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(api): update names of starter models, add ability to track previous_names so it does not mess up logic that prevents dupe starter model installs
This commit is contained in:
maryhipp
committed by
psychedelicious
psychedelicious
parent
32d9abe802
commit
9cd47fa857
@@ -808,7 +808,11 @@ def get_is_installed(
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for model in installed_models:
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if model.source == starter_model.source:
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return True
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if model.name == starter_model.name and model.base == starter_model.base and model.type == starter_model.type:
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if (
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(model.name == starter_model.name or model.name in starter_model.previous_names)
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and model.base == starter_model.base
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and model.type == starter_model.type
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):
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return True
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return False
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@@ -13,6 +13,9 @@ class StarterModelWithoutDependencies(BaseModel):
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type: ModelType
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format: Optional[ModelFormat] = None
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is_installed: bool = False
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# allows us to track what models a user has installed across name changes within starter models
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# if you update a starter model name, please add the old one to this list for that starter model
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previous_names: list[str] = []
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class StarterModel(StarterModelWithoutDependencies):
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@@ -243,44 +246,49 @@ easy_neg_sd1 = StarterModel(
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# endregion
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# region IP Adapter
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ip_adapter_sd1 = StarterModel(
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name="IP Adapter",
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name="Standard Reference (IP Adapter)",
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base=BaseModelType.StableDiffusion1,
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source="https://huggingface.co/InvokeAI/ip_adapter_sd15/resolve/main/ip-adapter_sd15.safetensors",
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description="IP-Adapter for SD 1.5 models",
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description="References images with a more generalized/looser degree of precision.",
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type=ModelType.IPAdapter,
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dependencies=[ip_adapter_sd_image_encoder],
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previous_names=["IP Adapter"],
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)
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ip_adapter_plus_sd1 = StarterModel(
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name="IP Adapter Plus",
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name="Precise Reference (IP Adapter Plus)",
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base=BaseModelType.StableDiffusion1,
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source="https://huggingface.co/InvokeAI/ip_adapter_plus_sd15/resolve/main/ip-adapter-plus_sd15.safetensors",
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description="Refined IP-Adapter for SD 1.5 models",
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description="References images with a higher degree of precision.",
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type=ModelType.IPAdapter,
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dependencies=[ip_adapter_sd_image_encoder],
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previous_names=["IP Adapter Plus"],
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)
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ip_adapter_plus_face_sd1 = StarterModel(
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name="IP Adapter Plus Face",
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name="Face Reference (IP Adapter Plus Face)",
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base=BaseModelType.StableDiffusion1,
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source="https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15/resolve/main/ip-adapter-plus-face_sd15.safetensors",
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description="Refined IP-Adapter for SD 1.5 models, adapted for faces",
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description="References images with a higher degree of precision, adapted for faces",
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type=ModelType.IPAdapter,
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dependencies=[ip_adapter_sd_image_encoder],
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previous_names=["IP Adapter Plus Face"],
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)
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ip_adapter_sdxl = StarterModel(
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name="IP Adapter SDXL",
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name="Standard Reference (IP Adapter ViT-H)",
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base=BaseModelType.StableDiffusionXL,
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source="https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h/resolve/main/ip-adapter_sdxl_vit-h.safetensors",
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description="IP-Adapter for SDXL models",
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description="References images with a higher degree of precision.",
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type=ModelType.IPAdapter,
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dependencies=[ip_adapter_sdxl_image_encoder],
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previous_names=["IP Adapter SDXL"],
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)
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ip_adapter_flux = StarterModel(
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name="XLabs FLUX IP-Adapter",
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name="Standard Reference (XLabs FLUX IP-Adapter)",
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base=BaseModelType.Flux,
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source="https://huggingface.co/XLabs-AI/flux-ip-adapter/resolve/main/flux-ip-adapter.safetensors",
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description="FLUX IP-Adapter",
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description="References images with a more generalized/looser degree of precision.",
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type=ModelType.IPAdapter,
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dependencies=[clip_vit_l_image_encoder],
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previous_names=["XLabs FLUX IP-Adapter"],
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)
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# endregion
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# region ControlNet
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@@ -299,157 +307,162 @@ qr_code_cnet_sdxl = StarterModel(
