Wan2.1 Video

Generate videos with Alibaba's Wan2.1 model on Clore.ai GPUs

Generate high-quality videos with Alibaba's Wan2.1 text-to-video and image-to-video models on CLORE.AI GPUs.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Why Wan2.1?

• High quality - State-of-the-art video generation

• Multiple modes - Text-to-video, image-to-video

• Various sizes - 1.3B to 14B parameters

• Long videos - Up to 81 frames

• Open weights - Apache 2.0 license

Model Variants

Model	Parameters	VRAM	Resolution	Frames
Wan2.1-T2V-1.3B	1.3B	8GB	480p	81
Wan2.1-T2V-14B	14B	24GB	720p	81
Wan2.1-I2V-14B	14B	24GB	720p	81

Quick Deploy on CLORE.AI

Docker Image:

pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel

Ports:

22/tcp
7860/http

Command:

pip install diffusers transformers accelerate gradio && \
python -c "
import gradio as gr
import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video

pipe = WanPipeline.from_pretrained('alibaba-pai/Wan2.1-T2V-1.3B', torch_dtype=torch.float16)
pipe.to('cuda')
pipe.enable_model_cpu_offload()

def generate(prompt, steps, frames, seed):
    generator = torch.Generator('cuda').manual_seed(seed) if seed > 0 else None
    output = pipe(prompt, num_frames=frames, num_inference_steps=steps, generator=generator)
    export_to_video(output.frames[0], 'output.mp4', fps=16)
    return 'output.mp4'

gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(label='Prompt'),
        gr.Slider(20, 100, value=50, label='Steps'),
        gr.Slider(16, 81, value=49, step=8, label='Frames'),
        gr.Number(value=-1, label='Seed')
    ],
    outputs=gr.Video(),
    title='Wan2.1 - Text to Video'
).launch(server_name='0.0.0.0', server_port=7860)
"

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Hardware Requirements

Model	Minimum GPU	Recommended	Optimal
1.3B T2V	RTX 3070 8GB	RTX 3090 24GB	RTX 4090
14B T2V	RTX 4090 24GB	A100 40GB	A100 80GB
14B I2V	RTX 4090 24GB	A100 40GB	A100 80GB

Installation

pip install diffusers transformers accelerate torch

Text-to-Video

Basic Usage (1.3B)

import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video

pipe = WanPipeline.from_pretrained(
    "alibaba-pai/Wan2.1-T2V-1.3B",
    torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.enable_model_cpu_offload()

prompt = "A cat playing with a ball in a sunny garden"

output = pipe(
    prompt=prompt,
    num_frames=49,
    num_inference_steps=50,
    guidance_scale=7.0
)

export_to_video(output.frames[0], "cat_video.mp4", fps=16)

High Quality (14B)

import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video

pipe = WanPipeline.from_pretrained(
    "alibaba-pai/Wan2.1-T2V-14B",
    torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()

prompt = "Cinematic shot of a dragon flying over mountains at sunset, 4K, detailed"

output = pipe(
    prompt=prompt,
    negative_prompt="blurry, low quality, distorted",
    num_frames=81,
    height=720,
    width=1280,
    num_inference_steps=50,
    guidance_scale=7.0
)

export_to_video(output.frames[0], "dragon.mp4", fps=24)

Image-to-Video

Animate an Image

import torch
from diffusers import WanI2VPipeline
from diffusers.utils import load_image, export_to_video

pipe = WanI2VPipeline.from_pretrained(
    "alibaba-pai/Wan2.1-I2V-14B",
    torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.enable_model_cpu_offload()

# Load input image
image = load_image("input.jpg")

prompt = "The person in the image starts walking forward"

output = pipe(
    prompt=prompt,
    image=image,
    num_frames=49,
    num_inference_steps=50,
    guidance_scale=7.0
)

export_to_video(output.frames[0], "animated.mp4", fps=16)

Image-to-Video with Wan2.1-I2V-14B

Wan2.1-I2V-14B animates a static image using a text prompt to guide the motion. Requires 24GB VRAM (RTX 4090 or A100 40GB recommended).

