StreamDiffusionTD v0.2.99 (Legacy)
This page documents version 0.2.99, which is still available alongside v0.3.1.
Do You Still Need v0.2.99?
With v0.3.1, StreamV2V temporal consistency is back via cached attention (requires TensorRT). The main reasons to keep v0.2.99 were V2V and simpler setup. Here’s the current comparison:
| Feature | v0.2.99 | v0.3.1 |
|---|---|---|
| V2V Temporal Consistency | Yes (no TRT required) | Yes (TRT required) |
| SDXL Support | No | Yes |
| IP Adapter / FaceID | No | Yes |
| TensorRT Local | Cloud only | Local + Cloud |
| FX Processors | No | Yes (5 built-in + custom) |
| Multi-ControlNet | No | Yes |
| Installer CLI | No | Yes |
| Default Model | sd-turbo | sdxl-turbo |
When v0.2.99 may still be useful:
- You want V2V without TensorRT acceleration
- You have a simpler setup that works and don’t need the new features
- Your GPU has limited VRAM and you prefer the lighter sd-turbo default
For most users, v0.3.1 is now the recommended version.
V2V Temporal Consistency (v0.2.99)
V2V (Video-to-Video) in v0.2.99 provides smooth frame transitions using a different approach than v0.3.1’s cached attention. This version does NOT require TensorRT.
V2V Parameters
| Parameter | Description | Default |
|---|---|---|
| Enable V2V | Enable temporal consistency | Off |
| Cache Attn Active | Store attention maps from past frames | On |
| Cache Interval | How often to update feature bank | 1 |
| Cache Max Frames | Number of frames stored | 3 |
| Use Feature Injection | Incorporate past frame details | On |
| Feature Injection Strength | Influence of past frames (0-1) | 0.7 |
| Feature Similarity Threshold | Minimum similarity for reuse | 0.3 |
| Use Tome Cache | Optimized cache storage | On |
| Tome Ratio | Cache allocation ratio | 0.5 |
| Use Grid | Grid-based feature organization | On |
V2V Detailed Descriptions
Cache Attn Active: Enables the model to store attention maps from past frames, reusing them for processing new frames. Helps maintain consistency and reduces flicker.
Cache Interval: Sets how often the feature bank updates with new frames. Lower values mean less frequent updates, capturing fewer changes and reducing computational load.
Cache Max Frames: Limits the number of frames stored in the feature bank. Higher values store more frames, enhancing temporal consistency but using more memory.
Use Feature Injection: Incorporates details from past frames into the current frame’s processing. Helps maintain continuity and reduces flicker.
Feature Injection Strength: Controls how much influence past frame details have on the current frame. Higher values increase consistency but may introduce artifacts.
Feature Similarity Threshold: Sets the minimum similarity score for features from past frames to be reused. Uses cosine similarity, only reusing features above this threshold.
Use Tome Cache: Activates an optimized cache for storing features efficiently. Reduces memory usage and enhances real-time processing performance.
Tome Ratio: Defines the proportion of total cache allocated to Tome Cache. Higher ratios optimize for speed at the cost of memory.
Use Grid: Activates a grid-based approach for organizing and processing features. Divides the frame into a grid, improving matching and fusion of features from past frames.
V2V Important Notes
- Not compatible with TensorRT acceleration (unlike v0.3.1’s cached attention which requires TRT)
- Increases VRAM usage
- Best for video sequences where frame-to-frame smoothness matters
- May reduce FPS compared to non-V2V mode
v0.2.99 Installation
Prerequisites
- Python 3.11.9 or 3.10.9
- CUDA 12.1 (or 12.8 for RTX 50-series)
- Git
Installation Steps
- Load operator: Drag
StreamDiffusionTD.toxinto TouchDesigner - Go to Install page
- Download StreamDiffusion to a folder
- Install Virtual Environment
- (Optional) Install TensorRT
Upgrading
If upgrading from earlier versions:
- v0.2.6+: Automatically detects previous installation
- Earlier: Set Basefolder parameter manually
v0.2.99 System Requirements
Local Mode
- OS: Windows 10/11, macOS (Apple Silicon limited)
- GPU: NVIDIA with 6GB+ VRAM (RTX 3060+)
- CUDA: 12.1 recommended (12.8 for RTX 50-series)
- Python: 3.11.9 or 3.10.9
v0.2.99 Key Features
- sd-turbo as default model
- Daydream cloud backend support
- RTX 5090/5080 compatibility (limited)
- Enhanced Mac installation (cloud mode)
- Feedback loop toggle
- V2V temporal consistency (works without TensorRT)
Running Both Versions
You can run both versions:
- Install v0.2.99 in one folder (e.g.,
C:/AI/StreamDiff_v0299/) - Install v0.3.1 in another folder (e.g.,
C:/AI/StreamDiff_v031/) - Use different TOX files pointing to different base folders
- Switch between them based on your needs
Download
v0.2.99 is available on Patreon alongside v0.3.1.