import copy
import math
import os
from glob import glob
from typing import Dict, List, Optional, Tuple, Union
import cv2
import numpy as np
import streamlit as st
import torch
import torch.nn as nn
import torchvision.transforms as TT
from einops import rearrange, repeat
from imwatermark import WatermarkEncoder
from omegaconf import ListConfig, OmegaConf
from PIL import Image
from safetensors.torch import load_file as load_safetensors
from torch import autocast
from torchvision import transforms
from torchvision.utils import make_grid, save_image
from scripts.demo.discretization import (Img2ImgDiscretizationWrapper,
Txt2NoisyDiscretizationWrapper)
from scripts.util.detection.nsfw_and_watermark_dectection import \
DeepFloydDataFiltering
from sgm.inference.helpers import embed_watermark
from sgm.modules.diffusionmodules.guiders import (LinearPredictionGuider,
VanillaCFG)
from sgm.modules.diffusionmodules.sampling import (DPMPP2MSampler,
DPMPP2SAncestralSampler,
EulerAncestralSampler,
EulerEDMSampler,
HeunEDMSampler,
LinearMultistepSampler)
from sgm.util import append_dims, default, instantiate_from_config
@st.cache_resource()
def init_st(version_dict, load_ckpt=True, load_filter=True):
state = dict()
if not "model" in state:
config = version_dict["config"]
ckpt = version_dict["ckpt"]
config = OmegaConf.load(config)
model, msg = load_model_from_config(config, ckpt if load_ckpt else None)
state["msg"] = msg
state["model"] = model
state["ckpt"] = ckpt if load_ckpt else None
state["config"] = config
if load_filter:
state["filter"] = DeepFloydDataFiltering(verbose=False)
return state
def load_model(model):
model.cuda()
lowvram_mode = False
def set_lowvram_mode(mode):
global lowvram_mode
lowvram_mode = mode
def initial_model_load(model):
global lowvram_mode
if lowvram_mode:
model.model.half()
else:
model.cuda()
return model
def unload_model(model):
global lowvram_mode
if lowvram_mode:
model.cpu()
torch.cuda.empty_cache()
def load_model_from_config(config, ckpt=None, verbose=True):
model = instantiate_from_config(config.model)
if ckpt is not None:
print(f"Loading model from {ckpt}")
if ckpt.endswith("ckpt"):
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
global_step = pl_sd["global_step"]
st.info(f"loaded ckpt from global step {global_step}")
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
elif ckpt.endswith("safetensors"):
sd = load_safetensors(ckpt)
else:
raise NotImplementedError
msg = None
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
else:
msg = None
model = initial_model_load(model)
model.eval()
return model, msg
def get_unique_embedder_keys_from_conditioner(conditioner):
return list(set([x.input_key for x in conditioner.embedders]))
def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
# Hardcoded demo settings; might undergo some changes in the future
value_dict = {}
for key in keys:
if key == "txt":
if prompt is None:
prompt = "A professional photograph of an astronaut riding a pig"
if negative_prompt is None:
negative_prompt = ""
prompt = st.text_input("Prompt", prompt)
negative_prompt = st.text_input("Negative prompt", negative_prompt)
value_dict["prompt"] = prompt
value_dict["negative_prompt"] = negative_prompt
if key == "original_size_as_tuple":
orig_width = st.number_input(
"orig_width",
value=init_dict["orig_width"],
min_value=16,
)
orig_height = st.number_input(
"orig_height",
value=init_dict["orig_height"],
min_value=16,
)
value_dict["orig_width"] = orig_width
value_dict["orig_height"] = orig_height
if key == "crop_coords_top_left":
