本文是关于如何使用cuda和Stable-Diffusion生成视频的完整指南,将使用cuda来加速视频生成,并且可以使用Kaggle的TESLA GPU来免费执行我们的模型。 #install the diffuser package #pip install --upgrade pip !pi…
A method to fine tune weights for CLIP and Unet, the language model and the actual image de-noiser used by Stable Diffusion, generously donated to the world by our friends at Novel AI in autumn 2022. Works in the same way as Lora except for sharing weights for some layers. Multiplier ca...
Pipeline 使用如下 # Load the pipelinemodel_id="stabilityai/stable-diffusion-2-1-base"pipe=StableDiffusionPipeline.from_pretrained(model_id).to(device)# Set up a generator for reproducibilitygenerator=torch.Generator(device=device).manual_seed(42)# Run the pipeline, showing some of the available a...
(如果是要从Hagging Face加载模型,也可以指定模型的url与token。URL的格式应为‘runwayml/stable-diffusion-v1-5’这种。原始checkpoint会提取到models/dreambooth/MODELNAME/working目录) 然后点击Create,耗时1-2分钟左右。创建完毕后,UI会显示新模型目录已经设置好。 选择刚创建好的模型: 回到顶部 3. Class概念解释...
简介:本文是关于如何使用cuda和Stable-Diffusion生成视频的完整指南,将使用cuda来加速视频生成,并且可以使用Kaggle的TESLA GPU来免费执行我们的模型。 #install the diffuser package#pip install --upgrade pip!pipinstall--upgradediffuserstransformersscipy#load the model from stable-diffusion model cardimporttorch ...
Before diving into the theoretical aspects of how Stable Diffusion functions, let's try it out a bit 🤗. In this section, we show how you can run text to image inference in just a few lines of code! Setup First, please make sure you are using a GPU runtime to run this notebook,...
LoRAs (Low-Rank Adaptations) are smaller files (anywhere from 1MB ~ 200MB) that you combine with an existing Stable Diffusion checkpoint models to introduce new concepts to your models, so that your m
--strength [STRENGTH]: diffusion strength to apply to the input image (default 0.75) --token [TOKEN]: specify a Huggingface user access token at the command line instead of reading it from a file (default is a file) --vae-slicing: use less memory when creating large batches of images ...
Kicking the resolution up to 768x768, Stable Diffusion likes to have quite a bit more VRAM in order to run well. Memory bandwidth also becomes more important, at least at the lower end of the spectrum. The relative positioning of the various Nvidia GPUs doesn't shift too much, and AMD'...
To get the lowest inference time per image, use the maximum batch size--n_samplesthat can fit on the GPU. Inference time per image will reduce on increasing the batch size, but the required VRAM will increase. If you get a CUDA out of memory error, try reducing the batch size--n_sam...