Delete t2v_enhanced/inference.py

t2v_enhanced/inference.py

@@ -1,82 +0,0 @@
-# General
-import os
-from os.path import join as opj
-import argparse
-import datetime
-from pathlib import Path
-import torch
-import gradio as gr
-import tempfile
-import yaml
-from t2v_enhanced.model.video_ldm import VideoLDM
-
-# Utilities
-from t2v_enhanced.inference_utils import *
-from t2v_enhanced.model_init import *
-from t2v_enhanced.model_func import *
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--prompt', type=str, default="A cat running on the street", help="The prompt to guide video generation.")
-    parser.add_argument('--image', type=str, default="", help="Path to image conditioning.")
-    # parser.add_argument('--video', type=str, default="", help="Path to video conditioning.")
-    parser.add_argument('--base_model', type=str, default="ModelscopeT2V", help="Base model to generate first chunk from", choices=["ModelscopeT2V", "AnimateDiff", "SVD"])
-    parser.add_argument('--num_frames', type=int, default=24, help="The number of video frames to generate.")
-    parser.add_argument('--negative_prompt', type=str, default="", help="The prompt to guide what to not include in video generation.")
-    parser.add_argument('--num_steps', type=int, default=50, help="The number of denoising steps.")
-    parser.add_argument('--image_guidance', type=float, default=9.0, help="The guidance scale.")
-
-    parser.add_argument('--output_dir', type=str, default="results", help="Path where to save the generated videos.")
-    parser.add_argument('--device', type=str, default="cuda")
-    parser.add_argument('--seed', type=int, default=33, help="Random seed")
-    args = parser.parse_args()
-
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-    result_fol = Path(args.output_dir).absolute()
-    device = args.device
-
-
-    # --------------------------
-    # ----- Configurations -----
-    # --------------------------
-    ckpt_file_streaming_t2v = Path("checkpoints/streaming_t2v.ckpt").absolute()
-    cfg_v2v = {'downscale': 1, 'upscale_size': (1280, 720), 'model_id': 'damo/Video-to-Video', 'pad': True}
-
-
-    # --------------------------
-    # ----- Initialization -----
-    # --------------------------
-    stream_cli, stream_model = init_streamingt2v_model(ckpt_file_streaming_t2v, result_fol)
-    if args.base_model == "ModelscopeT2V":
-        model = init_modelscope(device)
-    elif args.base_model == "AnimateDiff":
-        model = init_animatediff(device)
-    elif args.base_model == "SVD":
-        model = init_svd(device)
-        sdxl_model = init_sdxl(device)
-    
-
-    inference_generator = torch.Generator(device="cuda")
-
-
-    # ------------------
-    # ----- Inputs -----
-    # ------------------
-    now = datetime.datetime.now()
-    name = args.prompt[:100].replace(" ", "_") + "_" + str(now.time()).replace(":", "_").replace(".", "_")
-
-    inference_generator = torch.Generator(device="cuda")
-    inference_generator.manual_seed(args.seed)
-    
-    if args.base_model == "ModelscopeT2V":
-        short_video = ms_short_gen(args.prompt, model, inference_generator)
-    elif args.base_model == "AnimateDiff":
-        short_video = ad_short_gen(args.prompt, model, inference_generator)
-    elif args.base_model == "SVD":
-        short_video = svd_short_gen(args.image, args.prompt, model, sdxl_model, inference_generator)
-
-    n_autoreg_gen = args.num_frames // 8 - 8
-    stream_long_gen(args.prompt, short_video, n_autoreg_gen, args.negative_prompt, args.seed, args.num_steps, args.image_guidance, name, stream_cli, stream_model)
-    video2video(args.prompt, opj(result_fol, name+".mp4"), result_fol, cfg_v2v, msxl_model)