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Text2Video-Zero

A parameter-efficient framework for converting text to videos without training.

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Text2Video-Zero: Zero-Shot AI Video Generation from Text-to-Image Models

Introduction

Text2Video-Zero is an innovative open-source project developed by Picsart AI Research (PAIR) that enables zero-shot video generation and editing by leveraging the capabilities of existing pre-trained text-to-image diffusion models, such as Stable Diffusion. Its core contribution is a low-cost approach that adapts these powerful image models for the video domain without requiring any task-specific training on video datasets. This means it can generate or modify video clips based on textual prompts by intelligently manipulating latent codes and attention mechanisms across frames.

The project, available on GitHub, is primarily targeted at researchers, developers, and AI enthusiasts interested in the mechanics of video synthesis and exploring novel ways to create and edit video content using the strong priors learned by large-scale text-to-image models. It emphasizes privacy by design, as all processing can occur locally if the underlying text-to-image models are run locally.

Key Features

Text2Video-Zero offers a unique set of features for zero-shot video manipulation:

  • Zero-Shot Text-to-Video Generation:
    • Synthesizes short video clips directly from textual prompts by adapting pre-trained text-to-image models.
    • Does not require training on large video datasets, making it computationally less expensive than many traditional video generation models.
  • Text-Guided Video Editing (Video Instruct-Pix2Pix):
    • Allows users to edit existing videos based on textual instructions (e.g., "make it look like it's sunset time," "make the sand red," "make it in the style of a cartoon"). This builds upon the InstructPix2Pix methodology.
  • Leverages Pre-trained Text-to-Image Models:
    • Designed to work on top of powerful, existing text-to-image diffusion models like Stable Diffusion (e.g., Stable Diffusion v1.5). The quality and capabilities of the underlying image model significantly influence the video output.
  • Motion Dynamics & Temporal Consistency:
    • Latent Code Enrichment: Modifies the latent codes of generated frames to introduce motion dynamics while aiming to keep the global scene and background temporally consistent.
    • Cross-Frame Attention Reprogramming: Introduces a new cross-frame attention mechanism where each frame's self-attention is reprogrammed to attend to the first frame. This helps preserve the context, appearance, and identity of foreground objects across the video sequence.
  • Conditional Video Generation (Guidance):
    • Pose Control: Generate videos guided by a sequence of poses (e.g., OpenPose skeletons).
    • Edge Control (Canny Edges): Generate videos guided by edge maps, allowing for structural control over the generated content.
    • Depth Control: Utilize depth maps to guide video generation, influencing the 3D structure and perspective.
    • Dreambooth Specialization: Can be combined with Dreambooth-finetuned text-to-image models to generate videos featuring specific subjects or styles learned by Dreambooth.
  • Control Over Generation Parameters:
    • Motion Field Strength (motion_field_strength_x, motion_field_strength_y): Control the intensity of generated motion.
    • Latent Interpolation Timesteps (t0, t1): Parameters that influence how latent codes are manipulated between frames, affecting motion and consistency.
    • Video Length (video_length): Specify the number of frames to generate (typically short clips, e.g., 8 frames by default, can be extended by chunking).
    • Chunk Size (chunk_size): For generating longer videos in a chunk-by-chunk manner to manage memory.
    • Standard diffusion model parameters (inherited from the base T2I model) like prompt, negative prompt, guidance scale, steps, seed.
  • Open Source Code & Method: The research and Python-based codebase are publicly available, encouraging experimentation and further development by the community.
  • API & UI (Primarily for Research/Demo):
    • The GitHub repository provides inference scripts and example commands.
    • A Hugging Face Spaces demo was released for interactive testing.
    • While not a polished commercial product UI, the codebase can be integrated into other systems.

