multidiffusion-upscaler-for.../tile_utils/typing.py

29 lines
1.3 KiB
Python

import sys
from typing import *
from torch import Tensor
from gradio.components import Component
from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
from ldm.models.diffusion.ddpm import LatentDiffusion
from modules.processing import StableDiffusionProcessing as Processing, StableDiffusionProcessingImg2Img as ProcessingImg2Img, Processed
from modules.prompt_parser import MulticondLearnedConditioning, ScheduledPromptConditioning
from modules.extra_networks import ExtraNetworkParams
from modules.sd_samplers_kdiffusion import KDiffusionSampler, CFGDenoiser
from modules.sd_samplers_timesteps import CompVisSampler, CompVisTimestepsDenoiser, CompVisTimestepsVDenoiser
ModuleType = type(sys)
Sampler = Union[KDiffusionSampler, CompVisSampler]
Cond = MulticondLearnedConditioning
Uncond = List[List[ScheduledPromptConditioning]]
ExtraNetworkData = DefaultDict[str, List[ExtraNetworkParams]]
# 'c_crossattn' List[Tensor[B, L=77, D=768]] prompt cond (tcond)
# 'c_concat' List[Tensor[B, C=5, H, W]] latent mask (icond)
# 'c_adm' Tensor[?] unclip (icond)
# 'crossattn' Tensor[B, L=77, D=2048] sdxl (tcond)
# 'vector' Tensor[B, D] sdxl (tcond)
CondDict = Dict[str, Tensor]