vllm-omni/docs/user_guide/examples/offline_inference/image_to_video.md

Image-To-Video

Source https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_video.

This example demonstrates how to generate videos from images using Wan2.2, LTX-2, HunyuanVideo-1.5, and SANA-Video Image-to-Video models with vLLM-Omni's offline inference API.

Local CLI Usage

Download the example image:

wget https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg

Wan2.2-I2V-A14B-Diffusers (MoE)

python image_to_video.py \
  --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \
  --image cherry_blossom.jpg \
  --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
  --negative-prompt "<optional quality filter>" \
  --height 480 \
  --width 832 \
  --num-frames 48 \
  --guidance-scale 5.0 \
  --guidance-scale-high 6.0 \
  --num-inference-steps 40 \
  --boundary-ratio 0.875 \
  --flow-shift 12.0 \
  --fps 16 \
  --output i2v_output.mp4

Wan2.2-TI2V-5B-Diffusers (Unified)

python image_to_video.py \
  --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
  --image cherry_blossom.jpg \
  --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
  --negative-prompt "<optional quality filter>" \
  --height 480 \
  --width 832 \
  --num-frames 48 \
  --guidance-scale 4.0 \
  --num-inference-steps 40 \
  --flow-shift 12.0 \
  --fps 16 \
  --output i2v_output.mp4

LTX-2

python image_to_video.py \
  --model Lightricks/LTX-2 \
  --image cherry_blossom.jpg \
  --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
  --output ltx2_i2v_output.mp4

See the LTX-2 recipe for all checkpoints, pipeline selection, T2V, defaults, and advanced options.

SANA-Video-2B

SANA checkpoints identify their upstream T2V pipeline in model_index.json. Select the native I2V implementation explicitly:

python image_to_video.py \
  --model Efficient-Large-Model/SANA-Video_2B_480p_diffusers \
  --model-class-name SanaImageToVideoPipeline \
  --image cherry_blossom.jpg \
  --prompt "Cherry blossoms sway in the breeze as petals drift past the camera." \
  --negative-prompt "blurry, low quality, temporal artifacts" \
  --height 480 \
  --width 832 \
  --num-frames 81 \
  --num-inference-steps 50 \
  --guidance-scale 6.0 \
  --extra-body '{"motion_score": 30}' \
  --fps 16 \
  --seed 42 \
  --output sana_video_i2v_480p.mp4

For the 720p checkpoint, use Efficient-Large-Model/SANA-Video_2B_720p_diffusers with --height 704 --width 1280. The native I2V path supports both checkpoint variants: the 480p checkpoint uses the Wan VAE, while the 720p checkpoint uses the LTX-2 Video VAE.

For SANA-Video, 81 frames at 16 FPS is the standard checkpoint request (approximately five seconds), not minute-scale long-video generation. Minute-scale SANA generation requires the separate LongSANA/LongLive autoregressive workflow, which this pipeline does not implement.

See the SANA-Video recipe for online serving, backend boundaries, and the tested hardware profile.

Key arguments:

• --model: Model ID or local path (for example Wan I2V/TI2V, LTX-2, or SANA-Video).
• --model-class-name: Explicit pipeline override. SANA-Video I2V requires SanaImageToVideoPipeline.
• --image: Path to input image (required).
• --prompt: Text description of desired motion/animation.
• --height/--width: Output resolution (auto-calculated from image if not set). Wan dimensions should be multiples of 16; LTX dimensions should be multiples of 32.
• --num-frames: Number of frames (model-specific default; LTX-style models work best with 8k + 1).
• --guidance-scale and --guidance-scale-high: CFG scale (applied to low/high-noise stages for MoE).
• --negative-prompt: Optional list of artifacts to suppress.
• --boundary-ratio: Boundary split ratio for two-stage MoE models.
• --flow-shift: Scheduler flow shift (5.0 for 720p, 12.0 for 480p).
• --sample-solver: Wan2.2 sampling solver. Use unipc for the default multistep solver, or euler for Lightning/Distill checkpoints.
• --num-inference-steps: Number of denoising steps (default 50).
• --fps: Frames per second for the saved MP4 (requires diffusers export_to_video).
• --audio-sample-rate: fallback audio sample rate for embedded audio.
• --output: Path to save the generated video.
• --vae-use-slicing: Enable VAE slicing for memory optimization.
• --vae-use-tiling: Enable VAE tiling for memory optimization.
• --cfg-parallel-size: set it to 2 to enable CFG Parallel. See more examples in user_guide.
• --tensor-parallel-size: tensor parallel size (effective for models that support TP, e.g. LTX2).
• --enable-cpu-offload: enable CPU offloading for diffusion models.
• --use-hsdp: Enable Hybrid Sharded Data Parallel to shard model weights across GPUs.
• --hsdp-shard-size: Number of GPUs to shard model weights across within each replica group. -1 (default) auto-calculates as world_size / replicate_size.
• --hsdp-replicate-size: Number of replica groups for HSDP. Each replica holds a full sharded copy. Default 1 means pure sharding (no replication).

ℹ️ If you encounter OOM errors, try using --vae-use-slicing and --vae-use-tiling to reduce memory usage.

For Wan2.2 LightX2V-converted local Diffusers directories and related LoRA assets, see the LoRA guide.

Example materials

??? abstract "image_to_video.py" py --8<-- "examples/offline_inference/image_to_video/image_to_video.py"