Specific Use Cases

Text2Video-Zero is primarily a research project and tool for developers/enthusiasts, with use cases including:

  • Research in Zero-Shot Video Generation: Exploring and advancing techniques for generating video content without direct video training data.
  • Creative Video Synthesis & Experimentation: Creating short, artistic video clips from text prompts or by stylizing existing footage.
  • Text-Based Video Editing & Stylization: Applying stylistic changes or content modifications to videos using natural language instructions.
  • Rapid Prototyping of Video Concepts: Quickly visualizing short animated sequences or motion ideas.
  • Generating Video Content with Specialized Styles: Leveraging Dreambooth or other fine-tuned text-to-image models to create videos with specific characters or artistic styles.
  • Understanding Cross-Modal AI: Investigating how capabilities from text-to-image models can be transferred and adapted to the video domain.
  • Low-Resource Video Generation: Creating video content when large-scale video datasets or extensive video-specific model training are not feasible.

Usage Guide

Using Text2Video-Zero typically involves setting up a Python environment, downloading pre-trained text-to-image models, and running the provided scripts:

  1. Prerequisites & Setup:

    • Python: Version 3.9 is often recommended.
    • CUDA: NVIDIA CUDA >= 11.6 is typically required for GPU acceleration.
    • Hardware: A GPU with a minimum of 12GB VRAM is stated as sufficient, and this can potentially be reduced by adjusting the chunk_size parameter for generating longer videos. Performance is significantly better on more powerful GPUs.
    • Clone the Repository: 
      git clone [https://github.com/Picsart-AI-Research/Text2Video-Zero.git](https://github.com/Picsart-AI-Research/Text2Video-Zero.git)
cd Text2Video-Zero
    • Install Dependencies: Create a suitable Python environment (e.g., using Conda or venv) and install the required packages, usually listed in a requirements.txt file. This will include PyTorch, diffusers, transformers, and other libraries. 
      pip install -r requirements.txt

  2. Download Pre-trained Models:

    • Text2Video-Zero relies on pre-trained Stable Diffusion models (or other compatible text-to-image diffusion models). You will need to download the checkpoint files (e.g., Stable Diffusion v1.5) and place them in the appropriate directory or ensure they are accessible by your environment (e.g., via Hugging Face model identifiers).

  3. Running Inference Scripts:

    • The GitHub repository provides example Python scripts or command-line interfaces for various tasks.
    • Text-to-Video Generation:
      • Run a script (e.g., run_t2v.py or similar) with your text prompt and parameters.
      • Example (conceptual, refer to actual scripts in the repo): 
        python run_t2v.py --prompt "A panda playing guitar on Times Square" --model_id "runwayml/stable-diffusion-v1-5" --output_path "output_video.mp4"
    • Video Instruct-Pix2Pix (Video Editing):
      • Provide an input video and an editing instruction (text prompt).
      • Example (conceptual): 
        python run_vid2vid_instruct.py --input_video "path/to/camel.mp4" --edit_prompt "Make it look like it's sunset time" --model_id "timbrooks/instruct-pix2pix" --output_path "edited_video.mp4"
    • Conditional Generation (Pose, Edge, Depth):
      • Provide an input video or sequence for pose/edge/depth extraction, along with a text prompt.
      • Run the relevant script (e.g., run_pose_guided_t2v.py).
    • Parameters: Adjust parameters in the scripts or command line, such as:
      • prompt: Your text description.
      • video_length: Number of frames to generate.
      • motion_field_strength_x, motion_field_strength_y: Control motion intensity.
      • t0, t1: Latent interpolation timesteps.
      • chunk_size: For processing longer videos in chunks to save memory.
      • Path to base model, ControlNet models (if used), Dreambooth models (if used).

  4. Using with Hugging Face Diffusers Library:

    • Text2Video-Zero functionalities are also integrated or can be implemented using the Hugging Face diffusers library, which provides a TextToVideoZeroPipeline.
    from diffusers import TextToVideoZeroPipeline
import torch

model_id = "runwayml/stable-diffusion-v1-5" # Example base model
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A panda is playing guitar on Times Square"
result_frames = pipe(prompt=prompt).images # result_frames will be a list of PIL Images
# Process frames into a video (e.g., using imageio)

Pricing & Plans

Text2Video-Zero is an open-source research project released by Picsart AI Research.

  • The software and methods described are free to use under the terms of its license (CreativeML Open RAIL-M).
  • Costs are primarily associated with:
    • Your own hardware: Especially a capable GPU with sufficient VRAM (12GB+ recommended).
    • Electricity consumption.
    • Time for setup and experimentation.
  • There are no subscription fees for using the Text2Video-Zero codebase itself.

