Mochi 1

Overview

Mochi 1 is a revolutionary 10 billion parameter diffusion model from Genmo AI, released in late October 2024 following a successful $28.4 million Series A funding round led by NEA. As the largest video generative model ever openly released, Mochi 1 represents a significant milestone in democratizing access to state-of-the-art video AI technology.

Built on Genmo's novel Asymmetric Diffusion Transformer (AsymmDiT) architecture, Mochi 1 achieves exceptional performance in generating smooth, photorealistic videos at 30 frames per second for durations up to 5.4 seconds. The model excels at simulating complex physics including fluid dynamics, fur and hair movement, and expressing consistent, fluid human action with high temporal coherence and realistic motion dynamics.

Released under the permissive Apache 2.0 license, Mochi 1 is completely free for both personal and commercial use. The preview version generates videos at 480p resolution today, with full HD support planned before the end of the year. While optimized for photorealistic styles, Mochi 1 establishes new standards for open-source video generation quality.

Key Features

• 10 billion parameters - largest openly released video generation model
• Novel Asymmetric Diffusion Transformer (AsymmDiT) architecture
• Photorealistic video generation at 30 frames per second
• Video duration up to 5.4 seconds with high temporal coherence
• Advanced physics simulation: fluid dynamics, fur/hair, human motion
• Strong prompt adherence with high-fidelity motion
• Apache 2.0 license - free for personal and commercial use
• 480p in preview, HD support coming soon
• Open weights and architecture available on HuggingFace
• Active development with planned continuous improvements

Use Cases

• Commercial video production without licensing restrictions
• Photorealistic content creation for marketing and advertising
• Research into diffusion-based video generation architectures
• Custom model fine-tuning for specific visual styles
• Social media content generation (Reels, TikTok, Shorts)
• Product visualization with realistic physics
• Human action and motion studies
• Rapid video prototyping and storyboarding

Technical Specifications

Mochi 1 uses the Asymmetric Diffusion Transformer (AsymmDiT) architecture with 10 billion parameters. It outputs videos at 480p resolution (with HD support planned) at 30 fps for up to 5.4 seconds duration. The model is optimized for photorealistic styles (not optimized for animation) and excels at physics simulation including fluid dynamics, fur and hair movement, and human motion with high temporal coherence. Inference requires high-end GPUs (A100, H100 recommended) with 24GB+ VRAM.

Pricing and Availability

Mochi 1 is free and open source under the Apache 2.0 license, completely free for personal and commercial use. A free trial is available at genmo.ai/play. Self-hosting requires GPU infrastructure costs. Open weights and architecture are available on HuggingFace.

Resources and Links

Official website: https://www.genmo.ai/ | Playground: https://www.genmo.ai/play | Blog: https://www.genmo.ai/blog | GitHub: https://github.com/genmoai/mochi | HuggingFace: https://huggingface.co/genmo/mochi-1-preview | Documentation: https://github.com/genmoai/mochi/blob/main/README.md

Code Example: Local Inference with Hugging Face

Deploy Mochi 1 locally using Hugging Face Diffusers for photorealistic 30fps video generation. This implementation demonstrates advanced physics simulation capabilities including fluid dynamics, hair movement, and human action with production-ready error handling.

import torch
from diffusers import MochiPipeline
from diffusers.utils import export_to_video
import gc
import os

# Configuration for Mochi 1
MODEL_ID = "genmo/mochi-1-preview"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.bfloat16  # Mochi 1 optimized for bfloat16

# Verify GPU requirements
if torch.cuda.is_available():
    gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9
    print(f"GPU Memory: {gpu_memory:.1f} GB")
    if gpu_memory < 20:
        print("Warning: Mochi 1 requires 24GB+ VRAM for optimal performance")

try:
    # Load the Mochi 1 pipeline
    print("Loading Mochi 1 model (10B parameters)...")
    pipe = MochiPipeline.from_pretrained(
        MODEL_ID,
        torch_dtype=DTYPE,
        variant="bf16"
    )
    
    # Memory optimization techniques
    pipe.enable_model_cpu_offload()  # Sequential CPU offloading
    pipe.enable_vae_slicing()  # Process VAE in smaller batches
    
    # Example 1: Photorealistic human action
    prompt_human = "A professional athlete running on a beach at sunrise, slow motion, detailed facial expressions, flowing hair, photorealistic, 4K quality"
    negative_prompt = "cartoon, animated, low quality, blurry, distorted, unrealistic"
    
    print(f"\nGenerating: {prompt_human}")
    
    video_frames = pipe(
        prompt=prompt_human,
        negative_prompt=negative_prompt,
        num_frames=163,  # 5.4 seconds at 30fps
        height=480,  # Current preview version
        width=848,
        num_inference_steps=64,  # Higher for photorealistic quality
        guidance_scale=4.5,  # Mochi 1 optimized guidance
        generator=torch.Generator(device=DEVICE).manual_seed(123)
    ).frames[0]
    
    output_path = "mochi_human_action.mp4"
    export_to_video(video_frames, output_path, fps=30)
    print(f"Video saved: {output_path}")
    
