mirror of
https://github.com/Stability-AI/stablediffusion.git
synced 2024-12-22 15:44:58 +00:00
302 lines
18 KiB
Markdown
302 lines
18 KiB
Markdown
# Stable Diffusion Version 2
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This repository contains [Stable Diffusion](https://github.com/CompVis/stable-diffusion) models trained from scratch and will be continuously updated with
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new checkpoints. The following list provides an overview of all currently available models. More coming soon.
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## News
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**March 24, 2023**
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*Stable UnCLIP 2.1*
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- 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).
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- A public demo of SD-unCLIP is already available at clipdrop.co/stable-diffusion-reimagine
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**December 7, 2022**
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*Version 2.1*
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- 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.
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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>`
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**November 24, 2022**
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*Version 2.0*
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- 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.
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- 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.
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- Added a [x4 upscaling latent text-guided diffusion model](#image-upscaling-with-stable-diffusion).
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- 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.
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- A [text-guided inpainting model](#image-inpainting-with-stable-diffusion), finetuned from SD _2.0-base_.
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We follow the [original repository](https://github.com/CompVis/stable-diffusion) and provide basic inference scripts to sample from the models.
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________________
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*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:*
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[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
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[Robin Rombach](https://github.com/rromb)\*,
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[Andreas Blattmann](https://github.com/ablattmann)\*,
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[Dominik Lorenz](https://github.com/qp-qp)\,
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[Patrick Esser](https://github.com/pesser),
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[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
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_[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) |
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[GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_
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and [many others](#shout-outs).
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Stable Diffusion is a latent text-to-image diffusion model.
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________________________________
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## Requirements
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You can update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
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```
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conda install pytorch==1.12.1 torchvision==0.13.1 -c pytorch
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pip install transformers==4.19.2 diffusers invisible-watermark
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pip install -e .
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```
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#### xformers efficient attention
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For more efficiency and speed on GPUs,
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we highly recommended installing the [xformers](https://github.com/facebookresearch/xformers)
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library.
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Tested on A100 with CUDA 11.4.
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Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those, e.g., via
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```commandline
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export CUDA_HOME=/usr/local/cuda-11.4
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conda install -c nvidia/label/cuda-11.4.0 cuda-nvcc
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conda install -c conda-forge gcc
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conda install -c conda-forge gxx_linux-64==9.5.0
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```
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Then, run the following (compiling takes up to 30 min).
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```commandline
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cd ..
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git clone https://github.com/facebookresearch/xformers.git
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cd xformers
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git submodule update --init --recursive
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pip install -r requirements.txt
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pip install -e .
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cd ../stablediffusion
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```
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Upon successful installation, the code will automatically default to [memory efficient attention](https://github.com/facebookresearch/xformers)
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for the self- and cross-attention layers in the U-Net and autoencoder.
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## General Disclaimer
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Stable Diffusion models are general text-to-image diffusion models and therefore mirror biases and (mis-)conceptions that are present
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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.
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The weights are research artifacts and should be treated as such.**
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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).
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The weights are available via [the StabilityAI organization at Hugging Face](https://huggingface.co/StabilityAI) under the [CreativeML Open RAIL++-M License](LICENSE-MODEL).
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## Stable Diffusion v2
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Stable Diffusion v2 refers to a specific configuration of the model
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architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet
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and OpenCLIP ViT-H/14 text encoder for the diffusion model. The _SD 2-v_ model produces 768x768 px outputs.
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Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
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5.0, 6.0, 7.0, 8.0) and 50 DDIM sampling steps show the relative improvements of the checkpoints:
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### Text-to-Image
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Stable Diffusion 2 is a latent diffusion model conditioned on the penultimate text embeddings of a CLIP ViT-H/14 text encoder.
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We provide a [reference script for sampling](#reference-sampling-script).
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#### Reference Sampling Script
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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).
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We provide the configs for the _SD2-v_ (768px) and _SD2-base_ (512px) model.
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First, download the weights for [_SD2.1-v_](https://huggingface.co/stabilityai/stable-diffusion-2-1) and [_SD2.1-base_](https://huggingface.co/stabilityai/stable-diffusion-2-1-base).
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To sample from the _SD2.1-v_ model, run the following:
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```
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python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/v2-inference-v.yaml --H 768 --W 768
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```
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or try out the Web Demo: [](https://huggingface.co/spaces/stabilityai/stable-diffusion).
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To sample from the base model, use
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```
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python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt <path/to/model.ckpt/> --config <path/to/config.yaml/>
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```
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By default, this uses the [DDIM sampler](https://arxiv.org/abs/2010.02502), and renders images of size 768x768 (which it was trained on) in 50 steps.
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Empirically, the v-models can be sampled with higher guidance scales.
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Note: The inference config for all model versions is designed to be used with EMA-only checkpoints.
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For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
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non-EMA to EMA weights.
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#### Enable Intel® Extension for PyTorch* optimizations in Text-to-Image script
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If you're planning on running Text-to-Image on Intel® CPU, try to sample an image with TorchScript and Intel® Extension for PyTorch* optimizations. Intel® Extension for PyTorch* extends PyTorch by enabling up-to-date features optimizations for an extra performance boost on Intel® hardware. It can optimize memory layout of the operators to Channel Last memory format, which is generally beneficial for Intel CPUs, take advantage of the most advanced instruction set available on a machine, optimize operators and many more.
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**Prerequisites**
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Before running the script, make sure you have all needed libraries installed. (the optimization was checked on `Ubuntu 20.04`). Install [jemalloc](https://github.com/jemalloc/jemalloc), [numactl](https://linux.die.net/man/8/numactl), Intel® OpenMP and Intel® Extension for PyTorch*.
