Gemini 2.5 Pro Vision vs Gemini 3.1 Flash TTS

We've been building [Scenema Audio](https://scenema.ai/audio) as part of our video production platform at scenema.ai, and we're releasing the model weights and inference code.

The core idea: emotional performance and voice identity are independent. You describe how the speech should be performed (rage, grief, excitement, a child's wonder), and optionally provide reference audio for voice identity. The reference provides the "who." The prompt provides the "how." Any voice can perform any emotion, even if that voice has never been recorded in that emotional state.

# Limitations (and why we still use it)

This is a diffusion model, not a traditional TTS pipeline. Common issues include repetition and gibberish on some seeds. Different seeds give different results, and you will not get a perfect output with 0% error rate. This model is meant for a post-editing workflow: generate, pick the best take, trim if needed. Same way you'd work with any generative model.

That said, we keep coming back to Scenema Audio over even Gemini 3.1 Flash TTS, which is already more controllable than most TTS systems out there. The reason is simple: the output just sounds more natural and less robotic. There's a quality to diffusion-generated speech that autoregressive TTS doesn't quite match, especially for emotional delivery.

# Audio-first video generation

As [this video](https://www.youtube.com/watch?v=ZZO3XAy3KTo) points out, generating audio first and then using it to drive video generation is a powerful workflow. That's actually how we've used Scenema Audio in some cases. Generate the voice performance, then feed it into an A2V pipeline (LTX 2.3, Wan 2.6, Seedance 2.0, etc.) to generate video that matches the speech. [Here's an example of that workflow in action.](https://youtu.be/dcAjQhPKNLk?si=4iOwtpsLR-WzwDmF)

# On distillation and speed

A few people have asked this. Our bottleneck is not denoising steps. The diffusion pass is a small fraction of total generation time. The real costs are elsewhere in the pipeline. We're already at 8 steps (down from 50 in the base model), and that's the sweet spot where quality holds.

# Prompting matters

This model is sensitive to prompting, the same way LTX 2.3 is for video. A generic voice description gives you generic output. A specific, theatrical description with action tags gives you a performance. There's also a `pace` parameter that controls how much time the model gets per word. Takes some experimentation to find what works for your use case, but once you do, you can generate hours of audio with minimal quality loss.

Complex words and proper nouns benefit from phonetic spelling. Unlike traditional TTS, it doesn't have a phoneme-to-audio pipeline or a pronunciation dictionary. If it garbles "Tchaikovsky," you would spell it "Chai-koff-skee" or whatever makes sense to you.

# Docker REST API with automatic VRAM management

We ship this as a Docker container with a REST API. Same setup we use in production on scenema.ai. The service auto-detects your GPU and picks the right configuration:

|VRAM|Audio Model|Gemma|Notes|
|:-|:-|:-|:-|
|16 GB|INT8 (4.9 GB)|CPU streaming|Needs 32 GB system RAM|
|24 GB|INT8 (4.9 GB)|NF4 on GPU|Default config|
|48 GB|bf16 (9.8 GB)|bf16 on GPU|Best quality|

We went with Docker because that's how we serve it. No dependency hell, no conda environments. Pull, set your HF token for Gemma access, then `docker compose up`.

# ComfyUI

Native ComfyUI node support is planned. We're hoping to release it in the coming weeks, unless someone from the community beats us to it. In the meantime, the REST API is straightforward to call from a custom node since it's just a local HTTP service.

# Links

* **All demos + article:** [scenema.ai/audio](https://scenema.ai/audio)
* **Model weights:** [huggingface.co/ScenemaAI/scenema-audio](https://huggingface.co/ScenemaAI/scenema-audio)
* **Code + setup:** [github.com/ScenemaAI/scenema-audio](https://github.com/ScenemaAI/scenema-audio)
* **YouTube demo:** [youtu.be/VnEQ\_ImOaAc](https://youtu.be/VnEQ_ImOaAc)

This is fully open source. The model weights derive from the LTX-2 Community License but all inference and pipeline code is MIT.

We've been building [Scenema Audio](https://scenema.ai/audio) as part of our video production platform at scenema.ai, and we're releasing the model weights and inference code.

