---
license: apache-2.0
tags:
  - vision-language
  - diffusion
  - xlstm
  - vision-lstm
  - masked-diffusion
  - mdlm
  - multimodal
language: en
pipeline_tag: image-text-to-text
---

# ViL-DLM: Vision xLSTM Diffusion Language Model (~660M)

**The first vision-language model combining Vision xLSTM (ViL) with a discrete masked diffusion language model backbone.**

## Architecture

```
[Image] → ViL-S Encoder (57M) → MLP Projector (7M) → [196 Visual Tokens]
[Visual Tokens] + [Masked Text Tokens] → Bidirectional Diffusion LM (596M) → Denoised Text
```

| Component | Model | Params | Key Innovation |
|-----------|-------|--------|----------------|
| Vision Encoder | **Vision-xLSTM-S (ViL-S)** | ~57M | O(N) linear complexity, alternating bidirectional mLSTM with Conv2D |
| Projector | 2-layer MLP (GELU) | ~7M | Maps ViL features (384d) → LM space (1024d) |
| Language Backbone | **dLLM Qwen3-0.6B (MDLM)** | ~596M | Bidirectional masked diffusion, non-autoregressive |
| **Total** | | **~660M** | |

## How It Works

### Vision Encoder: Vision xLSTM (ViL-S)
- Based on [Vision-LSTM](https://arxiv.org/abs/2406.04303) (Alkin et al., 2024)
- Processes 224×224 images into 196 patch tokens (16×16 patches)
- Uses **mLSTM blocks** with matrix memory cells and exponential gating
- **Alternating bidirectional scanning**: odd blocks scan top-left→bottom-right, even blocks reverse
- **Conv2D for spatial QK context**: depthwise 3×3 convolution adds local spatial awareness
- **SwiGLU FFN** after each mLSTM block
- Linear O(N) complexity vs ViT's quadratic O(N²) — critical for scaling to many visual tokens

### Diffusion Language Model: MDLM via dLLM
- Based on [dLLM](https://arxiv.org/abs/2602.22661) (Berkeley, 2025) converting Qwen3-0.6B to diffusion
- **Training**: Forward diffusion progressively masks tokens with cosine schedule → model predicts masked tokens
- **Inference**: Start with all-masked output → iteratively unmask most-confident tokens
- **Key change from AR**: replaces causal attention mask with bidirectional padding-only mask
- Weighted cross-entropy loss on masked positions only (MDLM objective)

### Knowledge Distillation (Stage 3)
- Teacher: [Gemma 4 E2B](https://huggingface.co/google/gemma-4-E2B-it) (5.1B params, ~2B effective)
- **Sparse cross-tokenizer distillation**: prepare a teacher-scored candidate bank in the student token space, then blend sparse KL with diffusion loss
- Temperature τ=2.0, α_KD=0.5 (50% diffusion loss + 50% KD loss)

## Training Recipe

Multi-stage training inspired by LLaDA-V, LaViDa, LFM2, and Mistral/Pixtral:

| Stage | Components Trained | Dataset | Learning Rate | Epochs |
|-------|-------------------|---------|---------------|--------|
| 1 | Projector only (ViL & LM frozen) | [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) (558K) | 1e-3 | 1-2 |
| 2 | Full model (all components) | [The Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron) | ViL:2e-6, Proj:1e-5, LM:1e-5 | 3 |
| 3 | + KD from Gemma 4 E2B | Stage 2 data mix + cached teacher bank | Sparse cross-tokenizer KD (α=0.5) | 2 |

### Efficiency Tricks Applied
- **Per-component learning rates** (LLaDA-V recipe): vision encoder gets 5× lower LR
- **Gradient checkpointing** on LM backbone to reduce VRAM
- **Cosine LR schedule** with warmup
- **Gradient clipping** at 1.0
- AdamW optimizer with β=(0.9, 0.999), weight_decay=0.05

## Why This Combination Matters

This is a genuinely **unexplored frontier** in the literature:

1. **No published work** combines Vision xLSTM with a diffusion language model
2. ViL's **linear complexity** could be transformative for processing large numbers of visual tokens in multimodal diffusion models, where current Transformer-based approaches incur quadratic attention costs
3. The **bidirectional nature** of both ViL (alternating scan) and diffusion LM (full attention) creates natural synergy — both architectures process information non-autoregressively
4. **Distillation from Gemma 4 E2B** bridges the gap between the small student and a state-of-the-art multimodal teacher

