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Model_Loader用于从预训练好的stable diffusion模型提取VAE/Diffusion/CLIP模型的参数; Model_converter用于将预训练模型的参数转换到我们定义模型的参数,可直接从最上面的github下载; from clip import CLIP from encoder import VAE_Encoder from decoder import VAE_Decoder from diffusion import Diffusion import model_...
我们知道Diffusion的模型是一个生成式的模型,优化这类模型最直观的方法就是最大似然法。但是在VAE、GAN的时代,大家就已经知道直接优化一个复杂分布是不可计算的,Diffusion这种涉及复杂变化的模型也属于其中。因此我们要进行两个阶段的优化: ELBO:类似VAE的思想,但是分布不同的方法做公式转换 对具体计算公式做进一步变换...
Umar|多模态语言模型|Coding a Multimodal (Vision) Language Model from scratch in Pytorch 05:46:05 Umar《用PyTorch从零开始编写LLaMA2|Coding LLaMA 2 from scratch in PyTorch》deepseek翻译中英字幕 03:04:11 Umar 《用Pytorch从零开始编写SD|Coding Stable Diffusion from scratch in PyTorch》中英字幕 ...
git clone https://github.com/gmongaras/Diffusion_models_from_scratch.git cd Diffusion_models_from_scratch/ (Optional) If you don't want to change your environment, you can first create a virtual environment: pip install virtualenv python -m venv MyEnv/ Activate the virtual environment: http...
A diffusion model in general terms is a type of generative deep learning model that creates data from a learned denoising process. There are many variations of diffusion models with the most popular ones usually being text conditional models that can generate a certain image based on a prompt. ...
Diffusionbee默认的models是stable diffusion 1.5,要生成效果比较好的图,对prompt和negative propmpt的要求比较高,而且Diffusionbee不支持中文关键词。 Diffusionbee支持的models格式是PickleTensor,文件格式以.ckpt结尾。 stable diffusion早期训练的模型都是PickleTensor,因为这种模型容易混入有害的代码,因此后续的模型都是Safe...
开源的Stable Diffusion的发布,更让这种研究被推向火热。但是,作为生成对抗网络(GAN)的继任新秀,扩散模型虽然火热,但是背后的数学原理可能很多人也不太了解。因此,AI Summer的工作人员Sergios Karagiannakos,Nikolas Adaloglou几人发布了一篇从0开始讲解Diffusion Model背后的数学推导,全文很长,但是数学知识用到的并不...
跑通完整代码:DAhe大禾:Diffusion Model from Scratch in Pytorch中文翻译-代码 几点小纠正: 本文架构图右侧(红色框标识出)维度不应该是[192, 96],而是[256,128]。因为384/2+64=256 Introduction diffusion model是一类生成深度学习模型(generative deep learning model)。它是通过学习去噪过程(learned denoising proc...
# https://www.kaggle.com/code/vikramsandu/guided-diffusion-by-openai-from-scratch import math import os.path import matplotlib.pyplot as plt import numpy as np import torch import torch.nn.functional as F import torch.nn as nn from