💡 DeepGen 1.0 (Diffusers Format): A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing

This is the diffusers-compatible version of DeepGen-1.0. The model weights are stored in safetensors format with a self-contained pipeline script (deepgen_pipeline.py) — no need to clone the DeepGen repository.

DeepGen 1.0 is a lightweight unified multimodal model with only 5B parameters (3B VLM + 2B DiT). It integrates five core capabilities—general image generation, general image editing, reasoning image generation, reasoning image editing, and text rendering—within a single model. Across multiple authoritative benchmarks, DeepGen 1.0 is competitive with or surpassing the state-of-the-art unified multimodal models that are 3× to 16× larger.

🛠️ Quick Start

Installation

pip install torch diffusers transformers safetensors einops accelerate huggingface_hub
# Flash Attention (recommended)
pip install flash-attn --no-build-isolation

Load Pipeline

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "deepgenteam/DeepGen-1.0-diffusers",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
pipe.to("cuda")

# Optional: enable CPU offload for GPUs with limited memory (< 24GB)
# pipe.enable_model_cpu_offload()

Text-to-Image

result = pipe(
    prompt="a racoon holding a shiny red apple over its head",
    height=512, width=512,
    num_inference_steps=50,
    guidance_scale=4.0,
    seed=42,
)
result.images[0].save("output.png")

Image Editing

from PIL import Image

source_image = Image.open("guitar.png").convert("RGB")
result = pipe(
    prompt="Take a photo of this guitar placed on a sandy beach with the sunset in the background.",
    image=source_image,
    height=512, width=512,
    num_inference_steps=50,
    guidance_scale=4.0,
    seed=42,
)
result.images[0].save("edited.png")

📋 Parameters

💾 Memory Requirements

📁 Directory Structure

DeepGen-1.0-diffusers/
├── transformer/          # SD3 DiT weights (safetensors)
├── vae/                  # AutoencoderKL weights
├── connector/            # SCB Connector weights + config
├── scheduler/            # FlowMatchEulerDiscreteScheduler config
├── tokenizer/            # Qwen2.5-VL tokenizer
├── prompt_template.json  # Prompt formatting template
├── model_index.json      # Model metadata
└── deepgen_pipeline.py   # Self-contained pipeline script

Note: The VLM (Qwen2.5-VL-3B-Instruct) is loaded separately from Qwen/Qwen2.5-VL-3B-Instruct. You can override the VLM path using the vlm_model_path parameter in from_pretrained().

🧠 Method

Our core observation is that a lightweight model, when empowered by synergistic architecture design and data-centric training strategies, can achieve comprehensive capabilities competitive with or even surpassing much larger counterparts. To overcome the limitations of lightweight models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable "think tokens" to provide the generative backbone with structured, reasoning-rich guidance.

📊 Benchmarks

1. General Image Generation

2. General Image Editing

3. Reasoning Image Generation

4. Reasoning Image Editing

⭐ Citation

@article{wang2026deepgen,
  title={DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing},
  author={Wang, Dianyi and Li, Ruihang and Han, Feng and Ma, Chaofan and Song, Wei and Wang, Siyuan and Wang, Yibin and Xin, Yi and Liu, Hongjian and Zhang, Zhixiong and others},
  journal={arXiv preprint arXiv:2602.12205},
  year={2026}
}

License

Apache 2.0