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from diffusers import DiffusionPipeline
from .invert import Inverter
from .generate import Generator
from .utils import init_model, seed_everything, get_frame_ids
import torch
class VidToMePipeline(DiffusionPipeline):
# def __init__(self, device="cuda", sd_version="2.1", float_precision="fp16", height=512, width=512):
# # this will initlize the core pipeline components
# pipe, scheduler, model_key = init_model(device, sd_version, None, "none", float_precision)
# self.pipe = pipe
# self.scheduler = scheduler
# self.model_key = model_key
# self.device = device
# self.sd_version = sd_version
# self.float_precision = float_precision
# self.height = height
# self.width = width
def __init__(self, device="cuda", sd_version="1.5", float_precision="fp16", height=512, width=512):
# Register configuration parameters
self.register_to_config(device=device, sd_version=sd_version, float_precision=float_precision, height=height, width=width)
self.sd_version = sd_version
self.float_precision = float_precision
self.height = height
self.width = width
# this will initlize the core pipeline components
pipe, scheduler, model_key = init_model(device, sd_version, None, "none", float_precision)
self.pipe = pipe
self.scheduler = scheduler
self.model_key = model_key
super().__init__()
def __call__(self, video_path=None, video_prompt=None, edit_prompt=None,
control_type="none", n_timesteps=50, guidance_scale=7.5,
negative_prompt="ugly, blurry, low res", frame_range=None,
use_lora=False, seed=123, local_merge_ratio=0.9, global_merge_ratio=0.8):
# dynamic config built from user inputs
config = self._build_config(video_path, video_prompt, edit_prompt, control_type,
n_timesteps, guidance_scale, negative_prompt,
frame_range, use_lora, seed, local_merge_ratio, global_merge_ratio)
# seed for reproducibility - change as you need
seed_everything(config['seed'])
# inversion stage
print("Start inversion!")
inversion = Inverter(self.pipe, self.scheduler, config)
inversion(config['input_path'], config['inversion']['save_path'])
# generation stage
print("Start generation!")
generator = Generator(self.pipe, self.scheduler, config)
frame_ids = get_frame_ids(config['generation']['frame_range'], None)
generator(config['input_path'], config['generation']['latents_path'],
config['generation']['output_path'], frame_ids=frame_ids)
print(f"Output generated at: {config['generation']['output_path']}")
def _build_config(self, video_path, video_prompt, edit_prompt, control_type,
n_timesteps, guidance_scale, negative_prompt, frame_range,
use_lora, seed, local_merge_ratio, global_merge_ratio):
# constructing config dictionary from user prompts
config = {
'sd_version': self.sd_version,
'input_path': video_path,
'work_dir': "outputs/",
'height': self.height,
'width': self.width,
'inversion': {
'prompt': video_prompt or "Default video prompt.",
'save_path': "outputs/latents",
'steps': 50,
'save_intermediate': False
},
'generation': {
'control': control_type,
'guidance_scale': guidance_scale,
'n_timesteps': n_timesteps,
'negative_prompt': negative_prompt,
'prompt': edit_prompt or "Default edit prompt.",
'latents_path': "outputs/latents",
'output_path': "outputs/final",
'frame_range': frame_range or [0, 32],
'use_lora': use_lora,
'local_merge_ratio': local_merge_ratio,
'global_merge_ratio': global_merge_ratio
},
'seed': seed,
'device': "cuda",
'float_precision': self.float_precision
}
return config
# # Sample usage
# pipeline = VidToMePipeline(device="cuda", sd_version="2.1", float_precision="fp16")
# pipeline(video_path="path/to/video.mp4", video_prompt="A beautiful scene of a sunset",
# edit_prompt="Make the sunset look more vibrant", control_type="depth", n_timesteps=50)
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