This is not a useful package. It is a wrapper around imaginary to provide a Class interface.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for ahmed-mohamed-sn from gravatar.com ahmed-mohamed-sn

Unverified details

These details have not been verified by PyPI
Meta
  • Author: Hanoush
  • Requires: Python >=3.9, <4.0
Classifiers

Project description

ImaginAIry ๐Ÿค–๐Ÿง 

Downloads image image Code style: black Python Checks

AI imagined images. Pythonic generation of stable diffusion images.

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

Examples

# on macOS, make sure rust is installed first
>> pip install classy-imaginary
>> imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman"
# Stable Diffusion 2.1
>> imagine --model SD-2.1 "a forest"
Console Output 
๐Ÿค–๐Ÿง  received 4 prompt(s) and will repeat them 1 times to create 4 images.
Loading model onto mps backend...
Generating ๐Ÿ–ผ  : "a scenic landscape" 512x512px seed:557988237 prompt-strength:7.5 steps:40 sampler-type:PLMS
    PLMS Sampler: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 40/40 [00:29<00:00,  1.36it/s]
    ๐Ÿ–ผ  saved to: ./outputs/000001_557988237_PLMS40_PS7.5_a_scenic_landscape.jpg
Generating ๐Ÿ–ผ  : "a photo of a dog" 512x512px seed:277230171 prompt-strength:7.5 steps:40 sampler-type:PLMS
    PLMS Sampler: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 40/40 [00:28<00:00,  1.41it/s]
    ๐Ÿ–ผ  saved to: ./outputs/000002_277230171_PLMS40_PS7.5_a_photo_of_a_dog.jpg
Generating ๐Ÿ–ผ  : "photo of a fruit bowl" 512x512px seed:639753980 prompt-strength:7.5 steps:40 sampler-type:PLMS
    PLMS Sampler: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 40/40 [00:28<00:00,  1.40it/s]
    ๐Ÿ–ผ  saved to: ./outputs/000003_639753980_PLMS40_PS7.5_photo_of_a_fruit_bowl.jpg
Generating ๐Ÿ–ผ  : "portrait photo of a freckled woman" 512x512px seed:500686645 prompt-strength:7.5 steps:40 sampler-type:PLMS
    PLMS Sampler: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 40/40 [00:29<00:00,  1.37it/s]
    ๐Ÿ–ผ  saved to: ./outputs/000004_500686645_PLMS40_PS7.5_portrait_photo_of_a_freckled_woman.jpg


Prompt Based Editing by clipseg

Specify advanced text based masks using boolean logic and strength modifiers. Mask syntax:

  • mask descriptions must be lowercase
  • keywords (AND, OR, NOT) must be uppercase
  • parentheses are supported
  • mask modifiers may be appended to any mask or group of masks. Example: (dog OR cat){+5} means that we'll select any dog or cat and then expand the size of the mask area by 5 pixels. Valid mask modifiers:
    • {+n} - expand mask by n pixels
    • {-n} - shrink mask by n pixels
    • {*n} - multiply mask strength. will expand mask to areas that weakly matched the mask description
    • {/n} - divide mask strength. will reduce mask to areas that most strongly matched the mask description. probably not useful

When writing strength modifiers keep in mind that pixel values are between 0 and 1.

>> 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"

โžก๏ธ 

>> imagine \
    --init-image fruit-bowl.jpg \
    --mask-prompt "fruit OR fruit stem{*6}" \
    --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"

โžก๏ธ

Face Enhancement by CodeFormer

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

โžก๏ธ

Upscaling by RealESRGAN

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

โžก๏ธ

Tiled Images

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


360 degree images

imagine --tile-x -w 1024 -h 512 "360 degree equirectangular panorama photograph of the desert"  --upscale

Image-to-Image

Use depth maps for amazing "translations" of existing images.

>> imagine --model SD-2.0-depth --init-image girl_with_a_pearl_earring_large.jpg --init-image-strength 0.05  "professional headshot photo of a woman with a pearl earring" -r 4 -w 1024 -h 1024 --steps 50

โžก๏ธ

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)

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 where most, but not all, of the wordlists originate.

Generate image captions (via BLIP)

>> aimg describe assets/mask_examples/bowl001.jpg
a bowl full of gold bars sitting on a table

Features

  • It makes images from text descriptions! ๐ŸŽ‰
  • 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

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 (and CUDA installed) or an M1 processor.
  • Python installed. Preferably Python 3.10. (not conda)
  • For macOS rust 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)

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

ChangeLog

7.3.0

  • feature: ๐ŸŽ‰ depth-based image-to-image generations (and inpainting)
  • fix: k_euler_a produces more consistent images per seed (randomization respects the seed again)

7.2.0

  • feature: ๐ŸŽ‰ tile in a single dimension ("x" or "y"). This enables, with a bit of luck, generation of 360 VR images. Try this for example: imagine --tile-x -w 1024 -h 512 "360 degree equirectangular panorama photograph of the mountains" --upscale

7.1.1

  • fix: memory/speed regression introduced in 6.1.0
  • fix: model switching now clears memory better, thus avoiding out of memory errors

7.1.0

  • feature: ๐ŸŽ‰ Stable Diffusion 2.1. Generated people are no longer (completely) distorted. Use with --model SD-2.1 or --model SD-2.0-v

7.0.0

  • feature: negative prompting. --negative-prompt or ImaginePrompt(..., negative_prompt="ugly, deformed, extra arms, etc")
  • feature: a default negative prompt is added to all generations. Images in SD-2.0 don't look bad anymore. Images in 1.5 look improved as well.

