mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
244 lines
11 KiB
Python
244 lines
11 KiB
Python
from pathlib import Path
|
|
from typing import Literal
|
|
|
|
import torch
|
|
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler
|
|
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
|
|
from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
|
|
from optimum.quanto import qfloat8
|
|
from optimum.quanto.models import QuantizedDiffusersModel
|
|
from PIL import Image
|
|
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
|
|
|
|
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
|
|
from invokeai.app.invocations.fields import InputField, WithBoard, WithMetadata
|
|
from invokeai.app.invocations.primitives import ImageOutput
|
|
from invokeai.app.services.shared.invocation_context import InvocationContext
|
|
from invokeai.backend.util.devices import TorchDevice
|
|
|
|
TFluxModelKeys = Literal["flux-schnell"]
|
|
FLUX_MODELS: dict[TFluxModelKeys, str] = {"flux-schnell": "black-forest-labs/FLUX.1-schnell"}
|
|
|
|
|
|
class QuantizedFluxTransformer2DModel(QuantizedDiffusersModel):
|
|
base_class = FluxTransformer2DModel
|
|
|
|
|
|
@invocation(
|
|
"flux_text_to_image",
|
|
title="FLUX Text to Image",
|
|
tags=["image"],
|
|
category="image",
|
|
version="1.0.0",
|
|
)
|
|
class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
|
|
"""Text-to-image generation using a FLUX model."""
|
|
|
|
model: TFluxModelKeys = InputField(description="The FLUX model to use for text-to-image generation.")
|
|
use_8bit: bool = InputField(
|
|
default=False, description="Whether to quantize the transformer model to 8-bit precision."
|
|
)
|
|
positive_prompt: str = InputField(description="Positive prompt for text-to-image generation.")
|
|
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.")
|
|
guidance: float = InputField(
|
|
default=4.0,
|
|
description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images.",
|
|
)
|
|
seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
|
|
|
|
@torch.no_grad()
|
|
def invoke(self, context: InvocationContext) -> ImageOutput:
|
|
model_path = context.models.download_and_cache_model(FLUX_MODELS[self.model])
|
|
|
|
t5_embeddings, clip_embeddings = self._encode_prompt(context, model_path)
|
|
latents = self._run_diffusion(context, model_path, clip_embeddings, t5_embeddings)
|
|
image = self._run_vae_decoding(context, model_path, latents)
|
|
image_dto = context.images.save(image=image)
|
|
return ImageOutput.build(image_dto)
|
|
|
|
def _encode_prompt(self, context: InvocationContext, flux_model_dir: Path) -> tuple[torch.Tensor, torch.Tensor]:
|
|
# 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 the CLIP tokenizer.
|
|
clip_tokenizer_path = flux_model_dir / "tokenizer"
|
|
clip_tokenizer = CLIPTokenizer.from_pretrained(clip_tokenizer_path, local_files_only=True)
|
|
assert isinstance(clip_tokenizer, CLIPTokenizer)
|
|
|
|
# Load the T5 tokenizer.
|
|
t5_tokenizer_path = flux_model_dir / "tokenizer_2"
|
|
t5_tokenizer = T5TokenizerFast.from_pretrained(t5_tokenizer_path, local_files_only=True)
|
|
assert isinstance(t5_tokenizer, T5TokenizerFast)
|
|
|
|
clip_text_encoder_path = flux_model_dir / "text_encoder"
|
|
t5_text_encoder_path = flux_model_dir / "text_encoder_2"
|
|
with (
|
|
context.models.load_local_model(
|
|
model_path=clip_text_encoder_path, loader=self._load_flux_text_encoder
|
|
) as clip_text_encoder,
|
|
context.models.load_local_model(
|
|
model_path=t5_text_encoder_path, loader=self._load_flux_text_encoder_2
|
|
) as t5_text_encoder,
|
|
):
|
|
assert isinstance(clip_text_encoder, CLIPTextModel)
|
|
assert isinstance(t5_text_encoder, T5EncoderModel)
|
|
pipeline = FluxPipeline(
|
|
scheduler=None,
|
|
vae=None,
|
|
text_encoder=clip_text_encoder,
|
|
tokenizer=clip_tokenizer,
|
|
text_encoder_2=t5_text_encoder,
|
|
tokenizer_2=t5_tokenizer,
|
|
transformer=None,
|
|
)
|
|
|
|
# prompt_embeds: T5 embeddings
|
|
# pooled_prompt_embeds: CLIP embeddings
|
|
prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
|
|
prompt=self.positive_prompt,
|
|
prompt_2=self.positive_prompt,
|
|
device=TorchDevice.choose_torch_device(),
|
|
max_sequence_length=max_seq_len,
|
|
)
|
|
|
|
assert isinstance(prompt_embeds, torch.Tensor)
|
|
assert isinstance(pooled_prompt_embeds, torch.Tensor)
|
|
return prompt_embeds, pooled_prompt_embeds
|
|
|
|
def _run_diffusion(
|
|
self,
|
|
context: InvocationContext,
|
|
flux_model_dir: Path,
|
|
clip_embeddings: torch.Tensor,
|
|
t5_embeddings: torch.Tensor,
|
|
):
|
|
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(flux_model_dir / "scheduler", local_files_only=True)
|
|
|
|
# HACK(ryand): Manually empty the cache. Currently we don't check the size of the model before loading it from
|
|
# disk. Since the transformer model is large (24GB), there's a good chance that it will OOM on 32GB RAM systems
|
|
# if the cache is not empty.
|
|
context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)
|
|
|
|
transformer_path = flux_model_dir / "transformer"
|
|
with context.models.load_local_model(
|
|
model_path=transformer_path, loader=self._load_flux_transformer
|
|
) as transformer:
|
|
assert isinstance(transformer, FluxTransformer2DModel)
|
|
|
|
flux_pipeline_with_transformer = FluxPipeline(
|
|
scheduler=scheduler,
|
|
vae=None,
|
|
text_encoder=None,
|
|
tokenizer=None,
|
|
text_encoder_2=None,
|
|
tokenizer_2=None,
|
|
transformer=transformer,
|
|
)
|
|
|
|
latents = flux_pipeline_with_transformer(
|
|
height=self.height,
|
|
width=self.width,
|
|
num_inference_steps=self.num_steps,
|
|
guidance_scale=self.guidance,
|
|
generator=torch.Generator().manual_seed(self.seed),
|
|
prompt_embeds=t5_embeddings,
|
|
pooled_prompt_embeds=clip_embeddings,
|
|
output_type="latent",
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
assert isinstance(latents, torch.Tensor)
|
|
return latents
|
|
|
|
def _run_vae_decoding(
|
|
self,
|
|
context: InvocationContext,
|
|
flux_model_dir: Path,
|
|
latents: torch.Tensor,
|
|
) -> Image.Image:
|
|
vae_path = flux_model_dir / "vae"
|
|
with context.models.load_local_model(model_path=vae_path, loader=self._load_flux_vae) as vae:
|
|
assert isinstance(vae, AutoencoderKL)
|
|
|
|
flux_pipeline_with_vae = FluxPipeline(
|
|
scheduler=None,
|
|
vae=vae,
|
|
text_encoder=None,
|
|
tokenizer=None,
|
|
text_encoder_2=None,
|
|
tokenizer_2=None,
|
|
transformer=None,
|
|
)
|
|
|
|
latents = flux_pipeline_with_vae._unpack_latents(
|
|
latents, self.height, self.width, flux_pipeline_with_vae.vae_scale_factor
|
|
)
|
|
latents = (
|
|
latents / flux_pipeline_with_vae.vae.config.scaling_factor
|
|
) + flux_pipeline_with_vae.vae.config.shift_factor
|
|
latents = latents.to(dtype=vae.dtype)
|
|
image = flux_pipeline_with_vae.vae.decode(latents, return_dict=False)[0]
|
|
image = flux_pipeline_with_vae.image_processor.postprocess(image, output_type="pil")[0]
|
|
|
|
assert isinstance(image, Image.Image)
|
|
return image
|
|
|
|
@staticmethod
|
|
def _load_flux_text_encoder(path: Path) -> CLIPTextModel:
|
|
model = CLIPTextModel.from_pretrained(path, local_files_only=True)
|
|
assert isinstance(model, CLIPTextModel)
|
|
return model
|
|
|
|
@staticmethod
|
|
def _load_flux_text_encoder_2(path: Path) -> T5EncoderModel:
|
|
model = T5EncoderModel.from_pretrained(path, local_files_only=True)
|
|
assert isinstance(model, T5EncoderModel)
|
|
return model
|
|
|
|
def _load_flux_transformer(self, path: Path) -> FluxTransformer2DModel:
|
|
if self.use_8bit:
|
|
model_8bit_path = path / "quantized"
|
|
if model_8bit_path.exists():
|
|
# The quantized model exists, load it.
|
|
# TODO(ryand): The requantize(...) operation in from_pretrained(...) is very slow. This seems like
|
|
# something that we should be able to make much faster.
|
|
q_model = QuantizedFluxTransformer2DModel.from_pretrained(model_8bit_path)
|
|
|
|
# Access the underlying wrapped model.
|
|
# We access the wrapped model, even though it is private, because it simplifies the type checking by
|
|
# always returning a FluxTransformer2DModel from this function.
|
|
model = q_model._wrapped
|
|
else:
|
|
# The quantized model does not exist yet, quantize and save it.
|
|
# TODO(ryand): Loading in float16 and then quantizing seems to result in NaNs. In order to run this on
|
|
# GPUs that don't support bfloat16, we would need to host the quantized model instead of generating it
|
|
# here.
|
|
model = FluxTransformer2DModel.from_pretrained(path, local_files_only=True, torch_dtype=torch.bfloat16)
|
|
assert isinstance(model, FluxTransformer2DModel)
|
|
|
|
q_model = QuantizedFluxTransformer2DModel.quantize(model, weights=qfloat8)
|
|
|
|
model_8bit_path.mkdir(parents=True, exist_ok=True)
|
|
q_model.save_pretrained(model_8bit_path)
|
|
|
|
# (See earlier comment about accessing the wrapped model.)
|
|
model = q_model._wrapped
|
|
else:
|
|
model = FluxTransformer2DModel.from_pretrained(path, local_files_only=True, torch_dtype=torch.bfloat16)
|
|
|
|
assert isinstance(model, FluxTransformer2DModel)
|
|
return model
|
|
|
|
@staticmethod
|
|
def _load_flux_vae(path: Path) -> AutoencoderKL:
|
|
model = AutoencoderKL.from_pretrained(path, local_files_only=True)
|
|
assert isinstance(model, AutoencoderKL)
|
|
return model
|