Merge branch 'development' of github.com:lstein/stable-diffusion into…

… development

CHANGELOG.md → docs/CHANGELOG.md

@@ -134,4 +134,4 @@
 
 ## Links
 
-- **[Read Me](readme.md)**
+- **[Read Me](../readme.md)**

docs/CONTRIBUTORS.md

@@ -0,0 +1,61 @@
+# Contributors
+
+The list of all the amazing people who have contributed to the various features that you get to experience in this fork.
+
+We thank them for all of their time and hard work.
+
+_Original Author:_
+
+- Lincoln D. Stein <lincoln.stein@gmail.com>
+
+_Contributions by:_
+
+- [Sean McLellan](https://github.com/Oceanswave)
+- [Kevin Gibbons](https://github.com/bakkot)
+- [Tesseract Cat](https://github.com/TesseractCat)
+- [blessedcoolant](https://github.com/blessedcoolant)
+- [David Ford](https://github.com/david-ford)
+- [yunsaki](https://github.com/yunsaki)
+- [James Reynolds](https://github.com/magnusviri)
+- [David Wager](https://github.com/maddavid123)
+- [Jason Toffaletti](https://github.com/toffaletti)
+- [tildebyte](https://github.com/tildebyte)
+- [Cragin Godley](https://github.com/cgodley)
+- [BlueAmulet](https://github.com/BlueAmulet)
+- [Benjamin Warner](https://github.com/warner-benjamin)
+- [Cora Johnson-Roberson](https://github.com/corajr)
+- [veprogames](https://github.com/veprogames)
+- [JigenD](https://github.com/JigenD)
+- [Niek van der Maas](https://github.com/Niek)
+- [Henry van Megen](https://github.com/hvanmegen)
+- [Håvard Gulldahl](https://github.com/havardgulldahl)
+- [greentext2](https://github.com/greentext2)
+- [Simon Vans-Colina](https://github.com/simonvc)
+- [Gabriel Rotbart](https://github.com/gabrielrotbart)
+- [Eric Khun](https://github.com/erickhun)
+- [Brent Ozar](https://github.com/BrentOzar)
+- [nderscore](https://github.com/nderscore)
+- [Mikhail Tishin](https://github.com/tishin)
+- [Tom Elovi Spruce](https://github.com/ilovecomputers)
+- [spezialspezial](https://github.com/spezialspezial)
+- [Yosuke Shinya](https://github.com/shinya7y)
+- [Andy Pilate](https://github.com/Cubox)
+- [Muhammad Usama](https://github.com/SMUsamaShah)
+- [Arturo Mendivil](https://github.com/artmen1516)
+- [Paul Sajna](https://github.com/sajattack)
+- [Samuel Husso](https://github.com/shusso)
+- [nicolai256](https://github.com/nicolai256)
+
+_Original CompVis Authors:_
+
+- [Robin Rombach](https://github.com/rromb)
+- [Patrick von Platen](https://github.com/patrickvonplaten)
+- [ablattmann](https://github.com/ablattmann)
+- [Patrick Esser](https://github.com/pesser)
+- [owenvincent](https://github.com/owenvincent)
+- [apolinario](https://github.com/apolinario)
+- [Charles Packer](https://github.com/cpacker)
+
+---
+
+_If you have contributed and don't see your name on the list of contributors, please let one of the collaborators know about the omission, or feel free to make a pull request._

README-CompViz.md → docs/README-CompViz.md

@@ -1,5 +1,6 @@
 # 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:*
+
+_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)\*,
@@ -12,16 +13,15 @@
 
 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)
+![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), 
+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
@@ -44,16 +44,16 @@ pip install -e .
 