6.1.2

  • fix: add back in memory-efficient algorithms

6.1.1

  • feature: xformers will be used if available (for faster generation)
  • fix: version metadata was broken

6.1.0

  • feature: use different default steps and image sizes depending on sampler and model selceted
  • fix: #110 use proper version in image metadata
  • refactor: samplers all have their own class that inherits from ImageSampler
  • feature: ๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰ Stable Diffusion 2.0
    • --model SD-2.0 to use (it makes worse images than 1.5 though...)
    • Tested on macOS and Linux
    • All samplers working for new 512x512 model
    • New inpainting model working
    • 768x768 model working for all samplers except PLMS (--model SD-2.0-v )

5.1.0

  • feature: add progress image callback

5.0.1

  • fix: support larger images on M1. Fixes #8
  • fix: support CPU generation by disabling autocast on CPU. Fixes #81

5.0.0

  • feature: ๐ŸŽ‰ inpainting support using new inpainting model from RunwayML. It works really well! By default, the inpainting model will automatically be used for any image-masking task
  • feature: ๐ŸŽ‰ new default sampler makes image generation more than twice as fast
  • feature: added DPM++ 2S a and DPM++ 2M samplers.
  • feature: improve progress image logging
  • fix: fix bug with --show-work. fixes #84
  • fix: add workaround for pytorch bug affecting macOS users using the new DPM++ 2S a and DPM++ 2M samplers.
  • fix: add workaround for pytorch mps bug affecting k_dpm_fast sampler. fixes #75
  • fix: larger image sizes now work on macOS. fixes #8

4.1.0

  • feature: allow dynamic switching between models/weights --model SD-1.5 or --model SD-1.4 or --model path/my-custom-weights.ckpt)
  • feature: log total progress when generating images (image X out of Y)

4.0.0

  • feature: stable diffusion 1.5 (slightly improved image quality)
  • feature: dilation and erosion of masks Previously the + and - characters in a mask (example: face{+0.1}) added to the grayscale value of any masked areas. This wasn't very useful. The new behavior is that the mask will expand or contract by the number of pixel specified. The technical terms for this are dilation and erosion. This allows much greater control over the masked area.
  • feature: update k-diffusion samplers. add k_dpm_adaptive and k_dpm_fast
  • feature: img2img/inpainting supported on all samplers
  • refactor: consolidates img2img/txt2img code. consolidates schedules. consolidates masking
  • ci: minor logging improvements

3.0.1

  • fix: k-samplers were broken

3.0.0

  • feature: improved safety filter

2.4.0

  • ๐ŸŽ‰ 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

2.0.3

  • fix memory leak in face enhancer
  • fix blurry inpainting
  • fix for pillow compatibility

2.0.0

  • ๐ŸŽ‰ 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.
  • ๐ŸŽ‰ feature: interactive prompt added. access by running aimg
  • ๐ŸŽ‰ 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 (|, &, !)
  • ๐ŸŽ‰ 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 compatibility 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

Notable Stable Diffusion Implementations

Online Stable Diffusion Services

Further Reading

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for ahmed-mohamed-sn from gravatar.com ahmed-mohamed-sn

Unverified details

These details have not been verified by PyPI
Meta
  • Author: Hanoush
  • Requires: Python >=3.9, <4.0
Classifiers

Release history Release notifications | RSS feed

0.6.5

0.6.4

0.6.3

0.6.2

0.6.1

0.6.0

0.5.8

0.5.7

0.5.6

0.5.5

0.5.4

0.5.3

0.5.2

0.5.1

0.5.0

0.4.8

0.4.7

0.4.6

0.4.5

0.4.4

0.4.3

0.4.2

0.4.1

0.4.0

0.3.18

0.3.17

0.3.16

0.3.15

0.3.14

0.3.13

0.3.12

0.3.11

0.3.10

0.3.9

0.3.8

0.3.7

0.3.6

0.3.5

0.3.4

0.3.3

0.3.2

0.3.1

0.3.0

0.2.10

0.2.9

0.2.8

0.2.7

0.2.6

0.2.5

0.2.4

0.2.3

0.2.2

0.2.1

0.2.0

0.1.10

0.1.9

0.1.8

0.1.7

0.1.6

0.1.5

0.1.4

0.1.3

0.1.2

0.1.1

This version

0.1.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

classy_imaginary-0.1.0.tar.gz (6.2 MB view details)

Uploaded Source

Built Distribution

classy_imaginary-0.1.0-py3-none-any.whl (6.1 MB view details)

Uploaded Python 3

File details

Details for the file classy_imaginary-0.1.0.tar.gz.

File metadata

  • Download URL: classy_imaginary-0.1.0.tar.gz
  • Upload date:
  • Size: 6.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.1 CPython/3.9.12 Linux/6.0.12-76060006-generic

File hashes

Hashes for classy_imaginary-0.1.0.tar.gz
Algorithm Hash digest
SHA256 2693f12c50c5ed3e4c4fdbcf66c02fe01a75194b02789cc87555d1bb723dd08b
MD5 6b34aa9e8d3ead48cc938fb69cd679f0
BLAKE2b-256 5e5d25f0e3a358e9992f48617da487eacd4d3527e1acdb7f007f994fa82a3539

See more details on using hashes here.

File details

Details for the file classy_imaginary-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: classy_imaginary-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 6.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.1 CPython/3.9.12 Linux/6.0.12-76060006-generic

File hashes

Hashes for classy_imaginary-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 97c05bb45c6bb52df015e208a307471ecd6e267bc90eb63ada37fa5b227c30cb
MD5 1d068f9a76326ec8b882c91503cb372c
BLAKE2b-256 2f7eed8ba03553c29d51864bae11b0f8e05d3f773aa7928224021e38818af7fe

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page