TemporalDiff
Maintainer: CiaraRowles
151
📶
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
TemporalDiff is a finetuned version of the original AnimateDiff model, trained on a higher resolution dataset (512x512). According to the maintainer, CiaraRowles, this version demonstrates improved video coherency compared to the original model. Some key adjustments made include reducing the stride from 4 to 2 frames to create smoother motion, and addressing labeling issues in the training dataset that had slightly reduced the model's ability to interpret prompts.
Similar models include the original animate-diff from zsxkib, as well as other text-to-video diffusion models like animatediff-illusions and magic-animate.
Model inputs and outputs
The TemporalDiff model takes text prompts as input and generates corresponding videos as output. No additional memory is required to run this model compared to the base AnimateDiff model, as the training was done at 256x256 resolution.
Inputs
- Text prompts describing the desired video content
Outputs
- Generated videos corresponding to the input text prompts
Capabilities
The TemporalDiff model can generate animated videos based on text descriptions. It has been trained to improve video coherency and smoothness compared to the original AnimateDiff model.
What can I use it for?
The TemporalDiff model can be used for a variety of creative and experimental applications, such as generating animated content for design, art, or entertainment purposes. The maintainer notes it may also be useful for research into areas like probing the limitations and biases of generative models, or developing educational and creative tools.
Things to try
Experiment with different text prompts to see the range of videos the TemporalDiff model can generate. Try prompts that involve complex scenes, movement, or abstract concepts to test the model's capabilities. Additionally, compare the output of TemporalDiff to the original AnimateDiff model to assess the improvements in video coherency and smoothness.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🤯
TemporalNet
345
TemporalNet is a ControlNet model designed by CiaraRowles to enhance the temporal consistency of generated outputs. As demonstrated in this example, TemporalNet significantly reduces flickering, particularly at higher denoise levels. For optimal results, it is recommended to use TemporalNet in combination with other methods. Similar models include TemporalDiff, a finetuned version of the original AnimateDiff weights on a higher resolution dataset, and the QR code conditioned ControlNet models by DionTimmer for Stable Diffusion 1.5 and 2.1. Model inputs and outputs Inputs Input Images**: A folder containing the input frames Init Image**: A pre-stylized PNG file to be used as the initial image Outputs Video Frames**: The generated video frames with improved temporal consistency Capabilities TemporalNet can significantly reduce flickering in generated video outputs, making the transitions between frames more coherent and stable. This is particularly useful for creating higher-quality animations and dynamic content. What can I use it for? With TemporalNet, you can create more visually appealing and professional-looking video content for a variety of applications, such as social media posts, advertisements, or short films. The improved temporal consistency can help ensure a smooth and seamless viewing experience, making the content more engaging and impactful. Things to try One key thing to try with TemporalNet is experimenting with the combination of different methods and settings to find the optimal balance between temporal consistency and the desired visual style. By adjusting the control net weights, prompt, and other parameters, you can fine-tune the model to achieve your specific creative goals.
Updated Invalid Date
🗣️
LongAnimateDiff
42
The LongAnimateDiff model, developed by Lightricks Research, is an extension of the original AnimateDiff model. This model has been trained to generate videos with a variable frame count, ranging from 16 to 64 frames. The model is compatible with the original AnimateDiff model and can be used for a wide range of text-to-video generation tasks. Lightricks also released a specialized 32-frame video generation model, which typically produces higher-quality videos compared to the LongAnimateDiff model. The 32-frame model is designed for optimal results when using a motion scale of 1.15. Model inputs and outputs Inputs Text prompt**: The text prompt that describes the desired video content. Motion scale**: A parameter that controls the amount of motion in the generated video. The recommended values are 1.28 for the LongAnimateDiff model and 1.15 for the 32-frame model. Outputs Animated video**: The model generates videos with a variable frame count, ranging from 16 to 64 frames, based on the input text prompt and motion scale. Capabilities The LongAnimateDiff model is capable of generating high-quality animated videos from text prompts. The model can capture a wide range of visual elements, including characters, objects, and scenes, and animate them in a coherent and visually appealing way. What can I use it for? The LongAnimateDiff model can be used for a variety of applications, such as: Video generation for social media**: Create engaging and visually compelling videos for social media platforms. Animated marketing content**: Generate animated videos for product promotions, advertisements, and other marketing materials. Educational and explainer videos**: Use the model to create animated videos for educational or informational purposes. Creative projects**: Explore the model's capabilities to generate unique and imaginative animated videos for artistic or personal projects. Things to try One interesting aspect of the LongAnimateDiff model is its ability to generate videos with a variable frame count. Experiment with different frame counts and motion scales to see how they affect the visual quality and style of the generated videos. Additionally, try using the model in combination with the AnimateDiff-Lightning model, which is a lightning-fast text-to-video generation model, to explore the synergies between the two approaches.
