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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: masterpiece, bestquality, sunset.

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. I2VGenXLPipeline class diffusers.I2VGenXLPipeline < source > ( 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__ < source > ( prompt: Union = None image: Union = None height: Optional = 704 width: Optional = 1280 target_fps: Optional = 16 num_frames: int = 16 num_inference_steps: int = 50 guidance_scale: float = 9.0 negative_prompt: Union = None eta: float = 0.0 num_videos_per_prompt: Optional = 1 decode_chunk_size: Optional = 1 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None clip_skip: Optional = 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.FloatTensor) —

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.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 is 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 (prompt weighting). If not

provided, text embeddings are generated from the prompt input argument. negative_prompt_embeds (torch.FloatTensor, 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: Copied >>> import torch

>>> from diffusers import I2VGenXLPipeline

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

>>> pipeline.enable_model_cpu_offload()

>>> image_url = "https://github.com/ali-vilab/i2vgen-xl/blob/main/data/test_images/img_0009.png?raw=true"

>>> 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") disable_freeu < source > ( ) Disables the FreeU mechanism if enabled. disable_vae_slicing < source > ( ) Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to

computing decoding in one step. disable_vae_tiling < source > ( ) Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to

computing decoding in one step. enable_freeu < source > ( s1: float s2: float b1: float b2: float ) Parameters s1 (float) —

Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to

mitigate “oversmoothing effect” in the enhanced denoising process. s2 (float) —

Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to

mitigate “oversmoothing effect” in the enhanced denoising process. b1 (float) — Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (float) — Scaling factor for stage 2 to amplify the contributions of backbone features. Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497. The suffixes after the scaling factors represent the stages where they are being applied. Please refer to the official repository for combinations of the values

that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. enable_vae_slicing < source > ( ) Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to

compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. enable_vae_tiling < source > ( ) 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. encode_prompt < source > ( prompt device num_videos_per_prompt negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = 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.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. lora_scale (float, optional) —

A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. 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 < source > ( frames: Union ) Parameters frames (List[np.ndarray] or torch.FloatTensor) —

List of denoised frames (essentially images) as NumPy arrays of shape (height, width, num_channels) or as

a torch tensor. The length of the list denotes the video length (the number of frames). Output class for image-to-video pipeline.

Semantic Guidance Semantic Guidance for Diffusion Models was proposed in SEGA: Instructing Text-to-Image Models using Semantic Guidance and provides strong semantic control over image generation.

Small changes to the text prompt usually result in entirely different output images. However, with SEGA a variety of changes to the image are enabled that can be controlled easily and intuitively, while staying true to the original image composition. The abstract from the paper is: Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user’s intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) generalizes to any generative architecture using classifier-free guidance. More importantly, it allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA’s effectiveness on both latent and pixel-based diffusion models such as Stable Diffusion, Paella, and DeepFloyd-IF using a variety of tasks, thus providing strong evidence for its versatility, flexibility, and improvements over existing methods. 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. SemanticStableDiffusionPipeline class diffusers.SemanticStableDiffusionPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True ) 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) —