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import os
import sys
sys.path.append(os.path.realpath(os.path.join(os.path.dirname(__file__), "../../")))
from pytorch_lightning import seed_everything
from scripts.demo.streamlit_helpers import *
from scripts.demo.sv3d_helpers import *
SAVE_PATH = "outputs/demo/vid/"
VERSION2SPECS = {
"svd": {
"T": 14,
"H": 576,
"W": 1024,
"C": 4,
"f": 8,
"config": "configs/inference/svd.yaml",
"ckpt": "checkpoints/svd.safetensors",
"options": {
"discretization": 1,
"cfg": 2.5,
"sigma_min": 0.002,
"sigma_max": 700.0,
"rho": 7.0,
"guider": 2,
"force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
"num_steps": 25,
},
},
"svd_image_decoder": {
"T": 14,
"H": 576,
"W": 1024,
"C": 4,
"f": 8,
"config": "configs/inference/svd_image_decoder.yaml",
"ckpt": "checkpoints/svd_image_decoder.safetensors",
"options": {
"discretization": 1,
"cfg": 2.5,
"sigma_min": 0.002,
"sigma_max": 700.0,
"rho": 7.0,
"guider": 2,
"force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
"num_steps": 25,
},
},
"svd_xt": {
"T": 25,
"H": 576,
"W": 1024,
"C": 4,
"f": 8,
"config": "configs/inference/svd.yaml",
"ckpt": "checkpoints/svd_xt.safetensors",
"options": {
"discretization": 1,
"cfg": 3.0,
"min_cfg": 1.5,
"sigma_min": 0.002,
"sigma_max": 700.0,
"rho": 7.0,
"guider": 2,
"force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
"num_steps": 30,
"decoding_t": 14,
},
},
"svd_xt_image_decoder": {
"T": 25,
"H": 576,
"W": 1024,
"C": 4,
"f": 8,
"config": "configs/inference/svd_image_decoder.yaml",
"ckpt": "checkpoints/svd_xt_image_decoder.safetensors",
"options": {
"discretization": 1,
"cfg": 3.0,
"min_cfg": 1.5,
"sigma_min": 0.002,
"sigma_max": 700.0,
"rho": 7.0,
"guider": 2,
"force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
"num_steps": 30,
"decoding_t": 14,
},
},
"sv3d_u": {
"T": 21,
"H": 576,
"W": 576,
"C": 4,
"f": 8,
"config": "configs/inference/sv3d_u.yaml",
"ckpt": "checkpoints/sv3d_u.safetensors",
"options": {
"discretization": 1,
"cfg": 2.5,
"sigma_min": 0.002,
"sigma_max": 700.0,
"rho": 7.0,
"guider": 3,
"force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
"num_steps": 50,
"decoding_t": 14,
},
},
"sv3d_p": {
"T": 21,
"H": 576,
"W": 576,
"C": 4,
"f": 8,
"config": "configs/inference/sv3d_p.yaml",
"ckpt": "checkpoints/sv3d_p.safetensors",
"options": {
"discretization": 1,
"cfg": 2.5,
"sigma_min": 0.002,
"sigma_max": 700.0,
"rho": 7.0,
"guider": 3,
"force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
"num_steps": 50,
"decoding_t": 14,
},
},
}
if __name__ == "__main__":
st.title("Stable Video Diffusion / SV3D")
version = st.selectbox(
"Model Version",
[k for k in VERSION2SPECS.keys()],
0,
)
version_dict = VERSION2SPECS[version]
if st.checkbox("Load Model"):
mode = "img2vid"
else:
mode = "skip"
H = st.sidebar.number_input(
"H", value=version_dict["H"], min_value=64, max_value=2048
)
W = st.sidebar.number_input(
"W", value=version_dict["W"], min_value=64, max_value=2048
)
T = st.sidebar.number_input(
"T", value=version_dict["T"], min_value=0, max_value=128
)
C = version_dict["C"]
F = version_dict["f"]
options = version_dict["options"]
if mode != "skip":
state = init_st(version_dict, load_filter=True)
if state["msg"]:
st.info(state["msg"])
model = state["model"]
ukeys = set(
get_unique_embedder_keys_from_conditioner(state["model"].conditioner)
)
value_dict = init_embedder_options(
ukeys,
{},
)
if "fps" not in ukeys:
value_dict["fps"] = 10
value_dict["image_only_indicator"] = 0
if mode == "img2vid":
img = load_img_for_prediction(W, H)
if "sv3d" in version:
cond_aug = 1e-5
else:
cond_aug = st.number_input(
"Conditioning augmentation:", value=0.02, min_value=0.0
)
value_dict["cond_frames_without_noise"] = img
value_dict["cond_frames"] = img + cond_aug * torch.randn_like(img)
value_dict["cond_aug"] = cond_aug
if "sv3d_p" in version:
elev_deg = st.number_input("elev_deg", value=5, min_value=-90, max_value=90)
trajectory = st.selectbox(
"Trajectory",
["same elevation", "dynamic"],
0,
)
if trajectory == "same elevation":
value_dict["polars_rad"] = np.array([np.deg2rad(90 - elev_deg)] * T)
value_dict["azimuths_rad"] = np.linspace(0, 2 * np.pi, T + 1)[1:]
elif trajectory == "dynamic":
azim_rad, elev_rad = gen_dynamic_loop(length=21, elev_deg=elev_deg)
value_dict["polars_rad"] = np.deg2rad(90) - elev_rad
value_dict["azimuths_rad"] = azim_rad
elif "sv3d_u" in version:
elev_deg = st.number_input("elev_deg", value=5, min_value=-90, max_value=90)
value_dict["polars_rad"] = np.array([np.deg2rad(90 - elev_deg)] * T)
value_dict["azimuths_rad"] = np.linspace(0, 2 * np.pi, T + 1)[1:]
seed = st.sidebar.number_input(
"seed", value=23, min_value=0, max_value=int(1e9)
)
seed_everything(seed)
save_locally, save_path = init_save_locally(
os.path.join(SAVE_PATH, version), init_value=True
)
if "sv3d" in version:
plot_save_path = os.path.join(save_path, "plot_3D.png")
plot_3D(
azim=value_dict["azimuths_rad"],
polar=value_dict["polars_rad"],
save_path=plot_save_path,
dynamic=("sv3d_p" in version),
)
st.image(
plot_save_path,
f"3D camera trajectory",
)
options["num_frames"] = T
sampler, num_rows, num_cols = init_sampling(options=options)
num_samples = num_rows * num_cols
decoding_t = st.number_input(
"Decode t frames at a time (set small if you are low on VRAM)",
value=options.get("decoding_t", T),
min_value=1,
max_value=int(1e9),
)
if st.checkbox("Overwrite fps in mp4 generator", False):
saving_fps = st.number_input(
f"saving video at fps:", value=value_dict["fps"], min_value=1
)
else:
saving_fps = value_dict["fps"]
if st.button("Sample"):
out = do_sample(
model,
sampler,
value_dict,
num_samples,
H,
W,
C,
F,
T=T,
batch2model_input=["num_video_frames", "image_only_indicator"],
force_uc_zero_embeddings=options.get("force_uc_zero_embeddings", None),
force_cond_zero_embeddings=options.get(
"force_cond_zero_embeddings", None
),
return_latents=False,
decoding_t=decoding_t,
)
if isinstance(out, (tuple, list)):
samples, samples_z = out
else:
samples = out
samples_z = None
if save_locally:
save_video_as_grid_and_mp4(samples, save_path, T, fps=saving_fps)
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