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Found 11 papers, 8 papers with code
Seed1.5-VL Technical Report
We present Seed1. 5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning.
Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
This technical report highlights the key design decisions that enhance the performance of the medium-sized diffusion model.
FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism
Furthermore, we implement our method in a high-performance system that supports adaptive parallelization in distributed LLM training.
AffineQuant: Affine Transformation Quantization for Large Language Models
Among these techniques, Post-Training Quantization (PTQ) has emerged as a subject of considerable interest due to its noteworthy compression efficiency and cost-effectiveness in the context of training.
ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
Especially, after learning a deep understanding of pure resolution priors, ResAdapter trained on the general dataset, generates resolution-free images with personalized diffusion models while preserving their original style domain.
DiffusionGPT: LLM-Driven Text-to-Image Generation System
Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms.
AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration
Therefore, we propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training.
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
In particular, we first formulate the oscillation in PTQ and prove the problem is caused by the difference in module capacity.
Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios
Then, Next Hybrid Strategy (NHS) is designed to stack NCB and NTB in an efficient hybrid paradigm, which boosts performance in various downstream tasks.
Ranked #253 on
Image Classification
on ImageNet
(GFLOPs metric)
OMPQ: Orthogonal Mixed Precision Quantization
Instead of solving a problem of the original integer programming, we propose to optimize a proxy metric, the concept of network orthogonality, which is highly correlated with the loss of the integer programming but also easy to optimize with linear programming.
PAMS: Quantized Super-Resolution via Parameterized Max Scale
Specifically, most state-of-the-art SR models without batch normalization have a large dynamic quantization range, which also serves as another cause of performance drop.
