Create simple_video_sample.py

simple_video_sample.py

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+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/doggo.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)