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type=ModelType.ControlNet,
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)
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canny_sd1 = StarterModel(
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name="canny",
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name="Hard Edge Detection (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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description="Uses detected edges in the image to control composition.",
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type=ModelType.ControlNet,
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previous_names=["canny"],
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)
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inpaint_cnet_sd1 = StarterModel(
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name="inpaint",
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name="Inpainting",
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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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previous_names=["inpaint"],
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)
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mlsd_sd1 = StarterModel(
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name="mlsd",
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name="Line Drawing",
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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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description="Uses straight line detection for controlling the generation.",
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type=ModelType.ControlNet,
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previous_names=["mlsd"],
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)
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depth_sd1 = StarterModel(
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name="depth",
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name="Depth Map",
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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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description="Uses depth information in the image to control the depth in the generation.",
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type=ModelType.ControlNet,
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previous_names=["depth"],
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)
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normal_bae_sd1 = StarterModel(
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name="normal_bae",
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name="Lighting Detection (Normals)",
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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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description="Uses detected lighting information to guide the lighting of the composition.",
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type=ModelType.ControlNet,
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previous_names=["normal_bae"],
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)
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seg_sd1 = StarterModel(
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name="seg",
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name="Segmentation Map",
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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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description="Uses segmentation maps to guide the structure of the composition.",
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type=ModelType.ControlNet,
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previous_names=["seg"],
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)
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lineart_sd1 = StarterModel(
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name="lineart",
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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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description="Uses lineart detection to guide the lighting of the composition.",
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type=ModelType.ControlNet,
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previous_names=["lineart"],
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)
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lineart_anime_sd1 = StarterModel(
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name="lineart_anime",
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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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description="Uses anime lineart detection to guide the lighting of the composition.",
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type=ModelType.ControlNet,
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previous_names=["lineart_anime"],
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)
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openpose_sd1 = StarterModel(
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name="openpose",
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name="Pose Detection (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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description="Uses pose information to control the pose of human characters in the generation.",
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type=ModelType.ControlNet,
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previous_names=["openpose"],
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)
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scribble_sd1 = StarterModel(
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name="scribble",
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name="Contour Detection (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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description="Uses edges, contours, or line art in the image to control composition.",
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type=ModelType.ControlNet,
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previous_names=["scribble"],
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)
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softedge_sd1 = StarterModel(
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name="softedge",
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name="Soft Edge Detection",
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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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description="Uses a soft edge detection map to control composition.",
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type=ModelType.ControlNet,
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previous_names=["softedge"],
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)
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shuffle_sd1 = StarterModel(
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name="shuffle",
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name="Remix",
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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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previous_names=["shuffle"],
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)
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tile_sd1 = StarterModel(
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name="tile",