crop_coord_top = st.number_input("crop_coords_top", value=0, min_value=0)
crop_coord_left = st.number_input("crop_coords_left", value=0, min_value=0)
value_dict["crop_coords_top"] = crop_coord_top
value_dict["crop_coords_left"] = crop_coord_left
if key == "aesthetic_score":
value_dict["aesthetic_score"] = 6.0
value_dict["negative_aesthetic_score"] = 2.5
if key == "target_size_as_tuple":
value_dict["target_width"] = init_dict["target_width"]
value_dict["target_height"] = init_dict["target_height"]
if key in ["fps_id", "fps"]:
fps = st.number_input("fps", value=6, min_value=1)
value_dict["fps"] = fps
value_dict["fps_id"] = fps - 1
if key == "motion_bucket_id":
mb_id = st.number_input("motion bucket id", 0, 511, value=127)
value_dict["motion_bucket_id"] = mb_id
if key == "pool_image":
st.text("Image for pool conditioning")
image = load_img(
key="pool_image_input",
size=224,
center_crop=True,
)
if image is None:
st.info("Need an image here")
image = torch.zeros(1, 3, 224, 224)
value_dict["pool_image"] = image
return value_dict
def perform_save_locally(save_path, samples):
os.makedirs(os.path.join(save_path), exist_ok=True)
base_count = len(os.listdir(os.path.join(save_path)))
samples = embed_watermark(samples)
for sample in samples:
sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c")
Image.fromarray(sample.astype(np.uint8)).save(
os.path.join(save_path, f"{base_count:09}.png")
)
base_count += 1
def init_save_locally(_dir, init_value: bool = False):
save_locally = st.sidebar.checkbox("Save images locally", value=init_value)
if save_locally:
save_path = st.text_input("Save path", value=os.path.join(_dir, "samples"))
else:
save_path = None
return save_locally, save_path
def get_guider(options, key):
guider = st.sidebar.selectbox(
f"Discretization #{key}",
[
"VanillaCFG",
"IdentityGuider",
"LinearPredictionGuider",
],
options.get("guider", 0),
)
additional_guider_kwargs = options.pop("additional_guider_kwargs", {})
if guider == "IdentityGuider":
guider_config = {
"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
}
elif guider == "VanillaCFG":
scale = st.number_input(
f"cfg-scale #{key}",
value=options.get("cfg", 5.0),
min_value=0.0,
)
guider_config = {
"target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
"params": {
"scale": scale,
**additional_guider_kwargs,
},
}
elif guider == "LinearPredictionGuider":
max_scale = st.number_input(
f"max-cfg-scale #{key}",
value=options.get("cfg", 1.5),
min_value=1.0,
)
min_scale = st.number_input(
f"min guidance scale",
value=options.get("min_cfg", 1.0),
min_value=1.0,
max_value=10.0,
)
guider_config = {
"target": "sgm.modules.diffusionmodules.guiders.LinearPredictionGuider",
"params": {
"max_scale": max_scale,
"min_scale": min_scale,
"num_frames": options["num_frames"],
**additional_guider_kwargs,
},
}
else:
raise NotImplementedError
return guider_config
def init_sampling(
key=1,
img2img_strength: Optional[float] = None,
specify_num_samples: bool = True,
stage2strength: Optional[float] = None,
options: Optional[Dict[str, int]] = None,
):
options = {} if options is None else options
num_rows, num_cols = 1, 1
if specify_num_samples:
num_cols = st.number_input(
f"num cols #{key}", value=num_cols, min_value=1, max_value=10
)
steps = st.sidebar.number_input(
f"steps #{key}", value=options.get("num_steps", 40), min_value=1, max_value=1000
)
sampler = st.sidebar.selectbox(
f"Sampler #{key}",
[
"EulerEDMSampler",
"HeunEDMSampler",
"EulerAncestralSampler",
"DPMPP2SAncestralSampler",
"DPMPP2MSampler",
"LinearMultistepSampler",
],
options.get("sampler", 0),
)
discretization = st.sidebar.selectbox(
