QwenBaseDataLoader.py

import os

from modules.dataLoader.BaseDataLoader import BaseDataLoader
from modules.dataLoader.mixin.DataLoaderText2ImageMixin import DataLoaderText2ImageMixin
from modules.model.BaseModel import BaseModel
from modules.model.QwenModel import (
    DEFAULT_PROMPT_TEMPLATE,
    DEFAULT_PROMPT_TEMPLATE_CROP_START,
    PROMPT_MAX_LENGTH,
    QwenModel,
)
from modules.modelSetup.BaseModelSetup import BaseModelSetup
from modules.modelSetup.BaseQwenSetup import BaseQwenSetup
from modules.util import factory
from modules.util.config.TrainConfig import TrainConfig
from modules.util.enum.ModelType import ModelType
from modules.util.TrainProgress import TrainProgress

from mgds.pipelineModules.DecodeTokens import DecodeTokens
from mgds.pipelineModules.DecodeVAE import DecodeVAE
from mgds.pipelineModules.EncodeQwenText import EncodeQwenText
from mgds.pipelineModules.EncodeVAE import EncodeVAE
from mgds.pipelineModules.PadMaskedTokens import PadMaskedTokens
from mgds.pipelineModules.PruneMaskedTokens import PruneMaskedTokens
from mgds.pipelineModules.RescaleImageChannels import RescaleImageChannels
from mgds.pipelineModules.SampleVAEDistribution import SampleVAEDistribution
from mgds.pipelineModules.SaveImage import SaveImage
from mgds.pipelineModules.SaveText import SaveText
from mgds.pipelineModules.ScaleImage import ScaleImage
from mgds.pipelineModules.Tokenize import Tokenize


class QwenBaseDataLoader(
    BaseDataLoader,
    DataLoaderText2ImageMixin,
):
    def _preparation_modules(self, config: TrainConfig, model: QwenModel):
        rescale_image = RescaleImageChannels(image_in_name='image', image_out_name='image', in_range_min=0, in_range_max=1, out_range_min=-1, out_range_max=1)
        encode_image = EncodeVAE(in_name='image', out_name='latent_image_distribution', vae=model.vae, autocast_contexts=[model.autocast_context], dtype=model.train_dtype.torch_dtype())
        image_sample = SampleVAEDistribution(in_name='latent_image_distribution', out_name='latent_image', mode='mean')
        downscale_mask = ScaleImage(in_name='mask', out_name='latent_mask', factor=0.125)
        tokenize_prompt = Tokenize(in_name='prompt', tokens_out_name='tokens', mask_out_name='tokens_mask', tokenizer=model.tokenizer, max_token_length=PROMPT_MAX_LENGTH,
                                   format_text=DEFAULT_PROMPT_TEMPLATE, additional_format_text_tokens=DEFAULT_PROMPT_TEMPLATE_CROP_START)
        encode_prompt = EncodeQwenText(tokens_name='tokens', tokens_attention_mask_in_name='tokens_mask', hidden_state_out_name='text_encoder_hidden_state', tokens_attention_mask_out_name='tokens_mask',
                                       text_encoder=model.text_encoder, hidden_state_output_index=-1, autocast_contexts=[model.autocast_context], dtype=model.train_dtype.torch_dtype(), crop_start=DEFAULT_PROMPT_TEMPLATE_CROP_START)
        prune_masked_tokens = PruneMaskedTokens(tokens_name='tokens', tokens_mask_name='tokens_mask', hidden_state_name='text_encoder_hidden_state')

        modules = [rescale_image, encode_image, image_sample]
        if config.masked_training or config.model_type.has_mask_input():
            modules.append(downscale_mask)

        modules.append(tokenize_prompt)

        if not config.train_text_encoder_or_embedding():
            modules.append(encode_prompt)

        if config.latent_caching and not config.train_text_encoder_or_embedding():
            modules.append(prune_masked_tokens)

        return modules

    def _cache_modules(self, config: TrainConfig, model: QwenModel, model_setup: BaseQwenSetup):
        image_split_names = ['latent_image', 'original_resolution', 'crop_offset']

        if config.masked_training or config.model_type.has_mask_input():
            image_split_names.append('latent_mask')

        image_aggregate_names = ['crop_resolution', 'image_path']

        text_split_names = []

        sort_names = image_aggregate_names + image_split_names + [
            'prompt', 'tokens', 'tokens_mask', 'text_encoder_hidden_state',
            'concept'
        ]

