mirror of
https://github.com/invoke-ai/InvokeAI
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feat(mm): add starter models route
The models from INITIAL_MODELS.yaml have been recreated as a structured python object. This data is served on a new route. The model sources are compared against currently-installed models to determine if they are already installed or not.
This commit is contained in:
psychedelicious
parent
429f87c60b
commit
5ceaeb234d
@ -5,6 +5,7 @@ import io
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import pathlib
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import pathlib
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import shutil
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import shutil
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import traceback
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import traceback
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from copy import deepcopy
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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List, Optional
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from fastapi import Body, Path, Query, Response, UploadFile
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from fastapi import Body, Path, Query, Response, UploadFile
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@ -32,6 +33,7 @@ from invokeai.backend.model_manager.config import (
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from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
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from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
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from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
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from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
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from invokeai.backend.model_manager.search import ModelSearch
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from invokeai.backend.model_manager.search import ModelSearch
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from invokeai.backend.model_manager.starter_models import STARTER_MODELS, StarterModel
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from ..dependencies import ApiDependencies
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from ..dependencies import ApiDependencies
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@ -780,3 +782,15 @@ async def convert_model(
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# except ValueError as e:
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# except ValueError as e:
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# raise HTTPException(status_code=400, detail=str(e))
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# raise HTTPException(status_code=400, detail=str(e))
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# return response
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# return response
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@model_manager_router.get("/starter_models", operation_id="get_starter_models", response_model=list[StarterModel])
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async def get_starter_models() -> list[StarterModel]:
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installed_models = ApiDependencies.invoker.services.model_manager.store.search_by_attr()
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installed_model_sources = {m.source for m in installed_models}
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starter_models = deepcopy(STARTER_MODELS)
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for model in starter_models:
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if model.source in installed_model_sources:
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model.is_installed = True
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return starter_models
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392
invokeai/backend/model_manager/starter_models.py
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392
invokeai/backend/model_manager/starter_models.py
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@ -0,0 +1,392 @@
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from dataclasses import dataclass
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from typing import Optional
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from invokeai.backend.model_manager.config import BaseModelType, ModelType
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@dataclass
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class StarterModel:
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description: str
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source: str
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name: str
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base: BaseModelType
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type: ModelType
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# Optional list of model source dependencies that need to be installed before this model can be used
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dependencies: Optional[list[str]] = None
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is_installed: bool = False
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# List of starter models, displayed on the frontend.
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# The order/sort of this list is not changed by the frontend - set it how you want it here.
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STARTER_MODELS: list[StarterModel] = [
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# region: Main
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StarterModel(
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name="SD 1.5 (base)",
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base=BaseModelType.StableDiffusion1,
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source="runwayml/stable-diffusion-v1-5",
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description="Stable Diffusion version 1.5 diffusers model (4.27 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="SD 1.5 (inpainting)",
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base=BaseModelType.StableDiffusion1,
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source="runwayml/stable-diffusion-inpainting",
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description="RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="Analog Diffusion",
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base=BaseModelType.StableDiffusion1,
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source="wavymulder/Analog-Diffusion",
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description="An SD-1.5 model trained on diverse analog photographs (2.13 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="Deliberate v5",
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base=BaseModelType.StableDiffusion1,
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source="https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors",
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description="Versatile model that produces detailed images up to 768px (4.27 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="Dungeons and Diffusion",
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base=BaseModelType.StableDiffusion1,
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source="0xJustin/Dungeons-and-Diffusion",
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description="Dungeons & Dragons characters (2.13 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="dreamlike photoreal v2",
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base=BaseModelType.StableDiffusion1,
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source="dreamlike-art/dreamlike-photoreal-2.0",
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description="A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="Inkpunk Diffusion",
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base=BaseModelType.StableDiffusion1,
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source="Envvi/Inkpunk-Diffusion",
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description='Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)',
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type=ModelType.Main,
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),
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StarterModel(
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name="OpenJourney",
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base=BaseModelType.StableDiffusion1,
