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https://github.com/invoke-ai/InvokeAI
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81 lines
3.4 KiB
Python
81 lines
3.4 KiB
Python
from typing import Literal
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import torch
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from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
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from invokeai.app.invocations.model import CLIPField, T5EncoderField
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from invokeai.app.invocations.primitives import ConditioningOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.flux.modules.conditioner import HFEncoder
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
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@invocation(
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"flux_text_encoder",
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title="FLUX Text Encoding",
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tags=["image"],
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category="image",
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version="1.0.0",
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)
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class FluxTextEncoderInvocation(BaseInvocation):
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clip: CLIPField = InputField(
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title="CLIP",
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description=FieldDescriptions.clip,
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input=Input.Connection,
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)
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t5_encoder: T5EncoderField = InputField(
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title="T5Encoder",
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description=FieldDescriptions.t5Encoder,
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input=Input.Connection,
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)
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max_seq_len: Literal[256, 512] = InputField(description="Max sequence length for the desired flux model")
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positive_prompt: str = InputField(description="Positive prompt for text-to-image generation.")
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# TODO(ryand): Should we create a new return type for this invocation? This ConditioningOutput is clearly not
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# compatible with other ConditioningOutputs.
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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> ConditioningOutput:
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t5_embeddings, clip_embeddings = self._encode_prompt(context)
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conditioning_data = ConditioningFieldData(
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conditionings=[FLUXConditioningInfo(clip_embeds=clip_embeddings, t5_embeds=t5_embeddings)]
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)
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conditioning_name = context.conditioning.save(conditioning_data)
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return ConditioningOutput.build(conditioning_name)
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def _encode_prompt(self, context: InvocationContext) -> tuple[torch.Tensor, torch.Tensor]:
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max_seq_len = self.max_seq_len
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# Load CLIP.
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clip_tokenizer_info = context.models.load(self.clip.tokenizer)
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clip_text_encoder_info = context.models.load(self.clip.text_encoder)
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# Load T5.
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t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
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t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
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with (
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clip_text_encoder_info as clip_text_encoder,
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t5_text_encoder_info as t5_text_encoder,
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clip_tokenizer_info as clip_tokenizer,
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t5_tokenizer_info as t5_tokenizer,
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):
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assert isinstance(clip_text_encoder, CLIPTextModel)
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assert isinstance(t5_text_encoder, T5EncoderModel)
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assert isinstance(clip_tokenizer, CLIPTokenizer)
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assert isinstance(t5_tokenizer, T5Tokenizer)
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clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
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t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, max_seq_len)
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prompt = [self.positive_prompt]
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prompt_embeds = t5_encoder(prompt)
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pooled_prompt_embeds = clip_encoder(prompt)
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assert isinstance(prompt_embeds, torch.Tensor)
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assert isinstance(pooled_prompt_embeds, torch.Tensor)
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return prompt_embeds, pooled_prompt_embeds
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