imaginAIry 0.7.2

AI imagined images. Pythonic generation of stable diffusion images.

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

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)
• 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_images, imagine_image_files, ImaginePrompt, WeightedPrompt

prompts = [
    ImaginePrompt("a scenic landscape", seed=1),
    ImaginePrompt("a bowl of fruit"),
    ImaginePrompt([
       WeightedPrompt("cat", weight=1),
       WeightedPrompt("dog", weight=1),
    ])
]
for result in imagine_images(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.

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/neonsecret/stable-diffusion https://github.com/CompVis/stable-diffusion/pull/177
  • ✅ 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/
• deploy to pypi
• add tests
• set up ci (test/lint/format)
• add docs
• notify https://github.com/CompVis/stable-diffusion/issues/25
• remove yaml config
• delete more unused code
• Interface improvements
  • ✅ init-image at command line
  • prompt expansion
• Image Generation Features
  • upscaling
    • https://github.com/lowfuel/progrock-stable
  • face improvements
    • gfpgan - https://github.com/TencentARC/GFPGAN
    • codeformer - https://github.com/sczhou/CodeFormer
  • image describe feature - https://replicate.com/methexis-inc/img2prompt
  • outpainting
  • inpainting
    • https://github.com/andreas128/RePaint
  • add more sampling methods?
  • 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
  • img2img for plms?
  • images as actual prompts instead of just init images
  • 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
  • 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
  • zooming videos? a la disco diffusion
  • fix saturation at high CFG https://www.reddit.com/r/StableDiffusion/comments/xalo78/fixing_excessive_contrastsaturation_resulting/