Mochi 1 is a revolutionary 10 billion parameter diffusion model from Genmo AI, released in late October 2024 following a $28.4 million Series A funding round. 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.
Novel Asymmetric Diffusion Transformer Architecture
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 consistent human action.
Apache 2.0 License: Complete Commercial Freedom
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, with full HD support planned before the end of the year. Open weights and architecture are available on HuggingFace.
Advanced Physics Simulation
- Fluid dynamics simulation for water, smoke, and liquids
- Fur and hair physics with realistic movement
- Human motion capture with natural gestures
- High temporal coherence across frames
- Realistic motion dynamics without artifacts
Real-World Applications
Commercial video production without licensing restrictions, photorealistic content creation for marketing, research into diffusion-based architectures, custom model fine-tuning for specific visual styles, and social media content generation for Reels, TikTok, and Shorts.
Implementation Example: Basic Video Generation with Mochi 1
Here's how to get started with Mochi 1 for text-to-video generation using the Hugging Face Diffusers library:
import torch
from diffusers import MochiPipeline
from diffusers.utils import export_to_video
# Initialize Mochi 1 pipeline
pipe = MochiPipeline.from_pretrained(
"genmo/mochi-1-preview",
torch_dtype=torch.float16,
variant="fp16"
)
# Enable memory optimizations for consumer GPUs
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()
# Generate video with physics simulation
prompt = """A serene waterfall cascading into a crystal-clear pool,
sunlight filtering through mist creating rainbow effects,
realistic water physics with foam and splashes,
4K photorealistic quality, smooth 30fps motion."""
output = pipe(
prompt=prompt,
num_frames=163, # 5.4 seconds at 30 fps
height=480,
width=848, # Mochi 1 preview resolution
num_inference_steps=64,
guidance_scale=4.5,
generator=torch.Generator("cuda").manual_seed(42)
)
# Export to video file
export_to_video(output.frames[0], "waterfall.mp4", fps=30)
print("Video generated successfully!")Advanced Example: Physics-Based Animation with Custom Settings
This example demonstrates Mochi 1's advanced physics simulation capabilities for complex scenes:
import torch
from diffusers import MochiPipeline
from diffusers.utils import export_to_video
import numpy as np
# Initialize pipeline with optimizations
pipe = MochiPipeline.from_pretrained(
"genmo/mochi-1-preview",
torch_dtype=torch.float16,
variant="fp16"
)
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()
# Complex physics prompt focusing on fur, hair, and fluid dynamics
prompt = """A majestic lion with flowing mane running through
tall grass in golden hour light, fur and hair moving
naturally with realistic physics, wind creating waves
in the grass, dust particles in the air, cinematic
quality with motion blur, 30fps smooth animation."""
negative_prompt = """static, frozen, unnatural movement,
rigid hair, artificial physics, low quality, artifacts,
jerky motion, unrealistic dynamics"""
# Generate with custom parameters for best quality
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_frames=163,
height=480,
width=848,
num_inference_steps=80, # Higher steps for better quality
guidance_scale=5.0, # Balanced prompt adherence
generator=torch.Generator("cuda").manual_seed(123)
)
export_to_video(output.frames[0], "lion_running.mp4", fps=30)
# Generate multiple variations for best result selection
print("Generating variations...")
variations = []
for i, seed in enumerate([42, 123, 456, 789]):
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_frames=163,
height=480,
width=848,
num_inference_steps=64,
guidance_scale=4.5,
generator=torch.Generator("cuda").manual_seed(seed)
)
filename = f"variation_{i}_seed{seed}.mp4"
export_to_video(output.frames[0], filename, fps=30)
variations.append(filename)
print(f"Generated: {filename}")
print(f"Created {len(variations)} variations for selection")Production Example: Batch Video Generation for Social Media
For creating social media content at scale with Mochi 1's Apache 2.0 commercial license:
import torch
import gc
from diffusers import MochiPipeline
from diffusers.utils import export_to_video
from pathlib import Path
import json
from datetime import datetime
class MochiVideoGenerator:
"""Production-ready Mochi 1 video generator for social media."""
