FramePack is an open source video diffusion model developed by Lvmin Zhang's team (Stanford University). Based on the next-frame prediction architecture, 30fps video of up to 60 seconds can be generated on a notebook GPU with 6GB of video memory.

FramePack

Core parameters and statistics

Project	Specifications
Product Name	FramePack
Category	AI video generation
Delivery form	Desktop tool (Gradio GUI)/CLI
Supported platforms	Linux, Windows
Supported languages	en
Target users	Content creators, AI researchers, developers
User scale	GitHub 17.1k Stars
Pricing model	Free (Apache-2.0 open source)

Platform coverage and user scale data are based on the official real-time page and third-party statistics.

User and market recognition

With 17.1k Stars on GitHub, FramePack is one of the high-profile open source video generation projects of 2025. The paper "Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models" has been accepted by NeurIPS 2025. The community has 1.7k Forks, 456 Issues, 143 Watchers, and active community discussions. Several YouTubers and independent creators have posted installation and usage tutorials. Due to the characteristics of "running long videos with 6GB of video memory", it is recommended by a large number of low-configuration GPU users in communities such as Reddit.

Cost advantage

Cost Dimension	Description
Software License	Apache-2.0 open source, completely free
Model weights	Automatically downloaded from HuggingFace (approximately 30GB+), free
Hardware Cost	Minimum RTX 3060 6GB laptop to run, no cloud GPU required
Commercial use	Apache-2.0 license, free to use and modify

Compared with commercial tools such as Runway ($15/month) and Pika ($10/month), FramePack has no subscription fees and API billing, and the marginal usage cost is close to zero. There is zero additional cost for users who already have an RTX 30XX/40XX/50XX series GPU.

Main functions

• Image-to-Video: Upload a still picture and write a motion prompt word, automatically generate a 5-60 second continuous animation video, and output a 30fps MP4 file. The motion prompt words recommend the structure of "Main → Action → Details", which has the best effect on large-scale dynamics such as dancing and jumping.
• Next frame/next segment progressive generation: The video is generated segment by segment, and the latent preview can be viewed in real time in the GUI. You can preview it after the first segment is generated, without waiting for the entire video to be completed.
• TeaCache acceleration and quantification support: Built-in inference acceleration (can increase speed by about 40%), supports xformers, flash-attn, sage-attention and other attention implementation backends, and supports bf16 and fp8 quantization.
• Anti-drifting sampling: An original two-way sampling strategy solves the "forgetting-drift" problem in long video generation by establishing endpoints early, adjusting the sampling order, and discretizing historical representation.
• Gradio Web GUI: Friendly graphical interface, supports image upload, prompt word input, parameter adjustment, and supports the --share parameter to generate public network sharing links.

Model and version evolution

Version	Date	Key Changes
Windows One-Click Package	2025-04-18	Windows One-Click Installation Package, lowering the installation threshold
Initial Release (v0.1)	2025-04-17	Basic next-frame prediction, 13B HY model

The community extended version FramePack-F1 (2025-05-03) and the next-generation preview version FramePack-P1 (2025-06-26) have also been released.

Technical advantages

• Frame Context Packing: Core innovation - perform importance weighting compression on input frames, and allocate more token resources to important frames. O(1) computational complexity does not increase with the length of the video, and 6GB of video memory can handle thousands of frame contexts.
• Anti-drifting bidirectional sampling: Three plug-and-play methods (early establishment of endpoints, adjustment of sampling order, discrete history representation) solve the error accumulation problem of the next-frame model, and the 60-second video quality is stable and does not degrade.
• High training throughput: O(1) complexity enables a single 8×A100/H100 node to fine-tune a 13B video model with batch size 64.
• Modular Python code base: Built on PyTorch, it can easily replace components such as attention implementation, quantization scheme, and sampling strategy.

How to use

Entrance	How to use
Windows one-click package	Download framepack_cu126_torch26.7z → Unzip → Run run.bat → Browser operation
Linux/manual installation	git clone → pip install → python demo_gradio.py

The first run will automatically download model weights (approximately 30GB+) from HuggingFace. The prompt word recommends using the ChatGPT template to generate motion descriptions.

