2. 隐空间扩散强化学习 (Latent Diffusion Reinforcement Learning) 2.1 两阶段隐空间扩散模型的训练 (Two-Stage LDM training) 2.2 Latent Diffusion-Constrained Q-Learning (LDCQ) 2.3 Latent Diffusion Goal Conditioning (LDGC) 3. 实验与分析 3.1 分析隐空间多峰属性的时序抽象 (Temporal abstraction induces mul...
Detailed Information on Denoising Diffusion Models DM可以用信噪比SNR(t)=\frac {\alpha^2_t} {\sigma^2_t}组成的序列(\alpha_t)^T_{t=1}和(\sigma_t)^T_{t=1}来指定,故从数据样本x_0开始,定义一个前向diffusion过程q:q(x_t|x_0)=N(x_t|\alpha_tx_0,\sigma^2_tI)\\ 指定s <t时,...
The pre-training process of Latent Diffusion involves training a diffusion model on a large corpus of text. This diffusion model is trained to generate text in an autoregressive manner, where each token is generated conditioned on the previous tokens. The training objective is to minimize the reco...
Since CLIP offers a shared image/text feature space, and RDMs learn to cover a neighborhood of a given example during training, we can directly take a CLIP text embedding of a given prompt and condition on it. Run this mode viapython scripts/knn2img.py --prompt "a happy bear reading a...
The training process typically consists of the following steps: 4.Initialization: The model is initialized with a simple prior distribution, often a Gaussian or uniform distribution. 5.Diffusion Steps: The diffusion steps are performed iteratively by applying a series of transformation functions to the...
In this work, we introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation. Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model through text ...
Pre Idea Motivation&Solution Method(Model) Architecture & Scale Micro-Conditioning Multi-Aspect Training Improved Autoencoder Putting Everything Together Future Work EDM-framework Critique Unknown [论文速览] SDXL@ Improving Latent Diffusion Models for High-Resolution Image Synthesis Pre title: SDXL: Impro...
Latent Diffusion Models Requirements Model Zoo Pretrained Autoencoding Models Get the models Pretrained LDMs Get the models Sampling with unconditional models Inpainting Train your own LDMs Data preparation Faces LSUN ImageNet Model Training Training autoencoder models ...
To overcome this limitation, this paper introduces an innovative solution that utilizes a pretrained diffusion model, thereby obviating the necessity for additional training steps. The scheme proposes a Feature Normalization Mapping Module with Cross-Attention Mechanism (INN-FMM) based on the dual-path...
Training can be started by runningCUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,where <config_spec> is one of {celebahq-ldm-vq-4(f=4, VQ-reg. autoencoder, spatial size 64x64x3),ffhq-ldm-vq-4(f=4, VQ-reg. auto...