    # Example 2: Fluid dynamics simulation
    prompt_fluid = "Pouring honey into a glass jar, golden liquid flowing, light refraction, macro photography, photorealistic"
    
    print(f"\nGenerating: {prompt_fluid}")
    
    video_frames = pipe(
        prompt=prompt_fluid,
        negative_prompt=negative_prompt,
        num_frames=163,
        height=480,
        width=848,
        num_inference_steps=64,
        guidance_scale=4.5,
        generator=torch.Generator(device=DEVICE).manual_seed(456)
    ).frames[0]
    
    output_path = "mochi_fluid_dynamics.mp4"
    export_to_video(video_frames, output_path, fps=30)
    print(f"Video saved: {output_path}")
    
    # Example 3: Fur and hair movement
    prompt_fur = "A golden retriever dog running through tall grass, wind blowing fur, sunset lighting, photorealistic detail"
    
    print(f"\nGenerating: {prompt_fur}")
    
    video_frames = pipe(
        prompt=prompt_fur,
        negative_prompt=negative_prompt,
        num_frames=163,
        height=480,
        width=848,
        num_inference_steps=64,
        guidance_scale=4.5,
        generator=torch.Generator(device=DEVICE).manual_seed(789)
    ).frames[0]
    
    output_path = "mochi_fur_movement.mp4"
    export_to_video(video_frames, output_path, fps=30)
    print(f"Video saved: {output_path}")
    
    # Clean up
    del pipe
    gc.collect()
    torch.cuda.empty_cache()
    
    print("\nAll generations complete!")
    
except RuntimeError as e:
    if "out of memory" in str(e).lower():
        print("\nGPU out of memory error.")
        print("Solutions:")
        print("- Use GPU with 24GB+ VRAM (A100, RTX 4090, H100)")
        print("- Reduce num_frames (shorter videos)")
        print("- Use gradient checkpointing: pipe.unet.enable_gradient_checkpointing()")
    else:
        raise
except Exception as e:
    print(f"Error: {e}")
    raise

# Production tip: Batch processing for social media content
def batch_generate_social_media(prompts_list, output_dir="outputs"):
    """Generate multiple videos for social media campaigns"""
    os.makedirs(output_dir, exist_ok=True)
    
    for idx, prompt in enumerate(prompts_list):
        print(f"\nProcessing {idx+1}/{len(prompts_list)}: {prompt}")
        # Implementation follows pattern above
        # Save to output_dir with descriptive filenames

Code Example: Cloud API Inference

Access Mochi 1 through cloud providers for scalable video generation without managing GPU infrastructure. This example demonstrates integration with Replicate and Genmo's playground API for production workflows.

import replicate
import requests
import time
import os
from pathlib import Path
from typing import Optional, Dict, Any

# Configuration
REPLICATE_API_TOKEN = os.getenv("REPLICATE_API_TOKEN", "your_token_here")
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN

class MochiCloudClient:
    """Production-ready client for Mochi 1 cloud inference"""
    
    def __init__(self, api_token: str):
        self.api_token = api_token
        self.client = replicate.Client(api_token=api_token)
    
    def generate_video(
        self,
        prompt: str,
        negative_prompt: str = "cartoon, animated, low quality",
        duration_seconds: float = 5.4,
        seed: Optional[int] = None
    ) -> Dict[str, Any]:
        """
        Generate photorealistic video using Mochi 1
        
        Args:
            prompt: Detailed description for photorealistic video
            negative_prompt: Elements to avoid
            duration_seconds: Video length (up to 5.4s)
            seed: Random seed for reproducibility
        
        Returns:
            Dictionary with video_url and metadata
        """
        try:
            print(f"Submitting Mochi 1 generation...")
            print(f"Prompt: {prompt}")
            
            # Calculate frames (30 fps)
            num_frames = min(int(duration_seconds * 30), 163)
            
            # Run prediction
            output = self.client.run(
                "genmo/mochi-1-preview:latest",
                input={
                    "prompt": prompt,
                    "negative_prompt": negative_prompt,
                    "num_frames": num_frames,
                    "num_inference_steps": 64,
                    "guidance_scale": 4.5,
                    "seed": seed if seed else -1  # -1 for random
                }
            )
            
            video_url = output
            
            return {
                "video_url": video_url,
                "prompt": prompt,
                "duration": duration_seconds,
                "frames": num_frames,
                "seed": seed
            }
            
        except replicate.exceptions.ReplicateError as e:
            print(f"Replicate API Error: {e}")
            raise
        except Exception as e:
            print(f"Generation error: {e}")
            raise
    
    def download_video(self, video_url: str, output_path: Path) -> Path:
        """Download generated video from URL"""
        try:
            print(f"Downloading video...")
            response = requests.get(video_url, stream=True, timeout=300)
            response.raise_for_status()
            
            with open(output_path, "wb") as f:
                for chunk in response.iter_content(chunk_size=8192):
                    if chunk:
                        f.write(chunk)
            
            print(f"Video saved to: {output_path}")
            return output_path
            
        except requests.exceptions.RequestException as e:
            print(f"Download error: {e}")
            raise