## Running Training

```bash
# CPU smoke: Stage 1 projector path
python code/train_production.py --stage 1 --epochs 1 --batch_size 1 --grad_accum 1 --num_workers 0 --max_samples 1 --dry_run_batches 1

# CPU smoke: Stage 2 subset path
python code/train_production.py --stage 2 --resume_from ./vil-dlm-output/stage1_best --dataset_configs ai2d,aokvqa --epochs 1 --batch_size 1 --grad_accum 1 --num_workers 0 --max_samples 8 --dry_run_batches 1

# Stage 1: projector-only alignment
python code/train_production.py --stage 1 --require_cuda --epochs 1 --batch_size 8 --grad_accum 4

# Stage 2: full-model finetune on the balanced Cauldron mix
python code/train_production.py --stage 2 --require_cuda --epochs 3 --batch_size 2 --grad_accum 16

# Stage 3a: build the Gemma teacher candidate bank from a Stage 2 checkpoint (GPU only)
python code/train_production.py --stage 3a --require_cuda --resume_from ./vil-dlm-output/stage2_best --prepare_teacher_bank --teacher_batch_size 2 --kd_top_k 16 --kd_positions_per_sample 16 --kd_temperature 1.0

# Stage 3b: timestep-aware sparse KD training from the cached teacher bank (GPU only)
python code/train_production.py --stage 3b --require_cuda --resume_from ./vil-dlm-output/stage2_best --epochs 2 --batch_size 2 --grad_accum 16 --alpha_kd 0.5 --kd_temperature 1.0

# Cheap validation gate for any stage
python code/train_production.py --stage 1 --require_cuda --dry_run_batches 1 --max_samples 8

# Cheap Stage 3 KD-consumption gate after Stage 3a wrote a teacher bank
python code/train_production.py --stage 3b --require_cuda --resume_from ./vil-dlm-output/stage2_best --teacher_cache_dir ./vil-dlm-output/teacher-cache --epochs 1 --batch_size 1 --grad_accum 1 --dry_run_batches 1 --alpha_kd 0.5 --kd_temperature 1.0
```

Training now saves checkpoints locally by default. Add `--push_to_hub` only when you want to publish artifacts.
Stage 3b uses timestep-aware sparse KD by default; pass `--no-kd_timestep_weighting` only to reproduce the pilot's fixed-alpha KD behavior.
CPU sessions should stop after the Stage 2 subset smoke test. Stage 3 requires a CUDA GPU because Gemma 4 teacher-bank preparation uses quantized multimodal teacher inference.

### Hardware Requirements
- **Stage 1**: A10G (24GB) or T4 (16GB) — only projector gradients (~7M params)
- **Stage 2**: A10G (24GB) recommended — full model gradients (~660M params)
- **Stage 3**: H100 / A100 (80GB) recommended — Gemma 4 teacher bank prep + student distillation

### Dependencies
```
torch>=2.0
transformers>=5.5.0
datasets
trackio
accelerate
einops
pillow
huggingface_hub
```

## Pretrained Components Used

| Component | Source | License |
|-----------|--------|---------|
| Diffusion LM backbone | [dllm-hub/Qwen3-0.6B-diffusion-mdlm-v0.1](https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-mdlm-v0.1) | Apache 2.0 |
| Teacher (Stage 3) | [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) | Apache 2.0 |
| Pretraining data | [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) | CC BY 4.0 |
| Instruction data | [The Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron) | Mixed |

## Full Literature Context

This model was designed based on a comprehensive literature review covering:
- **Language Diffusion Models**: MDLM, LLaDA, Plaid, BD3-LM, EDLM, ADLM
- **Vision-Language Diffusion Models**: LaViDa, LLaDA-V, MMaDA, Muddit, LMFusion, DEEM
- **Vision xLSTM**: ViL (arxiv:2406.04303)
- **Knowledge Distillation for DLMs**: TCS distillation, DiffuGPT/DiffuLLaMA, dLLM
- **JEPA family**: I-JEPA, D-JEPA, VL-JEPA, LLM-JEPA
- **Efficiency recipes**: LFM2 (Liquid AI), Mistral/Pixtral, Minitron

## References

| Paper | arxiv | Role |
|-------|-------|------|
| Vision-LSTM (ViL) | [2406.04303](https://arxiv.org/abs/2406.04303) | Vision encoder architecture |
| dLLM | [2602.22661](https://arxiv.org/abs/2602.22661) | Diffusion LM backbone |
| MDLM | [2406.07524](https://arxiv.org/abs/2406.07524) | Core diffusion objective |
| LLaDA-V | [2505.16933](https://arxiv.org/abs/2505.16933) | VLM training recipe |
| LaViDa | — | Complementary masking, prefix KV cache |
| LFM2 | [2511.23404](https://arxiv.org/abs/2511.23404) | Top-K distillation recipe |
| Gemma 4 | [blog](https://huggingface.co/blog/gemma4) | Teacher model |
| Pixtral | [2410.07073](https://arxiv.org/abs/2410.07073) | RoPE-2D, variable-res ViT |
| Minitron | [2407.14679](https://arxiv.org/abs/2407.14679) | Prune+distill best practices |