320 lines
12 KiB
Python
320 lines
12 KiB
Python
import datetime, time
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import os, sys, argparse
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import math
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from glob import glob
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from pathlib import Path
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from typing import Optional
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import cv2
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import numpy as np
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import torch
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from einops import rearrange, repeat
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from fire import Fire
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from omegaconf import OmegaConf
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from PIL import Image
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from torchvision.transforms import ToTensor
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sys.path.insert(1, os.path.join(sys.path[0], '..', '..'))
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from sgm.util import default, instantiate_from_config
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def sample(
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input_path: str = "outputs/inputs/test_image.png", # Can either be image file or folder with image files
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ckpt: str = "checkpoints/svd.safetensors",
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num_frames: Optional[int] = None,
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num_steps: Optional[int] = None,
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version: str = "svd",
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fps_id: int = 6,
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motion_bucket_id: int = 127,
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cond_aug: float = 0.02,
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seed: int = 23,
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decoding_t: int = 1, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
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device: str = "cuda",
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output_folder: Optional[str] = None,
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save_fps: int = 10,
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resize: Optional[bool] = False,
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):
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"""
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Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
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image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
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"""
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if version == "svd":
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num_frames = default(num_frames, 14)
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num_steps = default(num_steps, 25)
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output_folder = default(output_folder, "outputs/svd/")
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model_config = "scripts/sampling/configs/svd.yaml"
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elif version == "svd_xt":
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num_frames = default(num_frames, 25)
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num_steps = default(num_steps, 30)
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output_folder = default(output_folder, "outputs/svd_xt/")
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model_config = "scripts/sampling/configs/svd_xt.yaml"
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elif version == "svd_image_decoder":
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num_frames = default(num_frames, 14)
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num_steps = default(num_steps, 25)
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output_folder = default(
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output_folder, "outputs/svd_image_decoder/"
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)
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model_config = "scripts/sampling/configs/svd_image_decoder.yaml"
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elif version == "svd_xt_image_decoder":
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num_frames = default(num_frames, 25)
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num_steps = default(num_steps, 30)
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output_folder = default(
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output_folder, "outputs/svd_xt_image_decoder/"
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)
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model_config = "scripts/sampling/configs/svd_xt_image_decoder.yaml"
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else:
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raise ValueError(f"Version {version} does not exist.")
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model, filter = load_model(
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model_config,
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ckpt,
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device,
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num_frames,
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num_steps,
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)
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torch.manual_seed(seed)
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path = Path(input_path)
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all_img_paths = []
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if path.is_file():
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if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
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all_img_paths = [input_path]
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else:
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raise ValueError("Path is not valid image file.")
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elif path.is_dir():
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all_img_paths = sorted(
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[
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f
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for f in path.iterdir()
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if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
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]
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)
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if len(all_img_paths) == 0:
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raise ValueError("Folder does not contain any images.")
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else:
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raise ValueError
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print(f'loaded {len(all_img_paths)} images.')
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os.makedirs(output_folder, exist_ok=True)
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for no, input_img_path in enumerate(all_img_paths):
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filepath, fullflname = os.path.split(input_img_path)
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filename, ext = os.path.splitext(fullflname)
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print(f'-sample {no+1}: {filename} ...')
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with Image.open(input_img_path) as image:
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if image.mode == "RGBA":
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image = image.convert("RGB")
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if resize:
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image = image.resize((1024,576))
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w, h = image.size
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if h % 64 != 0 or w % 64 != 0:
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width, height = map(lambda x: x - x % 64, (w, h))
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image = image.resize((width, height))
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print(
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f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
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)
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image = ToTensor()(image)
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image = image * 2.0 - 1.0
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image = image.unsqueeze(0).to(device)
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H, W = image.shape[2:]
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assert image.shape[1] == 3
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F = 8
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C = 4
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shape = (num_frames, C, H // F, W // F)
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if (H, W) != (576, 1024):
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print(
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"WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
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)
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if motion_bucket_id > 255:
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print(
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"WARNING: High motion bucket! This may lead to suboptimal performance."
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)
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if fps_id < 5:
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print("WARNING: Small fps value! This may lead to suboptimal performance.")
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if fps_id > 30:
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print("WARNING: Large fps value! This may lead to suboptimal performance.")
