Delete simple_video_sample.py

simple_video_sample.py

@@ -1,278 +0,0 @@
-import math
-import os
-from glob import glob
-from pathlib import Path
-from typing import Optional
-
-import cv2
-import numpy as np
-import torch
-from einops import rearrange, repeat
-from fire import Fire
-from omegaconf import OmegaConf
-from PIL import Image
-from torchvision.transforms import ToTensor
-
-from scripts.util.detection.nsfw_and_watermark_dectection import \
-    DeepFloydDataFiltering
-from sgm.inference.helpers import embed_watermark
-from sgm.util import default, instantiate_from_config
-
-
-def sample(
-    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
-    num_frames: Optional[int] = None,
-    num_steps: Optional[int] = None,
-    version: str = "svd",
-    fps_id: int = 6,
-    motion_bucket_id: int = 127,
-    cond_aug: float = 0.02,
-    seed: int = 23,
-    decoding_t: int = 14,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
-    device: str = "cuda",
-    output_folder: Optional[str] = None,
-):
-    """
-    Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
-    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
-    """
-
-    if version == "svd":
-        num_frames = default(num_frames, 14)
-        num_steps = default(num_steps, 25)
-        output_folder = default(output_folder, "outputs/simple_video_sample/svd/")
-        model_config = "scripts/sampling/configs/svd.yaml"
-    elif version == "svd_xt":
-        num_frames = default(num_frames, 25)
-        num_steps = default(num_steps, 30)
-        output_folder = default(output_folder, "outputs/simple_video_sample/svd_xt/")
-        model_config = "scripts/sampling/configs/svd_xt.yaml"
-    elif version == "svd_image_decoder":
-        num_frames = default(num_frames, 14)
-        num_steps = default(num_steps, 25)
-        output_folder = default(
-            output_folder, "outputs/simple_video_sample/svd_image_decoder/"
-        )
-        model_config = "scripts/sampling/configs/svd_image_decoder.yaml"
-    elif version == "svd_xt_image_decoder":
-        num_frames = default(num_frames, 25)
-        num_steps = default(num_steps, 30)
-        output_folder = default(
-            output_folder, "outputs/simple_video_sample/svd_xt_image_decoder/"
-        )
-        model_config = "scripts/sampling/configs/svd_xt_image_decoder.yaml"
-    else:
-        raise ValueError(f"Version {version} does not exist.")
-
-    model, filter = load_model(
-        model_config,
-        device,
-        num_frames,
-        num_steps,
-    )
-    torch.manual_seed(seed)
-
-    path = Path(input_path)
-    all_img_paths = []
-    if path.is_file():
-        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
-            all_img_paths = [input_path]
-        else:
-            raise ValueError("Path is not valid image file.")
-    elif path.is_dir():
-        all_img_paths = sorted(
-            [
-                f
-                for f in path.iterdir()
-                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
-            ]
-        )
-        if len(all_img_paths) == 0:
-            raise ValueError("Folder does not contain any images.")
-    else:
-        raise ValueError
-
-    for input_img_path in all_img_paths:
-        with Image.open(input_img_path) as image:
-            if image.mode == "RGBA":
-                image = image.convert("RGB")
-            w, h = image.size
-
-            if h % 64 != 0 or w % 64 != 0:
-                width, height = map(lambda x: x - x % 64, (w, h))
-                image = image.resize((width, height))
-                print(
-                    f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
-                )
-
-            image = ToTensor()(image)
-            image = image * 2.0 - 1.0
-
-        image = image.unsqueeze(0).to(device)
-        H, W = image.shape[2:]
-        assert image.shape[1] == 3
-        F = 8
-        C = 4
-        shape = (num_frames, C, H // F, W // F)
-        if (H, W) != (576, 1024):
-            print(
-                "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`."
-            )
-        if motion_bucket_id > 255:
-            print(
-                "WARNING: High motion bucket! This may lead to suboptimal performance."
-            )
-
-        if fps_id < 5:
-            print("WARNING: Small fps value! This may lead to suboptimal performance.")
-
-        if fps_id > 30:
-            print("WARNING: Large fps value! This may lead to suboptimal performance.")
