[Feat] support longcat image lora train

[wangshankun]

No description provided.

[gemini-code-assist]

Code Review

This pull request introduces a new LoRA training configuration for the longcat_image model and updates the model implementation to freeze the text encoder, include a text processor in the pipeline, and provide inference keyword arguments. Feedback includes correcting a typo in a method call, replacing absolute paths in the configuration with portable alternatives, restoring the device argument for consistency in prompt encoding, and ensuring the denoiser input preparation method remains compatible with the base class interface.

[gemini-code-assist]

Typo detected: rewire_prompt should likely be rewrite_prompt. The configuration key enable_prompt_rewrite_training and the previous version of the code both use "rewrite". This will cause an AttributeError at runtime if the method does not exist.

Suggested change

	prompt = self.text_pipeline.rewire_prompt(prompt, self.device)
	prompt = self.text_pipeline.rewrite_prompt(prompt, self.device)

[gemini-code-assist]

The configuration contains absolute paths specific to a local environment (e.g., /mnt/miaohua/wangshankun/...). This makes the configuration non-portable and likely to fail for other users or in different environments. Consider using relative paths or environment variables/placeholders for model and data paths.

[gemini-code-assist]

The device argument was removed from the encode_prompt call. While the pipeline might default to its own device, explicitly passing self.device ensures consistency and avoids potential device mismatch issues, especially since it was explicitly provided in the previous version.

Suggested change

	prompt_embed, text_ids = self.text_pipeline.encode_prompt(
	prompt=prompt,
	device=self.device,
	num_images_per_prompt=1,
	)
	prompt_embed, text_ids = self.text_pipeline.encode_prompt(
	prompt=prompt,
	device=self.device,
	num_images_per_prompt=1,
	)

[gemini-code-assist]

The signature of prepare_denoiser_input has been changed to accept only one argument (noisy_latent), which violates the interface defined in the base class BaseModel (which expects noisy_latent, sample, condition). To maintain compatibility with the base class while supporting callers that only provide one argument (like the current LoraTrainer), consider using optional arguments.

Suggested change

	def prepare_denoiser_input(self, noisy_latent):
	def prepare_denoiser_input(self, noisy_latent, sample=None, condition=None):