 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 
+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. 
+\*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.***
+**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) 
+[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form)
 
 ### Weights
 
@@ -64,36 +64,37 @@ which were trained as follows,
   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)).
+  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)
-
-
+![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.
+![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 
+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 
+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, 
+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
@@ -131,73 +132,72 @@ optional arguments:
                         evaluate at this precision
 
 ```
-Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints. 
+
+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", 
+	"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 = 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.  
+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. 
+
+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)
+![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)
+![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 
+## 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). 
+  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}, 
+      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},
@@ -206,5 +206,3 @@ Thanks for open-sourcing!
 }
 
 ```
-
-

docs/features/CLI.md

@@ -0,0 +1,50 @@
+# **Interactive Command-Line Interface**
+
+The `dream.py` script, located in `scripts/dream.py`, provides an interactive interface to image generation similar to the "dream mothership" bot that Stable AI provided on its Discord server.
+
+Unlike the txt2img.py and img2img.py scripts provided in the original CompViz/stable-diffusion source code repository, the time-consuming initialization of the AI model initialization only happens once. After that image generation
+from the command-line interface is very fast.
+
+The script uses the readline library to allow for in-line editing, command history (up and down arrows), autocompletion, and more. To help keep track of which prompts generated which images, the script writes a log file of image names and prompts to the selected output directory.
+
+In addition, as of version 1.02, it also writes the prompt into the PNG file's metadata where it can be retrieved using scripts/images2prompt.py
+
+The script is confirmed to work on Linux, Windows and Mac systems.
+
+_Note:_ This script runs from the command-line or can be used as a Web application. The Web GUI is currently rudimentary, but a much better replacement is on its way.
+
+```
+(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
+* Initializing, be patient...
+Loading model from models/ldm/text2img-large/model.ckpt
+(...more initialization messages...)
+
+* Initialization done! Awaiting your command...
+dream> ashley judd riding a camel -n2 -s150
+Outputs:
+   outputs/img-samples/00009.png: "ashley judd riding a camel" -n2 -s150 -S 416354203
+   outputs/img-samples/00010.png: "ashley judd riding a camel" -n2 -s150 -S 1362479620
+
+dream> "there's a fly in my soup" -n6 -g
+    outputs/img-samples/00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
+    seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
+dream> q
+
+# this shows how to retrieve the prompt stored in the saved image's metadata
+(ldm) ~/stable-diffusion$ python ./scripts/images2prompt.py outputs/img_samples/*.png
+00009.png: "ashley judd riding a camel" -s150 -S 416354203
+00010.png: "ashley judd riding a camel" -s150 -S 1362479620
+00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
+```
+
+<p align='center'>
+<img src="../assets/dream-py-demo.png"/>
+</p>
+
+The `dream>` prompt's arguments are pretty much identical to those used in the Discord bot, except you don't need to type "!dream" (it doesn't
+hurt if you do). A significant change is that creation of individual images is now the default unless --grid (-g) is given.
+
+For backward compatibility, the -i switch is recognized. For command-line help type -h (or --help) at the dream> prompt.
+
+The script itself also recognizes a series of command-line switches that will change important global defaults, such as the directory for
+image outputs and the location of the model weight files.

docs/features/IMG2IMG.md

@@ -0,0 +1,15 @@
+# **Image-to-Image**
+
+This script also provides an img2img feature that lets you seed your creations with an initial drawing or photo. This is a really cool feature that tells stable diffusion to build the prompt on top of the image you provide, preserving the original's basic shape and layout. To use it, provide the `--init_img` option as shown here:
+
+```
+dream> "waterfall and rainbow" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
+```
+
+The `--init_img (-I)` option gives the path to the seed picture. `--strength (-f)` controls how much the original will be modified, ranging from `0.0` (keep the original intact), to `1.0` (ignore
+the original completely). The default is `0.75`, and ranges from `0.25-0.75` give interesting results.
+
+You may also pass a `-v<count>` option to generate count variants on the original image. This is done by passing the first generated image back into img2img the requested number of times. It generates interesting variants.
+
+If the initial image contains transparent regions, then Stable Diffusion will only draw within the transparent regions, a process
+called "inpainting". However, for this to work correctly, the color information underneath the transparent needs to be preserved, not erased. See [Creating Transparent Images For Inpainting](./INPAINTING.md#creating-transparent-regions-for-inpainting) for details.