metadata

license: apache-2.0
language:
  - en
base_model:
  - Wan-AI/Wan2.1-T2V-14B
tags:
  - text-to-video
  - lora
  - diffusers
  - template:diffusion-lora
widget:
  - text: >-
      [conor] a man fighting another man in the MMA ring, landing a right hook
      towards the starting.
    output:
      url: videos/conor_2.mp4
  - text: >-
      [conor] a man sitting drinking whiskey, the bottle of whiskey says "proper
      twelve", with girls sitting around.
    output:
      url: videos/conor_1.mp4

Conor McGregor Lora for WanVideo2.1

Prompt: [conor] a man fighting another man in the MMA ring, landing a right hook towards the starting.

Prompt: [conor] a man sitting drinking whiskey, the bottle of whiskey says "proper twelve", with girls sitting around.

Trigger words

You should use conor to trigger the video generation.

Using with Diffusers

pip install git+https://github.com/huggingface/diffusers.git

import torch
from diffusers.utils import export_to_video
from diffusers import AutoencoderKLWan, WanPipeline
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler

# Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
flow_shift = 5.0  # 5.0 for 720P, 3.0 for 480P
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
pipe.to("cuda")

pipe.load_lora_weights("shauray/Mcgregor_WanLora")

pipe.enable_model_cpu_offload() #for low-vram environments

prompt = "conor a man walking through a bar"

output = pipe(
    prompt=prompt,
    height=480,
    width=720,
    num_frames=81,
    guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)

Download model

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.