Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fixes, first runable version

Browse Source
This commit is contained in:
Sergey Borisov
2023-06-11 16:42:40 +03:00
parent 738ba40f51
commit 694fd0c92f
4 changed files with 64 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
invokeai/backend/model_management/models/base.py
View File

@ -1,19 +1,21 @@
import sys
import typing
import inspect
from enum import Enum
import torch
from diffusers import DiffusionPipeline, ConfigMixin
from pydantic import BaseModel, Field
from typing import List, Dict, Optional, Type
from typing import List, Dict, Optional, Type, Literal
class BaseModelType(str, Enum):
#StableDiffusion1_5 = "stable_diffusion_1_5"
#StableDiffusion2 = "stable_diffusion_2"
#StableDiffusion2Base = "stable_diffusion_2_base"
# TODO: maybe then add sample size(512/768)?
StableDiffusion1_5 = "SD-1"
StableDiffusion2Base = "SD-2-base" # 512 pixels; this will have epsilon parameterization
StableDiffusion2 = "SD-2" # 768 pixels; this will have v-prediction parameterization
StableDiffusion1_5 = "sd-1.5"
StableDiffusion2Base = "sd-2-base" # 512 pixels; this will have epsilon parameterization
StableDiffusion2 = "sd-2" # 768 pixels; this will have v-prediction parameterization
#Kandinsky2_1 = "kandinsky_2_1"
class ModelType(str, Enum):
@ -21,7 +23,7 @@ class ModelType(str, Enum):
Vae = "vae"
Lora = "lora"
ControlNet = "controlnet"
#ControlNet = "controlnet"
TextualInversion = "embedding"
class SubModelType(str, Enum):