# ImaginAIry 🤖🧠

AI imagined images. Pythonic generation of stable diffusion images.

"just works" on Linux and macOS(M1) (and maybe windows?).

## Examples
```bash
# on macOS, make sure rust is installed first
>> pip install imaginairy
>> imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman"
```

<details closed>
<summary>Console Output</summary>

```bash
🤖🧠 received 4 prompt(s) and will repeat them 1 times to create 4 images.
Loading model onto mps backend...
Generating 🖼  : "a scenic landscape" 512x512px seed:557988237 prompt-strength:7.5 steps:40 sampler-type:PLMS
    PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:29<00:00,  1.36it/s]
    🖼  saved to: ./outputs/000001_557988237_PLMS40_PS7.5_a_scenic_landscape.jpg
Generating 🖼  : "a photo of a dog" 512x512px seed:277230171 prompt-strength:7.5 steps:40 sampler-type:PLMS
    PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:28<00:00,  1.41it/s]
    🖼  saved to: ./outputs/000002_277230171_PLMS40_PS7.5_a_photo_of_a_dog.jpg
Generating 🖼  : "photo of a fruit bowl" 512x512px seed:639753980 prompt-strength:7.5 steps:40 sampler-type:PLMS
    PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:28<00:00,  1.40it/s]
    🖼  saved to: ./outputs/000003_639753980_PLMS40_PS7.5_photo_of_a_fruit_bowl.jpg
Generating 🖼  : "portrait photo of a freckled woman" 512x512px seed:500686645 prompt-strength:7.5 steps:40 sampler-type:PLMS
    PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:29<00:00,  1.37it/s]
    🖼  saved to: ./outputs/000004_500686645_PLMS40_PS7.5_portrait_photo_of_a_freckled_woman.jpg
```
</details>

<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000019_786355545_PLMS50_PS7.5_a_scenic_landscape.jpg" height="256"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000032_337692011_PLMS40_PS7.5_a_photo_of_a_dog.jpg"  height="256"><br>
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000056_293284644_PLMS40_PS7.5_photo_of_a_bowl_of_fruit.jpg" height="256"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000078_260972468_PLMS40_PS7.5_portrait_photo_of_a_freckled_woman.jpg"  height="256">

### Prompt Based Editing  [by clipseg](https://github.com/timojl/clipseg)
Specify advanced text based masks using boolean logic and strength modifiers. Mask descriptions must be lowercase. Keywords uppercase.
Valid symbols: `AND`, `OR`, `NOT`, `()`, and mask strength modifier `{*1.5}` where `+` can be any of `+ - * /`. Single-character boolean 
operators also work.  When writing strength modifies know that pixel values are between 0 and 1.

```bash
>> imagine \
    --init-image pearl_earring.jpg \ 
    --mask-prompt "face AND NOT (bandana OR hair OR blue fabric){*6}" \
    --mask-mode keep \
    --init-image-strength .2 \
    --fix-faces \
    "a modern female president" "a female robot" "a female doctor" "a female firefighter"
```
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/mask_examples/pearl000.jpg" height="200">➡️ 
<img src="assets/mask_examples/pearl_pres.png" height="200">
<img src="assets/mask_examples/pearl_robot.png" height="200">
<img src="assets/mask_examples/pearl_doctor.png" height="200">
<img src="assets/mask_examples/pearl_firefighter.png" height="200">

```bash
>> imagine \
    --init-image fruit-bowl.jpg \
    --mask-prompt "fruit OR fruit stem{*1.5}" \
    --mask-mode replace \
    --mask-modify-original \
    --init-image-strength .1 \
    "a bowl of kittens" "a bowl of gold coins" "a bowl of popcorn" "a bowl of spaghetti"
```
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000056_293284644_PLMS40_PS7.5_photo_of_a_bowl_of_fruit.jpg" height="200">➡️ 
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/mask_examples/bowl004.jpg" height="200">
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/mask_examples/bowl001.jpg" height="200">
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/mask_examples/bowl002.jpg" height="200">
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/mask_examples/bowl003.jpg" height="200">


### Face Enhancement [by CodeFormer](https://github.com/sczhou/CodeFormer)

```bash
>> imagine "a couple smiling" --steps 40 --seed 1 --fix-faces
```
<img src="https://github.com/brycedrennan/imaginAIry/raw/master/assets/000178_1_PLMS40_PS7.5_a_couple_smiling_nofix.png" height="256"> ➡️ 
<img src="https://github.com/brycedrennan/imaginAIry/raw/master/assets/000178_1_PLMS40_PS7.5_a_couple_smiling_fixed.png" height="256"> 