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value_dict = {}
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value_dict["motion_bucket_id"] = motion_bucket_id
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value_dict["fps_id"] = fps_id
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value_dict["cond_aug"] = cond_aug
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value_dict["cond_frames_without_noise"] = image
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value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
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with torch.no_grad():
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with torch.autocast(device):
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batch, batch_uc = get_batch(
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get_unique_embedder_keys_from_conditioner(model.conditioner),
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value_dict,
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[1, num_frames],
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T=num_frames,
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device=device,
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)
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c, uc = model.conditioner.get_unconditional_conditioning(
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batch,
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batch_uc=batch_uc,
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force_uc_zero_embeddings=[
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"cond_frames",
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"cond_frames_without_noise",
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],
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)
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for k in ["crossattn", "concat"]:
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uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
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uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
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c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
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c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
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randn = torch.randn(shape, device=device)
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additional_model_inputs = {}
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additional_model_inputs["image_only_indicator"] = torch.zeros(
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2, num_frames
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).to(device)
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#additional_model_inputs["image_only_indicator"][:,0] = 1
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additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
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def denoiser(input, sigma, c):
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return model.denoiser(
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model.model, input, sigma, c, **additional_model_inputs
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)
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samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
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model.en_and_decode_n_samples_a_time = decoding_t
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samples_x = model.decode_first_stage(samples_z)
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samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
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#base_count = len(glob(os.path.join(output_folder, "*.mp4")))
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#video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
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video_path = os.path.join(output_folder, f"{filename}.mp4")
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writer = cv2.VideoWriter(
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video_path,
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cv2.VideoWriter_fourcc(*'mp4v'),
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save_fps,
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(samples.shape[-1], samples.shape[-2]),
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)
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#samples = embed_watermark(samples)
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#samples = filter(samples)
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vid = (
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(rearrange(samples, "t c h w -> t h w c") * 255)
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.cpu()
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.numpy()
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.astype(np.uint8)
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)
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for frame in vid:
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frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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writer.write(frame)
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writer.release()
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print(f'Done! results saved in {output_folder}.')
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def get_unique_embedder_keys_from_conditioner(conditioner):
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return list(set([x.input_key for x in conditioner.embedders]))
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def get_batch(keys, value_dict, N, T, device):
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batch = {}
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batch_uc = {}
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for key in keys:
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if key == "fps_id":
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batch[key] = (
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torch.tensor([value_dict["fps_id"]])
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.to(device)
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.repeat(int(math.prod(N)))
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)
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elif key == "motion_bucket_id":
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batch[key] = (
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torch.tensor([value_dict["motion_bucket_id"]])
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.to(device)
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.repeat(int(math.prod(N)))
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)
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elif key == "cond_aug":
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batch[key] = repeat(
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torch.tensor([value_dict["cond_aug"]]).to(device),
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"1 -> b",
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b=math.prod(N),
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)
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elif key == "cond_frames":
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batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
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elif key == "cond_frames_without_noise":
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batch[key] = repeat(
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value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
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)
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else:
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batch[key] = value_dict[key]
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if T is not None:
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batch["num_video_frames"] = T
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for key in batch.keys():
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if key not in batch_uc and isinstance(batch[key], torch.Tensor):
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batch_uc[key] = torch.clone(batch[key])
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return batch, batch_uc
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def load_model(
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config: str,
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ckpt: str,
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device: str,
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num_frames: int,
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num_steps: int,
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):
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config = OmegaConf.load(config)
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config.model.params.ckpt_path = ckpt
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if device == "cuda":
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config.model.params.conditioner_config.params.emb_models[
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0
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].params.open_clip_embedding_config.params.init_device = device
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config.model.params.sampler_config.params.num_steps = num_steps
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config.model.params.sampler_config.params.guider_config.params.num_frames = (
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num_frames
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)
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if device == "cuda":
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#with torch.device(device):
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model = instantiate_from_config(config.model).to(device).eval()
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else:
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model = instantiate_from_config(config.model).to(device).eval()
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filter = None #DeepFloydDataFiltering(verbose=False, device=device)
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return model, filter
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def get_parser():
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parser = argparse.ArgumentParser()
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parser.add_argument("--seed", type=int, default=23, help="seed for seed_everything")
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parser.add_argument("--ckpt", type=str, default=None, help="checkpoint path")
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parser.add_argument("--config", type=str, help="config (yaml) path")
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parser.add_argument("--input", type=str, default=None, help="image path or folder")
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parser.add_argument("--savedir", type=str, default=None, help="results saving path")
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parser.add_argument("--savefps", type=int, default=10, help="video fps to generate")
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parser.add_argument("--n_samples", type=int, default=1, help="num of samples per prompt",)
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parser.add_argument("--ddim_steps", type=int, default=50, help="steps of ddim if positive, otherwise use DDPM",)
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parser.add_argument("--ddim_eta", type=float, default=1.0, help="eta for ddim sampling (0.0 yields deterministic sampling)",)
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parser.add_argument("--frames", type=int, default=-1, help="frames num to inference")
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parser.add_argument("--fps", type=int, default=6, help="control the fps")
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parser.add_argument("--motion", type=int, default=127, help="control the motion magnitude")
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parser.add_argument("--cond_aug", type=float, default=0.02, help="adding noise to input image")
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parser.add_argument("--decoding_t", type=int, default=1, help="frames num to decoding per time")
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parser.add_argument("--resize", action='store_true', default=False, help="resize all input to default resolution")
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return parser
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if __name__ == "__main__":
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now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
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print("@SVD Inference: %s"%now)
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#Fire(sample)
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parser = get_parser()
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args = parser.parse_args()
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sample(input_path=args.input, ckpt=args.ckpt, num_frames=args.frames, num_steps=args.ddim_steps, \
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fps_id=args.fps, motion_bucket_id=args.motion, cond_aug=args.cond_aug, seed=args.seed, \
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decoding_t=args.decoding_t, output_folder=args.savedir, save_fps=args.savefps, resize=args.resize)
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