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DiscoArt vs. DD5.6

DiscoArt and DD5.6 follow the same algorithms, but DiscoArt has a much better implementation (guaranteed by me) with top code quality, the best user experience and rich features. It is perfect for integration to professional developers.

DiscoArt is synced with the upstream update of original DD notebook. The list below summarizes the major differences between DiscoArt and DD5.6:

Refactor & bug fixes

  • Completely refactored the notebook implementation and aim for best-in-class quality. (I'm serious about this.)
  • Fixes multiple bugs e.g. weighted prompts, cut scheduling in original DD5.6, which improves the generation quality.
  • No dependency of IPython when not using in notebook/colab.
  • Robust persistent storage for the generated images.
  • Simpler interface and Pythonic API.

K-shot sampling in one run

Here is a question: to generate 3 images with the same prompts & settings, would you rather do n_batches=3 and batch_size=1, or n_batches=1 and batch_size=3?

This is a trick question because in original DD5.6 one can not do batch_size > 1, it is not supported.

In DiscoArt, you can simply do:

from discoart import create

create(batch_size=3)

Which means you can generate three images in one run. Not only this is faster than three runs, but it leverages loss function better and potentially yields higher quality images.

Spellchecking

Typos in the prompts can lead to suboptimal image generation; and they are often hard to recognize. In DiscoArt, you will see a warning message when you have a typo as follows:

Artist names and common modifiers (e.g. dof, artstation) are added into whitelist to avoid the false alarm.

You can change on_misspelled_token parameter to enforce auto-correction as well.

IDE support

When writing DiscoArt applications in PyCharm/VSCode/Jupyter/Google Colab, you will get tab complete and parameter hinting out of the box.

Scheduling

The following variables can be either scalar variables in the type of bool, float or int; or can be scheduled via the syntax of [val]*400+[val1]*100+[val2]*500 over 1000 steps.

  • cut_overview
  • cut_innercut
  • cut_icgray_p
  • cut_ic_pow
  • use_secondary_model
  • cutn_batches
  • skip_augs
  • clip_guidance_scale
  • cutn_batches
  • tv_scale
  • range_scale
  • sat_scale
  • init_scale
  • clamp_grad
  • clamp_max

Also:

  • clip_models_schedules is added to control the scheduling of clip models, the syntax is the same as cut_overview but as a bool list [True]*400+[False]*600.

Custom diffusion models

Latest diffusion models are automatically synced to local when they become available, there is no need to update the codebase.

  • Support default 512x512, 256x256 diffusion model as well as Pixel Art Diffusion, Watercolor Diffusion, and Pulp SciFi Diffusion models.
  • diffusion_model and diffusion_model_config can be specified load custom diffusion model and override the default diffusion model.

To use a listed diffusion model, you can do:

from discoart import create

create(diffusion_model='portrait_generator', ...)

Note that you don't have to write the full name of the diffusion model, e.g. any prefix is enough.

To load a custom diffusion model from a local .pt file, you can do:

from discoart import create

create(diffusion_model='/path/to/diffusion-model.pt', diffusion_model_config={...}, ...)

To list all supported diffusion models,

from discoart.helper import list_diffusion_models

list_diffusion_models()

You can also specify the environment variable DISCOART_MODELS_YAML to build your list of diffusion models.

Feature changes

  • DiscoArt does not support video generation and image_prompt (which was marked as ineffective in DD 5.4).
  • Due to no video support, text_prompts in DiscoArt accepts a string or a list of strings, not a dictionary; i.e. no frame index 0: or 100:.
  • clip_models accepts a list of values from all open-clip pretrained models and weights.