f"Discretization #{key}",
[
"LegacyDDPMDiscretization",
"EDMDiscretization",
],
options.get("discretization", 0),
)
discretization_config = get_discretization(discretization, options=options, key=key)
guider_config = get_guider(options=options, key=key)
sampler = get_sampler(sampler, steps, discretization_config, guider_config, key=key)
if img2img_strength is not None:
st.warning(
f"Wrapping {sampler.__class__.__name__} with Img2ImgDiscretizationWrapper"
)
sampler.discretization = Img2ImgDiscretizationWrapper(
sampler.discretization, strength=img2img_strength
)
if stage2strength is not None:
sampler.discretization = Txt2NoisyDiscretizationWrapper(
sampler.discretization, strength=stage2strength, original_steps=steps
)
return sampler, num_rows, num_cols
def get_discretization(discretization, options, key=1):
if discretization == "LegacyDDPMDiscretization":
discretization_config = {
"target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
}
elif discretization == "EDMDiscretization":
sigma_min = st.number_input(
f"sigma_min #{key}", value=options.get("sigma_min", 0.03)
) # 0.0292
sigma_max = st.number_input(
f"sigma_max #{key}", value=options.get("sigma_max", 14.61)
) # 14.6146
rho = st.number_input(f"rho #{key}", value=options.get("rho", 3.0))
discretization_config = {
"target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
"params": {
"sigma_min": sigma_min,
"sigma_max": sigma_max,
"rho": rho,
},
}
return discretization_config
def get_sampler(sampler_name, steps, discretization_config, guider_config, key=1):
if sampler_name == "EulerEDMSampler" or sampler_name == "HeunEDMSampler":
s_churn = st.sidebar.number_input(f"s_churn #{key}", value=0.0, min_value=0.0)
s_tmin = st.sidebar.number_input(f"s_tmin #{key}", value=0.0, min_value=0.0)
s_tmax = st.sidebar.number_input(f"s_tmax #{key}", value=999.0, min_value=0.0)
s_noise = st.sidebar.number_input(f"s_noise #{key}", value=1.0, min_value=0.0)
if sampler_name == "EulerEDMSampler":
sampler = EulerEDMSampler(
num_steps=steps,
discretization_config=discretization_config,
guider_config=guider_config,
s_churn=s_churn,
s_tmin=s_tmin,
s_tmax=s_tmax,
s_noise=s_noise,
verbose=True,
)
elif sampler_name == "HeunEDMSampler":
sampler = HeunEDMSampler(
num_steps=steps,
discretization_config=discretization_config,
guider_config=guider_config,
s_churn=s_churn,
s_tmin=s_tmin,
s_tmax=s_tmax,
s_noise=s_noise,
verbose=True,
)
elif (
sampler_name == "EulerAncestralSampler"
or sampler_name == "DPMPP2SAncestralSampler"
):
s_noise = st.sidebar.number_input("s_noise", value=1.0, min_value=0.0)
eta = st.sidebar.number_input("eta", value=1.0, min_value=0.0)
if sampler_name == "EulerAncestralSampler":
sampler = EulerAncestralSampler(
num_steps=steps,
discretization_config=discretization_config,
guider_config=guider_config,
eta=eta,
s_noise=s_noise,
verbose=True,
)
elif sampler_name == "DPMPP2SAncestralSampler":
sampler = DPMPP2SAncestralSampler(
num_steps=steps,
discretization_config=discretization_config,
guider_config=guider_config,
eta=eta,
s_noise=s_noise,
verbose=True,
)
elif sampler_name == "DPMPP2MSampler":
sampler = DPMPP2MSampler(
num_steps=steps,
discretization_config=discretization_config,
guider_config=guider_config,
verbose=True,
)
elif sampler_name == "LinearMultistepSampler":
order = st.sidebar.number_input("order", value=4, min_value=1)
sampler = LinearMultistepSampler(
num_steps=steps,
discretization_config=discretization_config,
guider_config=guider_config,
order=order,
verbose=True,
)
else:
raise ValueError(f"unknown sampler {sampler_name}!")