        if not config.train_text_encoder_or_embedding():
            text_split_names += ['tokens', 'tokens_mask', 'text_encoder_hidden_state']

        return self._cache_modules_from_names(
            model, model_setup,
            image_split_names=image_split_names,
            image_aggregate_names=image_aggregate_names,
            text_split_names=text_split_names,
            sort_names=sort_names,
            config=config,
            text_caching=not config.train_text_encoder_or_embedding(),
        )

    def _output_modules(self, config: TrainConfig, model: QwenModel, model_setup: BaseQwenSetup, is_validation: bool = False):
        pad_masked_tokens = PadMaskedTokens(tokens_name='tokens', tokens_mask_name='tokens_mask', hidden_state_name='text_encoder_hidden_state', max_length=PROMPT_MAX_LENGTH)

        output_names = [
            'image_path', 'latent_image',
            'prompt',
            'tokens',
            'tokens_mask',
            'original_resolution', 'crop_resolution', 'crop_offset',
        ]

        if config.masked_training or config.model_type.has_mask_input():
            output_names.append('latent_mask')

        if not config.train_text_encoder_or_embedding():
            output_names.append('text_encoder_hidden_state')

        output_module_list = self._output_modules_from_out_names(
            model, model_setup,
            output_names=output_names,
            config=config,
            use_conditioning_image=False,
            vae=model.vae,
            autocast_context=[model.autocast_context],
            train_dtype=model.train_dtype,
            is_validation=is_validation,
        )

        if config.latent_caching and not config.train_text_encoder_or_embedding():
            output_module_list = [pad_masked_tokens] + output_module_list

        return output_module_list

    def _debug_modules(self, config: TrainConfig, model: QwenModel): #TODO clean up
        debug_dir = os.path.join(config.debug_dir, "dataloader")

        def before_save_fun():
            model.vae_to(self.train_device)

        decode_image = DecodeVAE(in_name='latent_image', out_name='decoded_image', vae=model.vae, autocast_contexts=[model.autocast_context], dtype=model.train_dtype.torch_dtype())
        upscale_mask = ScaleImage(in_name='latent_mask', out_name='decoded_mask', factor=8)
        decode_prompt = DecodeTokens(in_name='tokens', out_name='decoded_prompt', tokenizer=model.tokenizer)

        #FIXME https://github.com/Nerogar/OneTrainer/issues/1015
        #save_image = SaveImage(image_in_name='decoded_image', original_path_in_name='image_path', path=debug_dir, in_range_min=-1, in_range_max=1, before_save_fun=before_save_fun)

        # SaveImage(image_in_name='latent_mask', original_path_in_name='image_path', path=debug_dir, in_range_min=0, in_range_max=1, before_save_fun=before_save_fun)
        save_mask = SaveImage(image_in_name='decoded_mask', original_path_in_name='image_path', path=debug_dir, in_range_min=0, in_range_max=1, before_save_fun=before_save_fun)
        save_prompt = SaveText(text_in_name='decoded_prompt', original_path_in_name='image_path', path=debug_dir, before_save_fun=before_save_fun)

        # These modules don't really work, since they are inserted after a sorting operation that does not include this data
        # SaveImage(image_in_name='mask', original_path_in_name='image_path', path=debug_dir, in_range_min=0, in_range_max=1),
        # SaveImage(image_in_name='image', original_path_in_name='image_path', path=debug_dir, in_range_min=-1, in_range_max=1),

        modules = [decode_image]

        #FIXME https://github.com/Nerogar/OneTrainer/issues/1015
        #modules.append(save_image)

        if config.masked_training or config.model_type.has_mask_input():
            modules += [upscale_mask, save_mask]

        modules += [decode_prompt, save_prompt]

        return modules

    def _create_dataset(
            self,
            config: TrainConfig,
            model: BaseModel,
            model_setup: BaseModelSetup,
            train_progress: TrainProgress,
            is_validation: bool = False,
    ):
        return DataLoaderText2ImageMixin._create_dataset(self,
            config, model, model_setup, train_progress, is_validation,
            aspect_bucketing_quantization=64,
            allow_video_files=False, #don't allow video files, but...
            vae_frame_dim=True,  #...Qwen has a video-capable VAE. convert images to video dimensions
        )

factory.register(BaseDataLoader, QwenBaseDataLoader, ModelType.QWEN)