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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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ip2p_sd1 = 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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description="Uses image data to replicate exact colors/structure in the resulting generation.",
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type=ModelType.ControlNet,
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previous_names=["tile"],
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)
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canny_sdxl = StarterModel(
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name="canny-sdxl",
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name="Hard Edge Detection (canny)",
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base=BaseModelType.StableDiffusionXL,
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source="xinsir/controlNet-canny-sdxl-1.0",
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description="ControlNet weights trained on sdxl-1.0 with canny conditioning, by Xinsir.",
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description="Uses detected edges in the image to control composition.",
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type=ModelType.ControlNet,
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previous_names=["canny-sdxl"],
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)
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depth_sdxl = StarterModel(
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name="depth-sdxl",
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name="Depth Map",
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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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description="Uses depth information in the image to control the depth in the generation.",
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type=ModelType.ControlNet,
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previous_names=["depth-sdxl"],
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)
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softedge_sdxl = StarterModel(
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name="softedge-dexined-sdxl",
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name="Soft Edge Detection",
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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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depth_zoe_16_sdxl = 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,
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)
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depth_zoe_32_sdxl = StarterModel(
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name="depth-zoe-sdxl",
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base=BaseModelType.StableDiffusionXL,
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source="diffusers/controlNet-zoe-depth-sdxl-1.0",
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description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).",
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description="Uses a soft edge detection map to control composition.",
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type=ModelType.ControlNet,
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previous_names=["softedge-dexined-sdxl"],
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)
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openpose_sdxl = StarterModel(
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name="openpose-sdxl",
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name="Pose Detection (openpose)",
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base=BaseModelType.StableDiffusionXL,
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source="xinsir/controlNet-openpose-sdxl-1.0",
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description="ControlNet weights trained on sdxl-1.0 compatible with the DWPose processor by Xinsir.",
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description="Uses pose information to control the pose of human characters in the generation.",
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type=ModelType.ControlNet,
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previous_names=["openpose-sdxl", "controlnet-openpose-sdxl"],
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)
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scribble_sdxl = StarterModel(
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name="scribble-sdxl",
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name="Contour Detection (scribble)",
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base=BaseModelType.StableDiffusionXL,
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source="xinsir/controlNet-scribble-sdxl-1.0",
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description="ControlNet weights trained on sdxl-1.0 compatible with various lineart processors and black/white sketches by Xinsir.",
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description="Uses edges, contours, or line art in the image to control composition.",
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type=ModelType.ControlNet,
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previous_names=["scribble-sdxl", "controlnet-scribble-sdxl"],
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)
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tile_sdxl = StarterModel(
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name="tile-sdxl",
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name="Tile",
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base=BaseModelType.StableDiffusionXL,
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source="xinsir/controlNet-tile-sdxl-1.0",
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description="ControlNet weights trained on sdxl-1.0 with tiled image conditioning",
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description="Uses image data to replicate exact colors/structure in the resulting generation.",
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type=ModelType.ControlNet,
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previous_names=["tile-sdxl"],
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)
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union_cnet_sdxl = StarterModel(
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name="Multi-Guidance Detection (Union Pro)",
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base=BaseModelType.StableDiffusionXL,
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source="InvokeAI/Xinsir-SDXL_Controlnet_Union",
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description="A unified ControlNet for SDXL model that supports 10+ control types",
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type=ModelType.ControlNet,
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)
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union_cnet_flux = StarterModel(
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@@ -462,60 +475,52 @@ union_cnet_flux = StarterModel(
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# endregion
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# region T2I Adapter
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t2i_canny_sd1 = StarterModel(
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name="canny-sd15",
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name="Hard Edge Detection (canny)",
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base=BaseModelType.StableDiffusion1,
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source="TencentARC/t2iadapter_canny_sd15v2",
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description="T2I Adapter weights trained on sd-1.5 with canny conditioning.",
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description="Uses detected edges in the image to control composition",