Product Pricing

Package	Price	Contents
Software download	Free	Full functionality (Apache-2.0)
Model weights	Free	Automatically downloaded from HuggingFace
Commercial use	Free	Apache-2.0 License

Hidden costs: NVIDIA GPU required (minimum 6GB VRAM), model weight approximately 30GB+ storage space, first installation requires network download.

Application scenarios

• Material generation for content creators: Convert the AI ​​pictures generated by Midjourney into 5-15 second animation clips for short video backgrounds, transitions or opening animations, reducing the production time of a single clip from more than 30 minutes to 2-3 minutes.
• Advertising and Marketing Dynamic Visualization: Quickly generate a dynamic preview (storyboard animatic) of the product concept map, and complete the comparison and output of multiple versions within 15 minutes by a single person.
• Video Diffusion Experiments between Researchers and Students: The modular code base allows researchers to quickly prototype new ideas, and the 13B model can complete fine-tuning experiments on a single node.

Applicable people

• Individual users: Content creators and AI enthusiasts are recommended to use the Windows one-click package, no programming knowledge is required.
• SME Teams: Marketing and advertising teams need to quickly generate dynamic visual assets.
• Large Enterprise: The commercial-level stability of the research project needs to be evaluated, and FramePack has no official commercial support.
• Not suitable for boundaries: Not available for users without NVIDIA GPU; professional VFX scenes that require fine frame-by-frame editing; users who require a convenient SaaS experience.

Comparison of competing products

Comparative Dimensions	FramePack	Runway Gen-3	Pika
Core Differences	Running on local 6GB VRAM	Cloud Subscription	Cloud Subscription
Price	Free (Open Source)	Starting at $15/month	Starting at $10/month
Covered scenes	Image to video, research experiments	Professional video generation	Social media
User reviews	Low threshold, open source and controllable	High quality, full functionality	Good ease of use
Technical threshold	Medium (requires GPU + installation)	Low (Web use)	Low (Web use)

Summary and Outlook

FramePack brings large-scale video diffusion models from the cloud to consumer GPUs through two core innovations: frame context packaging and anti-drifting sampling. The combination of 6GB memory threshold, O(1) inference complexity, and Apache-2.0 completely open source makes it occupy a unique ecological niche among open source video generation tools.

Procurement/Adoption Risk Assessment: FramePack is an open source project (Apache-2.0) with no risk of vendor lock-in. Note: (1) The GitHub repository is the only official source, and domain name fraud exists on the Internet; (2) As a research project, long-term maintenance depends on the investment of core developers, and commercial-level stability is not guaranteed. Follow-up directions include FramePack-P1 to further improve stability. |---|---| | Product Type | Open Source Video Diffusion Model and Desktop Tools | | Number of model parameters | 13B (HY variant) | | Platform support | Linux, Windows | | Online date | April 2025 | | Current version | Windows One-Click Package (2025-04-18) | | Underlying Architecture | Next-Frame Prediction Neural Network | | Context compression | O(1) constant complexity frame context packing | | Output format | MP4 video (30fps) | | Minimum video memory requirements | 6GB VRAM (measured on notebook RTX 3060) | | Build Speed | ~2.5 seconds/frame (unoptimized) / ~1.5 seconds/frame (TeaCache) on RTX 4090 | | Longest video | 60 seconds (1800 frames @ 30fps) | | License | Apache-2.0 |

FramePack is an open source video diffusion system developed by researchers from Stanford University and MIT such as Lvmin Zhang (lllyasviel). Its core technology is Frame Context Packing - a method of importance-weighted compression of input frame context, which enables a 13B parameter-level video diffusion model to generate 30fps videos of up to 60 seconds on a consumer-grade GPU with only 6GB of video memory, and the generation workload does not increase with the length of the video (O(1) complexity).

Unlike traditional video diffusion models, FramePack uses a next-frame (or next-frame-section) prediction architecture: the model progressively generates video frame by frame (or segment by segment), and each step only predicts the next frame based on limited historical frame context. This design makes the video length theoretically not limited by the Transformer context window, and at the same time, combined with the anti-drifting sampling strategy, solves the common quality degradation (error accumulation) problem in long video generation.

