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import numpy as np
import torch
import torch.nn as nn
from .tools.t5 import T5EncoderModel
from .tools.wan_model import WanModel
class DiffForcingWanModel(nn.Module):
def __init__(
self,
checkpoint_path="deps/t5_umt5-xxl-enc-bf16/models_t5_umt5-xxl-enc-bf16.pth",
tokenizer_path="deps/t5_umt5-xxl-enc-bf16/google/umt5-xxl",
input_dim=256,
hidden_dim=1024,
ffn_dim=2048,
freq_dim=256,
num_heads=8,
num_layers=8,
time_embedding_scale=1.0,
chunk_size=5,
noise_steps=10,
use_text_cond=True,
text_len=128,
drop_out=0.1,
cfg_scale=5.0,
prediction_type="vel", # "vel", "x0", "noise"
causal=False,
):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.ffn_dim = ffn_dim
self.freq_dim = freq_dim
self.num_heads = num_heads
self.num_layers = num_layers
self.time_embedding_scale = time_embedding_scale
self.chunk_size = chunk_size
self.noise_steps = noise_steps
self.use_text_cond = use_text_cond
self.drop_out = drop_out
self.cfg_scale = cfg_scale
self.prediction_type = prediction_type
self.causal = causal
self.text_dim = 4096
self.text_len = text_len
self.text_encoder = T5EncoderModel(
text_len=self.text_len,
dtype=torch.bfloat16,
device=torch.device("cpu"),
checkpoint_path=checkpoint_path,
tokenizer_path=tokenizer_path,
shard_fn=None,
)
# Text encoding cache
self.text_cache = {}
self.model = WanModel(
model_type="t2v",
patch_size=(1, 1, 1),
text_len=self.text_len,
in_dim=self.input_dim,
dim=self.hidden_dim,
ffn_dim=self.ffn_dim,
freq_dim=self.freq_dim,
text_dim=self.text_dim,
out_dim=self.input_dim,
num_heads=self.num_heads,
num_layers=self.num_layers,
window_size=(-1, -1),
qk_norm=True,
cross_attn_norm=True,
eps=1e-6,
causal=self.causal,
)
self.param_dtype = torch.float32
def encode_text_with_cache(self, text_list, device):
"""Encode text using cache
Args:
text_list: List[str], list of texts
device: torch.device
Returns:
List[Tensor]: List of encoded text features
"""
text_features = []
indices_to_encode = []
texts_to_encode = []
# Check cache
for i, text in enumerate(text_list):
if text in self.text_cache:
# Get from cache and move to correct device
cached_feature = self.text_cache[text].to(device)
text_features.append(cached_feature)
else:
# Need to encode
text_features.append(None)
indices_to_encode.append(i)
texts_to_encode.append(text)
# Batch encode uncached texts
if texts_to_encode:
self.text_encoder.model.to(device)
encoded = self.text_encoder(texts_to_encode, device)
# Store in cache and update results
for idx, text, feature in zip(indices_to_encode, texts_to_encode, encoded):
# Cache to CPU to save GPU memory
self.text_cache[text] = feature.cpu()
text_features[idx] = feature
return text_features
def preprocess(self, x):
# (bs, T, C) -> (bs, C, T, 1, 1)
x = x.permute(0, 2, 1)[:, :, :, None, None]
return x
def postprocess(self, x):
# (bs, C, T, 1, 1) -> (bs, T, C)
x = x.permute(0, 2, 1, 3, 4).contiguous().view(x.size(0), x.size(2), -1)
return x
def _get_noise_levels(self, device, seq_len, time_steps):
"""Get noise levels"""
# noise_level[i] = clip(1 + i / chunk_size - time_steps, 0, 1)
noise_level = torch.clamp(
1
+ torch.arange(seq_len, device=device) / self.chunk_size
- time_steps.unsqueeze(1),
min=0.0,
max=1.0,
)
return noise_level
def add_noise(self, x, noise_level):
"""Add noise
Args:
x: (B, T, D)
noise_level: (B, T)
"""
noise = torch.randn_like(x)
# noise_level: (B, T) -> (B, T, 1)
noise_level = noise_level.unsqueeze(-1)
noisy_x = x * (1 - noise_level) + noise_level * noise
return noisy_x, noise
def forward(self, x):
feature = x["feature"] # (B, T, C)
feature_length = x["feature_length"] # (B,)
batch_size, seq_len, _ = feature.shape
device = feature.device
# Randomly use a time step
time_steps = []
for i in range(batch_size):
valid_len = feature_length[i].item()
# Random float from 0 to valid_len/chunk_size, not an integer
max_time = valid_len / self.chunk_size
