UpSample模块:Stable Diffusion Base XL U-Net中的上采样组件,由插值算法(nearest)+Conv组成。 ResNetBlock模块:借鉴ResNet模型的“残差结构”,让网络能够构建的更深的同时,将Time Embedding信息嵌入模型。 CrossAttention模块:将文本的语义信息与图像的语义信息进行Attention机制,增强输入文本Prompt对生成图像的控制。 Sel...
Stable Diffusion is great at many things, but not great at everything, and getting results in a particular style or appearance often involves a lot of work "prompt engineering". If you have a particular type of image you'd like to generate, then an alternative to spending a lo...
Subsequently, to relaunch the script, first activate the Anaconda command window (step 3), enter the stable-diffusion directory (step 5, "cd \path\to\stable-diffusion"), run "conda activate ldm" (step 6b), and then launch the dream script (step 9).Note: Tildebyte has written an alterna...
To run stable diffusion in Hugging Face, you can try one of the demos, such as the Stable Diffusion 2.1 demo. The tradeoff with Hugging Face is that you can’t customize properties as you can in DreamStudio, and it takes noticeably longer to generate an image. Stable Diffusion demo in ...
They were also trained on different data sets, with different design and implementation decisions made along the way. So although you can use both to do the same thing, they can give you totally different results. Here's the prompt I mentioned above from Stable Diffusion: And here it is ...
Along with conditioning image Stable Diffusion Video also allows providing micro-conditioning that allows more control over the generated video. It accepts the following arguments: Stable Diffusion Video also accepts micro-conditioning, in addition to the conditioning image, which allows more control over...
Next time you encounter slow download speeds when fetching PyTorch or any other package, consider implementing stable diffusion to optimize the download process. Happy coding!
(vi) 最后,我们在 GitHub - CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models 上发布了预训练的潜在扩散和自动编码模型,除了训练 DM 之外,它们还可以重复用于各种任务 [81]。 (vi) Finally, we release pretrained latent diffusion and autoencoding models at GitHub - Com...
This is a demo of what we’ll be doing to set it up and start using Stable Diffusion WebUI by AUTOMATIC1111. It’s not sped up so you can get an idea of how long it takes. As you can see, it’s very simple and straightforward. ...
Generation steps: This controls how many diffusion steps the model takes. More is generally better, though you do get diminishing returns. Seed: This controls the random seed used as the base of the image. It's a number between 1 and 4,294,967,295. If you use the same seed with the...