The core idea: emotional performance and voice identity are independent. You describe how the speech should be performed (rage, grief, excitement, a child's wonder), and optionally provide reference audio for voice identity. The reference provides the "who." The prompt provides the "how." Any voice can perform any emotion, even if that voice has never been recorded in that emotional state.

# Limitations (and why we still use it)

This is a diffusion model, not a traditional TTS pipeline. Common issues include repetition and gibberish on some seeds. Different seeds give different results, and you will not get a perfect output with 0% error rate. This model is meant for a post-editing workflow: generate, pick the best take, trim if needed. Same way you'd work with any generative model.

That said, we keep coming back to Scenema Audio over even Gemini 3.1 Flash TTS, which is already more controllable than most TTS systems out there. The reason is simple: the output just sounds more natural and less robotic. There's a quality to diffusion-generated speech that autoregressive TTS doesn't quite match, especially for emotional delivery.

# Audio-first video generation

As [this video](https://www.youtube.com/watch?v=ZZO3XAy3KTo) points out, generating audio first and then using it to drive video generation is a powerful workflow. That's actually how we've used Scenema Audio in some cases. Generate the voice performance, then feed it into an A2V pipeline (LTX 2.3, Wan 2.6, Seedance 2.0, etc.) to generate video that matches the speech. [Here's an example of that workflow in action.](https://youtu.be/dcAjQhPKNLk?si=4iOwtpsLR-WzwDmF)

# On distillation and speed

A few people have asked this. Our bottleneck is not denoising steps. The diffusion pass is a small fraction of total generation time. The real costs are elsewhere in the pipeline. We're already at 8 steps (down from 50 in the base model), and that's the sweet spot where quality holds.

# Prompting matters

This model is sensitive to prompting, the same way LTX 2.3 is for video. A generic voice description gives you generic output. A specific, theatrical description with action tags gives you a performance. There's also a `pace` parameter that controls how much time the model gets per word. Takes some experimentation to find what works for your use case, but once you do, you can generate hours of audio with minimal quality loss.

Complex words and proper nouns benefit from phonetic spelling. Unlike traditional TTS, it doesn't have a phoneme-to-audio pipeline or a pronunciation dictionary. If it garbles "Tchaikovsky," you would spell it "Chai-koff-skee" or whatever makes sense to you.

# Docker REST API with automatic VRAM management

We ship this as a Docker container with a REST API. Same setup we use in production on scenema.ai. The service auto-detects your GPU and picks the right configuration:

|VRAM|Audio Model|Gemma|Notes|
|:-|:-|:-|:-|
|16 GB|INT8 (4.9 GB)|CPU streaming|Needs 32 GB system RAM|
|24 GB|INT8 (4.9 GB)|NF4 on GPU|Default config|
|48 GB|bf16 (9.8 GB)|bf16 on GPU|Best quality|

We went with Docker because that's how we serve it. No dependency hell, no conda environments. We built it for production deployment.

# ComfyUI

Native ComfyUI node support is planned. We're hoping to release it in the coming weeks, unless someone from the community beats us to it. In the meantime, the REST API is straightforward to call from a custom node since it's just a local HTTP service.

# Links

* **All demos + article:** [scenema.ai/audio](https://scenema.ai/audio)
* **Model weights:** [huggingface.co/ScenemaAI/scenema-audio](https://huggingface.co/ScenemaAI/scenema-audio)
* **Code + setup:** [github.com/ScenemaAI/scenema-audio](https://github.com/ScenemaAI/scenema-audio)
* **YouTube demo:** [youtu.be/VnEQ\_ImOaAc](https://youtu.be/VnEQ_ImOaAc)

This is fully open source. The model weights derive from the LTX-2 Community License but all inference and pipeline code is MIT.

# How to Try Scenema Audio

1. You can clone the repo and run `docker compose up` locally or
2. Go to [Scenema](https://scenema.ai) and start a conversation to create a voiceover. You will be able to try voice design for free, iterate on your prompts, tune pacing, etc.

