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 text-to-video and video-to-video CogVideoX pipelines:
THUDM/CogVideoX-2b
: The recommended dtype for running this model is fp16
.THUDM/CogVideoX-5b
: The recommended dtype for running this model is bf16
.There is one model available that can be used with the image-to-video CogVideoX pipeline:
THUDM/CogVideoX-5b-I2V
: The recommended dtype for running this model is bf16
.Use torch.compile
to reduce the inference latency.
First, load the pipeline:
import torch
from diffusers import CogVideoXPipeline, CogVideoXImageToVideoPipeline
from diffusers.utils import export_to_video,load_image
pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b").to("cuda") # or "THUDM/CogVideoX-2b"
If you are using the image-to-video pipeline, load it as follows:
pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V").to("cuda")
Then change the memory layout of the pipelines transformer
component to torch.channels_last
:
pipe.transformer.to(memory_format=torch.channels_last)
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 T2V 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.
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()
:33 GB
19 GB
pipe.enable_sequential_cpu_offload()
:enable_model_cpu_offload
but can significantly reduce memory usage at the cost of slow inference4 GB
pipe.vae.enable_tiling()
:11 GB
pipe.vae.enable_slicing()
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.
( tokenizer: T5Tokenizer text_encoder: T5EncoderModel vae: AutoencoderKLCogVideoX transformer: CogVideoXTransformer3DModel scheduler: Union )
Parameters
T5EncoderModel
) —
Frozen text-encoder. CogVideoX uses
T5; specifically the
t5-v1_1-xxl variant. T5Tokenizer
) —
Tokenizer of class
T5Tokenizer. CogVideoXTransformer3DModel
to denoise the encoded video latents. 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.)
( 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 attention_kwargs: Optional = None callback_on_step_end: Union = None callback_on_step_end_tensor_inputs: List = ['latents'] max_sequence_length: int = 226 ) → CogVideoXPipelineOutput or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. 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
). 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. 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. 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. 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. 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. 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. int
, optional, defaults to 1) —
The number of videos to generate per prompt. torch.Generator
or List[torch.Generator]
, optional) —
One or a list of torch generator(s)
to make generation deterministic. 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
. 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. 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. str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
instead
of a plain tuple. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined under
self.processor
in
diffusers.models.attention_processor. 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
. 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. 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)
( 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
str
or List[str]
, optional) —
prompt to be encoded 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
). bool
, optional, defaults to True
) —
Whether to use classifier free guidance or not. int
, optional, defaults to 1) —
Number of videos that should be generated per prompt. torch device to place the resulting embeddings on 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. 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.
Enables fused QKV projections.
Disable QKV projection fusion if enabled.
( tokenizer: T5Tokenizer text_encoder: T5EncoderModel vae: AutoencoderKLCogVideoX transformer: CogVideoXTransformer3DModel scheduler: Union )
Parameters
T5EncoderModel
) —
Frozen text-encoder. CogVideoX uses
T5; specifically the
t5-v1_1-xxl variant. T5Tokenizer
) —
Tokenizer of class
T5Tokenizer. CogVideoXTransformer3DModel
to denoise the encoded video latents. transformer
to denoise the encoded video latents. Pipeline for image-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.)
( image: Union 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
PipelineImageInput
) —
The input video to condition the generation on. Must be an image, a list of images or a torch.Tensor
. str
or List[str]
, optional) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. 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
). 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. 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. 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. 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. 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. 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. int
, optional, defaults to 1) —
The number of videos to generate per prompt. torch.Generator
or List[torch.Generator]
, optional) —
One or a list of torch generator(s)
to make generation deterministic. 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
. 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. 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. str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
instead
of a plain tuple. 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
. 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. 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 CogVideoXImageToVideoPipeline
>>> from diffusers.utils import export_to_video, load_image
>>> pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V", torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")
>>> prompt = "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
>>> image = load_image(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
... )
>>> video = pipe(image, prompt, use_dynamic_cfg=True)
>>> export_to_video(video.frames[0], "output.mp4", fps=8)
( 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
str
or List[str]
, optional) —
prompt to be encoded 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
). bool
, optional, defaults to True
) —
Whether to use classifier free guidance or not. int
, optional, defaults to 1) —
Number of videos that should be generated per prompt. torch device to place the resulting embeddings on 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. 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.
Enables fused QKV projections.
Disable QKV projection fusion if enabled.
( tokenizer: T5Tokenizer text_encoder: T5EncoderModel vae: AutoencoderKLCogVideoX transformer: CogVideoXTransformer3DModel scheduler: Union )
Parameters
T5EncoderModel
) —
Frozen text-encoder. CogVideoX uses
T5; specifically the
t5-v1_1-xxl variant. T5Tokenizer
) —
Tokenizer of class
T5Tokenizer. CogVideoXTransformer3DModel
to denoise the encoded video latents. transformer
to denoise the encoded video latents. Pipeline for video-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.)
( video: List = None prompt: Union = None negative_prompt: Union = None height: int = 480 width: int = 720 num_inference_steps: int = 50 timesteps: Optional = None strength: float = 0.8 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 attention_kwargs: Optional = None callback_on_step_end: Union = None callback_on_step_end_tensor_inputs: List = ['latents'] max_sequence_length: int = 226 ) → CogVideoXPipelineOutput or tuple
Parameters
List[PIL.Image.Image]
) —
The input video to condition the generation on. Must be a list of images/frames of the video. str
or List[str]
, optional) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. 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
). 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. 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. 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. 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. float
, optional, defaults to 0.8) —
Higher strength leads to more differences between original video and generated video. 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. int
, optional, defaults to 1) —
The number of videos to generate per prompt. torch.Generator
or List[torch.Generator]
, optional) —
One or a list of torch generator(s)
to make generation deterministic. 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
. 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. 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. str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput
instead
of a plain tuple. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined under
self.processor
in
diffusers.models.attention_processor. 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
. 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. 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 CogVideoXDPMScheduler, CogVideoXVideoToVideoPipeline
>>> from diffusers.utils import export_to_video, load_video
>>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
>>> pipe = CogVideoXVideoToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")
>>> pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config)
>>> input_video = load_video(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hiker.mp4"
... )
>>> prompt = (
... "An astronaut stands triumphantly at the peak of a towering mountain. Panorama of rugged peaks and "
... "valleys. Very futuristic vibe and animated aesthetic. Highlights of purple and golden colors in "
... "the scene. The sky is looks like an animated/cartoonish dream of galaxies, nebulae, stars, planets, "
... "moons, but the remainder of the scene is mostly realistic."
... )
>>> video = pipe(
... video=input_video, prompt=prompt, strength=0.8, guidance_scale=6, num_inference_steps=50
... ).frames[0]
>>> export_to_video(video, "output.mp4", fps=8)
( 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
str
or List[str]
, optional) —
prompt to be encoded 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
). bool
, optional, defaults to True
) —
Whether to use classifier free guidance or not. int
, optional, defaults to 1) —
Number of videos that should be generated per prompt. torch device to place the resulting embeddings on 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. 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.
Enables fused QKV projections.
Disable QKV projection fusion if enabled.
( frames: Tensor )
Parameters
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.