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Address minor review comments.

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This commit is contained in:
Ryan Dick 2024-08-21 13:45:22 +00:00
parent 253b2b1dc6
commit e680cf76f6
3 changed files with 12 additions and 10 deletions
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2
invokeai/app/invocations/fields.py
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@ -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"

12
invokeai/app/invocations/flux_text_encoder.py
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@ -15,8 +15,8 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
@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 @@ class FluxTextEncoderInvocation(BaseInvocation):
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 @@ class FluxTextEncoderInvocation(BaseInvocation):
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)

8
invokeai/app/invocations/flux_text_to_image.py
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@ -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.")