# Business use cases
def marketing_campaign_example():
    """Generate videos for marketing campaign"""
    client = MochiCloudClient(REPLICATE_API_TOKEN)
    output_dir = Path("marketing_videos")
    output_dir.mkdir(exist_ok=True)
    
    # Product videos with physics simulation
    campaigns = [
        {
            "name": "perfume_commercial",
            "prompt": "Luxury perfume bottle on black silk fabric, liquid gold perfume splashing in slow motion, dramatic lighting, photorealistic, high-end commercial",
            "duration": 5.4
        },
        {
            "name": "coffee_pour",
            "prompt": "Barista pouring latte art in white ceramic cup, steam rising, morning sunlight through window, photorealistic cafe ambiance",
            "duration": 4.5
        },
        {
            "name": "athlete_training",
            "prompt": "Professional athlete doing high intensity workout, sweat details, muscle definition, gym environment, motivational energy, photorealistic",
            "duration": 5.0
        }
    ]
    
    results = []
    
    for campaign in campaigns:
        print(f"\nGenerating: {campaign['name']}")
        
        # Generate video
        result = client.generate_video(
            prompt=campaign["prompt"],
            duration_seconds=campaign["duration"],
            seed=42  # Reproducible results
        )
        
        # Download
        video_path = output_dir / f"{campaign['name']}.mp4"
        client.download_video(result["video_url"], video_path)
        
        results.append({
            "campaign": campaign["name"],
            "path": video_path,
            "url": result["video_url"]
        })
        
        # Rate limiting
        time.sleep(2)
    
    print("\n=== Marketing Campaign Complete ===")
    for r in results:
        print(f"{r['campaign']}: {r['path']}")
    
    return results

# Social media content generation
def social_media_batch():
    """Generate TikTok/Reels content batch"""
    client = MochiCloudClient(REPLICATE_API_TOKEN)
    
    social_prompts = [
        "Young woman dancing in urban street, colorful graffiti background, golden hour lighting, authentic movement",
        "Chef flambéing dish in professional kitchen, dramatic flames, culinary artistry, photorealistic",
        "Surfer riding perfect wave at sunset, water spray details, ocean dynamics, action photography style"
    ]
    
    for idx, prompt in enumerate(social_prompts, 1):
        print(f"\nGenerating social media clip {idx}/{len(social_prompts)}")
        result = client.generate_video(prompt, duration_seconds=5.4)
        
        output_path = Path(f"social_media_{idx}.mp4")
        client.download_video(result["video_url"], output_path)

if __name__ == "__main__":
    # Run marketing campaign example
    marketing_campaign_example()
    
    # Uncomment for social media batch
    # social_media_batch()

Professional Integration Services by 21medien

Mochi 1's photorealistic capabilities and advanced physics simulation make it ideal for commercial video production, but successful implementation requires deep expertise in video AI pipelines and production workflows. 21medien provides end-to-end integration services to help businesses harness Mochi 1's full potential under the Apache 2.0 license.

Our comprehensive services include: Production Infrastructure Setup for deploying Mochi 1 on high-performance GPU clusters (A100, H100) with optimized inference pipelines, Cloud Integration Strategy for hybrid deployment combining self-hosted and cloud API solutions based on your usage patterns and budget, Custom Video Pipeline Development including automated post-processing, format conversion, quality control, and content moderation workflows, Prompt Engineering and Quality Optimization to achieve consistent photorealistic results for your specific brand and content requirements, Physics Simulation Consulting for specialized use cases like product visualization, fluid dynamics demonstrations, or realistic human motion, Batch Processing Systems for high-volume video generation with queue management, progress tracking, and error recovery, and Commercial Licensing Guidance to ensure your implementation complies with Apache 2.0 terms for commercial applications.

Whether you're building a marketing video platform, integrating AI video into your creative workflow, or developing a video-first product, our team brings production-grade expertise in video AI implementation. We help you navigate GPU infrastructure costs, optimize generation parameters for your use cases, and build robust systems that scale with your business. Schedule a free consultation call through our contact page to discuss how Mochi 1 can revolutionize your video production capabilities while maintaining full control under an open-source license.