# max_time = valid_len / self.chunk_size + 1
time_steps.append(torch.FloatTensor(1).uniform_(0, max_time).item())
time_steps = torch.tensor(time_steps, device=device) # (B,)
noise_level = self._get_noise_levels(device, seq_len, time_steps) # (B, T)
# # Debug: Print noise levels
# print("Time steps and corresponding noise levels:")
# for i in range(batch_size):
# t = time_steps[i].item()
# # Get noise level at each position
# start_idx = int(self.chunk_size * (t - 1))
# end_idx = int(self.chunk_size * t) + 2
# # Limit to valid range
# start_idx = max(0, start_idx)
# end_idx = min(seq_len, end_idx)
# print(time_steps[i])
# print(noise_level[i, start_idx:end_idx])
# Add noise to entire sequence
noisy_feature, noise = self.add_noise(feature, noise_level) # (B, T, D)
# Debug: Print noise addition information
# print("Added noise levels at chunk positions:")
# for i in range(batch_size):
# t = time_steps[i].item()
# start_idx = int(self.chunk_size * (t - 1))
# end_idx = int(self.chunk_size * t) + 2
# # Limit to valid range
# start_idx = max(0, start_idx)
# end_idx = min(seq_len, end_idx)
# test1 = (
# feature[i, start_idx:end_idx, :] - noisy_feature[i, start_idx:end_idx, :]
# )
# test2 = (
# noise[i, start_idx:end_idx, :] - noisy_feature[i, start_idx:end_idx, :]
# )
# # Compute length on last dimension
# print(test1.norm(dim=-1))
# print(test2.norm(dim=-1))
feature = self.preprocess(feature) # (B, C, T, 1, 1)
noisy_feature = self.preprocess(noisy_feature) # (B, C, T, 1, 1)
noise = self.preprocess(noise) # (B, C, T, 1, 1)
feature_ref = []
noise_ref = []
noisy_feature_input = []
for i in range(batch_size):
t = time_steps[i].item()
end_index = int(self.chunk_size * t) + 1
valid_len = feature_length[i].item()
end_index = min(valid_len, end_index)
feature_ref.append(feature[i, :, :end_index, ...])
noise_ref.append(noise[i, :, :end_index, ...])
noisy_feature_input.append(noisy_feature[i, :, :end_index, ...])
# Encode text condition (using cache)
if self.use_text_cond and "text" in x:
text_list = x["text"] # List[str] or List[List[str]]
if isinstance(text_list[0], list):
text_end_list = x["feature_text_end"]
all_text_context = []
for single_text_list, single_text_end_list in zip(
text_list, text_end_list
):
if np.random.rand() > self.drop_out:
single_text_list = [""]
single_text_end_list = [0, seq_len]
else:
single_text_end_list = [0] + [
min(t, seq_len) for t in single_text_end_list
]
single_text_length_list = [
t - b
for t, b in zip(
single_text_end_list[1:], single_text_end_list[:-1]
)
]
single_text_context = self.encode_text_with_cache(
single_text_list, device
)
single_text_context = [
u.to(self.param_dtype) for u in single_text_context
]
for u, duration in zip(
single_text_context, single_text_length_list
):
all_text_context.extend([u for _ in range(duration)])
all_text_context.extend(
[
single_text_context[-1]
for _ in range(seq_len - single_text_end_list[-1])
]
)
else:
all_text_context = [
(u if np.random.rand() > self.drop_out else "") for u in text_list
]
all_text_context = self.encode_text_with_cache(all_text_context, device)
all_text_context = [u.to(self.param_dtype) for u in all_text_context]
else:
all_text_context = [""] * batch_size
all_text_context = self.encode_text_with_cache(all_text_context, device)
all_text_context = [u.to(self.param_dtype) for u in all_text_context]
# Through WanModel
predicted_result = self.model(
noisy_feature_input,
noise_level * self.time_embedding_scale,
all_text_context,
seq_len,
y=None,
) # (B, C, T, 1, 1)
loss = 0.0
for b in range(batch_size):
if self.prediction_type == "vel":
vel = feature_ref[b] - noise_ref[b] # (C, input_length, 1, 1)
squared_error = (
predicted_result[b][:, -self.chunk_size :, ...]
- vel[:, -self.chunk_size :, ...]
) ** 2
elif self.prediction_type == "x0":
squared_error = (
predicted_result[b][:, -self.chunk_size :, ...]
- feature_ref[b][:, -self.chunk_size :, ...]