User and market recognition

With 17.1k Stars on GitHub, FramePack is one of the high-profile open source video generation projects of 2025. Its influence is mainly reflected in:

• Academic Recognition: The paper "Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models" has been accepted by NeurIPS 2025 (The Thirty-ninth Annual Conference on Neural Information Processing Systems).
• Community activity: 1.7k Forks, 456 Issues, 143 Watchers on GitHub, community discussions are active, and users have voluntarily contributed extended versions such as FramePack-F1 and a large number of usage tutorials.
• Developer Ecosystem: Many YouTubers and independent creators have released tutorials on the installation and use of FramePack, covering the entire process from entry to advanced optimization.
• Adopted by content creators: FramePack has been recommended by a large number of low-configuration GPU users in communities such as Reddit due to its feature of "running long videos with 6GB of video memory". The comments focus on "finally a video diffusion tool that can run on a laptop".

Cost advantage

FramePack is a completely open source tool (Apache-2.0 license) with no hidden charges.

Cost Dimension	Description
The software itself	Free (open source, GitHub download)
Model weights	Automatically downloaded from HuggingFace (about 30GB+), free
Hardware Cost	Minimum RTX 3060 6GB laptop to run, no cloud GPU required
Commercial use	Apache-2.0 license, free to use and modify

Comparative Analysis: Compared with commercial video generation tools such as Runway Gen-3 (starting at $15/month, relying on cloud services), Pika (starting at $10/month, relying on cloud services), FramePack has a completely different cost structure - it has no subscription fees, no API call billing, and the only cost is the user's own GPU hardware. For users who already have RTX 30XX/40XX/50XX series GPUs, the marginal usage cost of FramePack is close to zero. For users without a GPU, the cost of renting a 6GB VRAM instance in the cloud is also much lower than the monthly subscription fees for commercial tools. However, users need to bear the download traffic of approximately 30GB model weight and the energy cost of first installation and configuration.

Main functions

• Image-to-Video: Upload a static picture and write a motion prompt word, and FramePack automatically generates a continuous animated video of 5 seconds to 60 seconds. Supports 30fps frame rate and outputs smooth MP4 files. It is recommended to use the structure of "subject → action → details" for sports prompt words (such as "girl dances gracefully, with clear movements and full of charm"). The model has the best effect on large-scale dynamics such as dancing, jumping, and running.
• Next frame/next segment progressive generation: FramePack is a next-frame-section prediction model, and the video will be generated segment by segment. In the GUI, users can see the latent preview and progress bar in real time, and can preview the effect after the first segment is generated without waiting for the entire video to be completed. The initial progress may be slow (the equipment warms up) and then gradually accelerate.
• TeaCache acceleration and quantification support: Built-in TeaCache inference acceleration (can speed up about 40%), and supports multiple attention implementation backends such as xformers, flash-attn, sage-attention, etc. Supports bf16 and fp8 quantization (via bnb/GGUF), allowing users to trade off quality and speed while reducing video memory usage. Note: TeaCache is not lossless. It is recommended to use TeaCache for creative exploration and full-precision diffusion for final rendering.
• Anti-drifting sampling: The original two-way anti-drifting sampling strategy solves the common "forgetting-drift" problem of next-frame models in long video generation by breaking the causal chain (introducing early establishment of endpoints, adjusting the sampling order, and discretizing historical representation). The measured 60-second video quality is stable and does not degrade.
• Gradio Web GUI: Provides a friendly Gradio interface, supporting image upload, prompt word input, parameter adjustment (video length, TeaCache switch, quantization options, etc.). Supports the --share parameter to generate public network sharing links for remote access and display.

Model and version evolution

Version	Release Date	Core Changes
Initial Release (v0.1)	2025-04-17	Basic next-frame prediction video generation Gradio GUI, 13B HY model
Windows One-Click Package	2025-04-18	Windows One-Click Installation Package (CUDA 12.6 + PyTorch 2.6), lowering the installation threshold
FramePack-F1	2025-05-03	Community extended version, enhanced model variant
FramePack-P1 (Preview)	2025-06-26	Next-generation version preview: Planned Anti-Drifting + History Discretization

FramePack iterates quickly from initial research prototypes. The Initial Release verified the feasibility of the core next-frame prediction architecture on 6GB of video memory; the release of the Windows one-click package greatly reduced the installation threshold for ordinary users (unzip and use); FramePack-F1 was driven by the community to expand model capabilities; FramePack-P1 introduced a more systematic anti-drift design (Planned Anti-Drifting) and history discretization (History Discretization) to further improve the stability of long videos.

