Framepack : groundbreaking video generation technology whose model size is only 0.13B parameters.

Community Article Published April 19, 2025

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Key Points

FramePack likely refers to an AI technology for video generation, enabling efficient long video creation on consumer hardware.
It may also refer to bike frame packs for carrying gear, but the AI context seems more relevant here.
Research suggests FramePack compresses input contexts, making video generation workload independent of length.

What is FramePack?

FramePack appears to be primarily an innovative neural network for video generation, developed by researchers from Stanford University. It uses next-frame prediction to create videos progressively, compressing input contexts to keep computational workload constant regardless of video length. This makes it possible to generate long videos on consumer-grade GPUs with minimal VRAM, such as 6GB.

Key Features

Processes thousands of frames with large models (13B parameters) on laptop GPUs.
Requires only 6GB VRAM for a 1-minute, 30fps video.
Supports training with batch sizes similar to image diffusion, enhancing efficiency.
Offers generation speeds of 1.5–2.5 seconds per frame on high-end GPUs.
Includes a user-friendly GUI for uploading images and writing prompts.

Additional Context

While the AI technology is the focus, "framepack" can also mean bike frame packs—bags attached to bicycle frames for bikepacking. Given the query, the AI technology seems more likely, but both interpretations are valid depending on context.

Survey Note: Detailed Analysis of FramePack

This section provides a comprehensive exploration of "framepack," examining its primary interpretations and the evidence supporting each, with a focus on the AI technology for video generation, given its prominence in recent research and relevance to current technological trends. The analysis is informed by web searches conducted on April 18, 2025, reflecting the latest available information.

Overview and Primary Interpretation

The term "framepack" most likely refers to FramePack, a next-frame prediction neural network structure designed for video generation, as detailed in recent publications and project pages. Developed by Lvmin Zhang and Maneesh Agrawala from Stanford University, FramePack was introduced in a 2025 Arxiv paper titled "Packing Input Frame Contexts in Next-Frame Prediction Models for Video Generation" (Packing Input Frame Contexts in Next-Frame Prediction Models for Video Generation). This technology compresses input contexts to a constant length, making the generation workload invariant to video length, which is a significant advancement for accessibility on consumer hardware.

The project page (FramePack Project Page) highlights its ability to diffuse thousands of frames at 30fps with 13B models using only 6GB of laptop GPU memory, and it supports finetuning at batch size 64 on a single 8xA100/H100 node for personal or lab experiments. Generation speeds are noted at 2.5 seconds per frame unoptimized, or 1.5 seconds with teacache on an RTX 4090, without requiring timestep distillation. This makes video diffusion feel akin to image diffusion, a notable simplification for users.

Key Features and Technical Details

FramePack's technical specifications include:

Efficiency: It processes numerous frames with 13B parameter models even on laptop GPUs, a feat enabled by compressing input contexts to a fixed GPU layout. For instance, a 480p frame might be encoded into 1536 tokens with a (1, 2, 2) patchifying kernel, or 192 tokens with a (2, 4, 4) kernel, allowing flexible resource allocation to "more important" frames, such as the nearest frame to the prediction target (F0).
Memory Usage: It requires only 6GB VRAM for generating a 60-second, 30fps video (1800 frames) with a 13B model, making it accessible for budget GPUs.
Training: Supports training with batch sizes similar to image diffusion, such as batch size 64, which is practical for researchers and developers.
Generation Speed: On an RTX 4090, it achieves 2.5 seconds per frame unoptimized, or 1.5 seconds with teacache, and is 4x to 8x slower on RTX 3070ti or 3060 laptops, providing real-time feedback during generation.
User Interface: Offers a GUI with features like uploading images, writing prompts, and viewing generated videos and latent previews, enhancing usability for creators and researchers.

The technology addresses common video generation challenges, such as drifting (quality degradation over time, also called error accumulation or exposure bias), by using FramePack Scheduling for different compression patterns. All schedulings are O(1) in complexity, contrasting with O(nlogn) or O(n) methods, making it efficient for streaming applications. The project page notes detailed evaluations in the paper, suggesting robust empirical support.

Significance and Applications

FramePack's low hardware barrier is transformative, ushering in a "consumer GPU era" for video generation, as noted in an article from AIbase (FramePack: Revolutionary Video Diffusion Technology - Only 6GB VRAM, 1.5 Seconds/Frame). It allows creators to generate minutes-long, high-quality diffusion videos for platforms like YouTube and TikTok, prototype storyboards, and iterate dynamic ads without costly render farms. Its modular Python codebase, supported by libraries like PyTorch, Xformers, and Flash-Attn, is extensible for researchers (FramePack – Practical Video Diffusion on Consumer GPUs).

Alternative Interpretation: Bike Frame Packs

While the AI technology is the primary focus, "framepack" also refers to bike frame packs, bags designed for bikepacking and attached within the bicycle frame's triangle. Websites like Apidura (Bike Frame Bags - Waterproof Packs for Bikepacking & Cycling) and Ortlieb (Frame-Pack) offer products with capacities ranging from 3L to 6.5L, used for storing essentials like tools and food, maintaining a low center of gravity for better bike handling. These are detailed in cycling resources like BIKEPACKING.com (Frame Packs Archives - BIKEPACKING.com), which describe their use for full suspension, hardtail, and rigid bikes, often custom-fitted or universally sized.

Given the query's brevity and the context of an AI assistant, the AI technology interpretation is more likely, but the bike context is noted for completeness. The decision to prioritize the technology is supported by recent GitHub activity (GitHub - lllyasviel/FramePack: Lets make video diffusion practical!), published on April 16, 2025, indicating active development and relevance.

Comparative Analysis and Decision

To ensure accuracy, additional searches for "framepack in video technology" and "frame packing in video" were conducted. The former reinforced the AI technology, with sources like ComfyUI Wiki (FramePack: Efficient Next-Frame Prediction Model for Video Generation | ComfyUI Wiki) and framepack.video (FramePack - Revolutionary Video Generation Technology) emphasizing its innovation. The latter revealed "frame packing" as a 3D video technique, combining left and right eye frames, but this is typically written as two words, contrasting with the single-word "framepack" in the query (What is Frame Packing 3D? – 3D Frame Packing Explained).

Given the single word and AI context, FramePack for video generation is the most plausible interpretation. However, the bike frame pack possibility is acknowledged, with resources like Apidura providing detailed product information for those contexts.

Tables for Clarity

Below is a table summarizing FramePack's technical specifications for video generation:

Feature	Details
VRAM Requirement	6GB for 60s, 30fps video with 13B model
Generation Speed (RTX 4090)	2.5s/frame (unoptimized), 1.5s/frame (teacache)
Batch Size for Training	Up to 64, similar to image diffusion
Model Size	Supports 13B parameters on laptop GPUs
Complexity	O(1) for streaming, not O(nlogn) or O(n)

And a table for bike frame pack capacities from Apidura, for completeness:

Capacity	Type	Weight	Width
3L	compact	145 g	6 cm
4.5L	compact	170 g	6 cm
5.3L	compact	200 g	6 cm
5L	tall	180 g	6 cm
6.5L	tall	210 g	6 cm

Conclusion

The evidence leans toward FramePack being the AI video generation technology, given its recent development, relevance to AI contexts, and alignment with the query's form. It offers significant advancements in accessibility and efficiency, with detailed support from project pages and papers. The bike frame pack interpretation is noted but less likely, with resources available for further exploration if needed.

Key Citations

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