Updated Invalid Date
🤿
TemporalNet2
120
TemporalNet2 is an evolution of the original TemporalNet model, designed to enhance the temporal consistency of generated outputs. The key difference is that TemporalNet2 uses both the last frame and an optical flow map between frames to guide the generation, improving the consistency of the output. This takes some modifications to the original ControlNet code, as outlined in the maintainer's description. Model inputs and outputs TemporalNet2 is a ControlNet model that takes in a sequence of input frames and generates a video output with improved temporal consistency. It can be used in conjunction with Stable Diffusion to create temporally coherent video content. Inputs Input Images**: A sequence of input frames to be processed Init Image**: A pre-stylized initial image to prevent drastic style changes Outputs Output Video**: A generated video with improved temporal consistency compared to the input frames Capabilities TemporalNet2 significantly reduces flickering and inconsistencies in generated video outputs, particularly at higher denoise levels. By leveraging both the last frame and an optical flow map, it can better maintain the visual coherence of the generated sequence. What can I use it for? TemporalNet2 can be a valuable tool for content creators and animators looking to generate temporally consistent video content using Stable Diffusion. It can be used to create smooth, visually coherent animations, video loops, and other dynamic media. The maintainer also suggests using it in conjunction with the HED model for additional benefits. Things to try Experimenting with the control net settings, such as the guidance scale and conditioning scale, can help find the right balance between maintaining the QR code shape and preserving the desired style. Additionally, generating the output at a higher resolution of 768x768 can improve the overall quality and detail of the generated video.
Updated Invalid Date
📈
stable-video-diffusion-img2vid-fp16
52
stable-video-diffusion-img2vid-fp16 is a generative image-to-video model developed by Stability AI that takes in a still image as input and generates a short video clip from it. This model is similar to lcm-video2video, which is a fast video-to-video model with a latent consistency, and animelike2d, though the latter's description is not provided. It is also related to stable-video-diffusion and stable-video-diffusion-img2vid, which are other image-to-video diffusion models. Model inputs and outputs The stable-video-diffusion-img2vid-fp16 model takes in a single still image as input and generates a short video clip of 14 frames at a resolution of 576x1024. The model was trained on a large dataset to learn how to convert a static image into a dynamic video sequence. Inputs Image**: A single input image at a resolution of 576x1024 pixels. Outputs Video**: A generated video clip of 14 frames at a resolution of 576x1024 pixels. Capabilities The stable-video-diffusion-img2vid-fp16 model is capable of generating short video sequences from static input images. The generated videos can capture motion, camera pans, and other dynamic elements, though they may not always achieve perfect photorealism. The model is intended for research purposes and can be used to explore generative models, study their limitations and biases, and generate artistic content. What can I use it for? The stable-video-diffusion-img2vid-fp16 model is intended for research purposes only. Possible applications include: Researching generative models and their capabilities Studying the limitations and biases of generative models Generating artistic content and using it in design or other creative processes Developing educational or creative tools that leverage the model's capabilities The model should not be used to generate factual or true representations of people or events, as it was not trained for that purpose. Any use of the model must comply with Stability AI's Acceptable Use Policy. Things to try With the stable-video-diffusion-img2vid-fp16 model, you can experiment with generating video sequences from a variety of input images. Try using different types of images, such as landscapes, portraits, or abstract art, to see how the model handles different subject matter. Explore the model's limitations by trying to generate videos with complex elements like faces, text, or fast-moving objects. Observe how the model's outputs evolve over the course of the video sequence and analyze the consistency and quality of the generated frames.
Updated Invalid Date