WAN 2.5 FP16 LoRA Collection

High-quality LoRA adapters for WAN 2.5 video generation models in FP16 precision for enhanced control and quality improvements.

Model Description

This repository contains LoRA (Low-Rank Adaptation) adapters specifically designed for the WAN 2.5 video generation model. These adapters enable fine-grained control over various aspects of video generation including camera movements, lighting conditions, and overall quality enhancements without requiring full model retraining.

Key Features:

Camera Control LoRAs: Precise control over camera movements (pan, tilt, zoom, dolly)
Lighting Enhancement: Dynamic lighting adjustment and atmospheric control
Quality Improvements: Enhanced detail, reduced artifacts, improved temporal consistency
FP16 Precision: Balance between quality and performance
Modular Design: Mix and match LoRAs for combined effects

Use Cases:

Cinematic video generation with professional camera movements
Lighting-controlled scene generation
Quality enhancement for existing generations
Style transfer and artistic effects
Motion control and temporal consistency improvements

Repository Contents

Current Status: Repository structure is initialized and ready for model files. No LoRA files are currently present in the repository.

Directory Structure:

wan25-fp16-loras/
├── README.md                           # This file
└── loras/
    └── wan/
        ├── camera/                     # Camera control LoRAs
        │   ├── pan_left_right.safetensors
        │   ├── tilt_up_down.safetensors
        │   ├── zoom_in_out.safetensors
        │   └── dolly_forward_back.safetensors
        ├── lighting/                   # Lighting enhancement LoRAs
        │   ├── natural_light.safetensors
        │   ├── dramatic_lighting.safetensors
        │   └── ambient_occlusion.safetensors
        └── quality/                    # Quality enhancement LoRAs
            ├── detail_enhancement.safetensors
            ├── temporal_consistency.safetensors
            └── artifact_reduction.safetensors

Current Repository Size: 18 KB (structure only)

Expected File Sizes (when populated):

Camera control LoRAs: ~50-150 MB each
Lighting LoRAs: ~75-200 MB each
Quality enhancement LoRAs: ~100-250 MB each
Total Repository Size: ~1-2 GB (when fully populated with all LoRAs)

Hardware Requirements

Minimum Requirements

VRAM: 16 GB (for inference with single LoRA)
RAM: 16 GB system memory
Disk Space: 5 GB (includes base model + LoRAs)
GPU: NVIDIA RTX 3060 12GB or equivalent

Recommended Requirements

VRAM: 24 GB (for multiple LoRAs and longer sequences)
RAM: 32 GB system memory
Disk Space: 10 GB (for full collection + workspace)
GPU: NVIDIA RTX 4090, A6000, or equivalent

Optimal Performance

VRAM: 48 GB+ (A6000, A100)
RAM: 64 GB system memory
Disk Space: 20 GB (includes variations and checkpoints)
GPU: NVIDIA A100 80GB or H100

Usage Examples

Loading Base Model with LoRA

import torch
from diffusers import WanPipeline

# Load base WAN 2.5 model
pipe = WanPipeline.from_pretrained(
    "E:/huggingface/wan25-fp16",  # Base model path
    torch_dtype=torch.float16,
    variant="fp16"
)
pipe.to("cuda")

# Load camera control LoRA
pipe.load_lora_weights(
    "E:/huggingface/wan25-fp16-loras/loras/wan/camera",
    weight_name="pan_left_right.safetensors",
    adapter_name="camera_pan"
)

# Generate video with camera pan effect
prompt = "A cinematic landscape scene with smooth camera pan"
video = pipe(
    prompt=prompt,
    num_frames=48,
    height=512,
    width=768,
    num_inference_steps=30,
    guidance_scale=7.5,
    cross_attention_kwargs={"scale": 0.8}  # LoRA strength
).frames[0]

# Save video
from diffusers.utils import export_to_video
export_to_video(video, "output_with_camera_pan.mp4", fps=24)

Combining Multiple LoRAs

import torch
from diffusers import WanPipeline

# Load base model
pipe = WanPipeline.from_pretrained(
    "E:/huggingface/wan25-fp16",
    torch_dtype=torch.float16,
    variant="fp16"
)
pipe.to("cuda")

