stable-diffusion-webui安装包，亲测有效

# Stable Diffusion Version 2    This repository contains [Stable Diffusion](https://github.com/CompVis/stable-diffusion) models trained from scratch and will be continuously updated with new checkpoints. The following list provides an overview of all currently available models. More coming soon. ## News **March 24, 2023** *Stable UnCLIP 2.1* - New stable diffusion finetune (_Stable unCLIP 2.1_, [Hugging Face](https://huggingface.co/stabilityai/)) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in [*Hierarchical Text-Conditional Image Generation with CLIP Latents*](https://arxiv.org/abs/2204.06125), and, thanks to its modularity, can be combined with other models such as [KARLO](https://github.com/kakaobrain/karlo). Comes in two variants: [*Stable unCLIP-L*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt) and [*Stable unCLIP-H*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-h.ckpt), which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available [here](doc/UNCLIP.MD). - A public demo of SD-unCLIP is already available at [clipdrop.co/stable-diffusion-reimagine](https://clipdrop.co/stable-diffusion-reimagine) **December 7, 2022** *Version 2.1* - New stable diffusion model (_Stable Diffusion 2.1-v_, [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-2-1)) at 768x768 resolution and (_Stable Diffusion 2.1-base_, [HuggingFace](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)) at 512x512 resolution, both based on the same number of parameters and architecture as 2.0 and fine-tuned on 2.0, on a less restrictive NSFW filtering of the [LAION-5B](https://laion.ai/blog/laion-5b/) dataset. Per default, the attention operation of the model is evaluated at full precision when `xformers` is not installed. To enable fp16 (which can cause numerical instabilities with the vanilla attention module on the v2.1 model) , run your script with `ATTN_PRECISION=fp16 python <thescript.py>` **November 24, 2022** *Version 2.0* - New stable diffusion model (_Stable Diffusion 2.0-v_) at 768x768 resolution. Same number of parameters in the U-Net as 1.5, but uses [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) as the text encoder and is trained from scratch. _SD 2.0-v_ is a so-called [v-prediction](https://arxiv.org/abs/2202.00512) model. - The above model is finetuned from _SD 2.0-base_, which was trained as a standard noise-prediction model on 512x512 images and is also made available. - Added a [x4 upscaling latent text-guided diffusion model](#image-upscaling-with-stable-diffusion). - New [depth-guided stable diffusion model](#depth-conditional-stable-diffusion), finetuned from _SD 2.0-base_. The model is conditioned on monocular depth estimates inferred via [MiDaS](https://github.com/isl-org/MiDaS) and can be used for structure-preserving img2img and shape-conditional synthesis.  - A [text-guided inpainting model](#image-inpainting-with-stable-diffusion), finetuned from SD _2.0-base_. We follow the [original repository](https://github.com/CompVis/stable-diffusion) and provide basic inference scripts to sample from the models. ________________ *The original Stable Diffusion model was created in a collaboration with [CompVis](https://arxiv.org/abs/2202.00512) and [RunwayML](https://runwayml.com/) and builds upon the work:* [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/> [Robin Rombach](https://github.com/rromb)\*, [Andreas Blattmann](https://github.com/ablattmann)\*, [Dominik Lorenz](https://github.com/qp-qp)\, [Patrick Esser](https://github.com/pesser), [Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/> _[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) | [GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_ and [many others](#shout-outs). Stable Diffusion is a latent text-to-image diffusion model. ________________________________ ## Requirements You can update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running ``` conda install pytorch==1.12.1 torchvision==0.13.1 -c pytorch pip install transformers==4.19.2 diffusers invisible-watermark pip install -e . ``` #### xformers efficient attention For more efficiency and speed on GPUs, we highly recommended installing the [xformers](https://github.com/facebookresearch/xformers) library. Tested on A100 with CUDA 11.4. Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those, e.g., via ```commandline export CUDA_HOME=/usr/local/cuda-11.4 conda install -c nvidia/label/cuda-11.4.0 cuda-nvcc conda install -c conda-forge gcc conda install -c conda-forge gxx_linux-64==9.5.0 ``` Then, run the following (compiling takes up to 30 min). ```commandline cd .. git clone https://github.com/facebookresearch/xformers.git cd xformers git submodule update --init --recursive pip install -r requirements.txt pip install -e . cd ../stablediffusion ``` Upon successful installation, the code will automatically default to [memory efficient attention](https://github.com/facebookresearch/xformers) for the self- and cross-attention layers in the U-Net and autoencoder. ## General Disclaimer Stable Diffusion models are general text-to-image diffusion models and therefore mirror biases and (mis-)conceptions that are present in their training data. Although efforts were made to reduce the inclusion of explicit pornographic material, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations. The weights are research artifacts and should be treated as such.** Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/stabilityai/stable-diffusion-2). The weights are available via [the StabilityAI organization at Hugging Face](https://huggingface.co/StabilityAI) under the [CreativeML Open RAIL++-M License](LICENSE-MODEL). ## Stable Diffusion v2 Stable Diffusion v2 refers to a specific configuration of the model architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. The _SD 2-v_ model produces 768x768 px outputs. Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 DDIM sampling steps show the relative improvements of the checkpoints:  ### Text-to-Image   Stable Diffusion 2 is a latent diffusion model conditioned on the penultimate text embeddings of a CLIP ViT-H/14 text encoder. We provide a [reference script for sampling](#reference-sampling-script). #### Reference Sampling Script This script incorporates an [invisible watermarking](https://github.com/ShieldMnt/invisible-watermark) of the outputs, to help viewers [identify the images as machine-generated](scripts/tests/test_watermark.py). We provide the configs for the _SD2-v_ (768px) and _SD2-base_ (512px) model. First, download the weights for [_SD2.1-v_](https://huggingface.co/stabilityai/stable-diffusion-2-1) and [_SD2.1-base_](https://huggingface.co/s