Intrinsic Distribution Matching
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper introduces Intrinsic Distribution Matching (IDM), which extends distribution matching distillation (DMD) for one- and few-step text-to-image generation. IDM keeps the full DMD correction and adds an explicit constraint matching feature means and per-coordinate standard deviations, computed from the generator's own EMA copy without an external encoder. Tested with full-parameter and LoRA tuning on Z-Image-Turbo 6B and Qwen-Image 20B, IDM reaches 94.25% (Chinese) and 90.20% (English) on LongText-Bench at two network evaluations. Ablations attribute gains to the moment loss, and EMA features perform competitively with DINOv2.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper introduces Intrinsic Distribution Matching (IDM), which extends distribution matching distillation (DMD) for one- and few-step text-to-image generation. IDM keeps the full DMD correction and adds an explicit constraint matching feature means and per-coordinate standard deviations, computed from the generator's own EMA copy without an external encoder. Tested with full-parameter and LoRA tuning on Z-Image-Turbo 6B and Qwen-Image 20B, IDM reaches 94.25% (Chinese) and 90.20% (English) on LongText-Bench at two network evaluations. Ablations attribute gains to the moment loss, and EMA features perform competitively with DINOv2.
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Key findings from the stored brief
- IDM adds an explicit loss on the means and per-coordinate standard deviations of the generator's EMA features to DMD.
- At 2 network evaluations, IDM scores 94.25% on Chinese and 90.20% on English LongText-Bench, the highest among compared methods.
- Controlled ablations show gains from the added moment loss.
- Generator EMA features perform competitively with an external DINOv2 encoder across the five reported metrics.
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Why this may matter
Few-step generators often struggle to render prompt details accurately. A method that improves long-text rendering without an external feature encoder could simplify training of efficient text-to-image models.
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Source claims not independently checked; the model weights are listed as coming soon, so results cannot yet be reproduced from released weights.
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