mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
84 lines
3.5 KiB
Python
84 lines
3.5 KiB
Python
import torch
|
|
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
|
|
|
|
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
|
|
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
|
|
from invokeai.app.invocations.model import CLIPField, T5EncoderField
|
|
from invokeai.app.invocations.primitives import ConditioningOutput
|
|
from invokeai.app.services.shared.invocation_context import InvocationContext
|
|
from invokeai.backend.flux.modules.conditioner import HFEncoder
|
|
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
|
|
|
|
|
|
@invocation(
|
|
"flux_text_encoder",
|
|
title="FLUX Text Encoding",
|
|
tags=["image"],
|
|
category="image",
|
|
version="1.0.0",
|
|
)
|
|
class FluxTextEncoderInvocation(BaseInvocation):
|
|
clip: CLIPField = InputField(
|
|
title="CLIP",
|
|
description=FieldDescriptions.clip,
|
|
input=Input.Connection,
|
|
)
|
|
t5Encoder: T5EncoderField = InputField(
|
|
title="T5Encoder",
|
|
description=FieldDescriptions.t5Encoder,
|
|
input=Input.Connection,
|
|
)
|
|
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
|
|
# compatible with other ConditioningOutputs.
|
|
@torch.no_grad()
|
|
def invoke(self, context: InvocationContext) -> ConditioningOutput:
|
|
t5_embeddings, clip_embeddings = self._encode_prompt(context)
|
|
conditioning_data = ConditioningFieldData(
|
|
conditionings=[FLUXConditioningInfo(clip_embeds=clip_embeddings, t5_embeds=t5_embeddings)]
|
|
)
|
|
|
|
conditioning_name = context.conditioning.save(conditioning_data)
|
|
return ConditioningOutput.build(conditioning_name)
|
|
|
|
def _encode_prompt(self, context: InvocationContext) -> tuple[torch.Tensor, torch.Tensor]:
|
|
# TODO: Determine the T5 max sequence length based on the model.
|
|
# if self.model == "flux-schnell":
|
|
max_seq_len = 256
|
|
# # elif self.model == "flux-dev":
|
|
# # max_seq_len = 512
|
|
# else:
|
|
# raise ValueError(f"Unknown model: {self.model}")
|
|
|
|
# Load CLIP.
|
|
clip_tokenizer_info = context.models.load(self.clip.tokenizer)
|
|
clip_text_encoder_info = context.models.load(self.clip.text_encoder)
|
|
|
|
# Load T5.
|
|
t5_tokenizer_info = context.models.load(self.t5Encoder.tokenizer)
|
|
t5_text_encoder_info = context.models.load(self.t5Encoder.text_encoder)
|
|
|
|
with (
|
|
clip_text_encoder_info as clip_text_encoder,
|
|
t5_text_encoder_info as t5_text_encoder,
|
|
clip_tokenizer_info as clip_tokenizer,
|
|
t5_tokenizer_info as t5_tokenizer,
|
|
):
|
|
assert isinstance(clip_text_encoder, CLIPTextModel)
|
|
assert isinstance(t5_text_encoder, T5EncoderModel)
|
|
assert isinstance(clip_tokenizer, CLIPTokenizer)
|
|
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)
|
|
|
|
prompt = [self.positive_prompt]
|
|
prompt_embeds = t5_encoder(prompt)
|
|
|
|
pooled_prompt_embeds = clip_encoder(prompt)
|
|
|
|
assert isinstance(prompt_embeds, torch.Tensor)
|
|
assert isinstance(pooled_prompt_embeds, torch.Tensor)
|
|
return prompt_embeds, pooled_prompt_embeds
|