Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

added assertion checks for out-of-bound arguments; added various copyright and license agreement files

Browse Source
This commit is contained in:
Lincoln Stein 2022-08-24 09:22:04 -04:00
parent 7a67d3d837
commit 3393b8cad1
7 changed files with 572 additions and 235 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
LICENSE
View File

@ -1,9 +1,27 @@
All rights reserved by the authors.
You must not distribute the weights provided to you directly or indirectly without explicit consent of the authors.
You must not distribute harmful, offensive, dehumanizing content or otherwise harmful representations of people or their environments, cultures, religions, etc. produced with the model weights
or other generated content described in the "Misuse and Malicious Use" section in the model card.
The model weights are provided for research purposes only.
MIT License
Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
This software is derived from a fork of the source code available from
https://github.com/pesser/stable-diffusion and
https://github.com/CompViz/stable-diffusion. They carry the following
copyrights:
Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
Please see individual source code files for copyright and authorship
attributions.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
@ -11,4 +29,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
SOFTWARE.

294
LICENSE-ModelWeights.txt Normal file
View File

@ -0,0 +1,294 @@
Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
CreativeML Open RAIL-M
dated August 22, 2022
Section I: PREAMBLE
Multimodal generative models are being widely adopted and used, and
have the potential to transform the way artists, among other
individuals, conceive and benefit from AI or ML technologies as a tool
for content creation.
Notwithstanding the current and potential benefits that these
artifacts can bring to society at large, there are also concerns about
potential misuses of them, either due to their technical limitations
or ethical considerations.
In short, this license strives for both the open and responsible
downstream use of the accompanying model. When it comes to the open
character, we took inspiration from open source permissive licenses
regarding the grant of IP rights. Referring to the downstream
responsible use, we added use-based restrictions not permitting the
use of the Model in very specific scenarios, in order for the licensor
to be able to enforce the license in case potential misuses of the
Model may occur. At the same time, we strive to promote open and
responsible research on generative models for art and content
generation.
Even though downstream derivative versions of the model could be
released under different licensing terms, the latter will always have
to include - at minimum - the same use-based restrictions as the ones
in the original license (this license). We believe in the intersection
between open and responsible AI development; thus, this License aims
to strike a balance between both in order to enable responsible
open-science in the field of AI.
This License governs the use of the model (and its derivatives) and is
informed by the model card associated with the model.
NOW THEREFORE, You and Licensor agree as follows:
1. Definitions
- "License" means the terms and conditions for use, reproduction, and
Distribution as defined in this document.
- "Data" means a collection of information and/or content extracted
from the dataset used with the Model, including to train, pretrain,
or otherwise evaluate the Model. The Data is not licensed under this
License.
- "Output" means the results of operating a Model as embodied in
informational content resulting therefrom.
- "Model" means any accompanying machine-learning based assemblies
(including checkpoints), consisting of learnt weights, parameters
(including optimizer states), corresponding to the model
architecture as embodied in the Complementary Material, that have
been trained or tuned, in whole or in part on the Data, using the
Complementary Material.
- "Derivatives of the Model" means all modifications to the Model,
works based on the Model, or any other model which is created or
initialized by transfer of patterns of the weights, parameters,
activations or output of the Model, to the other model, in order to
cause the other model to perform similarly to the Model, including -
but not limited to - distillation methods entailing the use of
intermediate data representations or methods based on the generation
of synthetic data by the Model for training the other model.
- "Complementary Material" means the accompanying source code and
scripts used to define, run, load, benchmark or evaluate the Model,
and used to prepare data for training or evaluation, if any. This
includes any accompanying documentation, tutorials, examples, etc,
if any.
- "Distribution" means any transmission, reproduction, publication or
other sharing of the Model or Derivatives of the Model to a third
party, including providing the Model as a hosted service made