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source="prompthero/openjourney",
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description='An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)',
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type=ModelType.Main,
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),
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StarterModel(
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name="seek.art MEGA",
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base=BaseModelType.StableDiffusion1,
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source="coreco/seek.art_MEGA",
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description='A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)',
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type=ModelType.Main,
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),
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StarterModel(
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name="TrinArt v2",
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base=BaseModelType.StableDiffusion1,
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source="naclbit/trinart_stable_diffusion_v2",
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description="An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="SD 2.1 (base)",
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base=BaseModelType.StableDiffusion2,
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source="stabilityai/stable-diffusion-2-1",
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description="Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="SD 2.0 (inpainting)",
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base=BaseModelType.StableDiffusion2,
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source="stabilityai/stable-diffusion-2-inpainting",
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description="Stable Diffusion version 2.0 inpainting model (5.21 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="SDXL (base)",
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base=BaseModelType.StableDiffusionXL,
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source="stabilityai/stable-diffusion-xl-base-1.0",
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description="Stable Diffusion XL base model (12 GB)",
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type=ModelType.Main,
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),
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StarterModel(
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name="SDXL Refiner",
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base=BaseModelType.StableDiffusionXLRefiner,
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source="stabilityai/stable-diffusion-xl-refiner-1.0",
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description="Stable Diffusion XL refiner model (12 GB)",
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type=ModelType.Main,
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),
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# endregion
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# region VAE
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StarterModel(
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name="sdxl-vae-fp16-fix",
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base=BaseModelType.StableDiffusionXL,
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source="madebyollin/sdxl-vae-fp16-fix",
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description="Version of the SDXL-1.0 VAE that works in half precision mode",
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type=ModelType.VAE,
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),
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# endregion
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# region LoRA
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StarterModel(
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name="FlatColor",
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base=BaseModelType.StableDiffusion1,
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source="https://civitai.com/models/6433/loraflatcolor",
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description="A LoRA that generates scenery using solid blocks of color",
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type=ModelType.LoRA,
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),
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StarterModel(
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name="Ink scenery",
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base=BaseModelType.StableDiffusion1,
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source="https://civitai.com/api/download/models/83390",
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description="Generate india ink-like landscapes",
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type=ModelType.LoRA,
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),
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# endregion
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# region IP Adapter
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StarterModel(
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name="IP Adapter",
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base=BaseModelType.StableDiffusion1,
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source="InvokeAI/ip_adapter_sd15",
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description="IP-Adapter for SD 1.5 models",
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type=ModelType.IPAdapter,
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dependencies=["InvokeAI/ip_adapter_sd_image_encoder"],
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),
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StarterModel(
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name="IP Adapter Plus",
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base=BaseModelType.StableDiffusion1,
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source="InvokeAI/ip_adapter_plus_sd15",
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description="Refined IP-Adapter for SD 1.5 models",
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type=ModelType.IPAdapter,
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dependencies=["InvokeAI/ip_adapter_sd_image_encoder"],
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),
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StarterModel(
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name="IP Adapter Plus Face",
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base=BaseModelType.StableDiffusion1,
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source="InvokeAI/ip_adapter_plus_face_sd15",
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description="Refined IP-Adapter for SD 1.5 models, adapted for faces",
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type=ModelType.IPAdapter,
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dependencies=["InvokeAI/ip_adapter_sd_image_encoder"],
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),
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StarterModel(
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name="IP Adapter SDXL",
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base=BaseModelType.StableDiffusionXL,
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source="InvokeAI/ip_adapter_sdxl",
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description="IP-Adapter for SDXL models",
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type=ModelType.IPAdapter,
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dependencies=["InvokeAI/ip_adapter_sdxl_image_encoder"],
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),
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# endregion
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# region ControlNet
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StarterModel(
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name="QRCode Monster",
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base=BaseModelType.StableDiffusion1,
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source="monster-labs/control_v1p_sd15_qrcode_monster",
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description="Controlnet model that generates scannable creative QR codes",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="canny",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_canny",
