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sample_utils.py
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sample_utils.py
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import math
import os
from typing import List, Optional, Union
import numpy as np
import torch
import torchvision
from PIL import Image
from einops import rearrange, repeat
from omegaconf import ListConfig, OmegaConf
from safetensors.torch import load_file as load_safetensors
from torch import autocast
from tqdm import tqdm
from train import save_img_seq_to_video
from vwm.modules.diffusionmodules.sampling import EulerEDMSampler
from vwm.util import default, instantiate_from_config
def init_model(version_dict, load_ckpt=True):
config = OmegaConf.load(version_dict["config"])
model = load_model_from_config(config, version_dict["ckpt"] if load_ckpt else None)
return model
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 load_model(model):
model.cuda()
def unload_model(model):
global lowvram_mode
if lowvram_mode:
model.cpu()
torch.cuda.empty_cache()
def load_model_from_config(config, ckpt=None):
model = instantiate_from_config(config.model)
if ckpt is not None:
print(f"Loading model from {ckpt}")
if ckpt.endswith("ckpt"):
pl_svd = torch.load(ckpt, map_location="cpu")
# dict contains:
# "epoch", "global_step", "pytorch-lightning_version",
# "state_dict", "loops", "callbacks", "optimizer_states", "lr_schedulers"
if "global_step" in pl_svd:
print(f"Global step: {pl_svd['global_step']}")
svd = pl_svd["state_dict"]
elif ckpt.endswith("safetensors"):
svd = load_safetensors(ckpt)
else:
raise NotImplementedError("Please convert the checkpoint to safetensors first")
missing, unexpected = model.load_state_dict(svd, strict=False)
if len(missing) > 0:
print(f"Missing keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected keys: {unexpected}")
model = initial_model_load(model)
model.eval()
return model
def init_embedder_options(keys):
# hardcoded demo settings, might undergo some changes in the future
value_dict = dict()
for key in keys:
if key in ["fps_id", "fps"]:
fps = 10
value_dict["fps"] = fps
value_dict["fps_id"] = fps - 1
elif key == "motion_bucket_id":
value_dict["motion_bucket_id"] = 127 # [0, 511]
return value_dict
def perform_save_locally(save_path, samples, mode, dataset_name, sample_index):
assert mode in ["images", "grids", "videos"]
merged_path = os.path.join(save_path, mode)
os.makedirs(merged_path, exist_ok=True)
samples = samples.cpu()
if mode == "images":
frame_count = 0
for sample in samples:
sample = rearrange(sample.numpy(), "c h w -> h w c")
if "real" in save_path:
sample = 255.0 * (sample + 1.0) / 2.0
else:
sample = 255.0 * sample
image_save_path = os.path.join(merged_path, f"{dataset_name}_{sample_index:06}_{frame_count:04}.png")
# if os.path.exists(image_save_path):
# return
Image.fromarray(sample.astype(np.uint8)).save(image_save_path)
frame_count += 1
elif mode == "grids":
grid = torchvision.utils.make_grid(samples, nrow=int(samples.shape[0] ** 0.5))
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1).numpy()
if "real" in save_path:
grid = 255.0 * (grid + 1.0) / 2.0
else:
grid = 255.0 * grid
grid_save_path = os.path.join(merged_path, f"{dataset_name}_{sample_index:06}.png")
# if os.path.exists(grid_save_path):
# return
Image.fromarray(grid.astype(np.uint8)).save(grid_save_path)
elif mode == "videos":
img_seq = rearrange(samples.numpy(), "t c h w -> t h w c")
if "real" in save_path:
img_seq = 255.0 * (img_seq + 1.0) / 2.0
else:
img_seq = 255.0 * img_seq
video_save_path = os.path.join(merged_path, f"{dataset_name}_{sample_index:06}.mp4")
# if os.path.exists(video_save_path):
# return
save_img_seq_to_video(video_save_path, img_seq.astype(np.uint8), 10)
else:
raise NotImplementedError
def init_sampling(sampler="EulerEDMSampler", guider="VanillaCFG", discretization="EDMDiscretization",
steps=50, cfg_scale=2.5, num_frames=25):
discretization_config = get_discretization(discretization)
guider_config = get_guider(guider, cfg_scale, num_frames)
sampler = get_sampler(sampler, steps, discretization_config, guider_config)
return sampler
def get_discretization(discretization):
if discretization == "LegacyDDPMDiscretization":
discretization_config = {
"target": "vwm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization"
}
elif discretization == "EDMDiscretization":
discretization_config = {
"target": "vwm.modules.diffusionmodules.discretizer.EDMDiscretization",
"params": {
"sigma_min": 0.002,
"sigma_max": 700.0,
"rho": 7.0
}
}
else:
raise NotImplementedError
return discretization_config
def get_guider(guider="LinearPredictionGuider", cfg_scale=2.5, num_frames=25):
if guider == "IdentityGuider":
guider_config = {
"target": "vwm.modules.diffusionmodules.guiders.IdentityGuider"
}
elif guider == "VanillaCFG":
scale = cfg_scale
guider_config = {
"target": "vwm.modules.diffusionmodules.guiders.VanillaCFG",
