chore(nodes): update titles of all model-specific nodes to reference their models

Also bump versions on all of them.

invokeai/app/invocations/compel.py

@@ -40,10 +40,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "compel",
-    title="Prompt",
+    title="Prompt - SD1.5",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.2.0",
+    version="1.2.1",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -233,10 +233,10 @@ class SDXLPromptInvocationBase:
 
 @invocation(
     "sdxl_compel_prompt",
-    title="SDXL Prompt",
+    title="Prompt - SDXL",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.2.0",
+    version="1.2.1",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -327,10 +327,10 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
 @invocation(
     "sdxl_refiner_compel_prompt",
-    title="SDXL Refiner Prompt",
+    title="Prompt - SDXL Refiner",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.1.1",
+    version="1.1.2",
 )
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -376,10 +376,10 @@ class CLIPSkipInvocationOutput(BaseInvocationOutput):
 
 @invocation(
     "clip_skip",
-    title="CLIP Skip",
+    title="Apply CLIP Skip - SD1.5, SDXL",
     tags=["clipskip", "clip", "skip"],
     category="conditioning",
-    version="1.1.0",
+    version="1.1.1",
 )
 class CLIPSkipInvocation(BaseInvocation):
     """Skip layers in clip text_encoder model."""

invokeai/app/invocations/controlnet_image_processors.py

@@ -87,7 +87,7 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.2")
+@invocation("controlnet", title="ControlNet - SD1.5, SDXL", tags=["controlnet"], category="controlnet", version="1.1.3")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 

invokeai/app/invocations/denoise_latents.py

@@ -127,10 +127,10 @@ def get_scheduler(
 
 @invocation(
     "denoise_latents",
-    title="Denoise Latents",
+    title="Denoise - SD1.5, SDXL",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.3",
+    version="1.5.4",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""

invokeai/app/invocations/flux_control_lora_loader.py

@@ -21,10 +21,10 @@ class FluxControlLoRALoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "flux_control_lora_loader",
-    title="Flux Control LoRA",
+    title="Control LoRA - FLUX",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.1.0",
+    version="1.1.1",
     classification=Classification.Prototype,
 )
 class FluxControlLoRALoaderInvocation(BaseInvocation):

invokeai/app/invocations/flux_model_loader.py

@@ -37,10 +37,10 @@ class FluxModelLoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "flux_model_loader",
-    title="Flux Main Model",
+    title="Main Model - FLUX",
     tags=["model", "flux"],
     category="model",
-    version="1.0.5",
+    version="1.0.6",
     classification=Classification.Prototype,
 )
 class FluxModelLoaderInvocation(BaseInvocation):

invokeai/app/invocations/flux_text_encoder.py

@@ -26,10 +26,10 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
 
 @invocation(
     "flux_text_encoder",
-    title="FLUX Text Encoding",
+    title="Prompt - FLUX",
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
-    version="1.1.1",
+    version="1.1.2",
     classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):

invokeai/app/invocations/flux_vae_decode.py

@@ -22,10 +22,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "flux_vae_decode",
-    title="FLUX Latents to Image",
+    title="Latents to Image - FLUX",
     tags=["latents", "image", "vae", "l2i", "flux"],
     category="latents",
-    version="1.0.1",
+    version="1.0.2",
 )
 class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""

invokeai/app/invocations/flux_vae_encode.py

@@ -19,10 +19,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "flux_vae_encode",
-    title="FLUX Image to Latents",
+    title="Image to Latents - FLUX",
     tags=["latents", "image", "vae", "i2l", "flux"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class FluxVaeEncodeInvocation(BaseInvocation):
     """Encodes an image into latents."""

invokeai/app/invocations/ideal_size.py

@@ -19,9 +19,9 @@ class IdealSizeOutput(BaseInvocationOutput):
 
 @invocation(
     "ideal_size",
-    title="Ideal Size",
+    title="Ideal Size - SD1.5, SDXL",
     tags=["latents", "math", "ideal_size"],
-    version="1.0.4",
+    version="1.0.5",
 )
 class IdealSizeInvocation(BaseInvocation):
     """Calculates the ideal size for generation to avoid duplication"""

invokeai/app/invocations/image_to_latents.py

@@ -31,10 +31,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "i2l",
-    title="Image to Latents",
+    title="Image to Latents - SD1.5, SDXL",
     tags=["latents", "image", "vae", "i2l"],
     category="latents",
-    version="1.1.0",
+    version="1.1.1",
 )
 class ImageToLatentsInvocation(BaseInvocation):
     """Encodes an image into latents."""