) ** 2
elif self.prediction_type == "noise":
squared_error = (
predicted_result[b][:, -self.chunk_size :, ...]
- noise_ref[b][:, -self.chunk_size :, ...]
) ** 2
sample_loss = squared_error.sum().mean()
loss += sample_loss
loss = loss / batch_size
loss_dict = {"total": loss, "mse": loss}
return loss_dict
def generate(self, x, num_denoise_steps=None):
"""
Generation - Diffusion Forcing inference
Uses triangular noise schedule, progressively generating from left to right
Generation process:
1. Start from t=0, gradually increase t
2. Each t corresponds to a noise schedule: clean on left, noisy on right, gradient in middle
3. After each denoising step, t increases slightly and continues
"""
feature_length = x["feature_length"]
batch_size = len(feature_length)
seq_len = max(feature_length).item()
# # debug
# x["text"] = [["walk forward.", "sit down.", "stand up."] for _ in range(batch_size)]
# x["feature_text_end"] = [[1, 2, 3] for _ in range(batch_size)]
# text = x["text"]
# text_end = x["feature_text_end"]
# print(text)
# print(text_end)
# print(batch_size, seq_len, self.chunk_size)
if num_denoise_steps is None:
num_denoise_steps = self.noise_steps
assert num_denoise_steps % self.chunk_size == 0
device = next(self.parameters()).device
# Initialize entire sequence as pure noise
generated = torch.randn(
batch_size, seq_len + self.chunk_size, self.input_dim, device=device
)
generated = self.preprocess(generated) # (B, C, T, 1, 1)
# Calculate total number of time steps needed
max_t = 1 + (seq_len - 1) / self.chunk_size
# Step size for each advancement
dt = 1 / num_denoise_steps
total_steps = int(max_t / dt)
# Encode text condition (using cache)
if self.use_text_cond and "text" in x:
text_list = x["text"] # List[str] or List[List[str]]
if isinstance(text_list[0], list):
generated_length = []
text_end_list = x["feature_text_end"]
full_text = []
all_text_context = []
for single_text_list, single_text_end_list in zip(
text_list, text_end_list
):
single_text_end_list = [0] + [
min(t, seq_len) for t in single_text_end_list
]
generated_length.append(single_text_end_list[-1])
single_text_length_list = [
t - b
for t, b in zip(
single_text_end_list[1:], single_text_end_list[:-1]
)
]
full_text.append(
" ////////// ".join(
[
f"{u} //dur:{t}"
for u, t in zip(
single_text_list, single_text_length_list
)
]
)
)
single_text_context = self.encode_text_with_cache(
single_text_list, device
)
single_text_context = [
u.to(self.param_dtype) for u in single_text_context
]
for u, duration in zip(
single_text_context, single_text_length_list
):
all_text_context.extend([u for _ in range(duration)])
all_text_context.extend(
[
single_text_context[-1]
for _ in range(
seq_len + self.chunk_size - single_text_end_list[-1]
)
]
)
else:
generated_length = feature_length
full_text = text_list
all_text_context = self.encode_text_with_cache(text_list, device)
all_text_context = [u.to(self.param_dtype) for u in all_text_context]
else:
generated_length = feature_length
full_text = [""] * batch_size
all_text_context = [""] * batch_size
all_text_context = self.encode_text_with_cache(all_text_context, device)
all_text_context = [u.to(self.param_dtype) for u in all_text_context]
# Get empty text condition encoding (for CFG)
text_null_list = [""] * batch_size
text_null_context = self.encode_text_with_cache(text_null_list, device)
text_null_context = [u.to(self.param_dtype) for u in text_null_context]
# print(len(all_text_context), len(text_null_context))
# Progressively advance from t=0 to t=max_t
for step in range(total_steps):
# Current time step
t = step * dt
start_index = max(0, int(self.chunk_size * (t - 1)) + 1)
end_index = int(self.chunk_size * t) + 1
time_steps = torch.full((batch_size,), t, device=device)
# Calculate current noise schedule
noise_level = self._get_noise_levels(
device, seq_len + self.chunk_size, time_steps
) # (B, T)
# Predict noise through WanModel
noisy_input = []
for i in range(batch_size):
noisy_input.append(generated[i, :, :end_index, ...])
predicted_result = self.model(
noisy_input,
noise_level * self.time_embedding_scale,
all_text_context,
seq_len + self.chunk_size,
y=None,
) # (B, C, T, 1, 1)
# Adjust using CFG
if self.cfg_scale != 1.0:
predicted_result_null = self.model(
noisy_input,
noise_level * self.time_embedding_scale,
text_null_context,
seq_len + self.chunk_size,
y=None,
) # (B, C, T, 1, 1)
predicted_result = [
self.cfg_scale * pv - (self.cfg_scale - 1) * pvn
for pv, pvn in zip(predicted_result, predicted_result_null)
]
for i in range(batch_size):
predicted_result_i = predicted_result[i] # (C, input_length, 1, 1)
if self.prediction_type == "vel":
predicted_vel = predicted_result_i[:, start_index:end_index, ...]
generated[i, :, start_index:end_index, ...] += predicted_vel * dt
elif self.prediction_type == "x0":
predicted_vel = (
predicted_result_i[:, start_index:end_index, ...]