return sampler
def get_interactive_image() -> Image.Image:
image = st.file_uploader("Input", type=["jpg", "JPEG", "png"])
if image is not None:
image = Image.open(image)
if not image.mode == "RGB":
image = image.convert("RGB")
return image
def load_img(
display: bool = True,
size: Union[None, int, Tuple[int, int]] = None,
center_crop: bool = False,
):
image = get_interactive_image()
if image is None:
return None
if display:
st.image(image)
w, h = image.size
print(f"loaded input image of size ({w}, {h})")
transform = []
if size is not None:
transform.append(transforms.Resize(size))
if center_crop:
transform.append(transforms.CenterCrop(size))
transform.append(transforms.ToTensor())
transform.append(transforms.Lambda(lambda x: 2.0 * x - 1.0))
transform = transforms.Compose(transform)
img = transform(image)[None, ...]
st.text(f"input min/max/mean: {img.min():.3f}/{img.max():.3f}/{img.mean():.3f}")
return img
def get_init_img(batch_size=1, key=None):
init_image = load_img(key=key).cuda()
init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
return init_image
def do_sample(
model,
sampler,
value_dict,
num_samples,
H,
W,
C,
F,
force_uc_zero_embeddings: Optional[List] = None,
force_cond_zero_embeddings: Optional[List] = None,
batch2model_input: List = None,
return_latents=False,
filter=None,
T=None,
additional_batch_uc_fields=None,
decoding_t=None,
):
force_uc_zero_embeddings = default(force_uc_zero_embeddings, [])
batch2model_input = default(batch2model_input, [])
additional_batch_uc_fields = default(additional_batch_uc_fields, [])
st.text("Sampling")
outputs = st.empty()
precision_scope = autocast
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
if T is not None:
num_samples = [num_samples, T]
else:
num_samples = [num_samples]
load_model(model.conditioner)
batch, batch_uc = get_batch(
get_unique_embedder_keys_from_conditioner(model.conditioner),
value_dict,
num_samples,
T=T,
additional_batch_uc_fields=additional_batch_uc_fields,
)
c, uc = model.conditioner.get_unconditional_conditioning(
batch,
batch_uc=batch_uc,
force_uc_zero_embeddings=force_uc_zero_embeddings,
force_cond_zero_embeddings=force_cond_zero_embeddings,
)
unload_model(model.conditioner)
for k in c:
if not k == "crossattn":
c[k], uc[k] = map(
lambda y: y[k][: math.prod(num_samples)].to("cuda"), (c, uc)
)
if k in ["crossattn", "concat"] and T is not None:
uc[k] = repeat(uc[k], "b ... -> b t ...", t=T)
uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=T)
c[k] = repeat(c[k], "b ... -> b t ...", t=T)
c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=T)
additional_model_inputs = {}
for k in batch2model_input:
if k == "image_only_indicator":
assert T is not None
if isinstance(
sampler.guider, (VanillaCFG, LinearPredictionGuider)
):
additional_model_inputs[k] = torch.zeros(
num_samples[0] * 2, num_samples[1]
).to("cuda")
else:
additional_model_inputs[k] = torch.zeros(num_samples).to(
"cuda"
)
else:
additional_model_inputs[k] = batch[k]
shape = (math.prod(num_samples), C, H // F, W // F)
randn = torch.randn(shape).to("cuda")
def denoiser(input, sigma, c):
return model.denoiser(
model.model, input, sigma, c, **additional_model_inputs
)
load_model(model.denoiser)
load_model(model.model)
samples_z = sampler(denoiser, randn, cond=c, uc=uc)
unload_model(model.model)
unload_model(model.denoiser)
load_model(model.first_stage_model)
model.en_and_decode_n_samples_a_time = (
decoding_t # Decode n frames at a time
)
samples_x = model.decode_first_stage(samples_z)
samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
unload_model(model.first_stage_model)
if filter is not None:
samples = filter(samples)
if T is None:
grid = torch.stack([samples])
grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
outputs.image(grid.cpu().numpy())
else:
