ImaginAIry 🤖🧠

AI imagined images. Pythonic generation of stable diffusion images.

"just works" on Linux and OSX(M1).

Examples

>> pip install imaginairy
>> imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman"

Console Output

🤖🧠 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

Automated Replacement (txt2mask) by clipseg

>> imagine --init-image pearl_earring.jpg --mask-prompt face --mask-mode keep --init-image-strength .4 "a female doctor" "an elegant woman"

➡️

>> imagine --init-image fruit-bowl.jpg --mask-prompt fruit --mask-mode replace --init-image-strength .1 "a bowl of pears" "a bowl of gold" "a bowl of popcorn" "a bowl of spaghetti"

➡️

Face Enhancement by CodeFormer

>> imagine "a couple smiling" --steps 40 --seed 1 --fix-faces

➡️

Upscaling by RealESRGAN

>> imagine "colorful smoke" --steps 40 --upscale

➡️

Tiled Images

>> imagine  "gold coins" "a lush forest" "piles of old books" leaves --tile

Image-to-Image

>> imagine "portrait of a smiling lady. oil painting" --init-image girl_with_a_pearl_earring.jpg

➡️

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

How To

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),
    ImaginePrompt("a bowl of fruit"),
    ImaginePrompt([
        WeightedPrompt("cat", weight=1),
        WeightedPrompt("dog", weight=1),
    ]),
    ImaginePrompt(
        "a spacious building", 
        init_image=LazyLoadingImage(url)
    )
]
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.

Running in Docker

See example Dockerfile (works on machine where you can pass the gpu into the container)

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

Improvements from CompVis

• img2img actually does # of steps you specify
• performance optimizations

Models Used

• CLIP - https://openai.com/blog/clip/
• LDM - Latent Diffusion
• Stable Diffusion
  • https://github.com/CompVis/stable-diffusion
  • https://huggingface.co/CompVis/stable-diffusion-v1-4
  • https://laion.ai/blog/laion-5b/

Not Supported

• a web interface. this is a python library

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
• ✅ deploy to pypi
• find similar images https://knn5.laion.ai/?back=https%3A%2F%2Fknn5.laion.ai%2F&index=laion5B&useMclip=false
• Development Environment
  • add tests
  • set up ci (test/lint/format)
  • add docs
  • remove yaml config
  • delete more unused code
• Interface improvements
  • ✅ init-image at command line
  • prompt expansion
• Image Generation Features
  • ✅ add k-diffusion sampling methods
  • why is k-diffusion so slow compared to plms? 2 it/s vs 8 it/s
  • negative prompting
    • some syntax to allow it in a text string
  • upscaling
    • ✅ realesrgan
    • ldm
    • https://github.com/lowfuel/progrock-stable
  • ✅ face enhancers
    • ✅ gfpgan - https://github.com/TencentARC/GFPGAN
    • ✅ codeformer - https://github.com/sczhou/CodeFormer
  • image describe feature -
    • https://replicate.com/methexis-inc/img2prompt
    • https://github.com/KaiyangZhou/CoOp
  • outpainting
  • inpainting
    • 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
    • bbb5298146
  • CPU support
  • img2img for plms?
  • 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/
  • 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
  • ✅ tiling
  • output show-work videos
  • 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
  • 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/

Noteable Stable Diffusion Implementations

• https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion
• https://github.com/lstein/stable-diffusion
• https://github.com/AUTOMATIC1111/stable-diffusion-webui

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