Model Details

Property	Value
Model ID	Wan-AI/Wan2.1-I2V-14B-480P
Parameters	14 Billion
VRAM Required	24GB
Max Resolution	480p (854×480) or 720p (1280×720)
Max Frames	81
License	Apache 2.0

Hardware Requirements

GPU	VRAM	Status
RTX 4090	24GB	✅ Recommended
RTX 3090	24GB	✅ Supported
A100 40GB	40GB	✅ Optimal
A100 80GB	80GB	✅ Best quality
RTX 3080	10GB	❌ Insufficient

Quick CLI Script

Save as generate_i2v.py and run:

python generate_i2v.py --model Wan-AI/Wan2.1-I2V-14B-480P --image input.jpg --prompt "camera slowly zooms out"

generate_i2v.py — Full Script

#!/usr/bin/env python3
"""
Wan2.1 Image-to-Video CLI script.
Usage: python generate_i2v.py --model Wan-AI/Wan2.1-I2V-14B-480P \
           --image input.jpg --prompt "camera slowly zooms out"
"""

import argparse
import os
import sys
import torch
from diffusers import WanImageToVideoPipeline
from diffusers.utils import load_image, export_to_video
from PIL import Image


def parse_args():
    parser = argparse.ArgumentParser(description="Wan2.1 Image-to-Video Generator")
    parser.add_argument(
        "--model",
        type=str,
        default="Wan-AI/Wan2.1-I2V-14B-480P",
        help="Model ID from Hugging Face (default: Wan-AI/Wan2.1-I2V-14B-480P)",
    )
    parser.add_argument(
        "--image",
        type=str,
        required=True,
        help="Path to input image (JPEG or PNG)",
    )
    parser.add_argument(
        "--prompt",
        type=str,
        required=True,
        help='Text prompt describing the desired motion (e.g. "camera slowly zooms out")',
    )
    parser.add_argument(
        "--negative-prompt",
        type=str,
        default="blurry, low quality, distorted, jerky motion, artifacts",
        help="Negative prompt to avoid unwanted artifacts",
    )
    parser.add_argument(
        "--frames",
        type=int,
        default=49,
        help="Number of video frames to generate (default: 49, max: 81)",
    )
    parser.add_argument(
        "--steps",
        type=int,
        default=50,
        help="Number of diffusion steps (default: 50)",
    )
    parser.add_argument(
        "--guidance",
        type=float,
        default=7.0,
        help="Classifier-free guidance scale (default: 7.0)",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=-1,
        help="Random seed for reproducibility (-1 = random)",
    )
    parser.add_argument(
        "--fps",
        type=int,
        default=16,
        help="Output video FPS (default: 16)",
    )
    parser.add_argument(
        "--output",
        type=str,
        default="output_i2v.mp4",
        help="Output video file path (default: output_i2v.mp4)",
    )
    parser.add_argument(
        "--height",
        type=int,
        default=480,
        help="Output video height in pixels (default: 480)",
    )
    parser.add_argument(
        "--width",
        type=int,
        default=854,
        help="Output video width in pixels (default: 854)",
    )
    parser.add_argument(
        "--cpu-offload",
        action="store_true",
        default=True,
        help="Enable model CPU offloading to save VRAM (default: True)",
    )
    parser.add_argument(
        "--vae-tiling",
        action="store_true",
        default=False,
        help="Enable VAE tiling for high-resolution outputs",
    )
    return parser.parse_args()


def load_and_resize_image(image_path: str, width: int, height: int) -> Image.Image:
    """Load image from path and resize to target dimensions."""
    if not os.path.exists(image_path):
        print(f"[ERROR] Image not found: {image_path}", file=sys.stderr)
        sys.exit(1)

    img = Image.open(image_path).convert("RGB")
    original_size = img.size
    img = img.resize((width, height), Image.LANCZOS)
    print(f"[INFO] Loaded image: {image_path} ({original_size[0]}x{original_size[1]}) → resized to {width}x{height}")
    return img


def load_pipeline(model_id: str, cpu_offload: bool, vae_tiling: bool):
    """Load the Wan I2V pipeline with memory optimizations."""
    print(f"[INFO] Loading model: {model_id}")
    print(f"[INFO] CUDA available: {torch.cuda.is_available()}")
    if torch.cuda.is_available():
        vram_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
        print(f"[INFO] GPU: {torch.cuda.get_device_name(0)} ({vram_gb:.1f} GB VRAM)")
        if vram_gb < 23:
            print("[WARN] Less than 24GB VRAM detected — enable --cpu-offload or use the 1.3B model")

    pipe = WanImageToVideoPipeline.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
    )

    if cpu_offload:
        print("[INFO] Enabling model CPU offload")
        pipe.enable_model_cpu_offload()
    else:
        pipe.to("cuda")

    if vae_tiling:
        print("[INFO] Enabling VAE tiling for high-res generation")
        pipe.enable_vae_tiling()

    return pipe


def generate_video(pipe, args) -> None:
    """Run the I2V pipeline and save the output video."""
    image = load_and_resize_image(args.image, args.width, args.height)

    generator = None
    if args.seed >= 0:
        generator = torch.Generator("cuda").manual_seed(args.seed)
        print(f"[INFO] Using seed: {args.seed}")
    else:
        print("[INFO] Using random seed")