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description="Controlnet weights trained on sd-1.5 with canny conditioning.",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="inpaint",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_inpaint",
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description="Controlnet weights trained on sd-1.5 with canny conditioning, inpaint version",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="mlsd",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_mlsd",
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description="Controlnet weights trained on sd-1.5 with canny conditioning, MLSD version",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="depth",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11f1p_sd15_depth",
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description="Controlnet weights trained on sd-1.5 with depth conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="normal_bae",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_normalbae",
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description="Controlnet weights trained on sd-1.5 with normalbae image conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="seg",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_seg",
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description="Controlnet weights trained on sd-1.5 with seg image conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="lineart",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_lineart",
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description="Controlnet weights trained on sd-1.5 with lineart image conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="lineart_anime",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15s2_lineart_anime",
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description="Controlnet weights trained on sd-1.5 with anime image conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="openpose",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_openpose",
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description="Controlnet weights trained on sd-1.5 with openpose image conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="scribble",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_scribble",
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description="Controlnet weights trained on sd-1.5 with scribble image conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="softedge",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11p_sd15_softedge",
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description="Controlnet weights trained on sd-1.5 with soft edge conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="shuffle",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11e_sd15_shuffle",
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description="Controlnet weights trained on sd-1.5 with shuffle image conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="tile",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11f1e_sd15_tile",
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description="Controlnet weights trained on sd-1.5 with tiled image conditioning",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="ip2p",
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base=BaseModelType.StableDiffusion1,
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source="lllyasviel/control_v11e_sd15_ip2p",
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description="Controlnet weights trained on sd-1.5 with ip2p conditioning.",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="canny-sdxl",
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base=BaseModelType.StableDiffusionXL,
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source="diffusers/controlnet-canny-sdxl-1.0",
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description="Controlnet weights trained on sdxl-1.0 with canny conditioning.",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="depth-sdxl",
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base=BaseModelType.StableDiffusionXL,
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source="diffusers/controlnet-depth-sdxl-1.0",
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description="Controlnet weights trained on sdxl-1.0 with depth conditioning.",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="softedge-dexined-sdxl",
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base=BaseModelType.StableDiffusionXL,
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source="SargeZT/controlnet-sd-xl-1.0-softedge-dexined",
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description="Controlnet weights trained on sdxl-1.0 with dexined soft edge preprocessing.",
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type=ModelType.ControlNet,
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),
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StarterModel(
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name="depth-16bit-zoe-sdxl",
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base=BaseModelType.StableDiffusionXL,
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source="SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe",
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description="Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (16 bits).",
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type=ModelType.ControlNet,
|
||||||
|
),
|
||||||
|
StarterModel(
|
||||||
|
name="depth-zoe-sdxl",
|
||||||
|
base=BaseModelType.StableDiffusionXL,
|
||||||
|
source="diffusers/controlnet-zoe-depth-sdxl-1.0",
|
||||||
|
description="Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).",
|
||||||
|
type=ModelType.ControlNet,
|
||||||
|
),
|
||||||
|
# endregion
|
||||||
|
# region T2I Adapter
|
||||||
|
StarterModel(
|
||||||
|
name="canny-sd15",
|
||||||
|
base=BaseModelType.StableDiffusion1,
|
||||||
|
source="TencentARC/t2iadapter_canny_sd15v2",
|
||||||
|
description="T2I Adapter weights trained on sd-1.5 with canny conditioning.",
|
||||||
|
type=ModelType.T2IAdapter,
|
||||||
|
),
|
||||||
|
StarterModel(
|
||||||
|
name="sketch-sd15",
|
||||||
|
base=BaseModelType.StableDiffusion1,
|
||||||
|
source="TencentARC/t2iadapter_sketch_sd15v2",
|
||||||
|
description="T2I Adapter weights trained on sd-1.5 with sketch conditioning.",
|
||||||
|
type=ModelType.T2IAdapter,
|
||||||
|
),
|
||||||
|
StarterModel(
|
||||||
|
name="depth-sd15",
|
||||||
|
base=BaseModelType.StableDiffusion1,
|
||||||
|
source="TencentARC/t2iadapter_depth_sd15v2",
|
||||||
|
description="T2I Adapter weights trained on sd-1.5 with depth conditioning.",
|
||||||
|
type=ModelType.T2IAdapter,
|
||||||
|
),
|
||||||
|
StarterModel(
|
||||||
|
name="zoedepth-sd15",
|
||||||
|
base=BaseModelType.StableDiffusion1,
|
||||||
|
source="TencentARC/t2iadapter_zoedepth_sd15v1",
|
||||||
|
description="T2I Adapter weights trained on sd-1.5 with zoe depth conditioning.",
|
||||||
|
type=ModelType.T2IAdapter,
|
||||||
|
),
|
||||||
|
StarterModel(
|
||||||
|
name="canny-sdxl",
|
||||||
|
base=BaseModelType.StableDiffusionXL,
|
||||||
|
source="TencentARC/t2i-adapter-canny-sdxl-1.0",
|
||||||
|
description="T2I Adapter weights trained on sdxl-1.0 with canny conditioning.",
|
||||||
|
type=ModelType.T2IAdapter,
|
||||||
|
),
|
||||||
|
StarterModel(
|
||||||
|
name="zoedepth-sdxl",
|
||||||
|
base=BaseModelType.StableDiffusionXL,
|
||||||
|
source="TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
|
||||||
|
description="T2I Adapter weights trained on sdxl-1.0 with zoe depth conditioning.",
|
||||||
|
type=ModelType.T2IAdapter,
|
||||||
|
),
|
||||||
|
StarterModel(
|
||||||
|
name="lineart-sdxl",
|
||||||
|
base=BaseModelType.StableDiffusionXL,
|
||||||
|
source="TencentARC/t2i-adapter-lineart-sdxl-1.0",
|
||||||
|
description="T2I Adapter weights trained on sdxl-1.0 with lineart conditioning.",
|
||||||
|
type=ModelType.T2IAdapter,
|
||||||
|
),
|
||||||
|
StarterModel(
|
||||||
|
name="sketch-sdxl",
|
||||||
|
base=BaseModelType.StableDiffusionXL,
|
||||||
|
source="TencentARC/t2i-adapter-sketch-sdxl-1.0",
|
||||||
|
description="T2I Adapter weights trained on sdxl-1.0 with sketch conditioning.",
|
||||||
|
type=ModelType.T2IAdapter,
|
||||||
|
),
|
||||||
|
# endregion
|
||||||
|
# region TI
|
||||||
|
StarterModel(
|
||||||
|
name="EasyNegative",
|
||||||
|
base=BaseModelType.StableDiffusion1,
|
||||||
|
source="https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors",
|
||||||
|
description="A textual inversion to use in the negative prompt to reduce bad anatomy",
|
||||||
|
type=ModelType.TextualInversion,
|
||||||
|
),
|
||||||
|
# endregion
|
||||||
|
]
|
||||||
|
|
||||||
|
assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models"
|
||||||
Loading…
Reference in New Issue
Block a user