### Upscaling [by RealESRGAN](https://github.com/xinntao/Real-ESRGAN)
```bash
>> imagine "colorful smoke" --steps 40 --upscale
```
<img src="https://github.com/brycedrennan/imaginAIry/raw/master/assets/000206_856637805_PLMS40_PS7.5_colorful_smoke.jpg" height="128"> ➡️ 
<img src="https://github.com/brycedrennan/imaginAIry/raw/master/assets/000206_856637805_PLMS40_PS7.5_colorful_smoke_upscaled.jpg" height="256"> 

### Tiled Images
```bash
>> imagine  "gold coins" "a lush forest" "piles of old books" leaves --tile
```

<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000066_801493266_PLMS40_PS7.5_gold_coins.jpg" height="128"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000066_801493266_PLMS40_PS7.5_gold_coins.jpg" height="128"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000066_801493266_PLMS40_PS7.5_gold_coins.jpg" height="128">
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000118_597948545_PLMS40_PS7.5_a_lush_forest.jpg" height="128"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000118_597948545_PLMS40_PS7.5_a_lush_forest.jpg" height="128"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000118_597948545_PLMS40_PS7.5_a_lush_forest.jpg" height="128">
<br>
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000075_961095192_PLMS40_PS7.5_piles_of_old_books.jpg" height="128"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000075_961095192_PLMS40_PS7.5_piles_of_old_books.jpg" height="128"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000075_961095192_PLMS40_PS7.5_piles_of_old_books.jpg" height="128">
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000040_527733581_PLMS40_PS7.5_leaves.jpg" height="128"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000040_527733581_PLMS40_PS7.5_leaves.jpg" height="128"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000040_527733581_PLMS40_PS7.5_leaves.jpg" height="128">

### Image-to-Image
```bash
>> imagine "portrait of a smiling lady. oil painting" --init-image girl_with_a_pearl_earring.jpg
```
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/tests/data/girl_with_a_pearl_earring.jpg" height="256"> ➡️ 
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000105_33084057_DDIM40_PS7.5_portrait_of_a_smiling_lady._oil_painting._.jpg" height="256"> 

### Prompt Expansion
You can use `{}` to randomly pull values from lists.  A list of values separated by `|` 
 and enclosed in `{ }` will be randomly drawn from in a non-repeating fashion. Values that are surrounded by `_ _` will 
 pull from a phrase list of the same name.   Folders containing .txt phraselist files may be specified via
`--prompt_library_path`. The option may be specified multiple times.  Built-in categories:
    
      3d-term, adj-architecture, adj-beauty, adj-detailed, adj-emotion, adj-general, adj-horror, animal, art-movement, 
      art-site, artist, artist-botanical, artist-surreal, aspect-ratio, bird, body-of-water, body-pose, camera-brand,
      camera-model, color, cosmic-galaxy, cosmic-nebula, cosmic-star, cosmic-term, dinosaur, eyecolor, f-stop, 
      fantasy-creature, fantasy-setting, fish, flower, focal-length, food, fruit, games, gen-modifier, hair, hd,
      iso-stop, landscape-type, national-park, nationality, neg-weight, noun-beauty, noun-fantasy, noun-general, 
      noun-horror, occupation, photo-term, pop-culture, pop-location, punk-style, quantity, rpg-item, scenario-desc, 
      skin-color, spaceship, style, tree-species, trippy, world-heritage-site

   Examples:

   `imagine "a {lime|blue|silver|aqua} colored dog" -r 4 --seed 0` (note that it generates a dog of each color without repetition)

<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000184_0_plms40_PS7.5_a_silver_colored_dog_[generated].jpg" height="200"><img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000186_0_plms40_PS7.5_a_aqua_colored_dog_[generated].jpg" height="200">
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000210_0_plms40_PS7.5_a_lime_colored_dog_[generated].jpg" height="200">
<img src="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000211_0_plms40_PS7.5_a_blue_colored_dog_[generated].jpg" height="200">

   `imagine "a {_color_} dog" -r 4 --seed 0` will generate four, different colored dogs. The colors will be pulled from an included 
   phraselist of colors.
    
   `imagine "a {_spaceship_|_fruit_|hot air balloon}. low-poly" -r 4 --seed 0` will generate images of spaceships or fruits or a hot air balloon

   Credit to [noodle-soup-prompts](https://github.com/WASasquatch/noodle-soup-prompts/) where most, but not all, of the wordlists originate.

