[Feat] Add f2v for sekotalk

[wangshankun]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @wangshankun, 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 introduces a new "f2v" (frame-to-video) processing mode for the seko_talk model, enhancing its ability to generate video conditioned on an initial reference image. The changes involve a new configuration file to activate this mode, modifications to the audio and input processing logic to integrate the reference image as a conditioning element, and a new script to demonstrate its usage. This feature aims to provide more control over the initial state of generated videos by leveraging a static image.

Highlights

• New F2V Configuration: Introduced configs/seko_talk/seko_talk_28_f2v.json to enable and configure the new "f2v_process" feature for the seko_talk model, including specific inference steps, FPS, and LoRA settings.
• Conditional Audio Input Processing: Modified lightx2v/models/networks/wan/infer/audio/pre_infer.py to conditionally adjust audio patch embedding concatenation based on the f2v_process flag, preventing audio embeddings from being concatenated with the main input x when f2v is active.
• Reference Image Integration: Updated lightx2v/models/runners/wan/wan_audio_runner.py to store the input image as a reference (self.ref_img) and use it to initialize self.prev_video when f2v_process is enabled, allowing the initial frame to condition video generation.
• F2V Run Script: Added scripts/seko_talk/run_seko_talk_28_f2v.sh to provide an example and facilitate running the seko_talk model with the new f2v configuration, including predefined prompts and input paths.

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[gemini-code-assist]

Code Review

This pull request introduces a 'first-frame-to-video' (f2v) feature for the sekotalk model. The changes include a new configuration file and run script for this feature, along with modifications in the audio runner and pre-inference logic to handle the f2v_process flag. The core logic correctly uses the input image as the initial frame for video generation. My review includes suggestions to improve the portability of the configuration and the robustness of the new run script by removing a hardcoded path and adding validation for required variables.

[gemini-code-assist]

The file path for the LoRA configuration is hardcoded to an absolute path. This makes the configuration not portable and will cause errors if run on a different machine or with a different directory structure. It's recommended to use a placeholder or a path relative to the model directory, which can be resolved at runtime.

[gemini-code-assist]

The variables lightx2v_path and model_path are initialized as empty, requiring users to edit the script. This can lead to errors if not set. A more robust and flexible approach is to read these paths from environment variables and validate that they are set, providing a clear error message to the user if they are missing.

Suggested change

	lightx2v_path=
	model_path=
	lightx2v_path=${LIGHTX2V_PATH:-}
	model_path=${MODEL_PATH:-}
	if [[ -z "$lightx2v_path" || -z "$model_path" ]]; then
	echo "Error: LIGHTX2V_PATH and MODEL_PATH environment variables must be set."
	exit 1
	fi