Address minor review comments.

Author: RyanJDick

invokeai/app/invocations/fields.py

@@ -135,7 +135,7 @@ class FieldDescriptions:
     vae_model = "VAE model to load"
     lora_model = "LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
-    flux_model = "Flux model (Transformer, VAE, CLIP) to load"
+    flux_model = "Flux model (Transformer) to load"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"

invokeai/app/invocations/flux_text_encoder.py

@@ -15,8 +15,8 @@
 @invocation(
     "flux_text_encoder",
     title="FLUX Text Encoding",
-    tags=["image", "flux"],
-    category="image",
+    tags=["prompt", "conditioning", "flux"],
+    category="conditioning",
     version="1.0.0",
 )
 class FluxTextEncoderInvocation(BaseInvocation):
@@ -32,7 +32,9 @@ class FluxTextEncoderInvocation(BaseInvocation):
         description=FieldDescriptions.t5_encoder,
         input=Input.Connection,
     )
-    max_seq_len: Literal[256, 512] = InputField(description="Max sequence length for the desired flux model")
+    t5_max_seq_len: Literal[256, 512] = InputField(
+        description="Max sequence length for the T5 encoder. Expected to be 256 for FLUX schnell models and 512 for FLUX dev models."
+    )
     positive_prompt: str = InputField(description="Positive prompt for text-to-image generation.")
 
     # TODO(ryand): Should we create a new return type for this invocation? This ConditioningOutput is clearly not
@@ -48,8 +50,6 @@ def invoke(self, context: InvocationContext) -> ConditioningOutput:
         return ConditioningOutput.build(conditioning_name)
 
     def _encode_prompt(self, context: InvocationContext) -> tuple[torch.Tensor, torch.Tensor]:
-        max_seq_len = self.max_seq_len
-
         # Load CLIP.
         clip_tokenizer_info = context.models.load(self.clip.tokenizer)
         clip_text_encoder_info = context.models.load(self.clip.text_encoder)
@@ -70,7 +70,7 @@ def _encode_prompt(self, context: InvocationContext) -> tuple[torch.Tensor, torc
             assert isinstance(t5_tokenizer, T5Tokenizer)
 
             clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
-            t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, max_seq_len)
+            t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
 
             prompt = [self.positive_prompt]
             prompt_embeds = t5_encoder(prompt)

invokeai/app/invocations/flux_text_to_image.py

@@ -33,7 +33,7 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Text-to-image generation using a FLUX model."""
 
     transformer: TransformerField = InputField(
-        description=FieldDescriptions.unet,
+        description=FieldDescriptions.flux_model,
         input=Input.Connection,
         title="Transformer",
     )
@@ -46,10 +46,12 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     )
     width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
     height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
-    num_steps: int = InputField(default=4, description="Number of diffusion steps.")
+    num_steps: int = InputField(
+        default=4, description="Number of diffusion steps. Recommend values are schnell: 4, dev: 50."
+    )
     guidance: float = InputField(
         default=4.0,
-        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images.",
+        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
     )
     seed: int = InputField(default=0, description="Randomness seed for reproducibility.")