encoders.py

# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.

from typing import Optional, List
from diffusers.modular_pipelines import (
    ModularPipelineBlocks,
    ComponentSpec,
    InputParam,
    OutputParam,
    ModularPipeline,
    PipelineState,
)
from diffusers.guiders import ClassifierFreeGuidance
from transformers import UMT5EncoderModel, AutoTokenizer
from diffusers.image_processor import PipelineImageInput
import torch
from diffusers.modular_pipelines.wan.encoders import WanTextEncoderStep
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from diffusers.video_processor import VideoProcessor
from diffusers.configuration_utils import FrozenDict


class ChronoEditImageEncoderStep(ModularPipelineBlocks):
    model_name = "chronoedit"

    @property
    def expected_components(self) -> List[ComponentSpec]:
        return [
            ComponentSpec("image_processor", CLIPImageProcessor),
            ComponentSpec("image_encoder", CLIPVisionModelWithProjection),
        ]

    @property
    def inputs(self) -> List[InputParam]:
        return [InputParam("image", type_hint=PipelineImageInput)]

    @property
    def intermediate_outputs(self) -> List[OutputParam]:
        return [
            OutputParam(
                "image_embeds",
                type_hint=torch.Tensor,
                description="Image embeddings to use as conditions during the denoising process.",
            )
        ]

    @staticmethod
    def encode_image(components, image: PipelineImageInput, device: Optional[torch.device] = None):
        device = device or components.image_encoder.device
        image = components.image_processor(images=image, return_tensors="pt").to(device)
        image_embeds = components.image_encoder(**image, output_hidden_states=True)
        return image_embeds.hidden_states[-2]

    @torch.no_grad()
    def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
        block_state = self.get_block_state(state)
        block_state.image_embeds = self.encode_image(components, block_state.image, components._execution_device)
        self.set_block_state(state, block_state)
        return components, state


class ChronoEditProcessImageStep(ModularPipelineBlocks):
    model_name = "chronoedit"

    @property
    def inputs(self) -> List[InputParam]:
        return [
            InputParam("image", type_hint=PipelineImageInput),
            InputParam("image_embeds", type_hint=torch.Tensor, required=False),
            InputParam("batch_size", type_hint=int, required=False),
            InputParam("height", type_hint=int),
            InputParam("width", type_hint=int),
        ]

    @property
    def intermediate_outputs(self) -> List[OutputParam]:
        return [
            OutputParam("processed_image", type_hint=PipelineImageInput),
            OutputParam("image_embeds", type_hint=torch.Tensor)
        ]

    @property
    def expected_components(self) -> List[ComponentSpec]:
        return [
            ComponentSpec(
                "video_processor",
                VideoProcessor,
                config=FrozenDict({"vae_scale_factor": 8}),
                default_creation_method="from_config",
            )
        ]

    @torch.no_grad()
    def __call__(self, components: ModularPipeline, state: PipelineState) -> PipelineState:
        block_state = self.get_block_state(state)
        image = block_state.image
        device = components._execution_device

        block_state.processed_image = components.video_processor.preprocess(
            image, height=block_state.height, width=block_state.width
        ).to(device, dtype=torch.bfloat16)

        if block_state.image_embeds is not None:
            image_embeds = block_state.image_embeds
            batch_size = block_state.batch_size
            block_state.image_embeds = image_embeds.repeat(batch_size, 1, 1).to(torch.bfloat16)

        self.set_block_state(state, block_state)
        
        return components, state


# Configure CFG with a guidance scale of 1.
class ChronoEditTextEncoderStep(WanTextEncoderStep):
    model_name = "chronoedit"

    @property
    def expected_components(self) -> List[ComponentSpec]:
        return [
            ComponentSpec("text_encoder", UMT5EncoderModel),
            ComponentSpec("tokenizer", AutoTokenizer),
            ComponentSpec(
                "guider",
                ClassifierFreeGuidance,
                config=FrozenDict({"guidance_scale": 1.0}),
                default_creation_method="from_config",
            ),
        ]

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

        block_state.prepare_unconditional_embeds = components.guider.num_conditions > 1
        block_state.device = components._execution_device

        block_state.negative_prompt_embeds = None
        # Encode input prompt
        (
            block_state.prompt_embeds,
            block_state.negative_prompt_embeds,
        ) = self.encode_prompt(
            components,
            block_state.prompt,
            block_state.device,
            1,
            block_state.prepare_unconditional_embeds,
            block_state.negative_prompt,
            prompt_embeds=None,
            negative_prompt_embeds=block_state.negative_prompt_embeds,
        )

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