Infinite Canvas is an open-source framework that lets you train and fine-tune advanced image and video AI models on your own Apple Silicon devices—no cloud required. With automated hardware detection, distributed training, and one-click workflows, we give businesses (starting with creative agencies) a near-cloud performance experience while keeping their private data fully in-house.
Our platform transforms everyday Apple Silicon Macs into powerful, on-prem AI studios, capable of handling complex generative tasks without massive GPU clusters. Designers and content teams can load their brand assets, style guides, and sensitive data locally, maintaining complete ownership and compliance from end to end. This approach minimizes external dependencies, slashes cloud inference costs, and harnesses local hardware that often goes underutilized.
We’re focusing initially on creative agencies—an underserved niche compared to text-based AI solutions. By proving that local model training can match cloud benchmarks and that customizing outputs with brand-specific data leads to better results, we’ll gain trust and momentum. From there, we plan to expand into any sector where privacy, data sovereignty, and tailored workflows are a priority—healthcare, finance, enterprise R&D, and beyond.
<aside> 💡
For the Technical Deep Dive and How It Works, See: Appendix: Product Memo & Tech Deep Dive
</aside>
Generated locally using Infinite Canvas on Apple Silicon (Macbook M3) using black-forest-labs/FLUX.1-dev
Generated locally on Apple Silicon (Macbook M3 + Mac Studio M2 Ultra) using stabilityai/stable-video-diffusion-img2vid-xt
Generated locally using Infinite Canvas on Apple Silicon (Macbook M3) using black-forest-labs/FLUX.1-dev
Generated locally using Infinite Canvas on Apple Silicon (Macbook M3) using black-forest-labs/FLUX.1-dev
Generated locally using Infinite Canvas on Apple Silicon (Macbook M3) using black-forest-labs/FLUX.1-dev
**Local Performance Metrics:**
- Model: FLUX.1-dev
- Resolution: 1024x1024
- Hardware: M3 Max 36 GB
- Generation Time: 17s
- Peak Memory Usage: 28 GB
**Cloud Performance Metrics:**
- Model: FLUX.1-dev
- Resolution: 1024x1024
- Cloud Instance Hardware: H100
- Generation Time: 7.8s
- Peak Memory Usage: N/A
Generated locally using Infinite Canvas on Apple Silicon (Macbook M3) using black-forest-labs/FLUX.1-dev
Generated locally using Infinite Canvas on Apple Silicon (Macbook M3) using black-forest-labs/FLUX.1-dev
Generated locally using Infinite Canvas on Apple Silicon (Macbook M3) using black-forest-labs/FLUX.1-dev
<aside> 💡
For the Technical Deep Dive, See: Appendix: Product Memo & Tech Deep Dive
</aside>
Infinite Canvas leverages Apple's MLX framework, designed specifically for Apple silicon, to optimize the performance of large text-to-video and image generation models. MLX allows us to efficiently utilize the unified memory architecture of Apple's M-series chips, maximizing GPU memory access and minimizing data transfer overhead. This translates to faster training and inference times, enabling near real-time experimentation and iteration with complex models.
flowchart TB
subgraph User["User / Creative Team"]
UI[Prompts & Video Assets]
end
subgraph CLI["Infinite Canvas Framework"]
ICLI[CLI / macOS App]
Config[MPI Configuration & Host Setup]
end
subgraph Node1["Node 1: Mac Mini M4"]
MLX1[MLX Runtime]
MPI1[MPI Process Rank 0]
GPU1[Apple Silicon GPU/ANE]
MEM1[Unified Memory]
end
subgraph Node2["Node 2: MacBook M2 Max"]
MLX2[MLX Runtime]
MPI2[MPI Process Rank 1]
GPU2[Apple Silicon GPU/ANE]
MEM2[Unified Memory]
end
subgraph Distribution["MLX Distribution"]
AllReduce[All-Reduce Operations]
DataSplit[Data Parallel Processing]
end
subgraph Output["Model Processing"]
Sync[Gradient Averaging]
Final[Model Output]
end
User --> CLI
CLI --> Config
Config --> Node1
Config --> Node2
MLX1 --> GPU1
MLX1 --> MEM1
MLX2 --> GPU2
MLX2 --> MEM2
MPI1 <--> |TCP Links| MPI2
Node1 --> DataSplit
Node2 --> DataSplit
DataSplit --> AllReduce
AllReduce --> Sync
Sync --> Final
classDef primary fill:#e1eaff,stroke:#9eb9ff,stroke-width:2px
classDef secondary fill:#fff5e1,stroke:#ffd591,stroke-width:2px
classDef hardware fill:#ffe1e1,stroke:#ff9191,stroke-width:2px
classDef output fill:#e1ffe9,stroke:#91ffb6,stroke-width:2px
classDef mpi fill:#f1e1ff,stroke:#d591ff,stroke-width:2px
class User,CLI primary
class Node1,Node2 secondary
class GPU1,GPU2,MEM1,MEM2 hardware
class Output output
class MPI1,MPI2,AllReduce mpi
Infinite Canvas leverages Apple's MLX framework to distribute AI workloads across multiple Mac computers, effectively creating a private AI cluster.
The system uses MPI (Message Passing Interface) to coordinate between machines - for example, a Mac Mini M4 and MacBook M2 Max can work together by splitting the workload, where each device processes a portion of the data in parallel.
When processing large video or image generation tasks, each Mac handles its assigned portion, and the results are efficiently combined through a process called "all-reduce" that averages and synchronizes the results across all devices.
This distributed approach allows creative teams to harness the combined computational power of their existing Mac devices, eliminating the need for expensive cloud GPU resources while keeping all data and processing local.
Note: MLX reduces communication overhead in multi-node Apple setups, accelerating generative tasks that would otherwise saturate VRAM