Image-To-Video¶

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

This shared example generates videos from images with VACE, Wan2.2, LTX2, HunyuanVideo-1.5, Cosmos3, and other compatible pipelines.

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 the first-frame or source image.
--last-image: Optional last-frame condition for models such as VACE.
--mask-image: Optional inpainting mask. White pixels are regenerated and black pixels are preserved.
--reference-image: Optional reference image; repeat it to provide multiple references.
--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.

Wan2.1 VACE Conditional Tasks¶

The shared script selects the VACE conditioning structure from the media inputs. No explicit mode parameter is required: the script constructs the source video, mask, or reference images consumed by the VACE pipeline. Download the same Hugging Face assets used by the original VACE example:

wget -O astronaut.jpg https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg
wget -O vace_first_frame.png https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png
wget -O vace_last_frame.png https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png

Image-to-Video (I2V)¶

python image_to_video.py \
  --model Wan-AI/Wan2.1-VACE-1.3B-diffusers \
  --image astronaut.jpg \
  --prompt "An astronaut emerging from a cracked, otherworldly egg on the surface of the moon" \
  --seed 42 --height 480 --width 832 --num-frames 81 \
  --num-inference-steps 30 --guidance-scale 5.0 --flow-shift 5.0 \
  --vae-use-tiling --output vace_i2v_output.mp4

Video-to-Last-Frame (V2LF)¶

python image_to_video.py \
  --model Wan-AI/Wan2.1-VACE-1.3B-diffusers \
  --last-image astronaut.jpg \
  --prompt "An astronaut emerging from a cracked, otherworldly egg on the surface of the moon" \
  --seed 42 --height 480 --width 832 --num-frames 81 \
  --num-inference-steps 30 --guidance-scale 5.0 --flow-shift 5.0 \
  --vae-use-tiling --output vace_v2lf_output.mp4

First-Last-Frame-to-Video (FLF2V)¶

python image_to_video.py \
  --model Wan-AI/Wan2.1-VACE-1.3B-diffusers \
  --image vace_first_frame.png \
  --last-image vace_last_frame.png \
  --prompt "CG animation style, a small blue bird takes off from a branch and lands on another branch" \
  --seed 42 --height 512 --width 512 --num-frames 81 \
  --num-inference-steps 30 --guidance-scale 5.0 --flow-shift 5.0 \
  --vae-use-tiling --output vace_flf2v_output.mp4

Inpainting¶

Create a mask matching the original VACE example: a 160-pixel-wide white vertical stripe marks the region to regenerate.

python - <<'PY'
from PIL import Image

mask = Image.new("L", (832, 480), 0)
mask.paste(255, (336, 0, 496, 480))
mask.save("vace_center_mask.png")
PY

python image_to_video.py \
  --model Wan-AI/Wan2.1-VACE-1.3B-diffusers \
  --image astronaut.jpg \
  --mask-image vace_center_mask.png \
  --prompt "Shrek, the ogre, walks out of a building in a happy mood" \
  --seed 42 --height 480 --width 832 --num-frames 81 \
  --num-inference-steps 30 --guidance-scale 5.0 --flow-shift 5.0 \
  --vae-use-tiling --output vace_inpaint_output.mp4

Reference-to-Video (R2V)¶

Repeat --reference-image to provide more than one reference image.

python image_to_video.py \
  --model Wan-AI/Wan2.1-VACE-1.3B-diffusers \
  --reference-image astronaut.jpg \
  --prompt "Camera slowly zooms out from the character walking in a garden" \
  --seed 42 --height 480 --width 832 --num-frames 81 \
  --num-inference-steps 30 --guidance-scale 5.0 --flow-shift 5.0 \
  --vae-use-tiling --output vace_r2v_output.mp4

The VACE T2V command is documented in the shared text_to_video.py example.

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

Example materials¶

image_to_video.py

Large file omitted from the rendered docs. View it on GitHub: https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/image_to_video/image_to_video.py.