    print(f"[INFO] Generating {args.frames} frames at {args.width}x{args.height}")
    print(f"[INFO] Steps: {args.steps} | Guidance: {args.guidance} | FPS: {args.fps}")
    print(f"[INFO] Prompt: {args.prompt}")

    output = pipe(
        prompt=args.prompt,
        negative_prompt=args.negative_prompt,
        image=image,
        num_frames=args.frames,
        height=args.height,
        width=args.width,
        num_inference_steps=args.steps,
        guidance_scale=args.guidance,
        generator=generator,
    )

    export_to_video(output.frames[0], args.output, fps=args.fps)
    print(f"[INFO] Video saved to: {os.path.abspath(args.output)}")
    duration = args.frames / args.fps
    print(f"[INFO] Duration: {duration:.1f}s at {args.fps}fps ({args.frames} frames)")


def main():
    args = parse_args()

    if not torch.cuda.is_available():
        print("[ERROR] CUDA GPU not found. Wan2.1-I2V-14B requires a CUDA-capable GPU.", file=sys.stderr)
        sys.exit(1)

    pipe = load_pipeline(args.model, args.cpu_offload, args.vae_tiling)
    generate_video(pipe, args)
    print("[DONE] Image-to-video generation complete!")


if __name__ == "__main__":
    main()

Advanced I2V Pipeline (Python API)

import torch
from diffusers import WanImageToVideoPipeline
from diffusers.utils import load_image, export_to_video
from PIL import Image

# ── Load pipeline ──────────────────────────────────────────────────────────────
pipe = WanImageToVideoPipeline.from_pretrained(
    "Wan-AI/Wan2.1-I2V-14B-480P",
    torch_dtype=torch.float16,
)
pipe.enable_model_cpu_offload()   # keeps VRAM under 24GB
pipe.enable_vae_tiling()          # optional: helps for 720p

# ── Load & prepare input image ─────────────────────────────────────────────────
image = load_image("input.jpg").resize((854, 480))

# ── Generate ───────────────────────────────────────────────────────────────────
prompt = "camera slowly zooms out, revealing the full landscape"
negative_prompt = "blurry, low quality, distorted, flickering, artifacts"

generator = torch.Generator("cuda").manual_seed(42)

output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    image=image,
    num_frames=49,          # ~3 seconds at 16fps
    height=480,
    width=854,
    num_inference_steps=50,
    guidance_scale=7.5,
    generator=generator,
)

export_to_video(output.frames[0], "i2v_output.mp4", fps=16)
print("Saved: i2v_output.mp4")

I2V Prompt Tips

Goal	Prompt Example
Camera movement	"camera slowly zooms out from the subject"
Parallax effect	"subtle parallax motion, depth of field shift"
Character animation	"the figure turns their head and smiles"
Nature animation	"leaves rustle in a gentle breeze, light shifts"
Abstract motion	"colors swirl and blend, fluid motion"

Memory Tips for I2V (24GB GPUs)

# Mandatory on 24GB GPUs
pipe.enable_model_cpu_offload()

# Optional: reduces peak VRAM ~10%
pipe.enable_vae_tiling()
pipe.enable_vae_slicing()

# Clear between runs
import gc
gc.collect()
torch.cuda.empty_cache()

Prompt Examples

Nature & Landscapes

prompts = [
    "Time-lapse of clouds moving over mountain peaks, dramatic lighting",
    "Ocean waves crashing on rocks, slow motion, cinematic",
    "Northern lights dancing in the night sky, vibrant colors",
    "Forest in autumn with leaves falling, peaceful atmosphere"
]

Animals & Characters

prompts = [
    "A golden retriever running through a field of flowers",
    "A butterfly emerging from its cocoon, macro shot",
    "Samurai warrior drawing sword, dramatic lighting",
    "Robot walking through futuristic city streets"
]

Abstract & Artistic

prompts = [
    "Colorful paint swirling in water, abstract art",
    "Geometric shapes transforming and morphing, neon colors",
    "Ink drops spreading in milk, macro photography"
]

Advanced Settings

Quality vs Speed

# Fast preview
output = pipe(
    prompt=prompt,
    num_frames=17,
    num_inference_steps=25,
    guidance_scale=5.0
)

# Balanced
output = pipe(
    prompt=prompt,
    num_frames=49,
    num_inference_steps=50,
    guidance_scale=7.0
)

# Maximum quality
output = pipe(
    prompt=prompt,
    num_frames=81,
    num_inference_steps=100,
    guidance_scale=7.5
)

