REVIEW 5 cited by
Grid: Omni Visual Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Visual generation has witnessed remarkable progress in single-image tasks, yet extending these capabilities to temporal sequences remains challenging. Current approaches either build specialized video models from scratch with enormous computational costs or add separate motion modules to image generators, both requiring learning temporal dynamics anew. We observe that modern image generation models possess underutilized potential in handling structured layouts with implicit temporal understanding. Building on this insight, we introduce GRID, which reformulates temporal sequences as grid layouts, enabling holistic processing of visual sequences while leveraging existing model capabilities. Through a parallel flow-matching training strategy with coarse-to-fine scheduling, our approach achieves up to 67 faster inference speeds while using <1/1000 of the computational resources compared to specialized models. Extensive experiments demonstrate that GRID not only excels in temporal tasks from Text-to-Video to 3D Editing but also preserves strong performance in image generation, establishing itself as an efficient and versatile omni-solution for visual generation.
Forward citations
Cited by 5 Pith papers
-
DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams
DataClaw0 introduces an agentic data-tailoring paradigm, a 9B model trained on a synthetically generated dataset, and a new benchmark, claiming improved downstream adaptation in video generation, VQA, and GUI navigati...
-
ReMoT: Reinforcement Learning with Motion Contrast Triplets
Training a 4B vision-language model on rule-generated motion-contrast triplets with GRPO lifts spatio-temporal QA accuracy by about 17 points on the authors' own benchmark and by smaller margins on standard benchmarks.
-
Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack
LBW embeds watermarks into autoregressive image token maps by biasing token sampling toward a secret green list and detects them with a z-test on green-token counts.
-
OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data
OmniConsistency is a style-agnostic consistency module for Flux that preserves structure and details during stylization with arbitrary LoRAs, reaching GPT-4o-level content consistency.
-
RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers
A decoupled-attention adapter transfers image-pair edits to new photos in diffusion transformers, trained with a new 218-task visual editing dataset.
Discussion (0). Sign in to comment.