Seba-AI-5B is a text-to-video generation model derived from THUDM's CogVideoX technology, supporting the generation of 720x480 resolution, 6-second videos from text prompts
Model Features
High-Quality Video Generation
Capable of generating high-quality 720x480 resolution, 6-second videos from text prompts
Multi-Precision Support
Supports various inference precisions including BF16, FP16, and FP32 to accommodate different hardware requirements
VRAM Optimization
Optimized via the diffusers library, capable of running on GPUs with as little as 4GB VRAM
Quantization Support
Supports INT8 quantization to further reduce VRAM requirements
Model Capabilities
Text-to-Video Generation
Video Content Creation
Creative Visual Expression
Use Cases
Creative Content Production
Animated Short Film Creation
Automatically generates animated short films based on scripts
Produces 6-second animated videos
Advertising Concept Production
Quickly generates advertising concept videos
Produces advertising videos that match product descriptions
Education & Entertainment
Educational Video Generation
Generates visual educational videos based on teaching content
Produces vivid and intuitive educational videos
Game Scene Generation
Quickly generates scene concept videos for game development
Produces scene videos that align with game settings
đ Seba-AI-5B
Seba-AI-5B is a video generation model. It offers high - quality video generation capabilities and provides users with a variety of video examples. You can visit related platforms to experience commercial video generation models.
The following are some video examples generated by the model:
Video Gallery with Captions
A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature's embrace.
A small boy, head bowed and determination etched on his face, sprints through the torrential downpour as lightning crackles and thunder rumbles in the distance. The relentless rain pounds the ground, creating a chaotic dance of water droplets that mirror the dramatic sky's anger. In the far background, the silhouette of a cozy home beckons, a faint beacon of safety and warmth amidst the fierce weather. The scene is one of perseverance and the unyielding spirit of a child braving the elements.
A suited astronaut, with the red dust of Mars clinging to their boots, reaches out to shake hands with an alien being, their skin a shimmering blue, under the pink-tinged sky of the fourth planet. In the background, a sleek silver rocket, a beacon of human ingenuity, stands tall, its engines powered down, as the two representatives of different worlds exchange a historic greeting amidst the desolate beauty of the Martian landscape.
An elderly gentleman, with a serene expression, sits at the water's edge, a steaming cup of tea by his side. He is engrossed in his artwork, brush in hand, as he renders an oil painting on a canvas that's propped up against a small, weathered table. The sea breeze whispers through his silver hair, gently billowing his loose-fitting white shirt, while the salty air adds an intangible element to his masterpiece in progress. The scene is one of tranquility and inspiration, with the artist's canvas capturing the vibrant hues of the setting sun reflecting off the tranquil sea.
In a dimly lit bar, purplish light bathes the face of a mature man, his eyes blinking thoughtfully as he ponders in close-up, the background artfully blurred to focus on his introspective expression, the ambiance of the bar a mere suggestion of shadows and soft lighting.
A golden retriever, sporting sleek black sunglasses, with its lengthy fur flowing in the breeze, sprints playfully across a rooftop terrace, recently refreshed by a light rain. The scene unfolds from a distance, the dog's energetic bounds growing larger as it approaches the camera, its tail wagging with unrestrained joy, while droplets of water glisten on the concrete behind it. The overcast sky provides a dramatic backdrop, emphasizing the vibrant golden coat of the canine as it dashes towards the viewer.
On a brilliant sunny day, the lakeshore is lined with an array of willow trees, their slender branches swaying gently in the soft breeze. The tranquil surface of the lake reflects the clear blue sky, while several elegant swans glide gracefully through the still water, leaving behind delicate ripples that disturb the mirror-like quality of the lake. The scene is one of serene beauty, with the willows' greenery providing a picturesque frame for the peaceful avian visitors.
A Chinese mother, draped in a soft, pastel-colored robe, gently rocks back and forth in a cozy rocking chair positioned in the tranquil setting of a nursery. The dimly lit bedroom is adorned with whimsical mobiles dangling from the ceiling, casting shadows that dance on the walls. Her baby, swaddled in a delicate, patterned blanket, rests against her chest, the child's earlier cries now replaced by contented coos as the mother's soothing voice lulls the little one to sleep. The scent of lavender fills the air, adding to the serene atmosphere, while a warm, orange glow from a nearby nightlight illuminates the scene with a gentle hue, capturing a moment of tender love and comfort.
Model Introduction
CogVideoX is an open - source version of the video generation model originating from QingYing. The table below displays the list of video generation models we currently offer, along with their foundational information.
Property
CogVideoX - 2B
CogVideoX - 5B (This Repository)
Model Name
CogVideoX - 2B
CogVideoX - 5B
Model Description
Entry - level model, balancing compatibility. Low cost for running and secondary development.
Larger model with higher video generation quality and better visual effects.
Inference Precision
FP16* (Recommended), BF16, FP32, FP8*, INT8, no support for INT4
BF16 (Recommended), FP16, FP32, FP8*, INT8, no support for INT4
Single GPU VRAM Consumption
SAT FP16: 18GB diffusers FP16: starting from 4GB* diffusers INT8(torchao): starting from 3.6GB*
SAT BF16: 26GB diffusers BF16: starting from 5GB* diffusers INT8(torchao): starting from 4.4GB*
Multi - GPU Inference VRAM Consumption
FP16: 10GB* using diffusers
BF16: 15GB* using diffusers
Inference Speed (Step = 50, FP/BF16)
Single A100: ~90 seconds Single H100: ~45 seconds
Single A100: ~180 seconds Single H100: ~90 seconds
720 x 480, no support for other resolutions (including fine - tuning)
720 x 480, no support for other resolutions (including fine - tuning)
Positional Encoding
3d_sincos_pos_embed
3d_rope_pos_embed
Data Explanation
â ī¸ Important Note
When testing using the diffusers library, all optimizations provided by the diffusers library were enabled. This solution has not been tested for actual VRAM/memory usage on devices other than NVIDIA A100 / H100. Generally, this solution can be adapted to all devices with NVIDIA Ampere architecture and above. If the optimizations are disabled, VRAM usage will increase significantly, with peak VRAM usage being about 3 times higher than the table shows. However, speed will increase by 3 - 4 times. You can selectively disable some optimizations, including:
When performing multi - GPU inference, the enable_model_cpu_offload() optimization needs to be disabled.
Using INT8 models will reduce inference speed. This is to ensure that GPUs with lower VRAM can perform inference normally while maintaining minimal video quality loss, though inference speed will decrease significantly.
The 2B model is trained with FP16 precision, and the 5B model is trained with BF16 precision. We recommend using the precision the model was trained with for inference.
PytorchAO and [Optimum - quanto](https://github.com/huggingface/optimum - quanto/) can be used to quantize the text encoder, Transformer, and VAE modules to reduce CogVideoX's memory requirements. This makes it possible to run the model on a free T4 Colab or GPUs with smaller VRAM!
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Transformers