### Generate image captions (via [BLIP](https://github.com/salesforce/BLIP))
```bash
>> aimg describe assets/mask_examples/bowl001.jpg
a bowl full of gold bars sitting on a table
```

## Features
 
 - It makes images from text descriptions! 🎉
 - Generate images either in code or from command line.
 - It just works. Proper requirements are installed. model weights are automatically downloaded. No huggingface account needed. 
    (if you have the right hardware... and aren't on windows)
 - No more distorted faces!
 - Noisy logs are gone (which was surprisingly hard to accomplish)
 - WeightedPrompts let you smash together separate prompts (cat-dog)
 - Tile Mode creates tileable images
 - Prompt metadata saved into image file metadata
 - Edit images by describing the part you want edited (see example above)
 - Have AI generate captions for images `aimg describe <filename-or-url>`
 - Interactive prompt: just run `aimg`

## How To

For full command line instructions run `aimg --help`

```python
from imaginairy import imagine, imagine_image_files, ImaginePrompt, WeightedPrompt, LazyLoadingImage

url = "https://upload.wikimedia.org/wikipedia/commons/thumb/6/6c/Thomas_Cole_-_Architect%E2%80%99s_Dream_-_Google_Art_Project.jpg/540px-Thomas_Cole_-_Architect%E2%80%99s_Dream_-_Google_Art_Project.jpg"
prompts = [
    ImaginePrompt("a scenic landscape", seed=1, upscale=True),
    ImaginePrompt("a bowl of fruit"),
    ImaginePrompt([
        WeightedPrompt("cat", weight=1),
        WeightedPrompt("dog", weight=1),
    ]),
    ImaginePrompt(
        "a spacious building", 
        init_image=LazyLoadingImage(url=url)
    ),
    ImaginePrompt(
        "a bowl of strawberries", 
        init_image=LazyLoadingImage(filepath="mypath/to/bowl_of_fruit.jpg"),
        mask_prompt="fruit OR stem{*2}",  # amplify the stem mask x2
        mask_mode="replace",
        mask_modify_original=True,
    ),
    ImaginePrompt("strawberries", tile_mode=True),
]
for result in imagine(prompts):
    # do something
    result.save("my_image.jpg")

# or

imagine_image_files(prompts, outdir="./my-art")

```

## Requirements
- ~10 gb space for models to download
- A decent computer with either a CUDA supported graphics card or M1 processor.
- Python installed. Preferably Python 3.10.
- For macOS [rust](https://www.rust-lang.org/tools/install) and setuptools-rust must be installed to compile the `tokenizer` library.
They can be installed via: `curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh` and `pip install setuptools-rust`
    

## Running in Docker
See example Dockerfile (works on machine where you can pass the gpu into the container)
```bash
docker build . -t imaginairy
# you really want to map the cache or you end up wasting a lot of time and space redownloading the model weights
docker run -it --gpus all -v $HOME/.cache/huggingface:/root/.cache/huggingface -v $HOME/.cache/torch:/root/.cache/torch -v `pwd`/outputs:/outputs imaginairy /bin/bash
```

## Running on Google Colab
[Example Colab](https://colab.research.google.com/drive/1rOvQNs0Cmn_yU1bKWjCOHzGVDgZkaTtO?usp=sharing)

## ChangeLog
**3.0.0**
- feature: improved safety filter

**2.4.0**
- 🎉 feature: prompt expansion
- feature: make (blip) photo captions more descriptive

**2.3.1**
 - fix: face fidelity default was broken

**2.3.0**
 - feature: model weights file can be specified via `--model-weights-path` argument at the command line
 - fix: set face fidelity default back to old value
 - fix: handle small images without throwing exception. credit to @NiclasEriksen
 - docs: add setuptools-rust as dependency for macos 

**2.2.1**
 - fix: init image is fully ignored if init-image-strength = 0

**2.2.0**
 - feature: face enhancement fidelity is now configurable

**2.1.0**
 - [improved masking accuracy from clipseg](https://github.com/timojl/clipseg/issues/8#issuecomment-1259150865)

