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211 lines
11 KiB
Markdown
211 lines
11 KiB
Markdown
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# Original README from CompViz/stable-diffusion
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*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous 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**
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which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).
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[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
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model.
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Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.
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Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
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this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
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With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
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See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
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## Requirements
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A suitable [conda](https://conda.io/) environment named `ldm` can be created
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and activated with:
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```
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conda env create -f environment.yaml
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conda activate ldm
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```
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You can also 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 torchvision -c pytorch
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pip install transformers==4.19.2
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pip install -e .
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```
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## Stable Diffusion v1
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Stable Diffusion v1 refers to a specific configuration of the model
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architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
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and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
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then finetuned on 512x512 images.
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*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
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in its training data.
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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/CompVis/stable-diffusion).
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Research into the safe deployment of general text-to-image models is an ongoing effort. To prevent misuse and harm, we currently provide access to the checkpoints only for [academic research purposes upon request](https://stability.ai/academia-access-form).
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**This is an experiment in safe and community-driven publication of a capable and general text-to-image model. We are working on a public release with a more permissive license that also incorporates ethical considerations.***
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[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form)
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### Weights
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We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
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which were trained as follows,
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- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
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194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
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- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
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515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
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filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
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- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
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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 PLMS sampling
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steps show the relative improvements of the checkpoints:
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### Text-to-Image with Stable Diffusion
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Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
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#### Sampling Script
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After [obtaining the weights](#weights), link them
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```
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mkdir -p models/ldm/stable-diffusion-v1/
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ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt
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```
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and sample with
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```
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python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
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```
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By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler,
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and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).
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```commandline
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usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA] [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS]
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[--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] [--seed SEED] [--precision {full,autocast}]
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optional arguments:
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-h, --help show this help message and exit
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--prompt [PROMPT] the prompt to render
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--outdir [OUTDIR] dir to write results to
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--skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples
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--skip_save do not save individual samples. For speed measurements.
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--ddim_steps DDIM_STEPS
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number of ddim sampling steps
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--plms use plms sampling
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--laion400m uses the LAION400M model
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--fixed_code if enabled, uses the same starting code across samples
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--ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling
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--n_iter N_ITER sample this often
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--H H image height, in pixel space
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--W W image width, in pixel space
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--C C latent channels
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--f F downsampling factor
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--n_samples N_SAMPLES
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how many samples to produce for each given prompt. A.k.a. batch size
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(note that the seeds for each image in the batch will be unavailable)
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--n_rows N_ROWS rows in the grid (default: n_samples)
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--scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
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--from-file FROM_FILE
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if specified, load prompts from this file
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--config CONFIG path to config which constructs model
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--ckpt CKPT path to checkpoint of model
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--seed SEED the seed (for reproducible sampling)
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--precision {full,autocast}
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evaluate at this precision
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```
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Note: The inference config for all v1 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. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
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which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
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#### Diffusers Integration
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Another way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers)
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```py
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# make sure you're logged in with `huggingface-cli login`
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from torch import autocast
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from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
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pipe = StableDiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-3-diffusers",
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use_auth_token=True
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)
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prompt = "a photo of an astronaut riding a horse on mars"
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with autocast("cuda"):
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image = pipe(prompt)["sample"][0]
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image.save("astronaut_rides_horse.png")
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```
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### Image Modification with Stable Diffusion
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By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different
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tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script,
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we provide a script to perform image modification with Stable Diffusion.
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The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
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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
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```
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Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
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Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example.
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**Input**
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**Outputs**
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This procedure can, for example, also be used to upscale samples from the base model.
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## Comments
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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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- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
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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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