---
license: apache-2.0
language:
- en
pipeline_tag: text-to-video
tags:
- art
- code
---
# RCNA MINI

**RCNA MINI** is a compact **LoRA** (Low-Rank Adaptation) model designed for generating high-quality, 4-step text-to-video outputs. It can create video clips ranging from 4 to 16 seconds long, making it ideal for generating short animations with rich details and smooth transitions.

## Key Features:
- **4-step Text-to-Video**: Generates videos from a text prompt in just 4 steps.
- **Video Length**: Can generate videos from 4 seconds to 16 seconds long.
- **High Quality**: Supports high-resolution and detailed outputs (up to 8K).
- **Fast Sampling**: Leveraging decoupled consistency learning, the model is optimized for speed while maintaining quality.

## Example Outputs:

- **Prompt**: "Astronaut in a jungle, cold color palette, muted colors, detailed, 8K"
  - Generates a high-quality video with rich details and smooth motion.

## How it Works:
RCNA MINI is based on the LoRA architecture, which fine-tunes diffusion models using low-rank adaptations. This results in faster generation and less computational overhead compared to full model retraining.

## Applications:
- Short-form animations for social media content
- Video generation for creative projects
- Artistic video generation based on textual descriptions

## Model Details:
- **Architecture**: LoRA applied to diffusion models
- **Inference Steps**: 4-step generation
- **Output Length**: 4 to 16 seconds
- 
## Using AnimateLCM with Diffusers

```python
import torch
from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter, DiffusionPipeline
from diffusers.utils import export_to_gif

# Load AnimateLCM for video generation
adapter = MotionAdapter.from_pretrained("Binarybardakshat/RCNA_MINI")
pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
pipe.load_lora_weights("Binarybardakshat/RCNA_MINI", weight_name="RCNA_LORA_MINI_1.safetensors", adapter_name="lcm-lora")
pipe.set_adapters(["lcm-lora"], [0.8])
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()

# Generate video using RCNA MINI
output = pipe(
    prompt="A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution",
    negative_prompt="bad quality, worse quality, low resolution",
    num_frames=16,
    guidance_scale=2.0,
    num_inference_steps=6,
    generator=torch.Generator("cpu").manual_seed(0),
)
frames = output.frames[0]
export_to_gif(frames, "animatelcm.gif")
print("Video and image generation complete!")

```
## License:
This model is licensed under the [MIT License](LICENSE).