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Unified Multimodal Image Generation & Editing

Most tools make you juggle a separate model for each job — one to generate, another to edit, another for reasoning-heavy prompts. DeepGen 1.0 folds all of that into a single lightweight model, like a Swiss Army knife instead of a drawer full of single-use gadgets. At just 5B parameters it stays small enough to run without a data-center GPU, yet holds its own against models 3× to 16× its size. Built by the DeepGen team.

🏆 GenEval 0.87 and DPGBench 87.90 (silver among open-source models), plus #1 open-source on the WISE reasoning benchmark (0.73) — at just 5B parameters

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What it does

DeepGen 1.0 is a unified multimodal image generation and editing model built on a hybrid VLM + DiT (Diffusion Transformer) architecture. With only 5B parameters (3B VLM + 2B DiT), it packs five core capabilities — general image generation, general image editing, reasoning-based generation, reasoning-based editing, and text rendering — into one model, while staying competitive with or beating models 3× to 16× larger. It is designed to show that massive scaling is not the only path to high-performance multimodal generation.

Problem it solves

• Lightweight efficiency – Achieves state-of-the-art performance at just 5B parameters, far smaller than comparable unified models (e.g., BAGEL at 14B, Hunyuan-Image-3.0 at 80B)
• Unified capabilities – Combines generation, editing, reasoning generation, reasoning editing, and text rendering in one model, eliminating the need for separate specialized models
• Semantic understanding – Overcomes limitations of lightweight models via Stacked Channel Bridging (SCB), enabling fine-grained semantic control
• Reasoning-aware generation – Supports complex, knowledge-intensive prompts that require multi-step reasoning before generating or editing an image
• Creative workflows – Suitable for design, content creation, and multimodal applications requiring both generation and editing

Input/Output

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• Input:

  • Text-to-image: a natural-language prompt describing the desired image

    

  • Image-to-image: one or more reference images + a text instruction for editing or transformation (supports general editing, style transfer, reasoning-based editing, and identity preservation) </aside>

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• Output: Generated or edited image

  • Quality: High-fidelity, semantically accurate, fine-grained detail

    

    

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• Options:
  • Diffusion steps — 10–50; controls how many refinement iterations the model uses (default: 50)
  • Output image height & width — default 512×512
  • Random seed (Advanced) — set for reproducible results </aside>

Accuracy & Speed

General Image Generation:

General Image Editing:

Reasoning Image Generation: