pythonProject/diffusers-main/src/diffusers/modular_pipelines/flux/encoders.py

# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import html
from typing import List, Optional, Union

import regex as re
import torch
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast

from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor
from ...loaders import FluxLoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL
from ...utils import USE_PEFT_BACKEND, is_ftfy_available, logging, scale_lora_layers, unscale_lora_layers
from ..modular_pipeline import ModularPipelineBlocks, PipelineState
from ..modular_pipeline_utils import ComponentSpec, ConfigSpec, InputParam, OutputParam
from .modular_pipeline import FluxModularPipeline


if is_ftfy_available():
    import ftfy


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()


def whitespace_clean(text):
    text = re.sub(r"\s+", " ", text)
    text = text.strip()
    return text


def prompt_clean(text):
    text = whitespace_clean(basic_clean(text))
    return text


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
def retrieve_latents(
    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
        return encoder_output.latent_dist.sample(generator)
    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
        return encoder_output.latent_dist.mode()
    elif hasattr(encoder_output, "latents"):
        return encoder_output.latents
    else:
        raise AttributeError("Could not access latents of provided encoder_output")


class FluxVaeEncoderStep(ModularPipelineBlocks):
    model_name = "flux"

    @property
    def description(self) -> str:
        return "Vae Encoder step that encode the input image into a latent representation"

    @property
    def expected_components(self) -> List[ComponentSpec]:
        return [
            ComponentSpec("vae", AutoencoderKL),
            ComponentSpec(
                "image_processor",
                VaeImageProcessor,
                config=FrozenDict({"vae_scale_factor": 16, "vae_latent_channels": 16}),
                default_creation_method="from_config",
            ),
        ]

    @property
    def inputs(self) -> List[InputParam]:
        return [
            InputParam("image", required=True),
            InputParam("height"),
            InputParam("width"),
            InputParam("generator"),
            InputParam("dtype", type_hint=torch.dtype, description="Data type of model tensor inputs"),
            InputParam(
                "preprocess_kwargs",
                type_hint=Optional[dict],
                description="A kwargs dictionary that if specified is passed along to the `ImageProcessor` as defined under `self.image_processor` in [diffusers.image_processor.VaeImageProcessor]",
            ),
        ]

    @property
    def intermediate_outputs(self) -> List[OutputParam]:
        return [
            OutputParam(
                "image_latents",
                type_hint=torch.Tensor,
                description="The latents representing the reference image for image-to-image/inpainting generation",
            )
        ]

    @staticmethod
    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_inpaint.StableDiffusion3InpaintPipeline._encode_vae_image with self.vae->vae
    def _encode_vae_image(vae, image: torch.Tensor, generator: torch.Generator):
        if isinstance(generator, list):
            image_latents = [
                retrieve_latents(vae.encode(image[i : i + 1]), generator=generator[i]) for i in range(image.shape[0])
            ]
            image_latents = torch.cat(image_latents, dim=0)
        else:
            image_latents = retrieve_latents(vae.encode(image), generator=generator)

        image_latents = (image_latents - vae.config.shift_factor) * vae.config.scaling_factor

        return image_latents

    @torch.no_grad()
    def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
        block_state = self.get_block_state(state)
        block_state.preprocess_kwargs = block_state.preprocess_kwargs or {}
        block_state.device = components._execution_device
        block_state.dtype = block_state.dtype if block_state.dtype is not None else components.vae.dtype

        block_state.image = components.image_processor.preprocess(
            block_state.image, height=block_state.height, width=block_state.width, **block_state.preprocess_kwargs
        )
        block_state.image = block_state.image.to(device=block_state.device, dtype=block_state.dtype)

        block_state.batch_size = block_state.image.shape[0]

        # if generator is a list, make sure the length of it matches the length of images (both should be batch_size)
        if isinstance(block_state.generator, list) and len(block_state.generator) != block_state.batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(block_state.generator)}, but requested an effective batch"
                f" size of {block_state.batch_size}. Make sure the batch size matches the length of the generators."
            )

        block_state.image_latents = self._encode_vae_image(
            components.vae, image=block_state.image, generator=block_state.generator
        )

        self.set_block_state(state, block_state)

        return components, state


class FluxTextEncoderStep(ModularPipelineBlocks):
    model_name = "flux"

    @property
    def description(self) -> str:
        return "Text Encoder step that generate text_embeddings to guide the video generation"

    @property
    def expected_components(self) -> List[ComponentSpec]:
        return [
            ComponentSpec("text_encoder", CLIPTextModel),
            ComponentSpec("tokenizer", CLIPTokenizer),
            ComponentSpec("text_encoder_2", T5EncoderModel),
            ComponentSpec("tokenizer_2", T5TokenizerFast),
        ]

    @property
    def expected_configs(self) -> List[ConfigSpec]:
        return []

    @property
    def inputs(self) -> List[InputParam]:
        return [
            InputParam("prompt"),
            InputParam("prompt_2"),
            InputParam("joint_attention_kwargs"),
        ]

    @property
    def intermediate_outputs(self) -> List[OutputParam]:
        return [
            OutputParam(
                "prompt_embeds",
                type_hint=torch.Tensor,
                description="text embeddings used to guide the image generation",
            ),
            OutputParam(
                "pooled_prompt_embeds",
                type_hint=torch.Tensor,
                description="pooled text embeddings used to guide the image generation",
            ),
            OutputParam(
                "text_ids",
                type_hint=torch.Tensor,
                description="ids from the text sequence for RoPE",
            ),
        ]

