Jiaqian LiuandCanlin Guo
8097747a5d
Signed-off-by: Celeste-jq <591998922@qq.com> Co-authored-by: Canlin Guo <canlinguosdu@gmail.com>
81 lines
3.4 KiB
Markdown
81 lines
3.4 KiB
Markdown
# Image-To-Video
|
||
|
||
This example demonstrates how to generate videos from images using Wan2.2 Image-to-Video models with vLLM-Omni's offline inference API.
|
||
|
||
## Local CLI Usage
|
||
|
||
Download the example image:
|
||
|
||
```bash
|
||
wget https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg
|
||
```
|
||
|
||
### Wan2.2-I2V-A14B-Diffusers (MoE)
|
||
|
||
```bash
|
||
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)
|
||
|
||
```bash
|
||
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
|
||
```
|
||
|
||
Key arguments:
|
||
|
||
- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V).
|
||
- `--image`: Path to input image (required).
|
||
- `--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 81).
|
||
- `--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).
|
||
- `--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`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism/cfg_parallel.md).
|
||
- `--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](../../../docs/user_guide/diffusion/lora.md#wan22-lightx2v-offline-assembly).
|