available by electronic or other remote means - e.g. API-based or
web access.
- "Licensor" means the copyright owner or entity authorized by the
copyright owner that is granting the License, including the persons
or entities that may have rights in the Model and/or distributing
the Model.
- "You" (or "Your") means an individual or Legal Entity exercising
permissions granted by this License and/or making use of the Model
for whichever purpose and in any field of use, including usage of
the Model in an end-use application - e.g. chatbot, translator,
image generator.
- "Third Parties" means individuals or legal entities that are not
under common control with Licensor or You.
- "Contribution" means any work of authorship, including the original
version of the Model and any modifications or additions to that
Model or Derivatives of the Model thereof, that is intentionally
submitted to Licensor for inclusion in the Model by the copyright
owner or by an individual or Legal Entity authorized to submit on
behalf of the copyright owner. For the purposes of this definition,
"submitted" means any form of electronic, verbal, or written
communication sent to the Licensor or its representatives, including
but not limited to communication on electronic mailing lists, source
code control systems, and issue tracking systems that are managed
by, or on behalf of, the Licensor for the purpose of discussing and
improving the Model, but excluding communication that is
conspicuously marked or otherwise designated in writing by the
copyright owner as "Not a Contribution."
- "Contributor" means Licensor and any individual or Legal Entity on
behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Model.
Section II: INTELLECTUAL PROPERTY RIGHTS
Both copyright and patent grants apply to the Model, Derivatives of
the Model and Complementary Material. The Model and Derivatives of the
Model are subject to additional terms as described in Section III.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare, publicly display, publicly
perform, sublicense, and distribute the Complementary Material, the
Model, and Derivatives of the Model.
3. Grant of Patent License. Subject to the terms and conditions of
this License and where and as applicable, each Contributor hereby
grants to You a perpetual, worldwide, non-exclusive, no-charge,
royalty-free, irrevocable (except as stated in this paragraph) patent
license to make, have made, use, offer to sell, sell, import, and
otherwise transfer the Model and the Complementary Material, where
such license applies only to those patent claims licensable by such
Contributor that are necessarily infringed by their Contribution(s)
alone or by combination of their Contribution(s) with the Model to
which such Contribution(s) was submitted. If You institute patent
litigation against any entity (including a cross-claim or counterclaim
in a lawsuit) alleging that the Model and/or Complementary Material or
a Contribution incorporated within the Model and/or Complementary
Material constitutes direct or contributory patent infringement, then
any patent licenses granted to You under this License for the Model
and/or Work shall terminate as of the date such litigation is asserted
or filed.
Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
4. Distribution and Redistribution. You may host for Third Party
remote access purposes (e.g. software-as-a-service), reproduce and
distribute copies of the Model or Derivatives of the Model thereof in
any medium, with or without modifications, provided that You meet the
following conditions: Use-based restrictions as referenced in
paragraph 5 MUST be included as an enforceable provision by You in any
type of legal agreement (e.g. a license) governing the use and/or
distribution of the Model or Derivatives of the Model, and You shall
give notice to subsequent users You Distribute to, that the Model or
Derivatives of the Model are subject to paragraph 5. This provision
does not apply to the use of Complementary Material. You must give
any Third Party recipients of the Model or Derivatives of the Model a
copy of this License; You must cause any modified files to carry
prominent notices stating that You changed the files; You must retain
all copyright, patent, trademark, and attribution notices excluding
those notices that do not pertain to any part of the Model,
Derivatives of the Model. You may add Your own copyright statement to
Your modifications and may provide additional or different license
terms and conditions - respecting paragraph 4.a. - for use,
reproduction, or Distribution of Your modifications, or for any such
Derivatives of the Model as a whole, provided Your use, reproduction,
and Distribution of the Model otherwise complies with the conditions
stated in this License.
5. Use-based restrictions. The restrictions set forth in Attachment A
are considered Use-based restrictions. Therefore You cannot use the
Model and the Derivatives of the Model for the specified restricted
uses. You may use the Model subject to this License, including only
