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InvokeAI/invokeai/backend/model_manager/starter_models.py

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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.
2024-03-19 04:57:16 +00:00
from typing import Optional
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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from pydantic import BaseModel
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.
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from invokeai.backend.model_manager.config import BaseModelType, ModelType
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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class StarterModelWithoutDependencies(BaseModel):
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.
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description: str
source: str
name: str
base: BaseModelType
type: ModelType
is_installed: bool = False
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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class StarterModel(StarterModelWithoutDependencies):
# Optional list of model source dependencies that need to be installed before this model can be used
dependencies: Optional[list[StarterModelWithoutDependencies]] = None
sdxl_fp16_vae_fix = StarterModel(
name="sdxl-vae-fp16-fix",
base=BaseModelType.StableDiffusionXL,
source="madebyollin/sdxl-vae-fp16-fix",
description="SDXL VAE that works with FP16.",
type=ModelType.VAE,
)
ip_adapter_sd_image_encoder = StarterModel(
name="IP Adapter SD1.5 Image Encoder",
base=BaseModelType.StableDiffusion1,
source="InvokeAI/ip_adapter_sd_image_encoder",
description="IP Adapter SD Image Encoder",
type=ModelType.CLIPVision,
)
ip_adapter_sdxl_image_encoder = StarterModel(
name="IP Adapter SDXL Image Encoder",
base=BaseModelType.StableDiffusionXL,
source="InvokeAI/ip_adapter_sdxl_image_encoder",
description="IP Adapter SDXL Image Encoder",
type=ModelType.CLIPVision,
)
cyberrealistic_negative = StarterModel(
name="CyberRealistic Negative v3",
base=BaseModelType.StableDiffusion1,
source="https://huggingface.co/cyberdelia/CyberRealistic_Negative/resolve/main/CyberRealistic_Negative_v3.pt",
description="Negative embedding specifically for use with CyberRealistic.",
type=ModelType.TextualInversion,
)
add FLUX schnell starter models and submodels as dependenices or adhoc download options
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t5_base_encoder = StarterModel(
name="t5_base_encoder",
base=BaseModelType.Any,
source="InvokeAI/flux_schnell::t5_xxl_encoder/base",
description="T5-XXL text encoder (used in FLUX pipelines). ~8GB",
type=ModelType.T5Encoder,
)
t5_8b_quantized_encoder = StarterModel(
name="t5_8b_quantized_encoder",
base=BaseModelType.Any,
source="invokeai/flux_schnell::t5_xxl_encoder/optimum_quanto_qfloat8",
description="T5-XXL text encoder with optimum-quanto qfloat8 quantization (used in FLUX pipelines). ~6GB",
type=ModelType.T5Encoder,
)
clip_l_encoder = StarterModel(
name="clip-vit-large-patch14",
base=BaseModelType.Any,
source="openai/clip-vit-large-patch14",
description="CLIP-L text encoder (used in FLUX pipelines). ~3GB",
type=ModelType.CLIPEmbed,
)
flux_vae = StarterModel(
name="FLUX.1-schnell_ae",
base=BaseModelType.Flux,
source="black-forest-labs/FLUX.1-schnell::ae.safetensors",
description="FLUX VAE compatible with both schnell and dev variants.",
type=ModelType.VAE,
)
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.
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# List of starter models, displayed on the frontend.
# The order/sort of this list is not changed by the frontend - set it how you want it here.
STARTER_MODELS: list[StarterModel] = [
# region: Main
add FLUX schnell starter models and submodels as dependenices or adhoc download options
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StarterModel(
name="FLUX Schnell (Quantized)",
base=BaseModelType.Flux,
source="InvokeAI/flux_schnell::transformer/bnb_nf4/flux1-schnell-bnb_nf4.safetensors",
description="FLUX schnell transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~14GB",
type=ModelType.Main,
dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
),
StarterModel(
name="FLUX Schnell",
base=BaseModelType.Flux,
source="InvokeAI/flux_schnell::transformer/base/flux1-schnell.safetensors",
description="FLUX schnell transformer in bfloat16. Total size with dependencies: ~33GB",
type=ModelType.Main,
dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
),
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.
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StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="CyberRealistic v4.1",
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.
