Image-Text-to-Video

Use Cases

Image Animation

Image-text-to-video models can be used to animate still images based on text descriptions. For example, you can provide a landscape photo and the instruction "A camera pan from left to right" to create a video with camera movement.

Dynamic Content Creation

Transform images into video by adding motion, transformations, or effects described in text prompts. This is useful for creating engaging social media content, presentations, or marketing materials.

Guided Video Generation

Use a reference image with text prompts to guide the video generation process. This provides more control over the visual style and composition compared to text-to-video models alone.

Story Visualization

Create video sequences from storyboards or concept art by providing scene descriptions. This can help filmmakers and animators visualize scenes before production.

Motion Control

Generate videos with specific camera movements, object motions, or scene transitions by combining reference images with detailed motion descriptions.

Task Variants

Image-to-Video with Motion Control

Models that generate videos from images while following specific motion instructions, such as camera movements, object animations, or scene dynamics.

Reference-guided Video Generation

Models that use a reference image to guide the visual style and composition of the generated video while incorporating text prompts for motion and transformation control.

Conditional Video Synthesis

Models that perform specific video transformations based on text conditions, such as adding weather effects, time-of-day changes, or environmental animations.

Inference

You can use the Diffusers library to interact with image-text-to-video models. Here's example snippet to use LTXImageToVideoPipeline.

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

pipe = LTXImageToVideoPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
pipe.to("cuda")

image = load_image(
    "https://huggingface.co/datasets/a-r-r-o-w/tiny-meme-dataset-captioned/resolve/main/images/8.png"
)
prompt = "A young girl stands calmly in the foreground, looking directly at the camera, as a house fire rages in the background. Flames engulf the structure, with smoke billowing into the air. Firefighters in protective gear rush to the scene, a fire truck labeled '38' visible behind them. The girl's neutral expression contrasts sharply with the chaos of the fire, creating a poignant and emotionally charged scene."
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"

video = pipe(
    image=image,
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=704,
    height=480,
    num_frames=161,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)

Useful Resources

• LTX-Video Model Card
• Text-to-Video: The Task, Challenges and the Current State
• Diffusers documentation on Video Generation