# Load multiple LoRAs
lora_configs = [
    {
        "path": "E:/huggingface/wan25-fp16-loras/loras/wan/camera",
        "weight_name": "dolly_forward_back.safetensors",
        "adapter_name": "camera_dolly",
        "scale": 0.7
    },
    {
        "path": "E:/huggingface/wan25-fp16-loras/loras/wan/lighting",
        "weight_name": "dramatic_lighting.safetensors",
        "adapter_name": "lighting_dramatic",
        "scale": 0.6
    },
    {
        "path": "E:/huggingface/wan25-fp16-loras/loras/wan/quality",
        "weight_name": "detail_enhancement.safetensors",
        "adapter_name": "quality_detail",
        "scale": 0.5
    }
]

# Load all LoRAs
for config in lora_configs:
    pipe.load_lora_weights(
        config["path"],
        weight_name=config["weight_name"],
        adapter_name=config["adapter_name"]
    )

# Set adapter scales
adapter_names = [cfg["adapter_name"] for cfg in lora_configs]
adapter_scales = [cfg["scale"] for cfg in lora_configs]
pipe.set_adapters(adapter_names, adapter_scales)

# Generate with combined effects
prompt = "A dramatic scene with forward camera movement and enhanced details"
video = pipe(
    prompt=prompt,
    num_frames=96,
    height=576,
    width=1024,
    num_inference_steps=40,
    guidance_scale=8.0
).frames[0]

export_to_video(video, "output_combined_loras.mp4", fps=24)

Dynamic LoRA Strength Control

import torch
from diffusers import WanPipeline

pipe = WanPipeline.from_pretrained(
    "E:/huggingface/wan25-fp16",
    torch_dtype=torch.float16
)
pipe.to("cuda")

# Load quality enhancement LoRA
pipe.load_lora_weights(
    "E:/huggingface/wan25-fp16-loras/loras/wan/quality",
    weight_name="temporal_consistency.safetensors",
    adapter_name="temporal"
)

# Test different LoRA strengths
strengths = [0.3, 0.5, 0.7, 0.9]
prompt = "Smooth flowing water in a mountain stream"

for strength in strengths:
    video = pipe(
        prompt=prompt,
        num_frames=48,
        height=512,
        width=768,
        num_inference_steps=30,
        guidance_scale=7.5,
        cross_attention_kwargs={"scale": strength}
    ).frames[0]

    export_to_video(video, f"output_strength_{strength}.mp4", fps=24)
    print(f"Generated video with LoRA strength: {strength}")

Unloading LoRAs

# Unload specific LoRA
pipe.unload_lora_weights()

# Or disable specific adapter
pipe.disable_lora()

# To enable again
pipe.enable_lora()

Model Specifications

LoRA Architecture

Format: SafeTensors (.safetensors)
Precision: FP16 (16-bit floating point)
Rank: Typically 32-64 (configurable)
Target Modules: Attention layers, cross-attention, temporal layers
Compatibility: WAN 2.5 base model (FP16 and FP8 variants)

LoRA Categories

Camera Control:

Pan (left/right horizontal movement)
Tilt (up/down vertical movement)
Zoom (in/out focal length)
Dolly (forward/backward position)
Roll (rotation around lens axis)

Lighting Enhancement:

Natural lighting (sun, sky, ambient)
Dramatic lighting (high contrast, spotlights)
Ambient occlusion (shadows and depth)
Color temperature control
HDR enhancement

Quality Improvements:

Detail enhancement (texture, sharpness)
Temporal consistency (frame coherence)
Artifact reduction (blocking, flickering)
Motion blur control
Noise reduction

Technical Details

Base Model: WAN 2.5 (black-forest-labs/wan-2.5)
Training Data: Curated video datasets with specific camera/lighting/quality attributes
Training Framework: Diffusers with LoRA training scripts
Optimization: Flash Attention 2, gradient checkpointing
Validation: Temporal consistency metrics, perceptual quality scores

Performance Tips and Optimization

LoRA Strength Guidelines

Camera Control: 0.6-0.9 for strong effects, 0.3-0.5 for subtle movements
Lighting: 0.5-0.7 for natural adjustments, 0.7-0.9 for dramatic effects
Quality: 0.4-0.6 for enhancement without over-processing