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base=BaseModelType.StableDiffusion1,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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source="https://huggingface.co/cyberdelia/CyberRealistic/resolve/main/CyberRealistic_V4.1_FP16.safetensors",
description="Photorealistic model. See other variants in HF repo 'cyberdelia/CyberRealistic'.",
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.
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type=ModelType.Main,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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dependencies=[cyberrealistic_negative],
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.
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),
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="ReV Animated",
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.
2024-03-19 04:57:16 +00:00
base=BaseModelType.StableDiffusion1,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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source="stablediffusionapi/rev-animated",
description="Fantasy and anime style images.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.Main,
),
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="Dreamshaper 8",
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.
2024-03-19 04:57:16 +00:00
base=BaseModelType.StableDiffusion1,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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source="Lykon/dreamshaper-8",
description="Popular versatile model.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.Main,
),
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="Dreamshaper 8 (inpainting)",
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.
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base=BaseModelType.StableDiffusion1,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
source="Lykon/dreamshaper-8-inpainting",
description="Inpainting version of Dreamshaper 8.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.Main,
),
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="Deliberate v5",
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.
2024-03-19 04:57:16 +00:00
base=BaseModelType.StableDiffusion1,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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source="https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors",
description="Popular versatile model",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.Main,
),
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="Deliberate v5 (inpainting)",
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.
2024-03-19 04:57:16 +00:00
base=BaseModelType.StableDiffusion1,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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source="https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5-inpainting.safetensors",
description="Inpainting version of Deliberate v5.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.Main,
),
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="Juggernaut XL v9",
base=BaseModelType.StableDiffusionXL,
source="RunDiffusion/Juggernaut-XL-v9",
description="Photograph-focused model.",
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.
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type=ModelType.Main,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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dependencies=[sdxl_fp16_vae_fix],
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.
2024-03-19 04:57:16 +00:00
),
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="Dreamshaper XL v2 Turbo",
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.
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base=BaseModelType.StableDiffusionXL,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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source="Lykon/dreamshaper-xl-v2-turbo",
description="For turbo, use CFG Scale 2, 4-8 steps, DPM++ SDE Karras. For non-turbo, use CFG Scale 6, 20-40 steps, DPM++ 2M SDE Karras.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.Main,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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dependencies=[sdxl_fp16_vae_fix],
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.
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),
StarterModel(
name="SDXL Refiner",
base=BaseModelType.StableDiffusionXLRefiner,
source="stabilityai/stable-diffusion-xl-refiner-1.0",
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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description="The OG Stable Diffusion XL refiner model.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.Main,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
dependencies=[sdxl_fp16_vae_fix],
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.
2024-03-19 04:57:16 +00:00
),
# endregion
# region VAE
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
sdxl_fp16_vae_fix,
add FLUX schnell starter models and submodels as dependenices or adhoc download options
2024-08-21 18:26:43 +00:00
flux_vae,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
# endregion
# region LoRA
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.
2024-03-19 04:57:16 +00:00
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="Alien Style",
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.
2024-03-19 04:57:16 +00:00
base=BaseModelType.StableDiffusionXL,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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source="https://huggingface.co/RalFinger/alien-style-lora-sdxl/resolve/main/alienzkin-sdxl.safetensors",
description="Futuristic, intricate alien styles. Trigger with 'alienzkin'.",
type=ModelType.LoRA,
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.
2024-03-19 04:57:16 +00:00
),
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
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name="Noodles Style",
base=BaseModelType.StableDiffusionXL,
source="https://huggingface.co/RalFinger/noodles-lora-sdxl/resolve/main/noodlez-sdxl.safetensors",
description="Never-ending, no-holds-barred, noodle nightmare. Trigger with 'noodlez'.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.LoRA,
),
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
# endregion
# region TI
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.
2024-03-19 04:57:16 +00:00
StarterModel(
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
name="EasyNegative",
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.
2024-03-19 04:57:16 +00:00
base=BaseModelType.StableDiffusion1,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
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,
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.
2024-03-19 04:57:16 +00:00
),
# endregion
# region IP Adapter
StarterModel(
name="IP Adapter",
base=BaseModelType.StableDiffusion1,
fix: update ip adapter starter models path
2024-04-24 03:18:25 +00:00
source="https://huggingface.co/InvokeAI/ip_adapter_sd15/resolve/main/ip-adapter_sd15.safetensors",
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.
