vllm-omni/examples/offline_inference/image_to_video

Image-To-Video

This example demonstrates how to generate videos from images (image-to-video) using Wan2.2, LTX2, HunyuanVideo-1.5, and Cosmos3 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

Cosmos3

# Cosmos3 bundles example frames under assets/ (any RGB image works too):
python image_to_video.py \
  --model nvidia/Cosmos3-Nano \
  --image /path/to/Cosmos3-Nano/assets/example_i2v_input.jpg \
  --prompt "The scene comes to life with smooth, natural motion." \
  --negative-prompt "blurry, distorted, low quality" \
  --height 720 --width 1280 --num-frames 189 --fps 24 \
  --num-inference-steps 35 --guidance-scale 6.0 \
  --extra-body '{"flow_shift": 10.0, "max_sequence_length": 4096, "guardrails": false}' \
  --output cosmos3_i2v.mp4

Key arguments:

• --model: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V).
• --image: Path to input image (required).
• --extra-body: JSON object of model-specific generation params, filtered against the model's declared extra_body_params (see vllm_omni/model_extras). Used by Cosmos3.
• --prompt: Text description of desired motion/animation.
• --height/--width: Output resolution (auto-calculated from image if not set). Dimensions should be multiples of 16.
• --num-frames: Number of frames (default: model-specific — Wan 81, LTX2 121, Cosmos3 189).
• --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 (default: model-specific — Wan/LTX2 5.0, Cosmos3 10.0).
• --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: model-specific — Wan 50, LTX2 40, Cosmos3 35).
• --fps: Frames per second for the saved MP4 (requires diffusers export_to_video).
• --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.