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license: apache-2.0
language:
- en
- zh
tags:
- image-to-video
- lora
- replicate
- text-to-video
- video
- video-generation
base_model: "Wan-AI/Wan2.1-T2V-14B-Diffusers"
pipeline_tag: text-to-video
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: woodward
---
# Thinking Out Loud
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) 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](https://replicate.com/) 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
```py
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
```py
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](https://huggingface.co/hanani/Thinking-Out-Loud/discussions) to add videos that show off what you've made with this LoRA.
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