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```bash
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apt-get install numactl libjemalloc-dev
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pip install intel-openmp
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pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable
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```
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To sample from the _SD2.1-v_ model with TorchScript+IPEX optimizations, run the following. Remember to specify desired number of instances you want to run the program on ([more](https://github.com/intel/intel-extension-for-pytorch/blob/master/intel_extension_for_pytorch/cpu/launch.py#L48)).
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```
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MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-v-fp32.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex
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```
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To sample from the base model with IPEX optimizations, use
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```
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MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-fp32.yaml --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex
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```
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If you're using a CPU that supports `bfloat16`, consider sample from the model with bfloat16 enabled for a performance boost, like so
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```bash
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# SD2.1-v
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MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16
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# SD2.1-base
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MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16
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```
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### Image Modification with Stable Diffusion
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#### Depth-Conditional Stable Diffusion
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To augment the well-established [img2img](https://github.com/CompVis/stable-diffusion#image-modification-with-stable-diffusion) functionality of Stable Diffusion, we provide a _shape-preserving_ stable diffusion model.
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Note that the original method for image modification introduces significant semantic changes w.r.t. the initial image.
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If that is not desired, download our [depth-conditional stable diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-depth) model and the `dpt_hybrid` MiDaS [model weights](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt), place the latter in a folder `midas_models` and sample via
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```
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python scripts/gradio/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
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```
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or
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```
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streamlit run scripts/streamlit/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
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```
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This method can be used on the samples of the base model itself.
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For example, take [this sample](assets/stable-samples/depth2img/old_man.png) generated by an anonymous discord user.
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Using the [gradio](https://gradio.app) or [streamlit](https://streamlit.io/) script `depth2img.py`, the MiDaS model first infers a monocular depth estimate given this input,
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and the diffusion model is then conditioned on the (relative) depth output.
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<p align="center">
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<b> depth2image </b><br/>
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<img src=assets/stable-samples/depth2img/d2i.gif>
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</p>
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This model is particularly useful for a photorealistic style; see the [examples](assets/stable-samples/depth2img).
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For a maximum strength of 1.0, the model removes all pixel-based information and only relies on the text prompt and the inferred monocular depth estimate.
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#### Classic Img2Img
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For running the "classic" img2img, use
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```
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python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8 --ckpt <path/to/model.ckpt>
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```
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and adapt the checkpoint and config paths accordingly.
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### Image Upscaling with Stable Diffusion
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After [downloading the weights](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler), run
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```
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python scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml <path-to-checkpoint>
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```
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or
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```
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streamlit run scripts/streamlit/superresolution.py -- configs/stable-diffusion/x4-upscaling.yaml <path-to-checkpoint>
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```
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for a Gradio or Streamlit demo of the text-guided x4 superresolution model.
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This model can be used both on real inputs and on synthesized examples. For the latter, we recommend setting a higher
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`noise_level`, e.g. `noise_level=100`.
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### Image Inpainting with Stable Diffusion
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[Download the SD 2.0-inpainting checkpoint](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) and run
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```
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python scripts/gradio/inpainting.py configs/stable-diffusion/v2-inpainting-inference.yaml <path-to-checkpoint>
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```
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or
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```
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streamlit run scripts/streamlit/inpainting.py -- configs/stable-diffusion/v2-inpainting-inference.yaml <path-to-checkpoint>
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```
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for a Gradio or Streamlit demo of the inpainting model.
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This scripts adds invisible watermarking to the demo in the [RunwayML](https://github.com/runwayml/stable-diffusion/blob/main/scripts/inpaint_st.py) repository, but both should work interchangeably with the checkpoints/configs.
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## Shout-Outs
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- Thanks to [Hugging Face](https://huggingface.co/) and in particular [Apolinário](https://github.com/apolinario) for support with our model releases!
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- Stable Diffusion would not be possible without [LAION](https://laion.ai/) and their efforts to create open, large-scale datasets.
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- The [DeepFloyd team](https://twitter.com/deepfloydai) at Stability AI, for creating the subset of [LAION-5B](https://laion.ai/blog/laion-5b/) dataset used to train the model.
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- Stable Diffusion 2.0 uses [OpenCLIP](https://laion.ai/blog/large-openclip/), trained by [Romain Beaumont](https://github.com/rom1504).
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- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
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and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
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Thanks for open-sourcing!
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- [CompVis](https://github.com/CompVis/stable-diffusion) initial stable diffusion release
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- [Patrick](https://github.com/pesser)'s [implementation](https://github.com/runwayml/stable-diffusion/blob/main/scripts/inpaint_st.py) of the streamlit demo for inpainting.
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- `img2img` is an application of [SDEdit](https://arxiv.org/abs/2108.01073) by [Chenlin Meng](https://cs.stanford.edu/~chenlin/) from the [Stanford AI Lab](https://cs.stanford.edu/~ermon/website/).
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- [Kat's implementation]((https://github.com/CompVis/latent-diffusion/pull/51)) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, and [more](https://github.com/crowsonkb/k-diffusion).
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- [DPMSolver](https://arxiv.org/abs/2206.00927) [integration](https://github.com/CompVis/stable-diffusion/pull/440) by [Cheng Lu](https://github.com/LuChengTHU).
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- Facebook's [xformers](https://github.com/facebookresearch/xformers) for efficient attention computation.
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- [MiDaS](https://github.com/isl-org/MiDaS) for monocular depth estimation.
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## License
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The code in this repository is released under the MIT License.
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The weights are available via [the StabilityAI organization at Hugging Face](https://huggingface.co/StabilityAI), and released under the [CreativeML Open RAIL++-M License](LICENSE-MODEL) License.
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## BibTeX
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```
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@misc{rombach2021highresolution,
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title={High-Resolution Image Synthesis with Latent Diffusion Models},
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author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
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year={2021},
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eprint={2112.10752},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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