    @staticmethod
    def check_inputs(block_state):
        for prompt in [block_state.prompt, block_state.prompt_2]:
            if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
                raise ValueError(f"`prompt` or `prompt_2` has to be of type `str` or `list` but is {type(prompt)}")

    @staticmethod
    def _get_t5_prompt_embeds(
        components,
        prompt: Union[str, List[str]],
        num_images_per_prompt: int,
        max_sequence_length: int,
        device: torch.device,
    ):
        dtype = components.text_encoder_2.dtype

        prompt = [prompt] if isinstance(prompt, str) else prompt
        batch_size = len(prompt)

        if isinstance(components, TextualInversionLoaderMixin):
            prompt = components.maybe_convert_prompt(prompt, components.tokenizer_2)

        text_inputs = components.tokenizer_2(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            return_length=False,
            return_overflowing_tokens=False,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids

        untruncated_ids = components.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids
        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
            removed_text = components.tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
            logger.warning(
                "The following part of your input was truncated because `max_sequence_length` is set to "
                f" {max_sequence_length} tokens: {removed_text}"
            )

        prompt_embeds = components.text_encoder_2(text_input_ids.to(device), output_hidden_states=False)[0]
        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
        _, seq_len, _ = prompt_embeds.shape

        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        return prompt_embeds

    @staticmethod
    def _get_clip_prompt_embeds(
        components,
        prompt: Union[str, List[str]],
        num_images_per_prompt: int,
        device: torch.device,
    ):
        prompt = [prompt] if isinstance(prompt, str) else prompt
        batch_size = len(prompt)

        if isinstance(components, TextualInversionLoaderMixin):
            prompt = components.maybe_convert_prompt(prompt, components.tokenizer)

        text_inputs = components.tokenizer(
            prompt,
            padding="max_length",
            max_length=components.tokenizer.model_max_length,
            truncation=True,
            return_overflowing_tokens=False,
            return_length=False,
            return_tensors="pt",
        )

        text_input_ids = text_inputs.input_ids
        tokenizer_max_length = components.tokenizer.model_max_length
        untruncated_ids = components.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
            removed_text = components.tokenizer.batch_decode(untruncated_ids[:, tokenizer_max_length - 1 : -1])
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                f" {tokenizer_max_length} tokens: {removed_text}"
            )
        prompt_embeds = components.text_encoder(text_input_ids.to(device), output_hidden_states=False)

        # Use pooled output of CLIPTextModel
        prompt_embeds = prompt_embeds.pooler_output
        prompt_embeds = prompt_embeds.to(dtype=components.text_encoder.dtype, device=device)

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt)
        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)

        return prompt_embeds

    @staticmethod
    def encode_prompt(
        components,
        prompt: Union[str, List[str]],
        prompt_2: Union[str, List[str]],
        device: Optional[torch.device] = None,
        num_images_per_prompt: int = 1,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        max_sequence_length: int = 512,
        lora_scale: Optional[float] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                used in all text-encoders
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            lora_scale (`float`, *optional*):
                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
        """
        device = device or components._execution_device

        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(components, FluxLoraLoaderMixin):
            components._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            if components.text_encoder is not None and USE_PEFT_BACKEND:
                scale_lora_layers(components.text_encoder, lora_scale)
            if components.text_encoder_2 is not None and USE_PEFT_BACKEND:
                scale_lora_layers(components.text_encoder_2, lora_scale)

        prompt = [prompt] if isinstance(prompt, str) else prompt

        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2

            # We only use the pooled prompt output from the CLIPTextModel
            pooled_prompt_embeds = FluxTextEncoderStep._get_clip_prompt_embeds(
                components,
                prompt=prompt,
                device=device,
                num_images_per_prompt=num_images_per_prompt,
            )
            prompt_embeds = FluxTextEncoderStep._get_t5_prompt_embeds(
                components,
                prompt=prompt_2,
                num_images_per_prompt=num_images_per_prompt,
                max_sequence_length=max_sequence_length,
                device=device,
            )

        if components.text_encoder is not None:
            if isinstance(components, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(components.text_encoder, lora_scale)

        if components.text_encoder_2 is not None:
            if isinstance(components, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(components.text_encoder_2, lora_scale)

        dtype = components.text_encoder.dtype if components.text_encoder is not None else torch.bfloat16
        text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)

        return prompt_embeds, pooled_prompt_embeds, text_ids

    @torch.no_grad()
    def __call__(self, components: FluxModularPipeline, state: PipelineState) -> PipelineState:
        # Get inputs and intermediates
        block_state = self.get_block_state(state)
        self.check_inputs(block_state)

        block_state.device = components._execution_device

        # Encode input prompt
        block_state.text_encoder_lora_scale = (
            block_state.joint_attention_kwargs.get("scale", None)
            if block_state.joint_attention_kwargs is not None
            else None
        )
        (block_state.prompt_embeds, block_state.pooled_prompt_embeds, block_state.text_ids) = self.encode_prompt(
            components,
            prompt=block_state.prompt,
            prompt_2=None,
            prompt_embeds=None,
            pooled_prompt_embeds=None,
            device=block_state.device,
            num_images_per_prompt=1,  # TODO: hardcoded for now.
            lora_scale=block_state.text_encoder_lora_scale,
        )

        # Add outputs
        self.set_block_state(state, block_state)
        return components, state