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type=ModelType.T2IAdapter,
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previous_names=["canny-sd15"],
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)
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t2i_sketch_sd1 = StarterModel(
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name="sketch-sd15",
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name="Sketch",
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base=BaseModelType.StableDiffusion1,
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source="TencentARC/t2iadapter_sketch_sd15v2",
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description="T2I Adapter weights trained on sd-1.5 with sketch conditioning.",
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description="Uses a sketch to control composition",
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type=ModelType.T2IAdapter,
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previous_names=["sketch-sd15"],
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)
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t2i_depth_sd1 = StarterModel(
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name="depth-sd15",
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name="Depth Map",
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base=BaseModelType.StableDiffusion1,
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source="TencentARC/t2iadapter_depth_sd15v2",
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description="T2I Adapter weights trained on sd-1.5 with depth conditioning.",
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type=ModelType.T2IAdapter,
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)
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t2i_zoe_depth_sd1 = StarterModel(
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name="zoedepth-sd15",
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base=BaseModelType.StableDiffusion1,
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source="TencentARC/t2iadapter_zoedepth_sd15v1",
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description="T2I Adapter weights trained on sd-1.5 with zoe depth conditioning.",
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description="Uses depth information in the image to control the depth in the generation.",
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type=ModelType.T2IAdapter,
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previous_names=["depth-sd15"],
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)
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t2i_canny_sdxl = StarterModel(
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name="canny-sdxl",
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name="Hard Edge Detection (canny)",
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base=BaseModelType.StableDiffusionXL,
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source="TencentARC/t2i-adapter-canny-sdxl-1.0",
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description="T2I Adapter weights trained on sdxl-1.0 with canny conditioning.",
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type=ModelType.T2IAdapter,
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)
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t2i_zoe_depth_sdxl = StarterModel(
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name="zoedepth-sdxl",
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base=BaseModelType.StableDiffusionXL,
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source="TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
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description="T2I Adapter weights trained on sdxl-1.0 with zoe depth conditioning.",
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description="Uses detected edges in the image to control composition",
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type=ModelType.T2IAdapter,
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previous_names=["canny-sdxl"],
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)
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t2i_lineart_sdxl = StarterModel(
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name="lineart-sdxl",
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name="Lineart",
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base=BaseModelType.StableDiffusionXL,
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source="TencentARC/t2i-adapter-lineart-sdxl-1.0",
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description="T2I Adapter weights trained on sdxl-1.0 with lineart conditioning.",
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description="Uses lineart detection to guide the lighting of the composition.",
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type=ModelType.T2IAdapter,
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previous_names=["lineart-sdxl"],
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)
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t2i_sketch_sdxl = StarterModel(
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name="sketch-sdxl",
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name="Sketch",
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base=BaseModelType.StableDiffusionXL,
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source="TencentARC/t2i-adapter-sketch-sdxl-1.0",
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description="T2I Adapter weights trained on sdxl-1.0 with sketch conditioning.",
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description="Uses a sketch to control composition",
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type=ModelType.T2IAdapter,
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previous_names=["sketch-sdxl"],
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)
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# endregion
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# region SpandrelImageToImage
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@@ -600,22 +605,18 @@ STARTER_MODELS: list[StarterModel] = [
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softedge_sd1,
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shuffle_sd1,
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tile_sd1,
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ip2p_sd1,
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canny_sdxl,
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depth_sdxl,
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softedge_sdxl,
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depth_zoe_16_sdxl,
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depth_zoe_32_sdxl,
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openpose_sdxl,
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scribble_sdxl,
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tile_sdxl,
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union_cnet_sdxl,
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union_cnet_flux,
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t2i_canny_sd1,
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t2i_sketch_sd1,
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||||
t2i_depth_sd1,
|
||||
t2i_zoe_depth_sd1,
|
||||
t2i_canny_sdxl,
|
||||
t2i_zoe_depth_sdxl,
|
||||
t2i_lineart_sdxl,
|
||||
t2i_sketch_sdxl,
|
||||
realesrgan_x4,
|
||||
@@ -646,7 +647,6 @@ sd1_bundle: list[StarterModel] = [
|
||||
softedge_sd1,
|
||||
shuffle_sd1,
|
||||
tile_sd1,
|
||||
ip2p_sd1,
|
||||
swinir,
|
||||
]
|
||||
|
||||
@@ -657,8 +657,6 @@ sdxl_bundle: list[StarterModel] = [
|
||||
canny_sdxl,
|
||||
depth_sdxl,
|
||||
softedge_sdxl,
|
||||
depth_zoe_16_sdxl,
|
||||
depth_zoe_32_sdxl,
|
||||
openpose_sdxl,
|
||||
scribble_sdxl,
|
||||
tile_sdxl,
|
||||
|
||||
Reference in New Issue
Block a user