import argparse import json import os import random import sys sys.path.insert(0, '../src') import torch from einops import rearrange, repeat from pytorch_lightning import seed_everything from safetensors import safe_open from torch import autocast from scripts.sampling.util import ( chunk, convert_load_lora, create_model, init_sampling, load_video_keyframes, model_load_ckpt, perform_save_locally_video, ) from sgm.util import append_dims if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--seed", type=int, default=42) parser.add_argument( "--config_path", type=str, default="configs/inference_ccedit/keyframe_no2ndca_depthmidas.yaml", ) parser.add_argument( "--ckpt_path", type=str, default="models/tv2v-no2ndca-depthmidas.ckpt", ) parser.add_argument( "--use_default", action="store_true", help="use default ckpt at first" ) parser.add_argument( "--basemodel_path", type=str, default="", help="load a new base model instead of original sd-1.5", ) parser.add_argument("--basemodel_listpath", type=str, default="") parser.add_argument("--lora_path", type=str, default="") parser.add_argument("--vae_path", type=str, default="") parser.add_argument( "--jsonl_path", type=str, required=True, help="path to jsonl file containing video paths, prompts, and edit prompts" ) parser.add_argument("--save_root", type=str, default="outputs") parser.add_argument("--H", type=int, default=512) parser.add_argument("--W", type=int, default=768) parser.add_argument("--original_fps", type=int, default=18) parser.add_argument("--target_fps", type=int, default=6) parser.add_argument("--num_keyframes", type=int, default=17) parser.add_argument("--negative_prompt", type=str, default="ugly, low quality") parser.add_argument("--sample_steps", type=int, default=30) parser.add_argument("--sampler_name", type=str, default="DPMPP2SAncestralSampler") parser.add_argument( "--discretization_name", type=str, default="LegacyDDPMDiscretization" ) parser.add_argument("--cfg_scale", type=float, default=7.5) parser.add_argument("--prior_coefficient_x", type=float, default=0.0) parser.add_argument("--prior_coefficient_noise", type=float, default=1.0) parser.add_argument("--sdedit_denoise_strength", type=float, default=0.0) parser.add_argument("--num_samples", type=int, default=2) parser.add_argument("--batch_size", type=int, default=1) parser.add_argument('--disable_check_repeat', action='store_true', help='disable check repeat') parser.add_argument('--lora_strength', type=float, default=0.8) parser.add_argument('--save_type', type=str, default='mp4', choices=['gif', 'mp4']) parser.add_argument('--inpainting_mode', action='store_true', help='inpainting mode') args = parser.parse_args() seed = args.seed if seed == -1: seed = random.randint(0, 1000000) seed_everything(seed) model = create_model(config_path=args.config_path).to("cuda") ckpt_path = args.ckpt_path print("--> load ckpt from: ", ckpt_path) model = model_load_ckpt(model, path=ckpt_path) model.eval() with open(args.jsonl_path, 'r') as f: lines = f.readlines() video_info_list = [json.loads(line) for line in lines] for video_info in video_info_list: video_name = video_info['video'] prompt = video_info['prompt'] add_prompt = video_info['edit_prompt'] video_path = os.path.join('/home/wangjuntong/video_editing_dataset/all_sourse', video_name) save_path = os.path.join(args.save_root, os.path.splitext(video_name)[0]) keyframes = load_video_keyframes( video_path, args.original_fps, args.target_fps, args.num_keyframes, (args.H, args.W), ) keyframes = keyframes.unsqueeze(0) keyframes = rearrange(keyframes, "b t c h w -> b c t h w").to(model.device) control_hint = keyframes batch = { "txt": [prompt], "control_hint": control_hint, } negative_prompt = args.negative_prompt batch_uc = { "txt": [negative_prompt], "control_hint": batch["control_hint"].clone(), } if add_prompt: batch["txt"] = [add_prompt + ", " + prompt] c, uc = model.conditioner.get_unconditional_conditioning( batch_c=batch, batch_uc=batch_uc, ) sampling_kwargs = {} for k in c: if isinstance(c[k], torch.Tensor): c[k], uc[k] = map(lambda y: y[k].to(model.device), (c, uc)) shape = (4, args.num_keyframes, args.H // 8, args.W // 8) precision_scope = autocast with torch.no_grad(): with torch.cuda.amp.autocast(): randn = torch.randn(1, *shape).to(model.device) if args.sdedit_denoise_strength == 0.0: def denoiser(input, sigma, c): return model.denoiser( model.model, input, sigma, c, **sampling_kwargs ) if args.prior_coefficient_x != 0.0: prior = model.encode_first_stage(keyframes) randn = ( args.prior_coefficient_x * prior + args.prior_coefficient_noise * randn ) sampler = init_sampling( sample_steps=args.sample_steps, sampler_name=args.sampler_name, discretization_name=args.discretization_name, guider_config_target="sgm.modules.diffusionmodules.guiders.VanillaCFGTV2V", cfg_scale=args.cfg_scale, ) sampler.verbose = True samples = sampler(denoiser, randn, c, uc=uc) else: assert ( args.sdedit_denoise_strength > 0.0 ), "sdedit_denoise_strength should be positive" assert ( args.sdedit_denoise_strength <= 1.0 ), "sdedit_denoise_strength should be less than 1.0" assert ( args.prior_coefficient_x == 0 ), "prior_coefficient_x should be 0 when using sdedit_denoise_strength" denoise_strength = args.sdedit_denoise_strength sampler = init_sampling( sample_steps=args.sample_steps, sampler_name=args.sampler_name, discretization_name=args.discretization_name, guider_config_target="sgm.modules.diffusionmodules.guiders.VanillaCFGTV2V", cfg_scale=args.cfg_scale, img2img_strength=denoise_strength, ) sampler.verbose = True z = model.encode_first_stage(keyframes) noise = torch.randn_like(z) sigmas = sampler.discretization(sampler.num_steps).to(z.device) sigma = sigmas[0] print(f"all sigmas: {sigmas}") print(f"noising sigma: {sigma}") noised_z = z + noise * append_dims(sigma, z.ndim) noised_z = noised_z / torch.sqrt( 1.0 + sigmas[0] ** 2.0 ) def denoiser(x, sigma, c): return model.denoiser(model.model, x, sigma, c) samples = sampler(denoiser, noised_z, cond=c, uc=uc) samples = model.decode_first_stage(samples) samples = (torch.clamp(samples, -1.0, 1.0) + 1.0) / 2.0 os.makedirs(save_path, exist_ok=True) perform_save_locally_video( save_path, samples, args.target_fps, args.save_type, save_grid=False ) print(f"Saved video to {save_path}")