Posts by Dong Li
Introducing AMD EVLM: Efficient Vision-Language Models with Parameter-Space Visual Conditioning
- 22 August 2025
This blog introduces a novel and computationally efficient paradigm for Vision-Language Models (VLMs), which diverges from the conventional method of prepending visual tokens to textual input. Instead of elongating the input sequence, this approach injects visual information directly into the Large Language Model’s (LLM) parameters. It achieves this by using a vision encoder to extract image features and then employing a perceptual weight generator to transform these features into dynamic, low-rank adapter weights. These weights are temporarily integrated with the LLM’s parameters, effectively conditioning the model on the image without increasing the input length. This mechanism allows the model to achieve performance comparable to traditional VLMs on standard benchmarks while significantly reducing computational costs during inference.
AMD Hummingbird Image to Video: A Lightweight Feedback-Driven Model for Efficient Image-to-Video Generation
- 03 August 2025
In this blog, we present AMD Hummingbird-I2V, a lightweight and feedback-driven image-to-video generation model designed to deliver high-quality results efficiently on resource-constrained hardware. Image-to-video (I2V) generation has become a significant challenge in computer vision, driven by the increasing demand for automated content creation in areas such as digital media production, animation, and advertising. While recent advancements have improved video quality, deploying I2V models in practical scenarios remains challenging due to their large model sizes and high inference costs. For example, DynamiCrafter [1] employs a 1.4B-parameter U-Net and typically requires 50 denoising steps to synthesize a single video. Step-Video [2], a DiT-based model with 30B parameters, takes approximately 30 minutes to generate one video on an AMD Instinct ™ MI250 GPU, making it impractical for latency-sensitive or resource-constrained environments, such as gaming-oriented desktop GPUs. In this work, we present AMD Hummingbird-I2V, a compact and efficient diffusion-based I2V model designed for high-quality video synthesis under limited computational budgets. Hummingbird-I2V adopts a lightweight U-Net architecture with 0.9B parameters and a novel two-stage training strategy guided by reward-based feedback, resulting in substantial improvements in inference speed, model efficiency, and visual quality. To further improve output resolution with minimal overhead, we introduce a super-resolution module at the end of the pipeline. Additionally, we leverage ReNeg [3], an AMD proposed reward-guided framework for learning negative embeddings via gradient descent, to further boost visual quality. As a result, Hummingbird-I2V can generate high-quality 4K video in just 11 seconds with 16 inference steps on an AMD Radeon™ RX 7900 XTX GPU. Quantitative results on the VBench-I2V [4] benchmark show that Hummingbird-I2V achieves state-of-the-art performance among U-Net-based diffusion models and competitive results compared to significantly larger DiT-based models. We provide a detailed analysis of the model architecture, training methodology, and benchmark performance.
GEAK: Introducing Triton Kernel AI Agent & Evaluation Benchmarks
- 01 August 2025
At AMD, we are pioneering ways to accelerate AI development using AI itself, by generating accurate and efficient GPU kernels. Specifically, we are starting with the automatic generation of kernels in Triton, an open-source Python-like language for writing parallel programming code for GPUs. Today, AMD is excited to announce (a) Generating Efficient AI-centric Kernels (GEAK) for AMD GPUs, and results on (b) two Triton kernel evaluation benchmarks, where we show how AI agents can perform inference-time scaling with frontier LLMs to generate accurate and efficient kernels for AMD Instinct™ GPUs like MI250X and MI300X.