for lawful purposes and in accordance with the License. Use may
include creating any content with, finetuning, updating, running,
training, evaluating and/or reparametrizing the Model. You shall
require all of Your users who use the Model or a Derivative of the
Model to comply with the terms of this paragraph (paragraph 5).
6. The Output You Generate. Except as set forth herein, Licensor
claims no rights in the Output You generate using the Model. You are
accountable for the Output you generate and its subsequent uses. No
use of the output can contravene any provision as stated in the
License.
Section IV: OTHER PROVISIONS
7. Updates and Runtime Restrictions. To the maximum extent permitted
by law, Licensor reserves the right to restrict (remotely or
otherwise) usage of the Model in violation of this License, update the
Model through electronic means, or modify the Output of the Model
based on updates. You shall undertake reasonable efforts to use the
latest version of the Model.
8. Trademarks and related. Nothing in this License permits You to make
use of Licensors’ trademarks, trade names, logos or to otherwise
suggest endorsement or misrepresent the relationship between the
parties; and any rights not expressly granted herein are reserved by
the Licensors.
9. Disclaimer of Warranty. Unless required by applicable law or agreed
to in writing, Licensor provides the Model and the Complementary
Material (and each Contributor provides its Contributions) on an "AS
IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
express or implied, including, without limitation, any warranties or
conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR
A PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Model, Derivatives of
the Model, and the Complementary Material and assume any risks
associated with Your exercise of permissions under this License.
10. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise, unless
required by applicable law (such as deliberate and grossly negligent
acts) or agreed to in writing, shall any Contributor be liable to You
for damages, including any direct, indirect, special, incidental, or
consequential damages of any character arising as a result of this
License or out of the use or inability to use the Model and the
Complementary Material (including but not limited to damages for loss
of goodwill, work stoppage, computer failure or malfunction, or any
and all other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
11. Accepting Warranty or Additional Liability. While redistributing
the Model, Derivatives of the Model and the Complementary Material
thereof, You may choose to offer, and charge a fee for, acceptance of
support, warranty, indemnity, or other liability obligations and/or
rights consistent with this License. However, in accepting such
obligations, You may act only on Your own behalf and on Your sole
responsibility, not on behalf of any other Contributor, and only if
You agree to indemnify, defend, and hold each Contributor harmless for
any liability incurred by, or claims asserted against, such
Contributor by reason of your accepting any such warranty or
additional liability.
12. If any provision of this License is held to be invalid, illegal or
unenforceable, the remaining provisions shall be unaffected thereby
and remain valid as if such provision had not been set forth herein.
END OF TERMS AND CONDITIONS
Attachment A
Use Restrictions
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national, federal, state,
local or international law or regulation;
- For the purpose of exploiting, harming or attempting to exploit or
harm minors in any way;
- To generate or disseminate verifiably false information and/or
content with the purpose of harming others;
- To generate or disseminate personal identifiable information that
can be used to harm an individual;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an
individual’s legal rights or otherwise creates or modifies a
binding, enforceable obligation;
pp- For any use intended to or which has the effect of discriminating
against or harming individuals or groups based on online or offline
social behavior or known or predicted personal or personality
characteristics;
- To exploit any of the vulnerabilities of a specific group of persons
based on their age, social, physical or mental characteristics, in
order to materially distort the behavior of a person pertaining to
that group in a manner that causes or is likely to cause that person
or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating
against individuals or groups based on legally protected
characteristics or categories;
- To provide medical advice and medical results interpretation;
- To generate or disseminate information for the purpose to be used
for administration of justice, law enforcement, immigration or
asylum processes, such as predicting an individual will commit
fraud/crime commitment (e.g. by text profiling, drawing causal
relationships between assertions made in documents, indiscriminate
and arbitrarily-targeted use).

