Wan2.1-I2V-14B-720P - AI Video Models Tool

Overview

Wan2.1-I2V-14B-720P is an image-to-video generative model in the Wan2.1 family from Wan-AI, hosted on Hugging Face. It produces high‑definition 720P videos from single or multiple input images and supports text-conditioned video synthesis. The model card describes multi-task capabilities including text-to-video generation, video editing (image-guided motion and temporal editing), and visual-text generation with bilingual (Chinese and English) prompt support. According to the Hugging Face model page, the release targets accessibility by being optimized for consumer‑grade GPUs while delivering HD (1280×720) outputs (Wan-AI/Wan2.1-I2V-14B-720P on Hugging Face). The model is distributed under the Apache-2.0 license and is available for direct use via the Hugging Face model hub. The Hugging Face page indicates active community interest (downloads and likes) and lists the pipeline type as image-to-video. Implementation details such as exact parameter count are not published on the model card; the repository instead focuses on usage examples, supported tasks, and performance claims. For developers and creators seeking a locally runnable HD image-to-video model for prototyping and consumer-level deployment, Wan2.1-I2V-14B-720P aims to balance fidelity and hardware practicality.

Model Statistics

• Downloads: 10,896
• Likes: 555
• Pipeline: image-to-video

License: apache-2.0

Model Details

Architecture and capabilities: Wan2.1-I2V-14B-720P is an image-to-video generation model in the Wan2.1 suite designed to extend single-image inputs into coherent short videos. The model supports multiple modes of operation shown on the model page: text-to-video (combine an image with a textual prompt to produce motion consistent with the prompt), image-guided video editing (apply temporal edits and inpainting across frames), and visual-text generation (generating or recognizing text overlays in Chinese and English). The model card lists the pipeline type as image-to-video and marks a target output resolution of 720P (1280×720). Implementation notes: The model is hosted on Hugging Face and released under the Apache-2.0 license. The model card does not specify a base pre-trained transformer or an exact parameter count. Wan-AI indicates the build was optimized for consumer-grade GPUs to make HD generation feasible for non-data-center setups. For inference, the model can be accessed through the Hugging Face hub (repository Wan-AI/Wan2.1-I2V-14B-720P) and used via standard hub clients or the Hugging Face Inference API. For detailed usage patterns, consult the model page on Hugging Face for code snippets and recommended runtime settings.

Key Features

• Image-to-video generation that converts single images into short 720P video clips.
• Text-conditioned synthesis: combine image inputs with English or Chinese prompts.
• Video editing: image-guided temporal edits and inpainting across generated frames.
• Optimized for consumer-grade GPUs for HD (720P) inference.
• Distributed under Apache-2.0 license on Hugging Face for easy access.

Example Usage

Example (python):

from huggingface_hub import InferenceApi
import base64

# Replace with your HF token if needed for rate limits or private models
HF_TOKEN = None  # or 'hf_...'
model_id = "Wan-AI/Wan2.1-I2V-14B-720P"

# Initialize the inference client
client = InferenceApi(repo_id=model_id, token=HF_TOKEN, pipeline="image-to-video")

# Prepare inputs: an image file and an optional text prompt
image_path = "input.jpg"
prompt = "A calm park scene where the trees gently sway and sunlight shifts across the path"

with open(image_path, "rb") as f:
    image_bytes = f.read()

# The exact input keys accepted by the hosted model may vary; common pattern shown here
inputs = {
    "image": image_bytes,
    "prompt": prompt,
    "max_length_seconds": 4,   # optional parameter examples; model-specific
    "resolution": "720p"
}

# Run inference (the return type may be bytes, base64 string, or JSON depending on the model)
result = client(inputs)

# If the model returns raw bytes for an MP4, save them. If it returns base64, decode first.
# The following tries to handle both common cases.
if isinstance(result, (bytes, bytearray)):
    with open("output.mp4", "wb") as out:
        out.write(result)
    print("Saved output.mp4")
elif isinstance(result, dict) and "video" in result:
    video_data = result["video"]
    if isinstance(video_data, str):
        # assume base64-encoded
        decoded = base64.b64decode(video_data)
        with open("output.mp4", "wb") as out:
            out.write(decoded)
        print("Saved output.mp4 (decoded)")
    else:
        print("Received structured response; inspect result keys:", result.keys())
else:
    print("Unexpected response format; inspect 'result' variable")

Benchmarks

Hugging Face downloads: 10,896 (Source: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)

Hugging Face likes: 555 (Source: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)

Target output resolution: 1280×720 (720P) (Source: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)

Key Information

• Category: Video Models
• Type: AI Video Models Tool