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Ltx2.3-VBVR-lora-I2V
hana1614/Ltx2.3-VBVR-lora-I2V
Ltx2.3-VBVR-lora-I2V is a machine learning model from hana1614. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as other.
LoRA fine-tuned weights for LTX-2.3 22B on the VBVR (A Very Big Video Reasoning Suite) dataset.
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- diffusers
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- other
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- Public
- Created
- Aug 14, 2026
- Updated
- Aug 14, 2026
- SHA
- d6f14eba
Base models
Model card
From the Hugging Face model README
LTX-2 VBVR LoRA - Video Reasoning
LoRA fine-tuned weights for LTX-2.3 22B on the VBVR (A Very Big Video Reasoning Suite) dataset.
Training Data
To ensure training quality, we preprocessed the full 1,000,000 videos from the official dataset and randomly sample during training to maintain data diversity. We adopt the official parameters with batch_size=16 and rank=32 to prevent catastrophic forgetting caused by excessively large rank.
The VBVR dataset contains 100 reasoning task categories, with ~10,000 variants per task, totaling ~1M videos. Main task types include:
- Object Trajectory: Objects moving to target positions
- Physical Reasoning: Rolling balls, collisions, gravity
- Causal Relationships: Conditional triggers, chain reactions
- Spatial Relationships: Relative positions, path planning
Model Details
| Item | Details |
|---|---|
| Base Model | ltx-2.3-22b-dev |
| Training Method | LoRA Fine-tuning |
| LoRA Rank | 32 |
| Effective Batch Size | 16 |
| Mixed Precision | BF16 |
TODO List
Dataset Release Plan
| Dataset | Videos | Status |
|---|---|---|
| VBVR-96K | 96,000 | ✅ Released |
| VBVR-240K | 240,000 | ✅ Released |
| VBVR-reinforce | 240K+150K | ✅ Released |
LoRA Capabilities
This LoRA adapter enhances the base LTX-2 model for production video generation workflows:
-
Enhanced Complex Prompt Understanding: Accurately interprets multi-object, multi-condition prompts with detailed spatial descriptions and temporal sequences, reducing prompt misinterpretation in production scenarios.
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Improved Motion Dynamics: Generates smooth, physically plausible object movements with natural acceleration, deceleration, and trajectory curves, avoiding robotic or unnatural motion patterns.
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Temporal Consistency: Maintains object appearance, lighting, and scene coherence throughout the video sequence, reducing flickering and frame-to-frame artifacts common in generated videos.
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Precise Timing Control: Enables accurate control over action duration, pacing, and synchronization between multiple moving elements based on prompt semantics.
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Multi-Object Interaction: Handles complex scenes with multiple objects interacting simultaneously, including collisions, following, avoiding, and coordinated movements.
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Camera and Framing Stability: Maintains consistent camera perspective and framing throughout the sequence, avoiding unwanted camera shake or unexpected viewpoint changes.
Training Configuration
Stage 1: VBVR Foundation (96K)
| Config | Value |
|---|---|
| Dataset | 96K VBVR general videos |
| Learning Rate | 1e-4 |
| Scheduler | Cosine |
| Batch Size | 1 × 16 (gradient accumulation) |
| Optimizer | AdamW |
| Max Grad Norm | 1.0 |
| Target Modules | to_q, to_k, to_v, to_out.0, ff.net.0.proj, ff.net.2 |
Stage 2: VBVR Extended (240K)
| Config | Value |
|---|---|
| Dataset | 240K general videos |
| Learning Rate | 1e-4 |
| Scheduler | Cosine |
| Batch Size | 1 × 16 (gradient accumulation) |
| Optimizer | AdamW |
| Max Grad Norm | 1.0 |
| Target Modules | to_q, to_k, to_v, to_out.0, ff.net.0.proj, ff.net.2 |
Stage 3: General + Hard Reasoning (390K)
| Config | Value |
|---|---|
| Dataset | 240K general videos + 150K high-difficulty reasoning videos |
| Learning Rate | 5e-5 |
| Scheduler | Cosine |
| Batch Size | 1 × 16 (gradient accumulation) |
| Optimizer | AdamW |
| Max Grad Norm | 1.0 |
| Target Modules | to_q, to_k, to_v, to_out.0 (FFN frozen) |
Video Demo
Training Progress Comparison
<div style="display: flex; gap: 10px; flex-wrap: wrap;"> <div style="flex: 1; min-width: 300px;"> <video src="https://huggingface.co/LiconStudio/Ltx2.3-VBVR-lora-I2V/resolve/main/original01.mp4" controls style="width: 100%;"></video> <p style="text-align: center; margin: 8px 0 0 0;"><strong>Original Model</strong></p> </div> <div style="flex: 1; min-width: 300px;"> <video src="https://huggingface.co/LiconStudio/Ltx2.3-VBVR-lora-I2V/resolve/main/240K.mp4" controls style="width: 100%;"></video> <p style="text-align: center; margin: 8px 0 0 0;"><strong>240K</strong></p> </div> <div style="flex: 1; min-width: 300px;"> <video src="https://huggingface.co/LiconStudio/Ltx2.3-VBVR-lora-I2V/resolve/main/S3.mp4" controls style="width: 100%;"></video> <p style="text-align: center; margin: 8px 0 0 0;"><strong>390K</strong></p> </div> </div>Dataset
This model is trained on the VBVR (Video Benchmark for Video Reasoning) dataset from video-reason.com.
Contact
For questions or suggestions, please open an issue on Hugging Face or contact the author directly.
- Library
- diffusers
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- other
- Languages
- en, zh
- Created
- Aug 14, 2026
- Updated
- Aug 14, 2026