210
README-CompViz.md Normal file
View File

@ -0,0 +1,210 @@
# Original README from CompViz/stable-diffusion
*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:*
[**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**
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/).
![txt2img-stable2](assets/stable-samples/txt2img/merged-0006.png)
[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
model.
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.
Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
## Requirements
A suitable [conda](https://conda.io/) environment named `ldm` can be created
and activated with:
```
conda env create -f environment.yaml
conda activate ldm
```
You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
```
conda install pytorch torchvision -c pytorch
pip install transformers==4.19.2
pip install -e .
```
## Stable Diffusion v1
Stable Diffusion v1 refers to a specific configuration of the model
architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
then finetuned on 512x512 images.
*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
in its training data.
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).
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).
**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.***
[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form)
### Weights
We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
which were trained as follows,
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
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`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
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)).
- `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).
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 PLMS sampling
steps show the relative improvements of the checkpoints:
![sd evaluation results](assets/v1-variants-scores.jpg)
### Text-to-Image with Stable Diffusion
![txt2img-stable2](assets/stable-samples/txt2img/merged-0005.png)
![txt2img-stable2](assets/stable-samples/txt2img/merged-0007.png)
Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
#### Sampling Script
After [obtaining the weights](#weights), link them
```
mkdir -p models/ldm/stable-diffusion-v1/
ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt
```
and sample with
```
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
```
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,
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`).
```commandline
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]
[--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] [--seed SEED] [--precision {full,autocast}]
optional arguments:
-h, --help show this help message and exit
--prompt [PROMPT] the prompt to render
--outdir [OUTDIR] dir to write results to
--skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples
--skip_save do not save individual samples. For speed measurements.
--ddim_steps DDIM_STEPS
number of ddim sampling steps
--plms use plms sampling
--laion400m uses the LAION400M model
--fixed_code if enabled, uses the same starting code across samples
--ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling
--n_iter N_ITER sample this often
--H H image height, in pixel space
--W W image width, in pixel space
--C C latent channels
--f F downsampling factor
--n_samples N_SAMPLES
how many samples to produce for each given prompt. A.k.a. batch size
(note that the seeds for each image in the batch will be unavailable)
--n_rows N_ROWS rows in the grid (default: n_samples)
--scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
--from-file FROM_FILE
if specified, load prompts from this file
--config CONFIG path to config which constructs model
--ckpt CKPT path to checkpoint of model
--seed SEED the seed (for reproducible sampling)
--precision {full,autocast}
evaluate at this precision
```
Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints.
For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
#### Diffusers Integration
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)
```py
# make sure you're logged in with `huggingface-cli login`
from torch import autocast
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-3-diffusers",
use_auth_token=True
)
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt)["sample"][0]
image.save("astronaut_rides_horse.png")
```
### Image Modification with Stable Diffusion
By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different
tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script,
we provide a script to perform image modification with Stable Diffusion.
The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
```
python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8
```
Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
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.
**Input**
![sketch-in](assets/stable-samples/img2img/sketch-mountains-input.jpg)
**Outputs**
![out3](assets/stable-samples/img2img/mountains-3.png)
![out2](assets/stable-samples/img2img/mountains-2.png)
This procedure can, for example, also be used to upscale samples from the base model.
## Comments
- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
Thanks for open-sourcing!
- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
## BibTeX
```
@misc{rombach2021highresolution,
title={High-Resolution Image Synthesis with Latent Diffusion Models},
author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
year={2021},
eprint={2112.10752},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```

243
README.md
View File

@ -102,6 +102,12 @@ do not need to add up to 1.
## Changes
* v1.08 (?? August 2022)
* Escape single quotes on the dream> command before trying to parse. This avoids
parse errors.
* Removed instruction to install Python3.8 as first step in Windows installed.
Turns out that anaconda3 does it for you!
* v1.07 (23 August 2022)
* Image filenames will now never fill gaps in the sequence, but will be assigned the
next higher name in the chosen directory. This ensures that the alphabetic and chronological
@ -236,34 +242,31 @@ This will bring your local copy into sync with the remote one.
### Windows
1. Install Python version 3.8.5 from here: https://www.python.org/downloads/windows/
(note that several users have reported that later versions do not work properly)
1. Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/
2. Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/
2. Install Git from here: https://git-scm.com/download/win
3. Install Git from here: https://git-scm.com/download/win
3. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
4. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
5. Run the command:
4. Run the command:
```
git clone https://github.com/lstein/stable-diffusion.git
```
This will create stable-diffusion folder where you will follow the rest of the steps.
6. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
5. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
```
cd stable-diffusion
```
7. Run the following two commands:
6. Run the following two commands:
```
conda env create -f environment.yaml (step 7a)
conda activate ldm (step 7b)
conda env create -f environment.yaml (step 6a)
conda activate ldm (step 6b)
```