"params": {
"scale": scale
}
}
elif guider == "LinearPredictionGuider":
max_scale = cfg_scale
min_scale = 1.0
guider_config = {
"target": "vwm.modules.diffusionmodules.guiders.LinearPredictionGuider",
"params": {
"max_scale": max_scale,
"min_scale": min_scale,
"num_frames": num_frames
}
}
elif guider == "TrianglePredictionGuider":
max_scale = cfg_scale
min_scale = 1.0
guider_config = {
"target": "vwm.modules.diffusionmodules.guiders.TrianglePredictionGuider",
"params": {
"max_scale": max_scale,
"min_scale": min_scale,
"num_frames": num_frames
}
}
else:
raise NotImplementedError
return guider_config
def get_sampler(sampler, steps, discretization_config, guider_config):
if sampler == "EulerEDMSampler":
s_churn = 0.0
s_tmin = 0.0
s_tmax = 999.0
s_noise = 1.0
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=False
)
else:
raise ValueError(f"Unknown sampler {sampler}")
return sampler
def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
# hardcoded demo setups, might undergo some changes in the future
batch = dict()
batch_uc = dict()
for key in keys:
if key in value_dict:
if key in ["fps", "fps_id", "motion_bucket_id", "cond_aug"]:
batch[key] = repeat(torch.tensor([value_dict[key]]).to(device), "1 -> b", b=math.prod(N))
elif key in ["command", "trajectory", "speed", "angle", "goal"]:
batch[key] = repeat(value_dict[key][None].to(device), "1 ... -> b ...", b=N[0])
elif key in ["cond_frames", "cond_frames_without_noise"]:
batch[key] = repeat(value_dict[key], "1 ... -> b ...", b=N[0])
else:
# batch[key] = value_dict[key]
raise NotImplementedError
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 get_condition(model, value_dict, num_samples, force_uc_zero_embeddings, device):
load_model(model.conditioner)
batch, batch_uc = get_batch(
list(set([x.input_key for x in model.conditioner.embedders])),
value_dict,
[num_samples]
)
c, uc = model.conditioner.get_unconditional_conditioning(
batch,
batch_uc=batch_uc,
force_uc_zero_embeddings=force_uc_zero_embeddings
)
unload_model(model.conditioner)
for k in c:
if isinstance(c[k], torch.Tensor):
c[k], uc[k] = map(lambda y: y[k][:num_samples].to(device), (c, uc))
if c[k].shape[0] < num_samples:
c[k] = c[k][[0]]
if uc[k].shape[0] < num_samples:
uc[k] = uc[k][[0]]
return c, uc
def fill_latent(cond, length, cond_indices, device):
latent = torch.zeros(length, *cond.shape[1:]).to(device)
latent[cond_indices] = cond
return latent
@torch.no_grad()
def do_sample(
images,
model,
sampler,
value_dict,
num_rounds,
num_frames,
force_uc_zero_embeddings: Optional[List] = None,
initial_cond_indices: Optional[List] = None,
device="cuda"
):
if initial_cond_indices is None:
initial_cond_indices = [0]
force_uc_zero_embeddings = default(force_uc_zero_embeddings, list())
precision_scope = autocast
with torch.no_grad(), precision_scope(device), model.ema_scope("Sampling"):
c, uc = get_condition(model, value_dict, num_frames, force_uc_zero_embeddings, device)
load_model(model.first_stage_model)
z = model.encode_first_stage(images)
unload_model(model.first_stage_model)
samples_z = torch.zeros((num_rounds * (num_frames - 3) + 3, *z.shape[1:])).to(device)
sampling_progress = tqdm(total=num_rounds, desc="Dreaming")
def denoiser(x, sigma, cond, cond_mask):
return model.denoiser(model.model, x, sigma, cond, cond_mask)
load_model(model.denoiser)
load_model(model.model)
initial_cond_mask = torch.zeros(num_frames).to(device)
prediction_cond_mask = torch.zeros(num_frames).to(device)
initial_cond_mask[initial_cond_indices] = 1
prediction_cond_mask[[0, 1, 2]] = 1
noise = torch.randn_like(z)
sample = sampler(
denoiser,
noise,
cond=c,
uc=uc,
cond_frame=z, # cond_frame will be rescaled when calling the sampler
cond_mask=initial_cond_mask
)
sampling_progress.update(1)
sample[0] = z[0]
samples_z[:num_frames] = sample
for n in range(num_rounds - 1):
load_model(model.first_stage_model)
samples_x_for_guidance = model.decode_first_stage(sample[-14:])
unload_model(model.first_stage_model)
value_dict["cond_frames_without_noise"] = samples_x_for_guidance[[-3]]
value_dict["cond_frames"] = sample[[-3]] / model.scale_factor
for embedder in model.conditioner.embedders:
if hasattr(embedder, "skip_encode"):
embedder.skip_encode = True
c, uc = get_condition(model, value_dict, num_frames, force_uc_zero_embeddings, device)
for embedder in model.conditioner.embedders:
if hasattr(embedder, "skip_encode"):
embedder.skip_encode = False
filled_latent = fill_latent(sample[-3:], num_frames, [0, 1, 2], device)
noise = torch.randn_like(filled_latent)
sample = sampler(
denoiser,
noise,
cond=c,
uc=uc,
cond_frame=filled_latent, # cond_frame will be rescaled when calling the sampler
cond_mask=prediction_cond_mask
)
sampling_progress.update(1)
samples_z[(n + 1) * (num_frames - 3) + 3: (n + 1) * (num_frames - 3) + num_frames] = sample[3:]
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)
return samples, samples_z, images