as_vids = rearrange(samples, "(b t) c h w -> b t c h w", t=T)
for i, vid in enumerate(as_vids):
grid = rearrange(make_grid(vid, nrow=4), "c h w -> h w c")
st.image(
grid.cpu().numpy(),
f"Sample #{i} as image",
)
if return_latents:
return samples, samples_z
return samples
def get_batch(
keys,
value_dict: dict,
N: Union[List, ListConfig],
device: str = "cuda",
T: int = None,
additional_batch_uc_fields: List[str] = [],
):
# Hardcoded demo setups; might undergo some changes in the future
batch = {}
batch_uc = {}
for key in keys:
if key == "txt":
batch["txt"] = [value_dict["prompt"]] * math.prod(N)
batch_uc["txt"] = [value_dict["negative_prompt"]] * math.prod(N)
elif key == "original_size_as_tuple":
batch["original_size_as_tuple"] = (
torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
.to(device)
.repeat(math.prod(N), 1)
)
elif key == "crop_coords_top_left":
batch["crop_coords_top_left"] = (
torch.tensor(
[value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
)
.to(device)
.repeat(math.prod(N), 1)
)
elif key == "aesthetic_score":
batch["aesthetic_score"] = (
torch.tensor([value_dict["aesthetic_score"]])
.to(device)
.repeat(math.prod(N), 1)
)
batch_uc["aesthetic_score"] = (
torch.tensor([value_dict["negative_aesthetic_score"]])
.to(device)
.repeat(math.prod(N), 1)
)
elif key == "target_size_as_tuple":
batch["target_size_as_tuple"] = (
torch.tensor([value_dict["target_height"], value_dict["target_width"]])
.to(device)
.repeat(math.prod(N), 1)
)
elif key == "fps":
batch[key] = (
torch.tensor([value_dict["fps"]]).to(device).repeat(math.prod(N))
)
elif key == "fps_id":
batch[key] = (
torch.tensor([value_dict["fps_id"]]).to(device).repeat(math.prod(N))
)
elif key == "motion_bucket_id":
batch[key] = (
torch.tensor([value_dict["motion_bucket_id"]])
.to(device)
.repeat(math.prod(N))
)
elif key == "pool_image":
batch[key] = repeat(value_dict[key], "1 ... -> b ...", b=math.prod(N)).to(
device, dtype=torch.half
)
elif key == "cond_aug":
batch[key] = repeat(
torch.tensor([value_dict["cond_aug"]]).to("cuda"),
"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])
elif key in additional_batch_uc_fields and key not in batch_uc:
batch_uc[key] = copy.copy(batch[key])
return batch, batch_uc
@torch.no_grad()
def do_img2img(
img,
model,
sampler,
value_dict,
num_samples,
force_uc_zero_embeddings: Optional[List] = None,
force_cond_zero_embeddings: Optional[List] = None,
additional_kwargs={},
offset_noise_level: int = 0.0,
return_latents=False,
skip_encode=False,
filter=None,
add_noise=True,
):
st.text("Sampling")
outputs = st.empty()
precision_scope = autocast
with torch.no_grad():
with precision_scope("cuda"):
with model.ema_scope():
load_model(model.conditioner)
batch, batch_uc = get_batch(
get_unique_embedder_keys_from_conditioner(model.conditioner),
value_dict,
[num_samples],
)
c, uc = model.conditioner.get_unconditional_conditioning(
batch,
batch_uc=batch_uc,
force_uc_zero_embeddings=force_uc_zero_embeddings,
force_cond_zero_embeddings=force_cond_zero_embeddings,
)
unload_model(model.conditioner)
for k in c:
c[k], uc[k] = map(lambda y: y[k][:num_samples].to("cuda"), (c, uc))
for k in additional_kwargs:
c[k] = uc[k] = additional_kwargs[k]
if skip_encode:
z = img
else:
load_model(model.first_stage_model)
z = model.encode_first_stage(img)
unload_model(model.first_stage_model)
noise = torch.randn_like(z)
sigmas = sampler.discretization(sampler.num_steps).cuda()
sigma = sigmas[0]
st.info(f"all sigmas: {sigmas}")
st.info(f"noising sigma: {sigma}")
if offset_noise_level > 0.0:
noise = noise + offset_noise_level * append_dims(
torch.randn(z.shape[0], device=z.device), z.ndim
)
if add_noise:
noised_z = z + noise * append_dims(sigma, z.ndim).cuda()
noised_z = noised_z / torch.sqrt(
1.0 + sigmas[0] ** 2.0
) # Note: hardcoded to DDPM-like scaling. need to generalize later.
