ControlVideo
A framework for controlling diffusion models for video generation.

A plug-and-play module turning community text-to-image models into animation generators without additional training.
A plug-and-play module turning community text-to-image models into animation generators without additional training.
AnimateDiff is an open-source implementation designed to animate personalized text-to-image diffusion models. It functions as a plug-and-play module, enabling the transformation of existing text-to-image models (like Stable Diffusion) into animation generators without requiring additional training of the base model. The architecture involves learning transferable motion priors that can be applied across various Stable Diffusion family models. Key stages include alleviating negative effects through domain adaptation, learning motion priors, and optionally adapting to new patterns using MotionLoRA. The tool also supports SparseCtrl for adding sparse controls (RGB images or sketches) to text-to-video models. This approach offers flexibility and control over animation content. It provides several pre-trained models and MotionLoRAs. AnimateDiff has multiple versions, including SDXL-Beta, each having specific motion modules trained on high-resolution videos. It's also officially supported by Diffusers.
A plug-and-play module turning community text-to-image models into animation generators without additional training.
Quick visual proof for AnimateDiff. Helps non-technical users understand the interface faster.
AnimateDiff is an open-source implementation designed to animate personalized text-to-image diffusion models.
Explore all tools that specialize in motion control. This domain focus ensures AnimateDiff delivers optimized results for this specific requirement.
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MotionLoRA allows efficient adaptation of motion modules for specific motion patterns like camera zooming and rolling using low-rank adaptation.
Adds control to text-to-video models using sparse inputs like RGB images or sketches, allowing users to guide the animation content.
Trained to mitigate defective visual artifacts in training datasets, benefiting the disentangled learning of motion and spatial appearance.
Supports Stable Diffusion XL, enabling the generation of high-resolution videos (1024x1024x16 frames) with various aspect ratios.
Designed to be compatible with existing community text-to-image models, allowing users to leverage their favorite models for animation.
1. Clone the AnimateDiff repository from GitHub: `git clone https://github.com/guoyww/AnimateDiff.git`
2. Navigate to the AnimateDiff directory: `cd AnimateDiff`
3. Install the required dependencies using pip: `pip install -r requirements.txt`
4. Download the necessary model checkpoints (automatically handled on first run).
5. Generate animations using the provided scripts: `python -m scripts.animate --config configs/prompts/1_animate/1_1_animate_RealisticVision.yaml`
6. For SDXL, checkout the `sdxl-beta` branch.
All Set
Ready to go
Verified feedback from other users.
“Users praise AnimateDiff for its ease of use and ability to create impressive animations, but some note the occasional flickering issue.”
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