invokeai/app/invocations/ip_adapter.py

@@ -69,7 +69,13 @@ CLIP_VISION_MODEL_MAP: dict[Literal["ViT-L", "ViT-H", "ViT-G"], StarterModel] =
 }
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.5.0")
+@invocation(
+    "ip_adapter",
+    title="IP-Adapter - SD1.5, SDXL",
+    tags=["ip_adapter", "control"],
+    category="ip_adapter",
+    version="1.5.1",
+)
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 

invokeai/app/invocations/latents_to_image.py

@@ -31,10 +31,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "l2i",
-    title="Latents to Image",
+    title="Latents to Image - SD1.5, SDXL",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.3.1",
+    version="1.3.2",
 )
 class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""

invokeai/app/invocations/metadata_linked.py

@@ -610,10 +610,10 @@ class LatentsMetaOutput(LatentsOutput, MetadataOutput):
 
 @invocation(
     "denoise_latents_meta",
-    title="Denoise Latents + metadata",
+    title=f"{DenoiseLatentsInvocation.UIConfig.title} + Metadata",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.1.0",
+    version="1.1.1",
 )
 class DenoiseLatentsMetaInvocation(DenoiseLatentsInvocation, WithMetadata):
     def invoke(self, context: InvocationContext) -> LatentsMetaOutput:
@@ -675,10 +675,10 @@ class DenoiseLatentsMetaInvocation(DenoiseLatentsInvocation, WithMetadata):
 
 @invocation(
     "flux_denoise_meta",
-    title="Flux Denoise + metadata",
+    title=f"{FluxDenoiseInvocation.UIConfig.title} + Metadata",
     tags=["flux", "latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class FluxDenoiseLatentsMetaInvocation(FluxDenoiseInvocation, WithMetadata):
     """Run denoising process with a FLUX transformer model + metadata."""

invokeai/app/invocations/model.py

@@ -122,10 +122,10 @@ class ModelIdentifierOutput(BaseInvocationOutput):
 
 @invocation(
     "model_identifier",
-    title="Model identifier",
+    title="Any Model",
     tags=["model"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
     classification=Classification.Prototype,
 )
 class ModelIdentifierInvocation(BaseInvocation):
@@ -144,10 +144,10 @@ class ModelIdentifierInvocation(BaseInvocation):
 
 @invocation(
     "main_model_loader",
-    title="Main Model",
+    title="Main Model - SD1.5",
     tags=["model"],
     category="model",
-    version="1.0.3",
+    version="1.0.4",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
@@ -244,7 +244,7 @@ class LoRASelectorOutput(BaseInvocationOutput):
     lora: LoRAField = OutputField(description="LoRA model and weight", title="LoRA")
 
 
-@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.1")
+@invocation("lora_selector", title="LoRA Model - SD1.5", tags=["model"], category="model", version="1.0.2")
 class LoRASelectorInvocation(BaseInvocation):
     """Selects a LoRA model and weight."""
 
@@ -257,7 +257,9 @@ class LoRASelectorInvocation(BaseInvocation):
         return LoRASelectorOutput(lora=LoRAField(lora=self.lora, weight=self.weight))
 
 
-@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.1.0")
+@invocation(
+    "lora_collection_loader", title="LoRA Collection - SD1.5", tags=["model"], category="model", version="1.1.1"
+)
 class LoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to the provided UNet and CLIP models."""
 
@@ -320,10 +322,10 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "sdxl_lora_loader",
-    title="SDXL LoRA",
+    title="LoRA Model - SDXL",
     tags=["lora", "model"],
     category="model",
-    version="1.0.3",
+    version="1.0.4",
 )
 class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
@@ -400,10 +402,10 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
 
 @invocation(
     "sdxl_lora_collection_loader",
-    title="SDXL LoRA Collection Loader",
+    title="LoRA Collection - SDXL",
     tags=["model"],
     category="model",
-    version="1.1.0",
+    version="1.1.1",
 )
 class SDXLLoRACollectionLoader(BaseInvocation):
     """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
@@ -469,7 +471,9 @@ class SDXLLoRACollectionLoader(BaseInvocation):
         return output
 
 
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.3")
+@invocation(
+    "vae_loader", title="VAE Model - SD1.5, SDXL, SD3, FLUX", tags=["vae", "model"], category="model", version="1.0.4"
+)
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
@@ -496,10 +500,10 @@ class SeamlessModeOutput(BaseInvocationOutput):
 
