arXiv:2606. 03746v1 Announce Type: cross Abstract: Few-step distillation has become an effective strategy for accelerating advanced visual generative models, yet prior work has largely focused on distillation objectives.
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arXiv:2606.03746v3 Announce Type: replace-cross Abstract: Few-step distillation has emerged as a critical component in the development of advanced visual generative foundation models, substantially r...
arXiv:2606. 27608v1 Announce Type: cross Abstract: We present Qwen-Image-2.
arXiv:2608. 20334v1 Announce Type: new Abstract: We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing.
Swift-Image is a compact unified model that performs text-to-image generation, single-image editing, and multi-image editing using a 6B parameter DiT architecture. It employs a progressive training pipeline, parallel expert reinforcement learning, and multi-teacher distillation to balance diverse objectives, while a Prompt Enhancer decouples high-level reasoning from pixel-level rendering. After training, structural pruning and few-step distillation produce efficient 3B and accelerated variants that maintain near‑lossless performance and improve editing efficiency.
SenseNova-U1.5 is an 8B‑MoT native unified multimodal model that can understand, reason about, and generate visual content without using an encoder or VAE. It improves visual fidelity and text rendering through spatially coherent patch reconstruction, large‑scale training on curated generation and editing data, and native resolutions up to 4K. Post‑training, specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing are optimized and distilled into a multi‑expert framework, yielding advances in image fidelity, complex composition, multi‑reference editing, and instruction following.
arXiv:2610.25404v2 Announce Type: replace Abstract: Multi-turn image editing aims to modify an image according to a sequence of instructions, enabling more effective interactive visual content creati...