2024-03-19 04:57:16 +00:00
description="IP-Adapter for SD 1.5 models",
type=ModelType.IPAdapter,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
dependencies=[ip_adapter_sd_image_encoder],
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.
2024-03-19 04:57:16 +00:00
),
StarterModel(
name="IP Adapter Plus",
base=BaseModelType.StableDiffusion1,
fix: update ip adapter starter models path
2024-04-24 03:18:25 +00:00
source="https://huggingface.co/InvokeAI/ip_adapter_plus_sd15/resolve/main/ip-adapter-plus_sd15.safetensors",
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.
2024-03-19 04:57:16 +00:00
description="Refined IP-Adapter for SD 1.5 models",
type=ModelType.IPAdapter,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
dependencies=[ip_adapter_sd_image_encoder],
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.
2024-03-19 04:57:16 +00:00
),
StarterModel(
name="IP Adapter Plus Face",
base=BaseModelType.StableDiffusion1,
fix: update ip adapter starter models path
2024-04-24 03:18:25 +00:00
source="https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15/resolve/main/ip-adapter-plus-face_sd15.safetensors",
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.
2024-03-19 04:57:16 +00:00
description="Refined IP-Adapter for SD 1.5 models, adapted for faces",
type=ModelType.IPAdapter,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
dependencies=[ip_adapter_sd_image_encoder],
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.
2024-03-19 04:57:16 +00:00
),
StarterModel(
name="IP Adapter SDXL",
base=BaseModelType.StableDiffusionXL,
fix: update SDXL IP Adpater starter model to be ViT-H
2024-04-24 03:59:42 +00:00
source="https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h/resolve/main/ip-adapter_sdxl_vit-h.safetensors",
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.
2024-03-19 04:57:16 +00:00
description="IP-Adapter for SDXL models",
type=ModelType.IPAdapter,
feat(mm): revised list of starter models - Enriched dependencies to not just be a string - allows reuse of a dependency as a starter model _and_ dependency of another model. For example, all the SDXL models have the fp16 VAE as a dependency, but you can also download it on its own. - Looked at popular models on the major model sites to select the list. No SD2 models. All hosted on HF.
2024-03-22 03:11:25 +00:00
dependencies=[ip_adapter_sdxl_image_encoder],
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.
2024-03-19 04:57:16 +00:00
),
# endregion
# region ControlNet
StarterModel(
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
name="QRCode Monster v2 (SD1.5)",
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.
2024-03-19 04:57:16 +00:00
base=BaseModelType.StableDiffusion1,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="monster-labs/control_v1p_sd15_qrcode_monster::v2",
description="ControlNet model that generates scannable creative QR codes",
type=ModelType.ControlNet,
),
StarterModel(
name="QRCode Monster (SDXL)",
base=BaseModelType.StableDiffusionXL,
source="monster-labs/control_v1p_sdxl_qrcode_monster",
description="ControlNet model that generates scannable creative QR codes",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="canny",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_canny",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with canny conditioning.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="inpaint",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_inpaint",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with canny conditioning, inpaint version",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="mlsd",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_mlsd",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with canny conditioning, MLSD version",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="depth",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11f1p_sd15_depth",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with depth conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="normal_bae",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_normalbae",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with normalbae image conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="seg",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_seg",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with seg image conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="lineart",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_lineart",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with lineart image conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="lineart_anime",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15s2_lineart_anime",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with anime image conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="openpose",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_openpose",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with openpose image conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="scribble",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_scribble",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with scribble image conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="softedge",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_softedge",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with soft edge conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="shuffle",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11e_sd15_shuffle",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with shuffle image conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="tile",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11f1e_sd15_tile",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with tiled image conditioning",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="ip2p",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11e_sd15_ip2p",
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
description="ControlNet weights trained on sd-1.5 with ip2p conditioning.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="canny-sdxl",
base=BaseModelType.StableDiffusionXL,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="xinsir/controlNet-canny-sdxl-1.0",
description="ControlNet weights trained on sdxl-1.0 with canny conditioning, by Xinsir.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="depth-sdxl",
base=BaseModelType.StableDiffusionXL,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="diffusers/controlNet-depth-sdxl-1.0",
description="ControlNet weights trained on sdxl-1.0 with depth conditioning.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="softedge-dexined-sdxl",
base=BaseModelType.StableDiffusionXL,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="SargeZT/controlNet-sd-xl-1.0-softedge-dexined",
description="ControlNet weights trained on sdxl-1.0 with dexined soft edge preprocessing.",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="depth-16bit-zoe-sdxl",
base=BaseModelType.StableDiffusionXL,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="SargeZT/controlNet-sd-xl-1.0-depth-16bit-zoe",
description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (16 bits).",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
name="depth-zoe-sdxl",
base=BaseModelType.StableDiffusionXL,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="diffusers/controlNet-zoe-depth-sdxl-1.0",
description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).",
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.