This will install all python requirements and activate the "ldm" environment which sets PATH and other environment variables properly.
8. Run the command:
7. Run the command:
```
python scripts\preload_models.py
```
@ -273,7 +276,7 @@ requires. (Note that this step is required. I created it because some people
are using GPU systems that are behind a firewall and the models can't be
downloaded just-in-time)
9. Now you need to install the weights for the big stable diffusion model.
8. Now you need to install the weights for the big stable diffusion model.
For running with the released weights, you will first need to set up
an acount with Hugging Face (https://huggingface.co). Use your
@ -299,7 +302,7 @@ you stashed this file. If you prefer not to copy or move the .ckpt file,
you may instead create a shortcut to it from within
"models\ldm\stable-diffusion-v1\".
10. Start generating images!
9. Start generating images!
```
# for the pre-release weights
python scripts\dream.py -l
@ -307,7 +310,7 @@ python scripts\dream.py -l
# for the post-release weights
python scripts\dream.py
```
11. Subsequently, to relaunch the script, first activate the Anaconda command window (step 4), enter the stable-diffusion directory (step 6, "cd \path\to\stable-diffusion"), run "conda activate ldm" (step 7b), and then launch the dream script (step 10).
10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3), enter the stable-diffusion directory (step 5, "cd \path\to\stable-diffusion"), run "conda activate ldm" (step 6b), and then launch the dream script (step 9).
#### Updating to newer versions of the script
@ -378,213 +381,3 @@ to send me an email if you use and like the script.
*Contributions by:* [Peter Kowalczyk](https://github.com/slix), [Henry Harrison](https://github.com/hwharrison), [xraxra](https://github.com/xraxra), and [bmaltais](https://github.com/bmaltais)
# Original README from CompViz/stable-diffusion
*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:*
[**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**
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/).
![txt2img-stable2](assets/stable-samples/txt2img/merged-0006.png)
[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
model.
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.
Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
## Requirements
A suitable [conda](https://conda.io/) environment named `ldm` can be created
and activated with:
```
conda env create -f environment.yaml
conda activate ldm
```
You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
```
conda install pytorch torchvision -c pytorch
pip install transformers==4.19.2
pip install -e .
```
## Stable Diffusion v1
Stable Diffusion v1 refers to a specific configuration of the model
architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
then finetuned on 512x512 images.
*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
in its training data.
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).
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).
**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.***
[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form)
### Weights
We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
which were trained as follows,
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
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`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
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)).
- `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).
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 PLMS sampling
steps show the relative improvements of the checkpoints:
![sd evaluation results](assets/v1-variants-scores.jpg)
### Text-to-Image with Stable Diffusion
![txt2img-stable2](assets/stable-samples/txt2img/merged-0005.png)
![txt2img-stable2](assets/stable-samples/txt2img/merged-0007.png)
Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
#### Sampling Script
After [obtaining the weights](#weights), link them
```
mkdir -p models/ldm/stable-diffusion-v1/
ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt
```
and sample with
```
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
```
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,
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`).
```commandline
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]
[--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] [--seed SEED] [--precision {full,autocast}]
optional arguments:
-h, --help show this help message and exit
--prompt [PROMPT] the prompt to render
--outdir [OUTDIR] dir to write results to
--skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples
--skip_save do not save individual samples. For speed measurements.
--ddim_steps DDIM_STEPS
number of ddim sampling steps
--plms use plms sampling
--laion400m uses the LAION400M model
--fixed_code if enabled, uses the same starting code across samples
--ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling
--n_iter N_ITER sample this often
--H H image height, in pixel space
--W W image width, in pixel space
--C C latent channels
--f F downsampling factor
--n_samples N_SAMPLES
how many samples to produce for each given prompt. A.k.a. batch size
(note that the seeds for each image in the batch will be unavailable)
--n_rows N_ROWS rows in the grid (default: n_samples)
--scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
--from-file FROM_FILE
if specified, load prompts from this file
--config CONFIG path to config which constructs model
--ckpt CKPT path to checkpoint of model
--seed SEED the seed (for reproducible sampling)
--precision {full,autocast}
evaluate at this precision
```
Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints.
For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
#### Diffusers Integration
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)
```py
# make sure you're logged in with `huggingface-cli login`
from torch import autocast
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-3-diffusers",
use_auth_token=True
)
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt)["sample"][0]
image.save("astronaut_rides_horse.png")
```
### Image Modification with Stable Diffusion
By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different
tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script,
we provide a script to perform image modification with Stable Diffusion.
The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
```
python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8
```
Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
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.
**Input**
![sketch-in](assets/stable-samples/img2img/sketch-mountains-input.jpg)
**Outputs**
![out3](assets/stable-samples/img2img/mountains-3.png)
![out2](assets/stable-samples/img2img/mountains-2.png)
This procedure can, for example, also be used to upscale samples from the base model.
## Comments
- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
Thanks for open-sourcing!
- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
## BibTeX
```
@misc{rombach2021highresolution,
title={High-Resolution Image Synthesis with Latent Diffusion Models},
author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
year={2021},
eprint={2112.10752},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```

