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273 lines
14 KiB
Markdown
273 lines
14 KiB
Markdown
FORK from https://github.com/darkhemic/stable-diffusion-cpuonly
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Debian 10 and Debian 11 work !
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Windows NOT TESTED, visit original repo !
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# Stable Diffusion CPU only
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This fork of Stable-Diffusion doesn't require a high end graphics card and runs exclusively on your cpu. It's been tested on Debian 11
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## Requirements
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Miniconda : https://docs.conda.io/en/latest/miniconda.html
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##### Install Git
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https://git-scm.com/downloads
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version control manager for code
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we just use it to download repos from GitHub
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Must be on system PATH, When installing select the option add to system PATH
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##### Install Wget
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used to download models for projects
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Windows users need this verison
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https://eternallybored.org/misc/wget/
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download the .exe and I copied it to my C:/Windows/System directory (this isn't the correct way just the fastest to get it working)
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Install git and wget with the following command
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```
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sudo apt-get -y install git wget build-essential
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```
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## Installation of Stable-Diffusion-cpuonly
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### Download Stable-Diffusion-cpuonly
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Copy this github repository and extract the files.
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### Download the CompVis Stable-diffusion model.
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Go here and download the correct mode from here. You'll have to agree to the license setup an account but this is the bread and butter AI art generating learning model.
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https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
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copy the file to your stable-diffusion-cpuonly-main directory
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### Download the GFPGAN model
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https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth
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copy the file to your stable-diffusion-cpuponly-main directory
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create manual conda enviroment and activate ! (optional, but strongly recommended)
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### Linux - Running the install script
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open a terminal or powershell and cd to your stable-diffusion-cpuonly-main directory and run
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```
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install_sdco.sh
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```
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## Linux - Starting Stable-Diffusion-cpuonly
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Run the following command
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activate manual conda enviroment ! (optional, but strongly recommended)
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run_sdco.sh
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```
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# 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](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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[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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##
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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](Stable_Diffusion_v1_Model_Card.md).*
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The weights are available via [the CompVis organization at Hugging Face](https://huggingface.co/CompVis) under [a license which contains specific use-based restrictions to prevent misuse and harm as informed by the model card, but otherwise remains permissive](LICENSE). While commercial use is permitted under the terms of the license, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations**, since there are [known limitations and biases](Stable_Diffusion_v1_Model_Card.md#limitations-and-bias) of the weights, and research on safe and ethical deployment of general text-to-image models is an ongoing effort. **The weights are research artifacts and should be treated as such.**
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[The CreativeML OpenRAIL M license](LICENSE) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
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### Weights
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We currently provide the following checkpoints:
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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-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally
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filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](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-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
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- `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" 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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We provide a [reference script for sampling](#reference-sampling-script), but
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there also exists a [diffusers integration](#diffusers-integration), which we
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expect to see more active community development.
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#### Reference Sampling Script
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We provide a reference sampling script, which incorporates
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- a [Safety Checker Module](https://github.com/CompVis/stable-diffusion/pull/36),
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to reduce the probability of explicit outputs,
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- an [invisible watermarking](https://github.com/ShieldMnt/invisible-watermark)
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of the outputs, to help viewers [identify the images as machine-generated](scripts/tests/test_watermark.py).
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After [obtaining the `stable-diffusion-v1-*-original` 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]
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[--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS] [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT]
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[--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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--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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A simple 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
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pipe = StableDiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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use_auth_token=True
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).to("cuda")
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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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