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import gradio as gr
import torch
from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
from diffusers.utils import export_to_video
import spaces
import os
# 1. Load the Model Components
print("Loading AnimateDiff-Lightning... this will be fast.")
# Load the motion adapter (the "video" part of the brain)
adapter = MotionAdapter.from_pretrained(
"ByteDance/AnimateDiff-Lightning-4step-T2V",
torch_dtype=torch.float16
)
# Load the base model (the "image" part of the brain)
# We use epiCRealism for high-quality realistic style
pipe = AnimateDiffPipeline.from_pretrained(
"emilianJR/epiCRealism",
motion_adapter=adapter,
torch_dtype=torch.float16
)
# Set up the scheduler specifically for Lightning (4-step generation)
pipe.scheduler = EulerDiscreteScheduler.from_config(
pipe.scheduler.config,
timestep_spacing="trailing",
beta_schedule="linear"
)
# Move to GPU immediately to speed up loading (ZeroGPU handles the swap)
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe.to(device)
# 2. Define the Generation Function
# @spaces.GPU ensures you get a powerful GPU for this function
@spaces.GPU(duration=60)
def generate_video(prompt, negative_prompt):
print(f"Generating video for: {prompt}")
# Generate the video frames
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=4, # Lightning needs only 4 steps!
guidance_scale=1.5, # Keep guidance low for Lightning
num_frames=16, # Standard length for AnimateDiff
)
frames = output.frames[0]
# Save to MP4
output_path = "output.mp4"
export_to_video(frames, output_path)
return output_path
# 3. Build the User Interface
with gr.Blocks(theme="soft") as demo:
gr.Markdown("# ⚡ AnimateDiff Lightning (Free & Fast)")
gr.Markdown("A truly free, open-source video generator using ByteDance's Lightning technology. fast generation.")
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(
label="Prompt",
placeholder="Close up portrait of a cyberpunk woman, neon city background, rainfall, 8k, realistic",
lines=3
)
neg_prompt_input = gr.Textbox(
label="Negative Prompt",
value="bad quality, worst quality, deformed, distorted, watermark",
lines=2
)
generate_btn = gr.Button("⚡ Generate Video", variant="primary")
with gr.Column():
video_output = gr.Video(label="Generated Result")
generate_btn.click(
fn=generate_video,
inputs=[prompt_input, neg_prompt_input],
outputs=video_output
)
# Launch
demo.launch()
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