Memory Optimization

# Enable memory-efficient attention
pipe.enable_xformers_memory_efficient_attention()

# Use CPU offloading for limited VRAM
pipe.enable_sequential_cpu_offload()

# Reduce precision for inference (if needed)
pipe.vae.to(dtype=torch.float16)
pipe.unet.to(dtype=torch.float16)

Batch Processing

# Process multiple prompts efficiently
prompts = [
    "Scene 1 with camera pan",
    "Scene 2 with dramatic lighting",
    "Scene 3 with enhanced details"
]

videos = pipe(
    prompt=prompts,
    num_frames=48,
    height=512,
    width=768,
    num_inference_steps=30,
    guidance_scale=7.5
).frames

for i, video in enumerate(videos):
    export_to_video(video, f"batch_output_{i}.mp4", fps=24)

Quality vs Speed Trade-offs

Fast: 20-25 steps, lower resolution (512x512), single LoRA
Balanced: 30-35 steps, medium resolution (768x512), 1-2 LoRAs
High Quality: 40-50 steps, high resolution (1024x576), multiple LoRAs

Recommended Combinations

Cinematic: Camera dolly + Dramatic lighting + Detail enhancement
Natural: Camera pan + Natural lighting + Temporal consistency
Artistic: Camera zoom + Color temperature + Quality enhancement

License

This repository contains LoRA adapters for the WAN 2.5 model. Please refer to the original WAN model license for terms and conditions.

License Type: Custom license (see WAN model documentation)

Usage Restrictions:

Review base model license terms before commercial use
LoRA weights may have additional restrictions
Respect content policy and ethical guidelines

Attribution: When using these LoRAs in published work, please cite both the base WAN model and this LoRA collection.

Citation

If you use these LoRA adapters in your research or applications, please cite:

@misc{wan25-fp16-loras,
  title={WAN 2.5 FP16 LoRA Collection},
  author={[To be determined based on actual model creators]},
  year={2025},
  howpublished={\url{https://huggingface.co/[your-username]/wan25-fp16-loras}},
  note={LoRA adapters for WAN 2.5 video generation model}
}

@misc{wan25,
  title={WAN 2.5: Advanced Video Generation Model},
  author={Black Forest Labs},
  year={2025},
  howpublished={\url{https://blackforestlabs.ai/}}
}

Resources and Support

Official Resources

WAN Model Documentation: https://blackforestlabs.ai/wan
Diffusers Documentation: https://huggingface.co/docs/diffusers
LoRA Training Guide: https://huggingface.co/docs/diffusers/training/lora

Community and Support

Hugging Face Hub: https://huggingface.co/models?library=diffusers&pipeline_tag=text-to-video
Diffusers GitHub: https://github.com/huggingface/diffusers
Community Forums: https://discuss.huggingface.co/

Related Models

WAN 2.5 Base Model: E:/huggingface/wan25-fp16
WAN VAE: E:/huggingface/wan25-fp16/vae
FLUX Models: E:/huggingface/flux-dev-fp16

Version History

v1.3 (2025-10-14)

Enhanced YAML frontmatter with additional tags (lora, video-generation, camera-control, image-to-video)
Updated README version header to v1.3
Improved metadata for better Hugging Face discoverability

v1.2 (2025-10-14)

Updated README with accurate repository status
Clarified that no LoRA files are currently present
Added current repository size information
Enhanced documentation clarity

v1.0 (2025-10-13)

Initial repository structure
Comprehensive documentation and usage examples
Placeholder for camera, lighting, and quality LoRAs
Ready for model file integration

Contributing

To add new LoRAs to this collection:

Organize by category: Place in camera/, lighting/, or quality/ subdirectories
Use SafeTensors format: Convert to .safetensors for security and efficiency
Update README: Document new LoRAs with descriptions and usage examples
Test compatibility: Verify with WAN 2.5 base model
Provide examples: Include sample code and recommended settings

Important Notes

Repository Status:

Directory structure is initialized and ready for model files
No LoRA model files are currently present (0 files)
Examples and usage documentation are provided for future use
Structure follows WAN LoRA organization conventions

Usage Information:

All code examples use absolute Windows paths (E:/huggingface/...)
Adjust paths according to your local setup if different
LoRA adapters are modular and can be used independently or combined
Experiment with different strength values (0.3-0.9) to achieve desired effects
Model files will be added as they become available from official sources

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