3 个仓库
Awesome GitHub RepositoriesDiffusion Model Memory Optimizers
Frameworks that reduce the memory footprint of diffusion models for inference on consumer hardware.
Distinct from Diffusion Weight Optimizers: Focuses on inference memory optimization and precision recovery rather than training weight optimization.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Diffusion Model Memory Optimizers. Refine with filters or upvote what's useful.
Awesome Diffusion Model Memory Optimizers GitHub Repositories
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- picsart-ai-research/text2video-zero
Picsart-AI-Research/Text2Video-Zero
4,244在 GitHub 上查看Text2Video-Zero 是一个文本转视频扩散模型和框架，旨在从文本提示中合成时间上连贯的视频序列。它作为一个零样本视频生成器，重新利用预训练的图像扩散模型来创建视频内容，而无需在视频数据集上进行额外训练。 该系统包括一个条件视频合成器，允许使用深度、边缘或姿态图进行引导生成，以控制结构布局和运动。它还提供基于文本的视频编辑功能，通过自然语言指令修改现有视频片段的风格或内容。 为了管理计算需求，该项目实现了 GPU 内存优化推理。这是通过诸如 Token 合并和帧分块等技术来实现的，以减少生成过程中的 VRAM 使用。
Optimizes GPU memory usage during video generation to enable inference on consumer-grade hardware.
Pythonvideo-editingvideo-generation - nunchaku-ai/nunchaku
nunchaku-ai/nunchaku
3,883在 GitHub 上查看Nunchaku is a 4-bit model quantization library and diffusion model inference engine designed to run large-scale neural networks on consumer GPUs. It functions as a GPU-accelerated optimizer that reduces VRAM usage and increases inference speed through weight compression and memory management. The project utilizes low-rank weight decomposition and SVD weight quantization to compress models to four-bit precision while maintaining visual fidelity. It employs kernel-level operator fusion to minimize data movement and hardware-aware precision mapping to adjust numerical precision based on the unde
Provides a specialized inference engine for running large-scale diffusion models with reduced memory overhead on consumer GPUs.
Pythoncomfyuidiffusion-modelsflux - city96/comfyui-gguf
city96/ComfyUI-GGUF
3,291在 GitHub 上查看ComfyUI-GGUF is a memory optimizer and model loader for ComfyUI that enables the execution of large transformer-based generative models using quantized weights. It provides a system for loading GGUF formatted weights within a node-based diffusion interface to reduce GPU memory consumption. The project includes a quantization tool for converting standard model checkpoints into compressed binary formats and a tensor fixer to restore missing keys and correct architectures in binary model files. These utilities ensure that compressed models remain functional during inference on hardware with limi
Provides a framework for running large transformer-based generative models using quantized weights.
Python
