load encoder optional

[qzzz95]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @qzzz95, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the QwenImagePipeline by introducing an optional mechanism to skip the loading and initialization of the encoder component. This provides users with greater control over resource allocation, allowing for potential memory savings and faster startup times in scenarios where the encoder is not required for a specific task. The default behavior remains unchanged, ensuring existing workflows are not disrupted.

Highlights

• Configuration Option: Introduced a new boolean field, load_encoder, in the QwenImagePipelineConfig class, defaulting to True, to control whether the encoder component should be loaded.
• Conditional Encoder Loading: Modified the from_pretrained method in qwen_image.py to conditionally load the encoder's state dictionary only if config.load_encoder is True.
• Conditional Component Initialization: Updated the _from_state_dict method to conditionally initialize the tokenizer, processor, and the encoder model itself, based on the config.load_encoder flag.
• Pipeline Construction: Ensured that the QwenImagePipeline constructor passes None for the tokenizer, processor, and encoder if config.load_encoder is False, preventing unnecessary object creation.

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[gemini-code-assist]

Summary of Changes

Hello @qzzz95, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the Qwen image pipeline by introducing an optional loading mechanism for its text encoder, tokenizer, and processor components. By adding a load_encoder flag to the pipeline configuration, users can now choose to bypass the loading and initialization of these elements, leading to more efficient resource utilization and potentially faster startup times for scenarios where the encoder is not essential.

Highlights

• Optional Encoder Loading: A new configuration option, load_encoder, has been introduced to control whether the text encoder, tokenizer, and processor components of the Qwen image pipeline are loaded during initialization.
• Conditional Component Initialization: The logic for loading the encoder's state dictionary and initializing the tokenizer, processor, and encoder models has been made conditional, executing only if load_encoder is set to True.
• Resource Optimization: This change allows for more flexible resource management, enabling users to skip loading potentially large encoder-related models when they are not required, which can reduce memory footprint and speed up pipeline setup.

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Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
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[gemini-code-assist]

Code Review

This pull request introduces a load_encoder flag to QwenImagePipelineConfig to make loading the text encoder optional. The changes correctly wrap the encoder loading logic in conditional blocks based on this flag.

However, I've found a high-severity issue that will cause a NameError if the pipeline is initialized with load_encoder=False. I've left a comment with a suggested fix.

Additionally, for improved robustness and maintainability, I recommend adding checks in methods that require the encoder (like __call__) to raise an informative error if it's not loaded. The type hints for tokenizer, processor, and encoder in QwenImagePipeline.__init__ should also be updated to Optional to reflect that they can be None. These changes are outside the current diff but are important for the overall quality of this feature.

[gemini-code-assist]

Code Review

This pull request introduces the ability to optionally load the encoder in the QwenImagePipeline by adding a load_encoder flag to the configuration. The implementation correctly gates the loading of the encoder's state dictionary and its initialization based on this flag. However, there is a critical issue in _from_state_dict that will lead to an UnboundLocalError when the encoder is not loaded. I've provided a detailed comment on how to fix this.