License

Text2Video-Zero is published under the CreativeML Open RAIL-M license. This license is designed for open and responsible AI development. Key aspects often include:

  • Permissive Use: Allows for copying, distribution, and modification of the software.
  • Use-Based Restrictions: Crucially, it includes restrictions on how the model and its outputs can be used, aiming to prevent harmful applications (e.g., generating illegal content, hate speech, misinformation, exploiting vulnerable groups).
  • Attribution: May require attribution depending on the context.

Users must review the full CreativeML Open RAIL-M license text available in the GitHub repository to understand all terms, conditions, and restrictions before using the software or models derived from it, especially for any public or commercial-facing applications.

Frequently Asked Questions (FAQ)

Q1: What is Text2Video-Zero? A1: Text2Video-Zero is an open-source AI method and codebase from Picsart AI Research that enables the generation and editing of short video clips from text prompts by leveraging pre-trained text-to-image diffusion models (like Stable Diffusion) without requiring specific video training data.

Q2: How does "zero-shot" video generation work in Text2Video-Zero? A2: It "adapts" existing text-to-image models for video tasks. Key techniques include enriching the latent codes of generated frames with motion dynamics (to ensure temporal consistency in background/scene) and reprogramming frame-level self-attention to a cross-frame attention mechanism (focusing on the first frame to maintain object appearance and context across the sequence).

Q3: Do I need to train a new model to use Text2Video-Zero? A3: No, the "zero-shot" aspect means it's designed to work with existing, pre-trained text-to-image diffusion models (like Stable Diffusion v1.5) without needing to fine-tune them on video data for basic text-to-video generation or Video Instruct-Pix2Pix editing.

Q4: What kind of videos can Text2Video-Zero create? A4: It can generate short video clips (typically a few seconds, e.g., 8 frames by default, extendable by chunking) from text prompts. It also supports text-guided video editing (Video Instruct-Pix2Pix) and conditional generation using poses, edges, or depth maps.

Q5: What hardware is required to run Text2Video-Zero? A5: A GPU with at least 12GB of VRAM is recommended for reasonable performance. The system can be configured to use less VRAM by processing videos in smaller chunks (chunk_size parameter), but this will increase generation time.

Q6: Is Text2Video-Zero free? A6: Yes, the code and research are open-source, released under the CreativeML Open RAIL-M license, making it free to use and modify according to the license terms.

Q7: Can I use Text2Video-Zero for commercial purposes? A7: The CreativeML Open RAIL-M license has specific use-based restrictions aimed at preventing harmful applications. While it might not explicitly forbid all commercial uses, users must carefully review the license to ensure their intended application complies with all its clauses and restrictions. The license often prioritizes responsible and ethical use over unrestricted commercial exploitation.

Q8: Where can I find the research paper for Text2Video-Zero? *A8: The paper is titled "Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators." It is typically available on arXiv and linked from the project's GitHub page or official project website. * Example arXiv link (check GitHub for the definitive one): https://arxiv.org/abs/2303.13439 (This is the typical format; the search results confirm similar papers).

Related Blog Articles & Tutorials

Here are examples of resources and discussions that can help you understand and use Text2Video-Zero:

Ethical Considerations & Safety

  • Zero-Shot Nature & Pre-trained Model Biases: Since Text2Video-Zero leverages existing text-to-image models like Stable Diffusion, it can inherit any biases, limitations, or safety concerns present in those base models (e.g., generating unrealistic faces, hands, or content reflecting societal biases from training data).
  • Misuse Potential: Like any powerful generative AI, the technology could potentially be misused for creating misleading or harmful content. The CreativeML Open RAIL-M license aims to restrict such uses.
  • Content Generation Limitations: The quality and coherence of the generated videos are highly dependent on the underlying text-to-image model and the effectiveness of Text2Video-Zero's motion and consistency techniques. Outputs are typically short and may not always be perfectly realistic or temporally smooth for complex scenes.
  • User Responsibility: Users are responsible for the content they generate and ensuring its use aligns with the model's license and ethical AI principles. The project is intended for research purposes.

Last updated: May 16, 2025

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