-
-        value_dict = {}
-        value_dict["motion_bucket_id"] = motion_bucket_id
-        value_dict["fps_id"] = fps_id
-        value_dict["cond_aug"] = cond_aug
-        value_dict["cond_frames_without_noise"] = image
-        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
-        value_dict["cond_aug"] = cond_aug
-
-        with torch.no_grad():
-            with torch.autocast(device):
-                batch, batch_uc = get_batch(
-                    get_unique_embedder_keys_from_conditioner(model.conditioner),
-                    value_dict,
-                    [1, num_frames],
-                    T=num_frames,
-                    device=device,
-                )
-                c, uc = model.conditioner.get_unconditional_conditioning(
-                    batch,
-                    batch_uc=batch_uc,
-                    force_uc_zero_embeddings=[
-                        "cond_frames",
-                        "cond_frames_without_noise",
-                    ],
-                )
-
-                for k in ["crossattn", "concat"]:
-                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
-                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
-                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
-                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
-
-                randn = torch.randn(shape, device=device)
-
-                additional_model_inputs = {}
-                additional_model_inputs["image_only_indicator"] = torch.zeros(
-                    2, num_frames
-                ).to(device)
-                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
-
-                def denoiser(input, sigma, c):
-                    return model.denoiser(
-                        model.model, input, sigma, c, **additional_model_inputs
-                    )
-
-                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
-                model.en_and_decode_n_samples_a_time = decoding_t
-                samples_x = model.decode_first_stage(samples_z)
-                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
-
-                os.makedirs(output_folder, exist_ok=True)
-                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
-                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
-                writer = cv2.VideoWriter(
-                    video_path,
-                    cv2.VideoWriter_fourcc(*"MP4V"),
-                    fps_id + 1,
-                    (samples.shape[-1], samples.shape[-2]),
-                )
-
-                samples = embed_watermark(samples)
-                samples = filter(samples)
-                vid = (
-                    (rearrange(samples, "t c h w -> t h w c") * 255)
-                    .cpu()
-                    .numpy()
-                    .astype(np.uint8)
-                )
-                for frame in vid:
-                    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-                    writer.write(frame)
-                writer.release()
-
-
-def get_unique_embedder_keys_from_conditioner(conditioner):
-    return list(set([x.input_key for x in conditioner.embedders]))
-
-
-def get_batch(keys, value_dict, N, T, device):
-    batch = {}
-    batch_uc = {}
-
-    for key in keys:
-        if key == "fps_id":
-            batch[key] = (
-                torch.tensor([value_dict["fps_id"]])
-                .to(device)
-                .repeat(int(math.prod(N)))
-            )
-        elif key == "motion_bucket_id":
-            batch[key] = (
-                torch.tensor([value_dict["motion_bucket_id"]])
-                .to(device)
-                .repeat(int(math.prod(N)))
-            )
-        elif key == "cond_aug":
-            batch[key] = repeat(
-                torch.tensor([value_dict["cond_aug"]]).to(device),
-                "1 -> b",
-                b=math.prod(N),
-            )
-        elif key == "cond_frames":
-            batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
-        elif key == "cond_frames_without_noise":
-            batch[key] = repeat(
-                value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
-            )
-        else:
-            batch[key] = value_dict[key]
-
-    if T is not None:
-        batch["num_video_frames"] = T
-
-    for key in batch.keys():
-        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
-            batch_uc[key] = torch.clone(batch[key])
-    return batch, batch_uc
-
-
-def load_model(
-    config: str,
-    device: str,
-    num_frames: int,
-    num_steps: int,
-):
-    config = OmegaConf.load(config)
-    if device == "cuda":
-        config.model.params.conditioner_config.params.emb_models[
-            0
-        ].params.open_clip_embedding_config.params.init_device = device
-
-    config.model.params.sampler_config.params.num_steps = num_steps
-    config.model.params.sampler_config.params.guider_config.params.num_frames = (
-        num_frames
-    )
-    if device == "cuda":
-        with torch.device(device):
-            model = instantiate_from_config(config.model).to(device).eval()
-    else:
-        model = instantiate_from_config(config.model).to(device).eval()
-
-    filter = DeepFloydDataFiltering(verbose=False, device=device)
-    return model, filter
-
-
-if __name__ == "__main__":
-    Fire(sample)