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import os
import torch
import gradio as gr
import spaces # Critical for ZeroGPU support in Spaces
from diffusers import DiffusionPipeline # Common for HF video models
from PIL import Image
# =============================================================
# INITIALIZATION & PIPELINE CONFIGURATION
# =============================================================
MODEL_ID = "MiniMaxAI/MiniMax-M1"
def load_pipeline():
# Load model with bfloat16 for modern GPU efficiency [17]
# Note: Ensure the model is available on the HF Hub or adjust path
pipe = DiffusionPipeline.from_pretrained(
MODEL_ID,
torch_dtype=torch.bfloat16,
use_safetensors=True
)
# Critical VRAM optimizations for deployment on 24GB or ZeroGPU [2]
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
return pipe
# Pipeline is initialized globally to avoid reloads on every click
pipe = load_pipeline()
# =============================================================
# GENERATION LOGIC
# =============================================================
@spaces.GPU(duration=300) # Allocated GPU time for complex generation [16]
def generate_video(prompt, negative_prompt, steps, guidance_scale, seed):
# Standard 5-second video length at 16 FPS [18-20]
num_frames = 81
# Use Generator for deterministic results [21, 22]
generator = torch.Generator("cuda").manual_seed(int(seed))
# Execution using VACE-style inputs [8, 23]
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=int(steps),
guidance_scale=float(guidance_scale),
num_frames=num_frames,
generator=generator
).frames
# Exporting generated frames to a video file [24, 25]
# For a real app, use diffusers.utils.export_to_video
from diffusers.utils import export_to_video
import tempfile
temp_path = tempfile.mktemp(suffix=".mp4")
export_to_video(output, temp_path, fps=16)
return temp_path
# =============================================================
# UI DESIGN (Using gr.Blocks for Professional Layout)
# =============================================================
# CSS for custom styling to improve "Joyful Experience" [26, 27]
css = """
.container { max-width: 1000px; margin: auto; }
.gen_btn { background-color: #7224f2 !important; color: white !important; }
"""
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
gr.Markdown(f"# {MODEL_ID} High-Fidelity Video Generator")
gr.Markdown("Leveraging ZeroGPU and VRAM offloading for cinematic AI video [2, 28].")
with gr.Row():
with gr.Column(scale=1):
prompt_input = gr.Textbox(
label="Prompt",
placeholder="Describe the action and scene details...",
lines=4
)
neg_prompt = gr.Textbox(
label="Negative Prompt",
value="blurry, distorted, low quality, watermark, text"
)
with gr.Accordion("Advanced Settings", open=False):
steps = gr.Slider(20, 50, value=30, step=1, label="Inference Steps")
guidance = gr.Slider(1.0, 15.0, value=7.0, label="Guidance Scale")
seed = gr.Number(value=42, label="Seed")
generate_btn = gr.Button("Generate Video", variant="primary", elem_classes="gen_btn")
with gr.Column(scale=1):
video_output = gr.Video(label="Generated Output")
# Event listener mapping [29, 30]
generate_btn.click(
fn=generate_video,
inputs=[prompt_input, neg_prompt, steps, guidance, seed],
outputs=video_output
)
# =============================================================
# LAUNCH
# =============================================================
if __name__ == "__main__":
# Ensure app.py is at the root for automatic HF detection [14, 15]
demo.launch()
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