2024-03-19 04:57:16 +00:00
type=ModelType.ControlNet,
),
update controlnet starter models
2024-06-23 17:30:16 +00:00
StarterModel(
Update starter models
2024-06-23 17:30:33 +00:00
name="openpose-sdxl",
update controlnet starter models
2024-06-23 17:30:16 +00:00
base=BaseModelType.StableDiffusionXL,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="xinsir/controlNet-openpose-sdxl-1.0",
description="ControlNet weights trained on sdxl-1.0 compatible with the DWPose processor by Xinsir.",
update controlnet starter models
2024-06-23 17:30:16 +00:00
type=ModelType.ControlNet,
),
StarterModel(
Update starter models
2024-06-23 17:30:33 +00:00
name="scribble-sdxl",
update controlnet starter models
2024-06-23 17:30:16 +00:00
base=BaseModelType.StableDiffusionXL,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="xinsir/controlNet-scribble-sdxl-1.0",
description="ControlNet weights trained on sdxl-1.0 compatible with various lineart processors and black/white sketches by Xinsir.",
update controlnet starter models
2024-06-23 17:30:16 +00:00
type=ModelType.ControlNet,
),
add sdxl tile to starter models
2024-07-19 00:38:37 +00:00
StarterModel(
name="tile-sdxl",
base=BaseModelType.StableDiffusionXL,
feat(ui): update qr code cnet starter model - For SD1.5, use the new V2 version - Add the SDXL version
2024-07-25 22:08:53 +00:00
source="xinsir/controlNet-tile-sdxl-1.0",
description="ControlNet weights trained on sdxl-1.0 with tiled image conditioning",
add sdxl tile to starter models
2024-07-19 00:38:37 +00:00
type=ModelType.ControlNet,
),
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.
2024-03-19 04:57:16 +00:00
# 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
Add some ESRGAN and SwinIR upscale models to the starter models list.
2024-07-12 20:28:31 +00:00
# region SpandrelImageToImage
StarterModel(
name="RealESRGAN_x4plus_anime_6B",
base=BaseModelType.Any,
source="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
description="A Real-ESRGAN 4x upscaling model (optimized for anime images).",
type=ModelType.SpandrelImageToImage,
),
StarterModel(
name="RealESRGAN_x4plus",
base=BaseModelType.Any,
source="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
description="A Real-ESRGAN 4x upscaling model (general-purpose).",
type=ModelType.SpandrelImageToImage,
),
StarterModel(
name="ESRGAN_SRx4_DF2KOST_official",
base=BaseModelType.Any,
source="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
description="The official ESRGAN 4x upscaling model.",
type=ModelType.SpandrelImageToImage,
),
StarterModel(
name="RealESRGAN_x2plus",
base=BaseModelType.Any,
source="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
description="A Real-ESRGAN 2x upscaling model (general-purpose).",
type=ModelType.SpandrelImageToImage,
),
StarterModel(
name="SwinIR - realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN",
base=BaseModelType.Any,
source="https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN-with-dict-keys-params-and-params_ema.pth",
description="A SwinIR 4x upscaling model.",
type=ModelType.SpandrelImageToImage,
),
# endregion
add FLUX schnell starter models and submodels as dependenices or adhoc download options
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# region TextEncoders
t5_base_encoder,
t5_8b_quantized_encoder,
clip_l_encoder,
# endregion
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.
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]
assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models"
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