 @invocation(
     "seamless",
-    title="Seamless",
+    title="Apply Seamless - SD1.5, SDXL",
     tags=["seamless", "model"],
     category="model",
-    version="1.0.1",
+    version="1.0.2",
 )
 class SeamlessModeInvocation(BaseInvocation):
     """Applies the seamless transformation to the Model UNet and VAE."""
@@ -539,7 +543,7 @@ class SeamlessModeInvocation(BaseInvocation):
         return SeamlessModeOutput(unet=unet, vae=vae)
 
 
-@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.1")
+@invocation("freeu", title="Apply FreeU - SD1.5, SDXL", tags=["freeu"], category="unet", version="1.0.2")
 class FreeUInvocation(BaseInvocation):
     """
     Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2):

invokeai/app/invocations/noise.py

@@ -72,10 +72,10 @@ class NoiseOutput(BaseInvocationOutput):
 
 @invocation(
     "noise",
-    title="Noise",
+    title="Create Latent Noise",
     tags=["latents", "noise"],
     category="latents",
-    version="1.0.2",
+    version="1.0.3",
 )
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""

invokeai/app/invocations/sd3_denoise.py

@@ -32,10 +32,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "sd3_denoise",
-    title="SD3 Denoise",
+    title="Denoise - SD3",
     tags=["image", "sd3"],
     category="image",
-    version="1.1.0",
+    version="1.1.1",
     classification=Classification.Prototype,
 )
 class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):

invokeai/app/invocations/sd3_image_to_latents.py

@@ -21,10 +21,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "sd3_i2l",
-    title="SD3 Image to Latents",
+    title="Image to Latents - SD3",
     tags=["image", "latents", "vae", "i2l", "sd3"],
     category="image",
-    version="1.0.0",
+    version="1.0.1",
     classification=Classification.Prototype,
 )
 class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):

invokeai/app/invocations/sd3_latents_to_image.py

@@ -24,10 +24,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "sd3_l2i",
-    title="SD3 Latents to Image",
+    title="Latents to Image - SD3",
     tags=["latents", "image", "vae", "l2i", "sd3"],
     category="latents",
-    version="1.3.1",
+    version="1.3.2",
 )
 class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""

invokeai/app/invocations/sd3_model_loader.py

@@ -30,10 +30,10 @@ class Sd3ModelLoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "sd3_model_loader",
-    title="SD3 Main Model",
+    title="Main Model - SD3",
     tags=["model", "sd3"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
     classification=Classification.Prototype,
 )
 class Sd3ModelLoaderInvocation(BaseInvocation):

invokeai/app/invocations/sd3_text_encoder.py

@@ -29,10 +29,10 @@ SD3_T5_MAX_SEQ_LEN = 256
 
 @invocation(
     "sd3_text_encoder",
-    title="SD3 Text Encoding",
+    title="Prompt - SD3",
     tags=["prompt", "conditioning", "sd3"],
     category="conditioning",
-    version="1.0.0",
+    version="1.0.1",
     classification=Classification.Prototype,
 )
 class Sd3TextEncoderInvocation(BaseInvocation):

invokeai/app/invocations/sdxl.py

@@ -24,7 +24,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.3")
+@invocation("sdxl_model_loader", title="Main Model - SDXL", tags=["model", "sdxl"], category="model", version="1.0.4")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
@@ -58,10 +58,10 @@ class SDXLModelLoaderInvocation(BaseInvocation):
 
 @invocation(
     "sdxl_refiner_model_loader",
-    title="SDXL Refiner Model",
+    title="Refiner Model - SDXL",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.3",
+    version="1.0.4",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""

invokeai/app/invocations/t2i_adapter.py

@@ -45,7 +45,11 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.3"
+    "t2i_adapter",
+    title="T2I-Adapter - SD1.5, SDXL",
+    tags=["t2i_adapter", "control"],
+    category="t2i_adapter",
+    version="1.0.4",
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -53,11 +53,11 @@ def crop_controlnet_data(control_data: ControlNetData, latent_region: TBLR) -> C
 
 @invocation(
     "tiled_multi_diffusion_denoise_latents",
-    title="Tiled Multi-Diffusion Denoise Latents",
+    title="Tiled Multi-Diffusion Denoise - SD1.5, SDXL",
     tags=["upscale", "denoise"],
     category="latents",
     classification=Classification.Beta,
-    version="1.0.0",
+    version="1.0.1",
 )
 class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
     """Tiled Multi-Diffusion denoising.