Gemini Omni Flash API

Gemini Omni Flash Skill

When to Use

Use this skill when you need use this skill for generative video editing, text-to-video, image-referenced video generation, and first-frame-to-video transition animations using the official google-genai SDK. Includes workflows for pre-processing/optimizing high-resolution or long source videos with ffmpeg,...

This skill uses the Gemini Omni Flash model (gemini-omni-flash-preview) to perform text to video generation, image to video generation and video editing.

[!WARNING] Important Regional Restrictions: Uploading videos to use for video edits is NOT available in the EEA, Switzerland, the United Kingdom, and some US states. If a video-to-video edit completes quickly with empty outputs (total_output_tokens: 0 or no video content), it is likely due to this restriction.

Core capabilities

1. Video editing and refinement: Editing existing videos (maximum duration 10 seconds), applying stylistic changes, or performing inpainting/outpainting.
2. Text to video: Generating videos from a text prompt.
3. First-frame to video: Generating videos from a single input image.
4. Image-referenced generation: Using style, character, or object references from images to guide video generation.

Workflow

1. Analyze request: Determine the target task (e.g., first-frame-to-video, reference-guided editing) and identify any input media assets.

2. Run SDK scripts:

   • Directly run the appropriate utility (scripts/video/generate_video.py or scripts/upload_file.py).
   • Configure settings like --aspect-ratio (e.g. 16:9, 9:16) and --duration (any integer between 3 and 10 seconds, e.g. 3, 5, 10).

3. Retrieve and process output: Outputs are saved to the local filesystem (e.g. media/). Report back the completed media path to the user.

Reference Documentation

• Interactions API: All operations and state management for the Gemini Omni Flash model (gemini-omni-flash-preview) are handled via the Interactions API.
• Files API: Input media files (such as reference images and videos) must be uploaded via the Files API first before being referenced in generations. The uploaded file URI and MIME type are then included in the interactions.create input parts array.
• Interactions API Skill Reference: Platform-wide guidelines, current model specifications, and SDK usage rules for the Interactions API.

Dependencies and Prerequisites

• Python SDK (google-genai): Requires google-genai >= 2.10.0 (Python) to support the new interactions client attribute. Install or upgrade using:

  pip install -U google-genai

• Python Runtime: Requires Python >= 3.10 (for compatibility with modern google-genai SDK types and methods).
• ffmpeg & ffprobe: prep_video.py, inspect_video.py, and generate_video.py (when stripping audio via --strip-audio) require ffmpeg and ffprobe binaries installed and available in your system PATH.

Available scripts

Use the following Python scripts to upload media with the Files API, prepare input videos with ffmpeg, and generate video outputs using the Interactions API.

1. upload_file.py: Uploads local media (images and videos) to the Files API and polls until ACTIVE. If uploading a video larger than 25MB, it prints an informative warning/tip highlighting that Gemini Omni Flash is optimized for editing 10s videos at 720p/24fps, and recommends pre-processing with prep_video.py first to speed up the upload.

   ./scripts/upload_file.py path/to/image.png

2. generate_video.py: Performs end-to-end video generation and downloads the output video. It detects and uploads local media references (images or videos) before calling the Interactions API. Large video assets (>25MB) will trigger informative pre-processing recommendations without blocking the upload.

   • Text to video:

     ./scripts/video/generate_video.py "A close-up of a cat drinking tea" --output media/cat_tea.mp4

   • Image to video (first frame and reference):

     ./scripts/video/generate_video.py "The waves crash against the shore." --image reference.png --output media/waves.mp4

   • Video interpolation:

     Provide exactly two images as keyframes to generate a transition video between them:

     ./scripts/video/generate_video.py "A smooth timelapse from sunrise to sunset" --image start.png --image end.png --output media/interpolation.mp4

   • Video editing (keep original audio):

     ./scripts/video/generate_video.py "Transform the style to Japanese anime" --video input.mp4 --output media/anime_style.mp4

   • Video editing (regenerate all audio from scratch):

     ./scripts/video/generate_video.py "Transform the style to Japanese anime" --video input.mp4 --strip-audio --output media/anime_style_new_audio.mp4

   • Turn-by-turn video editing (edit previous interaction):

     Edit a prior video generation without re-uploading assets by passing the interaction ID:

     ./scripts/video/generate_video.py "Change the setting to a snowy winter wonderland." --previous-interaction-id "abc123xyz..." --output media/winter_wonderland.mp4

   • Parallel batch execution (prompts file): Run multiple prompts from a line-by-line text file concurrently:

     ./scripts/video/generate_video.py --prompts-file prompts.txt --concurrency 3

   • Parallel batch execution (JSON config): Execute fully configured, distinct generation and editing jobs in parallel:

     ./scripts/video/generate_video.py --batch jobs.json --concurrency 3

     Example jobs.json:

     [
       {
         "prompt": "Transform the style to Japanese anime.",
         "video": "input.mp4",
         "output": "media/anime_style.mp4",
         "strip_audio": false,
         "aspect_ratio": "16:9"
       },
       {
         "prompt": "A smooth timelapse from sunrise to sunset.",
         "image": ["start.png", "end.png"],
         "output": "media/interpolation.mp4"
       }
     ]

3. inspect_video.py: Inspects a local video file (using ffprobe) to check its duration, resolution, frame rate (FPS), audio stream presence, and format details.

   ./scripts/video/inspect_video.py media/output.mp4

   • To get a pre-parsed, structured JSON summary:

     ./scripts/video/inspect_video.py media/output.mp4 --json

   • To get the complete, unmodified ffprobe raw JSON dump:

     ./scripts/video/inspect_video.py media/output.mp4 --raw

4. prep_video.py: Normalizes, trims, and formats any video file to fit standard Gemini Omni Flash generation and editing limits. It handles timecode-based trimming, optional frame rate conversion, and proportional scaling of large videos (max 1280x720 for landscape, 720x1280 for portrait) to optimize upload times without stretching. If the video is longer than 10 seconds and the script is run interactively (in a TTY), it prompts the user to select the first 10s, last 10s, or enter a custom timecode (defaulting to the first 10s).

   • Trim first 10s (default):

    ./scripts/video/prep_video.py path/to/source.mp4

   or explicitly specify the start and duration:

   ./scripts/video/prep_video.py path/to/source.mp4 --start 0 --duration 10

   • Trim last 10s (automatically calculates starting point based on source length):

     ./scripts/video/prep_video.py path/to/source.mp4 --start last

   • Trim 10s starting at specific timecode (MM:SS or HH:MM:SS):

     ./scripts/video/prep_video.py path/to/source.mp4 --start 00:03 --output media/custom.mp4

   • Custom frame rate and resolution:

     ./scripts/video/prep_video.py path/to/source.mp4 --fps 30 --resolution 1920x1080

   • Strip audio for audio regeneration:

     ./scripts/video/prep_video.py path/to/source.mp4 --strip-audio --output media/video_with_no_audio.mp4

Using tags in prompts to set image roles

You can use tags in your prompt to make it clear whether each uploaded media is an initial frame or a reference.

1. Simple tags (recommended)

For simple cases where image roles are clear from the prompt, you can bind images to roles directly:

• <FIRST_FRAME>: Use the image as the starting frame of the video, for example: <FIRST_FRAME> a woman is walking
• <IMAGE_REF_N>: Use the image as a reference, for example: in the style of <IMAGE_REF_0> a woman <IMAGE_REF_1> is walking (combines style reference from the first image and subject reference from the second image). Image references start from 0.

An example with 6 reference images:

[0-3s] A studio fashion sequence. Starting with woman <IMAGE_REF_0>, she is holding <IMAGE_REF_1>
[3-6s] Then we see the man <IMAGE_REF_2> holding <IMAGE_REF_3>
[6-10s] And finally another woman <IMAGE_REF_4> who is holding <IMAGE_REF_5> while walking.

2. Explicitly declare sources and references

For more complex cases with multiple images and multiple roles, you can use explicit prefix tags paired with natural language instruction suffixes.

• Declaring sources and reference images:

  • [# Sources <FIRST_FRAME>@Image1] will use the first image as the starting frame.
  • [# References <IMAGE_REF_0>@Image1] will use the first image as a reference.
  • [# References <IMAGE_REF_1>@Image2] will use the second image as a reference.
  • [# References <IMAGE_REF_0>@Image1 <IMAGE_REF_1>@Image2] will use both images as references.
  • [# Sources <FIRST_FRAME>@Image1] [# References <IMAGE_REF_0>@Image2] will use the first image as the starting frame and the second image as a reference.

• Guiding instructions: Add guiding instructions at the end of your prompt:

  • For starting frame: "Use the given image as the starting frame."
  • For reference images: "Use the given image(s) as references for video generation. The images should not be used as literal initial frames."

• Example Expanded Prompt:

  [# Sources <FIRST_FRAME>@Image1] [# References <IMAGE_REF_0>@Image2] a woman <IMAGE_REF_0> is walking. Use Image1 as the starting frame. Use Image2 as a reference for the video generation.