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I2VGen-XL

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I2VGen-XL

I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models by Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, and Jingren Zhou.

The abstract from the paper is:

Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accuracy, clarity and spatio-temporal continuity. They primarily arise from the scarcity of well-aligned text-video data and the complex inherent structure of videos, making it difficult for the model to simultaneously ensure semantic and qualitative excellence. In this report, we propose a cascaded I2VGen-XL approach that enhances model performance by decoupling these two factors and ensures the alignment of the input data by utilizing static images as a form of crucial guidance. I2VGen-XL consists of two stages: i) the base stage guarantees coherent semantics and preserves content from input images by using two hierarchical encoders, and ii) the refinement stage enhances the video’s details by incorporating an additional brief text and improves the resolution to 1280×720. To improve the diversity, we collect around 35 million single-shot text-video pairs and 6 billion text-image pairs to optimize the model. By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos. Through extensive experiments, we have investigated the underlying principles of I2VGen-XL and compared it with current top methods, which can demonstrate its effectiveness on diverse data. The source code and models will be publicly available at this https URL.

The original codebase can be found here. The model checkpoints can be found here.

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. Also, to know more about reducing the memory usage of this pipeline, refer to the [“Reduce memory usage”] section here.

Sample output with I2VGenXL:

library.
library

Notes

  • I2VGenXL always uses a clip_skip value of 1. This means it leverages the penultimate layer representations from the text encoder of CLIP.
  • It can generate videos of quality that is often on par with Stable Video Diffusion (SVD).
  • Unlike SVD, it additionally accepts text prompts as inputs.
  • It can generate higher resolution videos.
  • When using the DDIMScheduler (which is default for this pipeline), less than 50 steps for inference leads to bad results.
  • This implementation is 1-stage variant of I2VGenXL. The main figure in the I2VGen-XL paper shows a 2-stage variant, however, 1-stage variant works well. See this discussion for more details.

I2VGenXLPipeline

class diffusers.I2VGenXLPipeline

< >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer image_encoder: CLIPVisionModelWithProjection feature_extractor: CLIPImageProcessor unet: I2VGenXLUNet scheduler: DDIMScheduler )

Parameters

  • vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
  • text_encoder (CLIPTextModel) — Frozen text-encoder (clip-vit-large-patch14).
  • tokenizer (CLIPTokenizer) — A CLIPTokenizer to tokenize text.
  • unet (I2VGenXLUNet) — A I2VGenXLUNet to denoise the encoded video latents.
  • scheduler (DDIMScheduler) — A scheduler to be used in combination with unet to denoise the encoded image latents.

Pipeline for image-to-video generation as proposed in I2VGenXL.

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

__call__

< >

( prompt: typing.Union[str, typing.List[str]] = None image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] = None height: typing.Optional[int] = 704 width: typing.Optional[int] = 1280 target_fps: typing.Optional[int] = 16 num_frames: int = 16 num_inference_steps: int = 50 guidance_scale: float = 9.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None eta: float = 0.0 num_videos_per_prompt: typing.Optional[int] = 1 decode_chunk_size: typing.Optional[int] = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True cross_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None clip_skip: typing.Optional[int] = 1 ) → pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput or tuple

Parameters

  • prompt (str or List[str], optional) — The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds.
  • image (PIL.Image.Image or List[PIL.Image.Image] or torch.Tensor) — Image or images to guide image generation. If you provide a tensor, it needs to be compatible with CLIPImageProcessor.
  • height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The height in pixels of the generated image.
  • width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — The width in pixels of the generated image.
  • target_fps (int, optional) — Frames per second. The rate at which the generated images shall be exported to a video after generation. This is also used as a “micro-condition” while generation.
  • num_frames (int, optional) — The number of video frames to generate.
  • num_inference_steps (int, optional) — The number of denoising steps.
  • guidance_scale (float, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the text prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1.
  • negative_prompt (str or List[str], optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1).
  • eta (float, optional) — Corresponds to parameter eta (η) from the DDIM paper. Only applies to the DDIMScheduler, and is ignored in other schedulers.
  • num_videos_per_prompt (int, optional) — The number of images to generate per prompt.
  • decode_chunk_size (int, optional) — The number of frames to decode at a time. The higher the chunk size, the higher the temporal consistency between frames, but also the higher the memory consumption. By default, the decoder will decode all frames at once for maximal quality. Reduce decode_chunk_size to reduce memory usage.
  • generator (torch.Generator or List[torch.Generator], optional) — A torch.Generator to make generation deterministic.
  • latents (torch.Tensor, optional) — Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random generator.
  • prompt_embeds (torch.Tensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the prompt input argument.
  • negative_prompt_embeds (torch.Tensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, negative_prompt_embeds are generated from the negative_prompt input argument.
  • output_type (str, optional, defaults to "pil") — The output format of the generated image. Choose between PIL.Image or np.array.
  • return_dict (bool, optional, defaults to True) — Whether or not to return a StableDiffusionPipelineOutput instead of a plain tuple.
  • cross_attention_kwargs (dict, optional) — A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in self.processor.
  • 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.

Returns

pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput or tuple

If return_dict is True, pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput is returned, otherwise a tuple is returned where the first element is a list with the generated frames.

The call function to the pipeline for image-to-video generation with I2VGenXLPipeline.

Examples:

>>> import torch
>>> from diffusers import I2VGenXLPipeline
>>> from diffusers.utils import export_to_gif, load_image

>>> pipeline = I2VGenXLPipeline.from_pretrained(
...     "ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16"
... )
>>> pipeline.enable_model_cpu_offload()

>>> image_url = (
...     "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/i2vgen_xl_images/img_0009.png"
... )
>>> image = load_image(image_url).convert("RGB")

>>> prompt = "Papers were floating in the air on a table in the library"
>>> negative_prompt = "Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms"
>>> generator = torch.manual_seed(8888)

>>> frames = pipeline(
...     prompt=prompt,
...     image=image,
...     num_inference_steps=50,
...     negative_prompt=negative_prompt,
...     guidance_scale=9.0,
...     generator=generator,
... ).frames[0]
>>> video_path = export_to_gif(frames, "i2v.gif")

encode_prompt

< >

( prompt device num_videos_per_prompt negative_prompt = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None clip_skip: typing.Optional[int] = None )

Parameters

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

Encodes the prompt into text encoder hidden states.

I2VGenXLPipelineOutput

class diffusers.pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput

< >

( frames: typing.Union[torch.Tensor, numpy.ndarray, typing.List[typing.List[PIL.Image.Image]]] )

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

Output class for image-to-video pipeline.

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)

< > Update on GitHub

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