Resolution Options

# 480p (1.3B model)
output = pipe(prompt, height=480, width=854, num_frames=49)

# 720p (14B model)
output = pipe(prompt, height=720, width=1280, num_frames=49)

# 1080p (14B model, high VRAM)
output = pipe(prompt, height=1080, width=1920, num_frames=33)

Batch Generation

import os
import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video

pipe = WanPipeline.from_pretrained("alibaba-pai/Wan2.1-T2V-1.3B", torch_dtype=torch.float16)
pipe.to("cuda")
pipe.enable_model_cpu_offload()

prompts = [
    "A rocket launching into space",
    "Fish swimming in coral reef",
    "Rain falling on a city street at night"
]

output_dir = "./videos"
os.makedirs(output_dir, exist_ok=True)

for i, prompt in enumerate(prompts):
    print(f"Generating {i+1}/{len(prompts)}: {prompt[:40]}...")

    output = pipe(
        prompt=prompt,
        num_frames=49,
        num_inference_steps=50
    )

    export_to_video(output.frames[0], f"{output_dir}/video_{i:03d}.mp4", fps=16)
    torch.cuda.empty_cache()

Gradio Interface

import gradio as gr
import torch
from diffusers import WanPipeline
from diffusers.utils import export_to_video
import tempfile

pipe = WanPipeline.from_pretrained("alibaba-pai/Wan2.1-T2V-1.3B", torch_dtype=torch.float16)
pipe.to("cuda")
pipe.enable_model_cpu_offload()

def generate_video(prompt, negative_prompt, frames, steps, guidance, seed):
    generator = torch.Generator("cuda").manual_seed(seed) if seed > 0 else None

    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_frames=frames,
        num_inference_steps=steps,
        guidance_scale=guidance,
        generator=generator
    )

    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as f:
        export_to_video(output.frames[0], f.name, fps=16)
        return f.name

demo = gr.Interface(
    fn=generate_video,
    inputs=[
        gr.Textbox(label="Prompt", lines=2),
        gr.Textbox(label="Negative Prompt", value="blurry, low quality"),
        gr.Slider(17, 81, value=49, step=8, label="Frames"),
        gr.Slider(20, 100, value=50, step=5, label="Steps"),
        gr.Slider(3, 12, value=7, step=0.5, label="Guidance"),
        gr.Number(value=-1, label="Seed")
    ],
    outputs=gr.Video(label="Generated Video"),
    title="Wan2.1 - Text to Video Generation",
    description="Generate videos from text prompts. Running on CLORE.AI."
)

demo.launch(server_name="0.0.0.0", server_port=7860)

Memory Optimization

# Enable all optimizations
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()
pipe.enable_vae_slicing()

# For very low VRAM
pipe.enable_sequential_cpu_offload()

# Clear cache between generations
torch.cuda.empty_cache()

Performance

Model	Resolution	Frames	GPU	Time
1.3B	480p	49	RTX 4090	~2 min
1.3B	480p	49	A100 40GB	~1.5 min
14B	720p	49	A100 40GB	~5 min
14B	720p	81	A100 80GB	~8 min

Cost Estimate

Typical CLORE.AI marketplace rates:

GPU	Hourly Rate	~49 frame videos/hour
RTX 3090 24GB	~$0.06	~20 (1.3B)
RTX 4090 24GB	~$0.10	~30 (1.3B)
A100 40GB	~$0.17	~40 (1.3B) / ~12 (14B)
A100 80GB	~$0.25	~8 (14B high-res)

Prices vary. Check CLORE.AI Marketplace for current rates.

Troubleshooting

Out of Memory

# Use smaller model
pipe = WanPipeline.from_pretrained("alibaba-pai/Wan2.1-T2V-1.3B")

# Enable all optimizations
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()

# Reduce frames
output = pipe(prompt, num_frames=17)

# Reduce resolution
output = pipe(prompt, height=480, width=854)

Poor Quality

• Increase steps (75-100)

• Write more detailed prompts

• Use negative prompts

• Try 14B model for better quality

Video Too Short

• Increase num_frames (max 81)

• Use RIFE interpolation for frame interpolation

• Chain multiple generations

Artifacts/Flickering

• Increase guidance scale

• Use fixed seed for consistency

• Post-process with video stabilization

Wan2.1 vs Others

Feature	Wan2.1	Hunyuan	SVD	CogVideoX
Quality	Excellent	Excellent	Good	Great
Speed	Fast	Medium	Fast	Slow
Max Frames	81	129	25	49
Resolution	720p	720p	576p	720p
I2V Support	Yes	Yes	Yes	Yes
License	Apache 2.0	Open	Open	Open

Use Wan2.1 when:

• Need open-source video generation

• Want fast generation speed

• Apache 2.0 license required

• Balanced quality/speed needed

Next Steps

• Hunyuan Video - Alternative T2V

• OpenSora - Open Sora alternative

• Stable Video Diffusion - Image animation

• RIFE Interpolation - Frame interpolation