Add Echo modular pipeline

[Gelercatty]

What does this PR do?

Adds official Diffusers Modular Pipeline support for Echo, a long-video model that generates synchronized video and audio with cross-shot memory.

This supersedes #13910 with a modular implementation that incorporates the earlier review feedback:

• adds the standalone diffusers.modular_pipelines.echo package and the public EchoModularPipeline / EchoBlocks APIs
• reuses existing lower-level Diffusers components without exposing LTX in Echo's public API
• keeps checkpoint conversion in scripts/ instead of adding an original-checkpoint runtime pipeline
• supports an optional first frame and up to seven paired image/audio memory slots
• supports variable-length memory audio and crops audio longer than 9.62 seconds to its highest-response window
• implements Echo's positive-only conditioning and stochastic 8-step DMD denoising workflow
• adds mixed-precision-safe audio decoding, documentation, and tests using a public tiny fixture

Official resources:

• Source implementation: https://github.com/jd-opensource/JoyAI-Echo/tree/echo-long-diffusers
• Model weights: https://huggingface.co/Echo-Team/JoyAI-Echo
• Tracking issue: [New Pipeline/Model] Add JoyAI-Echo multi-shot audio-video generation pipeline #13909

Closes #13909
Supersedes #13910

Testing

• make style
• make fix-copies
• make quality
• PYTHONPATH=src python -m pytest -q tests/modular_pipelines/echo/test_modular_pipeline_echo.py
  • 25 passed, 12 skipped

AI-assisted self-review

Codex was used to help review, refactor, test, and prepare this contribution. The final diff was reviewed using the repository's model-integration and self-review guidance.

The final self-review found no blocking correctness issues. Echo has its own public package and naming throughout; existing lower-level Diffusers component types are reused only as implementation dependencies. Memory-slot count, short-audio handling, variable audio lengths, response-aware 9.62-second cropping, deterministic DMD re-noising, mixed-precision decoding, loading, save/load, workflow, and memory behavior are covered by the test suite.

Validation scope: the final pass used the public Echo-Team/tiny-echo-modular-pipe fixture on CPU. A full production-checkpoint conversion and end-to-end GPU inference run was not repeated in this final pass.

Before submitting

• Did you use an AI agent (Claude Code, Codex, Cursor, etc.) to help with this PR?
  • Did you read the Coding with AI agents guide?
  • Did you run the self-review skill on the diff?
  • Did you share the final self-review notes in the PR description or a comment?
• Did you read the contributor guideline?
• Did you read our philosophy doc?
• Was this discussed/approved via a GitHub issue or the forum? See [New Pipeline/Model] Add JoyAI-Echo multi-shot audio-video generation pipeline #13909 and the maintainer discussion on [feat] Add JoyAI-Echo multi-shot audio-video generation pipeline #13910.
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?
• Are you the author (or part of the team) of the model/pipeline?

Who can review?

@asomoza @yiyixuxu

[HuggingFaceDocBuilderDev]

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