**2.0.3**
 - fix memory leak in face enhancer
 - fix blurry inpainting 
 - fix for pillow compatibility

**2.0.0**
 - 🎉 fix: inpainted areas correlate with surrounding image, even at 100% generation strength.  Previously if the generation strength was high enough the generated image
would be uncorrelated to the rest of the surrounding image.  It created terrible looking images.   
 - 🎉 feature: interactive prompt added. access by running `aimg`
 - 🎉 feature: Specify advanced text based masks using boolean logic and strength modifiers. Mask descriptions must be lowercase. Keywords uppercase.
   Valid symbols: `AND`, `OR`, `NOT`, `()`, and mask strength modifier `{+0.1}` where `+` can be any of `+ - * /`. Single character boolean operators also work (`|`, `&`, `!`)
 - 🎉 feature: apply mask edits to original files with `mask_modify_original` (on by default)
 - feature: auto-rotate images if exif data specifies to do so
 - fix: mask boundaries are more accurate
 - fix: accept mask images in command line
 - fix: img2img algorithm was wrong and wouldn't at values close to 0 or 1

**1.6.2**
 - fix: another bfloat16 fix

**1.6.1**
 - fix: make sure image tensors come to the CPU as float32 so there aren't compatability issues with non-bfloat16 cpus

**1.6.0**
 - fix: *maybe* address #13 with `expected scalar type BFloat16 but found Float`
   - at minimum one can specify `--precision full` now and that will probably fix the issue  
 - feature: tile mode can now be specified per-prompt

**1.5.3**
 - fix: missing config file for describe feature

**1.5.1**
 - img2img now supported with PLMS (instead of just DDIM)
 - added image captioning feature `aimg describe dog.jpg` => `a brown dog sitting on grass`
 - added new commandline tool `aimg` for additional image manipulation functionality

