mmagic.models.editors.disco_diffusion.disco

Module Contents

Classes

DiscoDiffusion

Disco Diffusion (DD) is a Google Colab Notebook which leverages an AI

Attributes

ModelType

mmagic.models.editors.disco_diffusion.disco.ModelType

class mmagic.models.editors.disco_diffusion.disco.DiscoDiffusion(unet, diffusion_scheduler, secondary_model=None, clip_models=[], use_fp16=False, pretrained_cfgs=None)

Bases: torch.nn.Module

Disco Diffusion (DD) is a Google Colab Notebook which leverages an AI Image generating technique called CLIP-Guided Diffusion to allow you to create compelling and beautiful images from just text inputs. Created by Somnai, augmented by Gandamu, and building on the work of RiversHaveWings, nshepperd, and many others.

Ref:

Github Repo: https://github.com/alembics/disco-diffusion Colab: https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb # noqa

Parameters

• unet (ModelType) – Config of denoising Unet.

• diffusion_scheduler (ModelType) – Config of diffusion_scheduler scheduler.

• secondary_model (ModelType) – A smaller secondary diffusion model trained by Katherine Crowson to remove noise from intermediate timesteps to prepare them for CLIP. Ref: https://twitter.com/rivershavewings/status/1462859669454536711 # noqa Defaults to None.

• clip_models (list) – Config of clip models. Defaults to [].

• use_fp16 (bool) – Whether to use fp16 for unet model. Defaults to False.

• pretrained_cfgs (dict) – Path Config for pretrained weights. Usually this is a dict contains module name and the corresponding ckpt path. Defaults to None.

property device

Get current device of the model.

Returns

The current device of the model.

Return type

torch.device

load_pretrained_models(pretrained_cfgs)

Loading pretrained weights to model. pretrained_cfgs is a dict consist of module name as key and checkpoint path as value.

Parameters

• pretrained_cfgs (dict) – Path Config for pretrained weights.

• the (Usually this is a dict contains module name and) –

• None. (corresponding ckpt path. Defaults to) –

infer(scheduler_kwargs=None, height=None, width=None, init_image=None, batch_size=1, num_inference_steps=100, skip_steps=0, show_progress=True, text_prompts=[], image_prompts=[], eta=0.8, clip_guidance_scale=5000, init_scale=1000, tv_scale=0.0, sat_scale=0.0, range_scale=150, cut_overview=[12] * 400 + [4] * 600, cut_innercut=[4] * 400 + [12] * 600, cut_ic_pow=[1] * 1000, cut_icgray_p=[0.2] * 400 + [0] * 600, cutn_batches=4, seed=None)

Inference API for disco diffusion.

Parameters

• scheduler_kwargs (dict) – Args for infer time diffusion scheduler. Defaults to None.

• height (int) – Height of output image. Defaults to None.

• width (int) – Width of output image. Defaults to None.

• init_image (str) – Initial image at the start point of denoising. Defaults to None.

• batch_size (int) – Batch size. Defaults to 1.

• num_inference_steps (int) – Number of inference steps. Defaults to 1000.

• skip_steps (int) – Denoising steps to skip, usually set with init_image. Defaults to 0.

• show_progress (bool) – Whether to show progress. Defaults to False.

• text_prompts (list) – Text prompts. Defaults to [].

• image_prompts (list) – Image prompts, this is not the same as init_image, they works the same way with text_prompts. Defaults to [].

• eta (float) – Eta for ddim sampling. Defaults to 0.8.

• clip_guidance_scale (int) – The Scale of influence of prompts on output image. Defaults to 1000.

• seed (int) – Sampling seed. Defaults to None.