Tasks

Image-to-Video

Image-to-video models take a still image as input and generate a video. These models can be guided by text prompts to influence the content and style of the output video.

Inputs
Optional Text Prompt

This penguin is dancing

Image-to-Video Model
Output

About Image-to-Video

Use Cases

Image-to-video models transform a static image into a video sequence. This can be used for a variety of creative and practical applications.

Animated Images

Bring still photos to life by adding subtle motion or creating short animated clips. This is great for social media content or dynamic presentations.

Storytelling from a Single Frame

Expand on the narrative of an image by generating a short video that imagines what happened before or after the moment captured in the photo.

Video Generation with Visual Consistency

Use an input image as a strong visual anchor to guide the generation of a video, ensuring that the style, characters, or objects in the video remain consistent with the source image.

Controllable Motion

Image-to-video models can be used to specify the direction or intensity of motion or camera control, giving more fine-grained control over the generated animation.

Inference

Running the model Wan 2.1 T2V 1.3B with diffusers

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

model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = "A cat walks on the grass, realistic"
negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"

output = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=480,
    width=832,
    num_frames=81,
    guidance_scale=5.0
).frames[0]
export_to_video(output, "output.mp4", fps=15)

Useful Resources

To train image-to-video LoRAs check out finetrainers and musubi trainer.

Compatible libraries

Image-to-Video demo

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Models for Image-to-Video
Browse Models (209)

Note LTX-Video, a 13B parameter model for high quality video generation

Note A 14B parameter model for reference image controlled video generation

Note An image-to-video generation model using FramePack F1 methodology with Hunyuan-DiT architecture

Note A distilled version of the LTX-Video-0.9.7-dev model for faster inference

Note An image-to-video generation model by Skywork AI, 14B parameters, producing 720p videos.

Note Image-to-video variant of Tencent's HunyuanVideo.

Note A 14B parameter model for 720p image-to-video generation by Wan-AI.

Note A Diffusers version of the Wan2.1-I2V-14B-720P model for 720p image-to-video generation.

Datasets for Image-to-Video
Browse Datasets (48)

Note A benchmark dataset for reference image controlled video generation.

Note A dataset with videos and captions throughout the videos.

Spaces using Image-to-Video

Note An application to generate videos fast.

Note Generate videos with the FramePack-F1

Note Generate videos with the FramePack

Note Wan2.1 with CausVid LoRA

Note A demo for Stable Video Diffusion

Metrics for Image-to-Video
fvd
Fréchet Video Distance (FVD) measures the perceptual similarity between the distributions of generated videos and a set of real videos, assessing overall visual quality and temporal coherence of the video generated from an input image.
clip_score
CLIP Score measures the semantic similarity between a textual prompt (if provided alongside the input image) and the generated video frames. It evaluates how well the video's generated content and motion align with the textual description, conditioned on the initial image.
lpips
First Frame Fidelity, often measured using LPIPS (Learned Perceptual Image Patch Similarity), PSNR, or SSIM, quantifies how closely the first frame of the generated video matches the input conditioning image.
identity_preservation
Identity Preservation Score measures the consistency of identity (e.g., a person's face or a specific object's characteristics) between the input image and throughout the generated video frames, often calculated using features from specialized models like face recognition (e.g., ArcFace) or re-identification models.
motion_score
Motion Score evaluates the quality, realism, and temporal consistency of motion in the video generated from a static image. This can be based on optical flow analysis (e.g., smoothness, magnitude), consistency of object trajectories, or specific motion plausibility assessments.