
class diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput

< source >

( frames: Tensor )

Parameters

- frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]) — List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`.

Output class for CogVideo pipelines.

< > Update on GitHub