Gemini 3.1 Flash TTS introduces audio tags for controlling vocal style, delivery, and pace with natural language commands, scene direction, speaker-level specificity, and more natural expressive voices. The model supports over 70 languages including Hindi, Japanese, and German, with features like SynthID watermarking and multi-speaker audio. It is available in preview via the Gemini API, Google AI Studio, Vertex AI, and rolling out in Google Workspace via Google Vids.

We've been building [Scenema Audio](https://scenema.ai/audio) as part of our video production platform at scenema.ai, and we're releasing the model weights and inference code.

The core idea: emotional performance and voice identity are independent. You describe how the speech should be performed (rage, grief, excitement, a child's wonder), and optionally provide reference audio for voice identity. The reference provides the "who." The prompt provides the "how." Any voice can perform any emotion, even if that voice has never been recorded in that emotional state.

# Limitations (and why we still use it)

This is a diffusion model, not a traditional TTS pipeline. Common issues include repetition and gibberish on some seeds. Different seeds give different results, and you will not get a perfect output with 0% error rate. This model is meant for a post-editing workflow: generate, pick the best take, trim if needed. Same way you'd work with any generative model.

That said, we keep coming back to Scenema Audio over even Gemini 3.1 Flash TTS, which is already more controllable than most TTS systems out there. The reason is simple: the output just sounds more natural and less robotic. There's a quality to diffusion-generated speech that autoregressive TTS doesn't quite match, especially for emotional delivery.

# Audio-first video generation

As [this video](https://www.youtube.com/watch?v=ZZO3XAy3KTo) points out, generating audio first and then using it to drive video generation is a powerful workflow. That's actually how we've used Scenema Audio in some cases. Generate the voice performance, then feed it into an A2V pipeline (LTX 2.3, Wan 2.6, Seedance 2.0, etc.) to generate video that matches the speech. [Here's an example of that workflow in action.](https://youtu.be/dcAjQhPKNLk?si=4iOwtpsLR-WzwDmF)

# On distillation and speed

A few people have asked this. Our bottleneck is not denoising steps. The diffusion pass is a small fraction of total generation time. The real costs are elsewhere in the pipeline. We're already at 8 steps (down from 50 in the base model), and that's the sweet spot where quality holds.

# Prompting matters

This model is sensitive to prompting, the same way LTX 2.3 is for video. A generic voice description gives you generic output. A specific, theatrical description with action tags gives you a performance. There's also a `pace` parameter that controls how much time the model gets per word. Takes some experimentation to find what works for your use case, but once you do, you can generate hours of audio with minimal quality loss.

Complex words and proper nouns benefit from phonetic spelling. Unlike traditional TTS, it doesn't have a phoneme-to-audio pipeline or a pronunciation dictionary. If it garbles "Tchaikovsky," you would spell it "Chai-koff-skee" or whatever makes sense to you.

# Docker REST API with automatic VRAM management

We ship this as a Docker container with a REST API. Same setup we use in production on scenema.ai. The service auto-detects your GPU and picks the right configuration:

|VRAM|Audio Model|Gemma|Notes|
|:-|:-|:-|:-|
|16 GB|INT8 (4.9 GB)|CPU streaming|Needs 32 GB system RAM|
|24 GB|INT8 (4.9 GB)|NF4 on GPU|Default config|
|48 GB|bf16 (9.8 GB)|bf16 on GPU|Best quality|

We went with Docker because that's how we serve it. No dependency hell, no conda environments. Pull, set your HF token for Gemma access, then `docker compose up`.

# ComfyUI

Native ComfyUI node support is planned. We're hoping to release it in the coming weeks, unless someone from the community beats us to it. In the meantime, the REST API is straightforward to call from a custom node since it's just a local HTTP service.

# Links

* **All demos + article:** [scenema.ai/audio](https://scenema.ai/audio)
* **Model weights:** [huggingface.co/ScenemaAI/scenema-audio](https://huggingface.co/ScenemaAI/scenema-audio)
* **Code + setup:** [github.com/ScenemaAI/scenema-audio](https://github.com/ScenemaAI/scenema-audio)
* **YouTube demo:** [youtu.be/VnEQ\_ImOaAc](https://youtu.be/VnEQ_ImOaAc)

This is fully open source. The model weights derive from the LTX-2 Community License but all inference and pipeline code is MIT.