else:
noised_z = z / torch.sqrt(1.0 + sigmas[0] ** 2.0)
def denoiser(x, sigma, c):
return model.denoiser(model.model, x, sigma, c)
load_model(model.denoiser)
load_model(model.model)
samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
unload_model(model.model)
unload_model(model.denoiser)
load_model(model.first_stage_model)
samples_x = model.decode_first_stage(samples_z)
unload_model(model.first_stage_model)
samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
if filter is not None:
samples = filter(samples)
grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
outputs.image(grid.cpu().numpy())
if return_latents:
return samples, samples_z
return samples
def get_resizing_factor(
desired_shape: Tuple[int, int], current_shape: Tuple[int, int]
) -> float:
r_bound = desired_shape[1] / desired_shape[0]
aspect_r = current_shape[1] / current_shape[0]
if r_bound >= 1.0:
if aspect_r >= r_bound:
factor = min(desired_shape) / min(current_shape)
else:
if aspect_r < 1.0:
factor = max(desired_shape) / min(current_shape)
else:
factor = max(desired_shape) / max(current_shape)
else:
if aspect_r <= r_bound:
factor = min(desired_shape) / min(current_shape)
else:
if aspect_r > 1:
factor = max(desired_shape) / min(current_shape)
else:
factor = max(desired_shape) / max(current_shape)
return factor
def get_interactive_image(key=None) -> Image.Image:
image = st.file_uploader("Input", type=["jpg", "JPEG", "png"], key=key)
if image is not None:
image = Image.open(image)
if not image.mode == "RGB":
image = image.convert("RGB")
return image
def load_img_for_prediction(
W: int, H: int, display=True, key=None, device="cuda"
) -> torch.Tensor:
image = get_interactive_image(key=key)
if image is None:
return None
if display:
st.image(image)
w, h = image.size
image = np.array(image).transpose(2, 0, 1)
image = torch.from_numpy(image).to(dtype=torch.float32) / 255.0
image = image.unsqueeze(0)
rfs = get_resizing_factor((H, W), (h, w))
resize_size = [int(np.ceil(rfs * s)) for s in (h, w)]
top = (resize_size[0] - H) // 2
left = (resize_size[1] - W) // 2
image = torch.nn.functional.interpolate(
image, resize_size, mode="area", antialias=False
)
image = TT.functional.crop(image, top=top, left=left, height=H, width=W)
if display:
numpy_img = np.transpose(image[0].numpy(), (1, 2, 0))
pil_image = Image.fromarray((numpy_img * 255).astype(np.uint8))
st.image(pil_image)
return image.to(device) * 2.0 - 1.0
def save_video_as_grid_and_mp4(
video_batch: torch.Tensor, save_path: str, T: int, fps: int = 5
):
os.makedirs(save_path, exist_ok=True)
base_count = len(glob(os.path.join(save_path, "*.mp4")))
video_batch = rearrange(video_batch, "(b t) c h w -> b t c h w", t=T)
video_batch = embed_watermark(video_batch)
for vid in video_batch:
save_image(vid, fp=os.path.join(save_path, f"{base_count:06d}.png"), nrow=4)
video_path = os.path.join(save_path, f"{base_count:06d}.mp4")
writer = cv2.VideoWriter(
video_path,
cv2.VideoWriter_fourcc(*"MP4V"),
fps,
(vid.shape[-1], vid.shape[-2]),
)
vid = (
(rearrange(vid, "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()
video_path_h264 = video_path[:-4] + "_h264.mp4"
os.system(f"ffmpeg -i {video_path} -c:v libx264 {video_path_h264}")
with open(video_path_h264, "rb") as f:
video_bytes = f.read()
st.video(video_bytes)
base_count += 1