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from dataclasses import dataclass
from typing import Dict, Any, Optional
import base64
import logging
import random
import torch
from diffusers import HunyuanVideoPipeline
from varnish import Varnish
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class GenerationConfig:
"""Configuration for video generation"""
# Content settings
prompt: str
negative_prompt: str = ""
# Model settings
num_frames: int = 49 # Should be 4k + 1 format
height: int = 320
width: int = 576
num_inference_steps: int = 50
guidance_scale: float = 7.0
# Reproducibility
seed: int = -1
# Varnish post-processing settings
fps: int = 30
double_num_frames: bool = False
super_resolution: bool = False
grain_amount: float = 0.0
quality: int = 18 # CRF scale (0-51, lower is better)
# Audio settings
enable_audio: bool = False
audio_prompt: str = ""
audio_negative_prompt: str = "voices, voice, talking, speaking, speech"
def validate_and_adjust(self) -> 'GenerationConfig':
"""Validate and adjust parameters"""
# Ensure num_frames follows 4k + 1 format
k = (self.num_frames - 1) // 4
self.num_frames = (k * 4) + 1
# Set random seed if not specified
if self.seed == -1:
self.seed = random.randint(0, 2**32 - 1)
return self
class EndpointHandler:
"""Handles video generation requests using HunyuanVideo and Varnish"""
def __init__(self, path: str = ""):
"""Initialize handler with models
Args:
path: Path to model weights
"""
self.device = "cuda" if torch.cuda.is_available() else "cpu"
# Initialize HunyuanVideo pipeline
self.pipeline = HunyuanVideoPipeline.from_pretrained(
path,
torch_dtype=torch.float16,
).to(self.device)
# Initialize text encoders in float16
self.pipeline.text_encoder = self.pipeline.text_encoder.half()
self.pipeline.text_encoder_2 = self.pipeline.text_encoder_2.half()
# Initialize transformer in bfloat16
self.pipeline.transformer = self.pipeline.transformer.to(torch.bfloat16)
# Initialize VAE in float16
self.pipeline.vae = self.pipeline.vae.half()
# Initialize Varnish for post-processing
self.varnish = Varnish(
device=self.device,
model_base_dir="/repository/varnish"
)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Process video generation requests
Args:
data: Request data containing:
- inputs (str): Prompt for video generation
- parameters (dict): Generation parameters
Returns:
Dictionary containing:
- video: Base64 encoded MP4 data URI
- content-type: MIME type
- metadata: Generation metadata
"""
# Extract inputs
inputs = data.pop("inputs", data)
if isinstance(inputs, dict):
prompt = inputs.get("prompt", "")
else:
prompt = inputs
params = data.get("parameters", {})
# Create and validate config
config = GenerationConfig(
prompt=prompt,
negative_prompt=params.get("negative_prompt", ""),
num_frames=params.get("num_frames", 49),
height=params.get("height", 320),
width=params.get("width", 576),
num_inference_steps=params.get("num_inference_steps", 50),
guidance_scale=params.get("guidance_scale", 7.0),
seed=params.get("seed", -1),
fps=params.get("fps", 30),
double_num_frames=params.get("double_num_frames", False),
super_resolution=params.get("super_resolution", False),
grain_amount=params.get("grain_amount", 0.0),
quality=params.get("quality", 18),
enable_audio=params.get("enable_audio", False),
audio_prompt=params.get("audio_prompt", ""),
audio_negative_prompt=params.get("audio_negative_prompt", "voices, voice, talking, speaking, speech"),
).validate_and_adjust()
try:
# Set random seeds
if config.seed != -1:
torch.manual_seed(config.seed)
random.seed(config.seed)
generator = torch.Generator(device=self.device).manual_seed(config.seed)
else:
generator = None
# Generate video frames
with torch.inference_mode():
output = self.pipeline(
prompt=config.prompt,
negative_prompt=config.negative_prompt,
num_frames=config.num_frames,
height=config.height,
width=config.width,
num_inference_steps=config.num_inference_steps,
guidance_scale=config.guidance_scale,
generator=generator,
output_type="pt",
).frames
# Process with Varnish
import asyncio
try:
loop = asyncio.get_event_loop()
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
result = loop.run_until_complete(
self.varnish(
input_data=output,
fps=config.fps,
double_num_frames=config.double_num_frames,
super_resolution=config.super_resolution,
grain_amount=config.grain_amount,
enable_audio=config.enable_audio,
audio_prompt=config.audio_prompt,
audio_negative_prompt=config.audio_negative_prompt,
)
)
# Get video data URI
video_uri = loop.run_until_complete(
result.write(
type="data-uri",
quality=config.quality
)
)
return {
"video": video_uri,
"content-type": "video/mp4",
"metadata": {
"width": result.metadata.width,
"height": result.metadata.height,
"num_frames": result.metadata.frame_count,
"fps": result.metadata.fps,
"duration": result.metadata.duration,
"seed": config.seed,
}
}
except Exception as e:
logger.error(f"Error generating video: {str(e)}")
raise RuntimeError(f"Failed to generate video: {str(e)}") |