13
ldm/simplet2i.py
View File

@ -1,3 +1,10 @@
# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
# Derived from source code carrying the following copyrights
# Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
# Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
"""Simplified text to image API for stable diffusion/latent diffusion
Example Usage:
@ -161,6 +168,9 @@ The vast majority of these arguments default to reasonable values.
model = self.load_model() # will instantiate the model or return it from cache
assert strength<1.0 and strength>=0.0, "strength (-f) must be >=0.0 and <1.0"
assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
# grid and individual are mutually exclusive, with individual taking priority.
# not necessary, but needed for compatability with dream bot
if (grid is None):
@ -282,6 +292,9 @@ The vast majority of these arguments default to reasonable values.
iterations = iterations or self.iterations
strength = strength or self.strength
assert strength<1.0 and strength>=0.0, "strength (-f) must be >=0.0 and <1.0"
assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
if init_img is None:
print("no init_img provided!")
return []

15
scripts/dream.py
View File

@ -1,4 +1,6 @@
#!/usr/bin/env python3
# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
import argparse
import shlex
import atexit
@ -158,10 +160,14 @@ def main_loop(t2i,parser,log,infile):
print("Try again with a prompt!")
continue
if opt.init_img is None:
results = t2i.txt2img(**vars(opt))
else:
results = t2i.img2img(**vars(opt))
try:
if opt.init_img is None:
results = t2i.txt2img(**vars(opt))
else:
results = t2i.img2img(**vars(opt))
except AssertionError as e:
print(e)
continue
print("Outputs:")
write_log_message(t2i,opt,results,log)
@ -374,3 +380,4 @@ if readline_available:
if __name__ == "__main__":
main()

2
scripts/preload_models.py
View File

@ -1,4 +1,5 @@
#!/usr/bin/env python3
# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
# Before running stable-diffusion on an internet-isolated machine,
# run this script from one with internet connectivity. The
# two machines must share a common .cache directory.
@ -30,3 +31,4 @@ tokenizer =CLIPTokenizer.from_pretrained(version)
transformer=CLIPTextModel.from_pretrained(version)
print('\n\n...success')