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Diffusion models generate high-quality images but require hundreds of forward passes. MIT CSAIL and Adobe Research introduce Distribution Matching Distillation (DMD), a distillation approach that converts costly multi-step diffusion models into fast one-step generators. A thread 🧵

MIT CSAIL

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34,383 görüntüleme • 2 yıl önce •via X (Twitter)

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DMD trains a one-step generator that maps random noise into realistic images, consisting of two key components. First up: it uses a regression loss to anchor the mapping process, ensuring a coarse organization of the image space, enhancing the stability of the training phase.

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Additionally, it employs a distribution matching loss to guarantee that the likelihood of generating a specific image w/the student model aligns w/its actual frequency of occurrence in the real world.

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The gradient of this loss is formulated as the difference between two diffusion models’ output, trained on real and fake samples respectively.

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DMD achieves a strong 11.49 FID on zero-shot COCO-30K, comparable to Stable Diffusion v1.5 while being 30X faster. Compared to previous approaches, it notably balances image quality with sample diversity.

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DMD paves the way for real-time visual generation. This same approach could improve diffusion-based generative models across various fields, from design, to scientific discovery and beyond, by significantly enhancing speed and effectiveness.

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Paper: Authors: @TianweiY, @m_gharbi, @rzhang88, @elishechtman, @fredodurand, Bill Freeman, and Taesung Park. Project page: MIT News:

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@AdobeResearch will you release the code / model for this?

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@AdobeResearch Could this approach of distribution matching loss be applied to other generative AI tasks besides image generation? For example, text generation or music composition?

𝗦𝗼𝘂𝗹𝘀𝗳𝗲𝗻𝗴 𝗡𝗲𝘄 𝗬𝗼𝗿𝗸 profil fotoğrafı

𝗦𝗼𝘂𝗹𝘀𝗳𝗲𝗻𝗴 𝗡𝗲𝘄 𝗬𝗼𝗿𝗸2 yıl önce

@rzhang88 @AdobeResearch Good for you my friend, we are try use your model colorization(which is 4 years ago) for sneakers now, lol, thank you for everything.

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机器之心 JIQIZHIXIN

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TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

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Introducing PAN — MBZUAI’s New World Model for Interactive Intelligence Developed by MBZUAI’s Institute of Foundation Models, PAN is built for simulation, prediction, and agentic reasoning. Unlike traditional video generators that only output frames, PAN maintains a persistent internal state that evolves when guided with natural language. Its Generative Latent Prediction architecture combines: • A latent encoder to capture the world state • A dynamics module that evolves that state step-by-step • A video diffusion decoder that visualizes outcomes By decoding at every step using a causal sliding-window diffusion process, PAN stays grounded in real-world physics and maintains long-horizon continuity, a leap beyond single-shot models. Evaluated on action fidelity, long-horizon stability, and simulative planning, PAN delivers state-of-the-art performance compared to open models and rivals leading commercial systems. For robotics, autonomy, and decision support, PAN is a foundation for the next wave of intelligent, foresight-driven AI.

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WorldExplorer: Towards Generating Fully Navigable 3D Scenes Contributions: • We introduce the first method for generating 3D scenes from text that supports high-quality view synthesis while enabling exploration across a wide range of camera poses. • We propose an iterative scene expansion strategy using video diffusion models, driven by trajectory sampling and adaptive collision detection. • We design a scene memory mechanism that conditions each video generation step on relevant past frames, improving view consistency and overall scene coherence.

MrNeRF

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🤔The more I studied diffusion language models, the more I came to appreciate the simplicity of autoregressive (AR) language models. AR models are trained to agree with what they generate, and their serving stacks are built to preserve that structure. DLMs often do neither: they lack introspective consistency, and high TPF does not necessarily translate into high real-world TPS. We propose Introspective Diffusion Language Model (I-DLM), which unifies introspection and generation in a single pass: 1. 🧑‍🎓I-DLM brings introspective consistency to DLMs with only 5B training tokens, achieving AR-thinking-level quality. 2. 🚀 I-DLM carefully trades compute for higher TPF while converting that advantage into real TPS under high-concurrency serving. 📖Website: ⌨️Code:

Chenfeng_X

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While frontier closed models like Google’s Nano Banana can autonomously produce rich interleaved content (e.g., illustrated tutorials), open-source models still lag in both task coverage and generation quality. We introduce DuoGen, a dual transformer–diffusion framework that narrows this gap via an efficient decoupled design: a pretrained Multimodal LLM performs semantic reasoning and decides when to generate images, while a Video Diffusion Transformer ensures high-fidelity, consistent visuals—without costly mixed-modality pretraining. Enabled by a new large-scale interleaved instruction-tuning dataset. Code & data will be open-sourced. Paper: Project: Work led by Min Shi 🐝 originating from his summer internship at NVIDIA Research and continuing beyond, in collaboration with Ming-Yu Liu, Xiaohui Zeng, Jiannan Huang, Yin Cui, Jialuo Li, Tsung-Yi Lin, Max Zhaoshuo Li 李赵硕 ✈️ RSS, Francesco Ferroni, Xiao Fu, Yogesh Balaji, Chieh-Yun Chen, and other colleagues 🚀

Humphrey Shi

16,824 görüntüleme • 5 ay önce

📢GaussianGPT: autoregressive 3D Gaussian scene generation. We introduce a GPT-style model that directly generates 3D Gaussian scenes, token by token, in a series of small, discrete decision steps. Generation, completion, and large-scale outpainting in a single pipeline. Unlike diffusion-based approaches, GaussianGPT explicitly models the scene distribution at every step, allowing for quite flexible scene synthesis. 🌐 ▶️ Great work by Nicolas von Lützow, Barbara Roessle, Katharina Schmid

📢GaussianGPT: autoregressive 3D Gaussian scene generation. We introduce a GPT-style model that directly generates 3D Gaussian scenes, token by token, in a series of small, discrete decision steps. Generation, completion, and large-scale outpainting in a single pipeline. Unlike diffusion-based approaches, GaussianGPT explicitly models the scene distribution at every step, allowing for quite flexible scene synthesis. 🌐 ▶️ Great work by Nicolas von Lützow, Barbara Roessle, Katharina Schmid

Matthias Niessner

152,098 görüntüleme • 3 ay önce

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

AK

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