def __init__(self, output_dir="social_media_videos"):
self.output_dir = Path(output_dir)
self.output_dir.mkdir(exist_ok=True)
# Initialize pipeline
self.pipe = MochiPipeline.from_pretrained(
"genmo/mochi-1-preview",
torch_dtype=torch.float16,
variant="fp16"
)
self.pipe.enable_model_cpu_offload()
self.pipe.enable_vae_slicing()
self.pipe.enable_vae_tiling()
def generate_video(self, prompt, platform="instagram", seed=None):
"""Generate video optimized for specific social media platform."""
# Platform-specific settings
platform_configs = {
"instagram": {"height": 480, "width": 480, "fps": 30}, # Square
"tiktok": {"height": 480, "width": 270, "fps": 30}, # 9:16
"youtube": {"height": 480, "width": 848, "fps": 30}, # 16:9
"twitter": {"height": 480, "width": 848, "fps": 30} # 16:9
}
config = platform_configs.get(platform, platform_configs["youtube"])
if seed is None:
seed = torch.randint(0, 2**32 - 1, (1,)).item()
print(f"Generating {platform} video (seed: {seed})...")
try:
output = self.pipe(
prompt=prompt,
num_frames=163, # ~5.4 seconds
height=config["height"],
width=config["width"],
num_inference_steps=64,
guidance_scale=4.5,
generator=torch.Generator("cuda").manual_seed(seed)
)
# Save video
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = self.output_dir / f"{platform}_{timestamp}_seed{seed}.mp4"
export_to_video(output.frames[0], str(filename), fps=config["fps"])
# Save metadata
metadata = {
"platform": platform,
"prompt": prompt,
"seed": seed,
"timestamp": timestamp,
"resolution": f"{config['width']}x{config['height']}",
"fps": config["fps"]
}
metadata_file = filename.with_suffix(".json")
with open(metadata_file, "w") as f:
json.dump(metadata, f, indent=2)
return {"success": True, "file": str(filename), "metadata": metadata}
except Exception as e:
return {"success": False, "error": str(e), "seed": seed}
finally:
# Cleanup GPU memory
torch.cuda.empty_cache()
gc.collect()
def batch_generate(self, prompts_config):
"""Generate multiple videos from configuration."""
results = []
for idx, config in enumerate(prompts_config):
print(f"\nGenerating video {idx + 1}/{len(prompts_config)}")
result = self.generate_video(
prompt=config["prompt"],
platform=config.get("platform", "youtube"),
seed=config.get("seed")
)
results.append(result)
# Summary
successful = sum(1 for r in results if r["success"])
print(f"\nBatch complete: {successful}/{len(results)} successful")
return results
# Example usage for social media campaign
generator = MochiVideoGenerator(output_dir="campaign_videos")
campaign_prompts = [
{
"prompt": "Product showcase: premium headphones rotating on pedestal, "
"studio lighting, reflective surface, smooth motion",
"platform": "instagram",
"seed": 42
},
{
"prompt": "Nature scene: cherry blossoms falling in slow motion, "
"soft pink petals, spring atmosphere, cinematic",
"platform": "tiktok",
"seed": 123
},
{
"prompt": "Tech demo: futuristic UI hologram with particle effects, "
"neon glow, smooth animations, sci-fi aesthetic",
"platform": "youtube",
"seed": 456
}
]
results = generator.batch_generate(campaign_prompts)
print(f"\nGenerated {len([r for r in results if r['success']])} videos for campaign")Conclusion
Mochi 1 establishes new standards for open-source video generation quality and proves that world-class generative models can thrive as community-driven projects. With Apache 2.0 licensing and 10 billion parameters, it provides developers complete freedom to build, modify, and deploy advanced video AI.