Technical advantages

• Frame Context Packing: The core innovation of FramePack is the importance-weighted compression of input frames - more important frames (such as the nearest frame next to the prediction target) are allocated more token resources (larger patchifying kernel), and less important frames are allocated fewer tokens. This greatly increases the number of frames that can be encoded within a fixed context length, and the computational complexity is O(1) (does not grow with the length of the video). For the 13B model, 6GB of video memory can handle the context of thousands of frames.
• Anti-drifting bidirectional sampling: The traditional next-frame model has error accumulation (exposure bias) during the generation process, causing video quality to degrade as the length increases. FramePack proposes a bidirectional sampling strategy—not only referring to past frames during each generation step, but also making the generation process "anchored" by establishing endpoints early. Specifically, it includes three methods: early-established endpoints, adjusted sampling orders, and discrete history representation. All three methods can be applied plug-and-play to fine-tune existing video diffusion models.
• High training throughput: Due to the O(1) complexity brought by context packing technology, FramePack can be trained with very large batch sizes. A single 8×A100/H100 node can fine-tune the 13B video model with batch size 64, and the training efficiency is close to the image diffusion model - "Video diffusion, but feels like image diffusion".
• Modular Python code base: Based on the modular architecture built by PyTorch, researchers can easily replace components such as attention implementation, quantization scheme, and sampling strategy for experiments. The code base contains a complete Gradio demo, diffusers compatibility layer and automatic download mechanism of model weights.

How to use

FramePack provides two usage methods:

Method 1: Windows one-click package (recommended for novices)

1. Download framepack_cu126_torch26.7z (~1.69 GB) from GitHub Releases.
2. Unzip it to any directory and run update.bat to update the components.
3. Run run.bat to start Gradio Web GUI.
4. In the pop-up browser interface, upload the image, enter the exercise prompt word, adjust the parameters, and click Generate.
5. Model weights will be automatically downloaded from HuggingFace (about 30GB+, required for the first run).

Method 2: Linux/Manual installation (recommended for developers)

# Create a standalone Python 3.10 context
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
pip install -r requirements.txt

# Start Gradio GUI
python demo_gradio.py

#Supported parameters: --share (public network sharing link), --port, --server, etc.

• FramePack uses PyTorch attention by default, and optional acceleration backends such as xformers, flash-attn, and sage-attention can be installed.
• For prompt words, it is recommended to use the ChatGPT template to generate motion descriptions in the format of "Subject → Action → Details".
• Initial progress may be slow when first generated (device warms up), and will gradually speed up in subsequent segments.

Product Pricing

FramePack is a completely open source and free project (Apache-2.0 license) without any packages or billing.

Project	Cost
Software download	Free
Model weights	Free (automatically downloaded from HuggingFace)
Commercial use	Free (Apache-2.0 license)
Cloud GPU rental	User is responsible for it (such as renting a 6GB+ video memory instance)

Hidden Cost Tip:

1. Hardware Threshold: Requires NVIDIA RTX 30XX/40XX/50XX series GPU (GTX 10XX/20XX not tested), minimum 6GB video memory. Users without GPUs need to rent additional cloud instances.
2. Storage Space: The model weight is about 30GB+, and sufficient disk space must be ensured.
3. First time installation: Although the Windows one-click package simplifies the process, you still need to wait for the model weights to be downloaded during the first run (depending on network bandwidth).
4. Learning Cost: Writing effective sports prompt words requires some practice. It is recommended to use the ChatGPT template to assist in the generation.

