hanani/Thinking-Out-Loud

Thinking Out Loud

About this LoRA

This is a LoRA for the Wan 14B Text-to-Video model.

It can be used with diffusers or ComfyUI, and can be loaded against the Wan 14B models.

It was trained on Replicate with 500 steps at a learning rate of 5e-05 and LoRA rank of 32.

Trigger word

You should use woodward to trigger the video generation.

Use this LoRA

Replicate has a collection of Wan models that are optimised for speed and cost. They can also be used with this LoRA:

• https://replicate.com/collections/wan-video
• https://replicate.com/fofr/wan-with-lora

Run this LoRA with an API using Replicate

import replicate

input = {
    "prompt": "woodward",
    "lora_url": "https://huggingface.co/hanani/Thinking-Out-Loud/resolve/main/wan-14b-t2v-woodward-lora.safetensors"
}

output = replicate.run(
    "fofr/wan-with-lora:latest",
    model="14B",
    input=input
)
for index, item in enumerate(output):
    with open(f"output_{index}.mp4", "wb") as file:
        file.write(item.read())

Using with Diffusers

import torch
from diffusers.utils import export_to_video
from diffusers import WanVidAdapter, WanVid

# Load base model
base_model = WanVid.from_pretrained("Wan-AI/Wan2.1-T2V-14B-Diffusers", torch_dtype=torch.float16)

# Load and apply LoRA adapter
adapter = WanVidAdapter.from_pretrained("hanani/Thinking-Out-Loud")
base_model.load_adapter(adapter)

# Generate video
prompt = "woodward"
negative_prompt = "blurry, low quality, low resolution"

# Generate video frames
frames = base_model(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    guidance_scale=5.0,
    width=832,
    height=480,
    fps=16,
    num_frames=32,
).frames[0]

# Save as video
video_path = "output.mp4"
export_to_video(frames, video_path, fps=16)
print(f"Video saved to: {video_path}")

Training details

• Steps: 500
• Learning rate: 5e-05
• LoRA rank: 32

Contribute your own examples

You can use the community tab to add videos that show off what you've made with this LoRA.