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Wan2.1-T2V-14B - AI Video Models Tool

Overview

Wan2.1-T2V-14B is an advanced text-to-video generation model released on Hugging Face that targets high-quality short-video synthesis and editing workflows. According to the model repository on Hugging Face, the model supports both 480P and 720P output modes and is designed to handle text-to-video, image-to-video, and video-editing tasks, including the generation of on-screen multilingual text in Chinese and English (https://huggingface.co/Wan-AI/Wan2.1-T2V-14B). The project provides practical instructions for single- and multi-GPU inference, prompt extension strategies, and direct integration with popular tooling such as Diffusers and ComfyUI. Wan2.1-T2V-14B is part of the Wan2.1 suite and emphasizes an end-to-end workflow for creators and researchers: compose a textual prompt (or supply a reference image/video), run inference on GPU(s), and produce a ready-to-play video. The model card and repository include step-by-step guidance for deploying on one or multiple GPUs, and the package is distributed under an Apache-2.0 license. Community adoption is measurable on Hugging Face by the repository statistics (tens of thousands of downloads and over a thousand likes), indicating active usage and experimentation in the space.

Model Statistics

Downloads: 34,291
Likes: 1446
Pipeline: text-to-video

License: apache-2.0

Model Details

Architecture and scale: Wan2.1-T2V-14B is presented as a 14B-class text-to-video model in the Wan2.1 family. The official Hugging Face model page does not publish a detailed parameter breakdown or full architecture diagram; the published card notes the model name and capabilities but lists parameter count as unknown on the model page (https://huggingface.co/Wan-AI/Wan2.1-T2V-14B). Capabilities: The model supports text-to-video generation, image-to-video conversion (single-image animation/extension), and video editing (inpainting/attribute editing workflows as described in the repository). It can render multilingual in-video text, explicitly supporting Chinese and English. Output resolution modes documented by the authors include 480P and 720P. Deployment & tooling: The repository supplies guidance for single- and multi-GPU inference, and shows example integrations with Diffusers and ComfyUI pipelines. The model is distributed under the Apache-2.0 license, enabling broad reuse and modification. For hardware, the authors document multi-GPU inference patterns (pipeline sharding and memory optimizations) though exact GPU-memory footprints depend on configuration and are not published in the model card.

Key Features

Text-to-video generation supporting cinematic prompts and motion-aware outputs
Supports both 480P and 720P output modes for faster or higher-quality renders
Image-to-video: extend or animate a single image into a short video sequence
Video editing and inpainting workflows for frame-level corrections and attribute edits
Generates on-screen multilingual text (Chinese and English) within videos
Repository includes single- and multi-GPU inference instructions and optimization tips
Integrates with Diffusers and ComfyUI for pipeline orchestration and GUI-based workflows

Example Usage

Example (python):

from diffusers import DiffusionPipeline
import torch

# Load the model (this example assumes the repository provides a Diffusers-compatible pipeline)
# Replace device and model name as needed; multi-GPU / optimization flags may be required for large models.
model_id = "Wan-AI/Wan2.1-T2V-14B"

pipe = DiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to("cuda")

prompt = "A cinematic timelapse of a futuristic city at sunset, filmic lighting, fluid camera motion"

# Basic generation parameters; the exact argument names may vary by pipeline implementation.
result = pipe(prompt, num_inference_steps=30, guidance_scale=7.5)

# result may provide a video file, list of frames, or a tensor depending on the pipeline.
# This example assumes the pipeline returns a sequence of frames as a NumPy array or PIL images.
outputs = getattr(result, "videos", None) or getattr(result, "frames", None) or result

# Simple saving helper (adapts to returned format)
import imageio

if hasattr(outputs, "__len__") and len(outputs) > 0:
    # If outputs is a list/array of frames
    frames = outputs
    imageio.mimwrite("output.mp4", frames, fps=24, macro_block_size=1)
else:
    # If pipeline already returned a file-like object or filepath
    try:
        result.save("output.mp4")
    except Exception:
        print("Check the pipeline output type and adapt saving logic accordingly.")

print("Saved output to output.mp4")

# Note: For multi-GPU inference and memory optimization, consult the model repository
# for recommended flags, Offload/Accelerate recipes, or ComfyUI connectors.