device = "cuda" model = UNet().to(device) ckpt = torch.load("unconditional_ckpt.pt") model.load_state_dict(ckpt) diffusion = Diffusion(img_size=64, device=device) x = diffusion.sample(model, n=16) plot_images(x) Conditional Model This model was trained on CIFAR-10 64x64 with 10 ...
PyTorch允许我们直接用self.alpha_bars[t]从self.alpha_bars里取出batch_size个数,就像用一个普通的整型索引来从数组中取出一个数一样。有些实现会用torch.gather从self.alpha_bars里取数,其作用是一样的。 我们可以随机从训练集取图片做测试,看看它们在前向过程中是怎么逐步变成噪声的。
Implementation of Denoising Diffusion Probabilistic Model in Pytorch - lucidrains/denoising-diffusion-pytorch
torch.uniform_函数详见AlexGoAlex:使用pytorch编写机器学习代码——torch中一些常用函数解析 首先这个函数用torch.zeros(batch_size,)限制维度,再使用uniform_(0, 1)将数值限制在0和1之间 def random_times(self, batch_size): # times are now uniform from 0 to 1 with shape of (batch_size,) return torc...
扩散模型(Diffusion Model)详解:直观理解、数学原理、PyTorch 实现 在过去的大半年里,以Stable Diffusion为代表的AI绘画是世界上最为火热的AI方向之一。或许大家会有疑问,Stable Diffusion里的这个"Diffusion"是什么意思?其实,扩散模型(Diffusion Model)正是Stable Diffusion中负责生成图像的模型。想要理解Stable Diffusion的...
Diffusion Model 首先定义了一个前向扩散过程,总共包含T个时间步,如下图所示: 最左边的蓝色圆圈x0表示真实自然图像,对应下方的狗子图片。 最右边的蓝色圆圈xT则表示纯高斯噪声,对应下方的噪声图片。 最中间的蓝色圆圈xt则表示加了噪声的x0,对应下方加了噪声的狗子图片。
进入ioclab/control_v1p_sd15_brightness首页。进入Files and versions页,下载diffusion_pytorch_model.safetensors。放入stable-diffusion-webui目录下的extensions/sd-webui-controlnet/models目录下。 下载config.json,放入stable-diffusion-webui目录下的extensions/sd-webui-controlnet/models目录下。
This release comprises a Python package for converting Stable Diffusion models from PyTorch to Core ML using diffusers and coremltools, as well as a Swift package to deploy the models. To get started, visit theCore ML Stable Diffusioncode repository for detailed instructions on benchmarking and ...
Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase and https://github.com/lucidrains/denoising-diffusion-pytorch. Thanks for open-sourcing! The implementation of the transformer encoder is from x-transformers by lucidrains. BibTeX @misc{rombach2021highresolution, title=...
model = instantiate_from_config(config.model) # trainer and callbacks trainer_kwargs = dict() # default logger configs default_logger_cfgs = { "wandb": { "target": "pytorch_lightning.loggers.WandbLogger", "params": { "name": nowname, "save_dir": logdir, "offline": ...