Application scenarios

• Create generation for content creators: Video creators on YouTube, TikTok, Instagram and other platforms can convert static images into dynamic footage. For example, the AI ​​pictures generated by Midjourney are converted into 5-15 second animation clips through FramePack, which can be used as the background, transition or opening animation of the short video. The traditional process requires frame-by-frame production in After Effects, and FramePack reduces the production time of a single clip from more than 30 minutes to 2-3 minutes (including prompt word debugging).
• Advertising and Marketing Dynamic Visuals: The marketing team uses product concept maps and storyboards to quickly generate dynamic previews (storyboard animatic) through FramePack for customer proposals or A/B testing. Without using a 3D rendering pipeline or a video team, one person can complete the comparison output of multiple versions of dynamic footage in 15 minutes.
• Video Diffusion Experiments by Researchers and Students: FramePack’s modular code base allows researchers to quickly prototype new video diffusion ideas—changing attention mechanisms, testing different sampling strategies, and fine-tuning model weights. The 13B model can complete fine-tuning experiments on a single node, lowering the hardware threshold for video generation research.
• Personal creativity and artistic expression: Independent artists use FramePack to transform illustrations and photography into dynamic art (AI animation), exploring the creative possibilities of "static → dynamic". The unrestricted nature of open source means that art creators do not need to worry about licensing issues with commercial tools.

Applicable people

• Content Creators and Video Producers: Need to quickly convert static images into dynamic videos for use in social media, advertising, short films and other scenarios. It is recommended to use the Windows one-click package. No programming knowledge is required and you can get started in 10 minutes. Not recommended: Professional VFX scenes that require fine control over the details of each frame (FramePack's next-frame prediction does not provide a frame-by-frame editing interface).
• AI researchers and students: Researchers engaged in video diffusion models and generative AI. FramePack provides a concise code base and pre-trained model, which can be used as a baseline or experimental platform. Modular design makes it easy to replace components for ablation experiments.
• AI Enthusiasts and Open Source Community Contributors: Enthusiasts who are interested in AI video generation and have RTX 30XX series or above GPUs. FramePack's active community and rich tutorials lower the barrier to exploration.
• Not suitable for the crowd:
  • Users without NVIDIA GPU: FramePack requires an NVIDIA GPU (6GB+ VRAM) and will not run on a pure CPU or AMD GPU.
  • Users who need the convenient experience of cloud SaaS: FramePack is a local running tool and needs to be installed, configured and downloaded by yourself. It is not as easy to use out of the box as cloud services such as Runway/Pika.
  • Scenarios requiring very long videos (>60 seconds) or high-resolution output: FramePack currently mainly supports 60-second video generation at 480p level.
  • Users who want to get finished videos directly instead of experimental tools: As a research project, the output quality of FramePack still lags behind commercial products, and artifacts may appear in some results.

Summary and Outlook

FramePack brings the large-scale video diffusion model from the cloud to consumer-grade GPUs through two core innovations: Frame Context Packing and anti-drifting sampling. The hard threshold of 6GB of video memory, the inference complexity of O(1), and the complete open source of Apache-2.0 - the combination of these three places FramePack in a unique ecological niche among open source video generation tools in 2025. The paper was accepted by NeurIPS 2025, and its technical impact has been verified by 17.1k GitHub Stars and an active community.

Not Fitting Boundaries: FramePack is not a commercial-grade video generation product, but a research-driven open source tool. It requires users to have certain technical capabilities (especially Linux users), and there is still a gap with commercial tools such as Runway and Pika in terms of resolution, video length, and output quality precision. Not available to users without an NVIDIA GPU.

Procurement/Adoption Risk Assessment: FramePack is an open source project (Apache-2.0) with no risk of vendor lock-in. However, please note: (1) The GitHub repository clearly states that "this GitHub repository is the only official FramePack website". There are a large number of fraudulent websites on the Internet that pretend to be framepack.ai, framepack.net and other domain names. Do not pay fees or download files on these websites; (2) As a research project, the long-term maintenance of FramePack depends on the investment of the core developer (Lvmin Zhang), and commercial-level stability is not guaranteed. Subsequent development directions include FramePack-P1 (Planned Anti-Drifting + History Discretization) to further improve stability, as well as more model variants and application integrations contributed by the community.

Related tools: runway, pika

Version Info

• Windows One-Click Package ：Windows one-click installation package (CUDA 12.6 + PyTorch 2.6), with built-in 13B HY model weights automatically downloaded. （2025-04-18）
• Initial Release ：The initial version is released, including basic next-frame prediction video generation capabilities, Gradio GUI interface 13B model weights. （2025-04-17）