**1.4.0**
 - support multiple additive targets for masking with `|` symbol.  Example: "fruit|stem|fruit stem"

**1.3.0**
 - added prompt based image editing. Example: "fruit => gold coins"
 - test coverage improved

**1.2.0**
 - allow urls as init-images

**previous**
 - img2img actually does # of steps you specify  
 - performance optimizations
 - numerous other changes


## Not Supported
 - a GUI. this is a python library
 - training
 - exploratory features that don't work well

## Todo

 - Performance Optimizations
   - ✅ https://github.com/huggingface/diffusers/blob/main/docs/source/optimization/fp16.mdx
   - ✅ https://github.com/CompVis/stable-diffusion/compare/main...Doggettx:stable-diffusion:autocast-improvements#
   - ✅ https://www.reddit.com/r/StableDiffusion/comments/xalaws/test_update_for_less_memory_usage_and_higher/
   - https://github.com/neonsecret/stable-diffusion  
   - https://github.com/CompVis/stable-diffusion/pull/177
   - https://github.com/huggingface/diffusers/pull/532/files
   - https://github.com/HazyResearch/flash-attention
   - xformers improvments https://www.photoroom.com/tech/stable-diffusion-100-percent-faster-with-memory-efficient-attention/
 - Development Environment
   - ✅ add tests
   - ✅ set up ci (test/lint/format)
   - setup parallel testing
   - add docs
   - remove yaml config
   - delete more unused code
 - Interface improvements
   - ✅ init-image at command line
   - ✅ prompt expansion
   - ✅ interactive cli
 - Image Generation Features
   - ✅ add k-diffusion sampling methods
   - ✅ tiling
   - generation videos/gifs
   - Compositional Visual Generation
     - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
     - https://colab.research.google.com/github/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch/blob/main/notebooks/demo.ipynb#scrollTo=wt_j3uXZGFAS
   - negative prompting
     - some syntax to allow it in a text string
   - images as actual prompts instead of just init images
     - requires model fine-tuning since SD1.4 expects 77x768 text encoding input
     - https://twitter.com/Buntworthy/status/1566744186153484288
     - https://github.com/justinpinkney/stable-diffusion
     - https://github.com/LambdaLabsML/lambda-diffusers
     - https://www.reddit.com/r/MachineLearning/comments/x6k5bm/n_stable_diffusion_image_variations_released/
 - Image Editing
   - outpainting
     - https://github.com/parlance-zz/g-diffuser-bot/search?q=noise&type=issues
     - lama cleaner
   - ✅ inpainting
     - https://github.com/Jack000/glid-3-xl-stable 
     - https://github.com/andreas128/RePaint
     - img2img but keeps img stable
     - https://www.reddit.com/r/StableDiffusion/comments/xboy90/a_better_way_of_doing_img2img_by_finding_the/
     - https://gist.github.com/trygvebw/c71334dd127d537a15e9d59790f7f5e1
     - https://github.com/pesser/stable-diffusion/commit/bbb52981460707963e2a62160890d7ecbce00e79
     - https://github.com/SHI-Labs/FcF-Inpainting https://praeclarumjj3.github.io/fcf-inpainting/
   - ✅ text based image masking
     - ✅ ClipSeg - https://github.com/timojl/clipseg
     - https://github.com/facebookresearch/detectron2
 - Image Enhancement
   - Photo Restoration - https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life
   - Upscaling
     - ✅ realesrgan 
     - ldm
     - https://github.com/lowfuel/progrock-stable
     - gobig
     - stable super-res?
       - todo: try with 1-0-0-0 mask at full image resolution (rencoding entire image+predicted image at every step)
       - todo: use a gaussian pyramid and only include the "high-detail" level of the pyramid into the next step
       - https://www.reddit.com/r/StableDiffusion/comments/xkjjf9/upscale_to_huge_sizes_and_add_detail_with_sd/
   - ✅ face enhancers
     - ✅ gfpgan - https://github.com/TencentARC/GFPGAN
     - ✅ codeformer - https://github.com/sczhou/CodeFormer
   - ✅ image describe feature - 
     - ✅ https://github.com/salesforce/BLIP
     - 🚫 CLIP brute-force prompt reconstruction
       - The accuracy of this approach is too low for me to include it in imaginAIry
       - https://github.com/rmokady/CLIP_prefix_caption
       - https://github.com/pharmapsychotic/clip-interrogator (blip + clip)
     - https://github.com/KaiyangZhou/CoOp
   - 🚫 CPU support.  While the code does actually work on some CPUs, the generation takes so long that I don't think it's
    worth the effort to support this feature
   - ✅ img2img for plms
   - img2img for kdiff functions
 - Other
   - Enhancement pipelines
   - text-to-3d https://dreamfusionpaper.github.io/
   - make a video https://github.com/lucidrains/make-a-video-pytorch
   - animations
     - https://github.com/francislabountyjr/stable-diffusion/blob/main/inferencing_notebook.ipynb
     - https://www.youtube.com/watch?v=E7aAFEhdngI
     - https://github.com/pytti-tools/frame-interpolation
   - cross-attention control: 
     - https://github.com/bloc97/CrossAttentionControl/blob/main/CrossAttention_Release_NoImages.ipynb
   - guided generation 
     - https://colab.research.google.com/drive/1dlgggNa5Mz8sEAGU0wFCHhGLFooW_pf1#scrollTo=UDeXQKbPTdZI
     - https://colab.research.google.com/github/aicrumb/doohickey/blob/main/Doohickey_Diffusion.ipynb#scrollTo=PytCwKXCmPid
     - https://github.com/mlfoundations/open_clip
     - https://github.com/openai/guided-diffusion
   - image variations https://github.com/lstein/stable-diffusion/blob/main/VARIATIONS.md
   - textual inversion 
     - https://www.reddit.com/r/StableDiffusion/comments/xbwb5y/how_to_run_textual_inversion_locally_train_your/
     - https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb#scrollTo=50JuJUM8EG1h
     - https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion_textual_inversion_library_navigator.ipynb
     - https://github.com/Jack000/glid-3-xl-stable
   - fix saturation at high CFG https://www.reddit.com/r/StableDiffusion/comments/xalo78/fixing_excessive_contrastsaturation_resulting/
   - https://www.reddit.com/r/StableDiffusion/comments/xbrrgt/a_rundown_of_twenty_new_methodsoptions_added_to/
   - ✅ deploy to pypi
   - find similar images https://knn5.laion.ai/?back=https%3A%2F%2Fknn5.laion.ai%2F&index=laion5B&useMclip=false

## Noteable Stable Diffusion Implementations
 - https://github.com/ahrm/UnstableFusion
 - https://github.com/AUTOMATIC1111/stable-diffusion-webui
 - https://github.com/blueturtleai/gimp-stable-diffusion
 - https://github.com/hafriedlander/stable-diffusion-grpcserver
 - https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion
 - https://github.com/lkwq007/stablediffusion-infinity
 - https://github.com/lstein/stable-diffusion
 - https://github.com/parlance-zz/g-diffuser-lib

## Further Reading
 - Differences between samplers
   - https://www.reddit.com/r/StableDiffusion/comments/xbeyw3/can_anyone_offer_a_little_guidance_on_the/
 - https://www.reddit.com/r/bigsleep/comments/xb5cat/wiskkeys_lists_of_texttoimage_systems_and_related/
 - https://huggingface.co/blog/annotated-diffusion
 - https://huggingface.co/blog/assets/78_annotated-diffusion/unet_architecture.jpg