- generated[i, :, start_index:end_index, ...]
) / (
noise_level[i, start_index:end_index]
.unsqueeze(0)
.unsqueeze(-1)
.unsqueeze(-1)
)
generated[i, :, start_index:end_index, ...] += predicted_vel * dt
elif self.prediction_type == "noise":
predicted_vel = (
generated[i, :, start_index:end_index, ...]
- predicted_result_i[:, start_index:end_index, ...]
) / (
1
+ dt
- noise_level[i, start_index:end_index]
.unsqueeze(0)
.unsqueeze(-1)
.unsqueeze(-1)
)
generated[i, :, start_index:end_index, ...] += predicted_vel * dt
generated = self.postprocess(generated) # (B, T, C)
y_hat_out = []
for i in range(batch_size):
# cut off the padding
single_generated = generated[i, : generated_length[i], :]
y_hat_out.append(single_generated)
out = {}
out["generated"] = y_hat_out
out["text"] = full_text
return out
@torch.no_grad()
def stream_generate(self, x, num_denoise_steps=None):
"""
Streaming generation - Diffusion Forcing inference
Uses triangular noise schedule, progressively generating from left to right
Generation process:
1. Start from t=0, gradually increase t
2. Each t corresponds to a noise schedule: clean on left, noisy on right, gradient in middle
3. After each denoising step, t increases slightly and continues
"""
feature_length = x["feature_length"]
batch_size = len(feature_length)
seq_len = max(feature_length).item()
# # debug
# x["text"] = [["walk forward.", "sit down.", "stand up."] for _ in range(batch_size)]
# x["feature_text_end"] = [[1, 2, 3] for _ in range(batch_size)]
# text = x["text"]
# text_end = x["feature_text_end"]
# print(text)
# print(text_end)
# print(batch_size, seq_len, self.chunk_size)
if num_denoise_steps is None:
num_denoise_steps = self.noise_steps
assert num_denoise_steps % self.chunk_size == 0
device = next(self.parameters()).device
# Initialize entire sequence as pure noise
generated = torch.randn(
batch_size, seq_len + self.chunk_size, self.input_dim, device=device
)
generated = self.preprocess(generated) # (B, C, T, 1, 1)
# Calculate total number of time steps needed
max_t = 1 + (seq_len - 1) / self.chunk_size
# Step size for each advancement
dt = 1 / num_denoise_steps
total_steps = int(max_t / dt)
# Encode text condition (using cache)
if self.use_text_cond and "text" in x:
text_list = x["text"] # List[str] or List[List[str]]
if isinstance(text_list[0], list):
generated_length = []
text_end_list = x["feature_text_end"]
full_text = []
all_text_context = []
for single_text_list, single_text_end_list in zip(
text_list, text_end_list
):
single_text_end_list = [0] + [
min(t, seq_len) for t in single_text_end_list
]
generated_length.append(single_text_end_list[-1])
single_text_length_list = [
t - b
for t, b in zip(
single_text_end_list[1:], single_text_end_list[:-1]
)
]
full_text.append(
" ////////// ".join(
[
f"{u} //dur:{t}"
for u, t in zip(
single_text_list, single_text_length_list
)
]
)
)
single_text_context = self.encode_text_with_cache(
single_text_list, device
)
single_text_context = [
u.to(self.param_dtype) for u in single_text_context
]
for u, duration in zip(
single_text_context, single_text_length_list
):
all_text_context.extend([u for _ in range(duration)])
all_text_context.extend(
[
single_text_context[-1]
for _ in range(
seq_len + self.chunk_size - single_text_end_list[-1]
)
]
)
else:
generated_length = feature_length
full_text = text_list
all_text_context = self.encode_text_with_cache(text_list, device)
all_text_context = [u.to(self.param_dtype) for u in all_text_context]
else:
generated_length = feature_length
full_text = [""] * batch_size
all_text_context = [""] * batch_size
all_text_context = self.encode_text_with_cache(all_text_context, device)
all_text_context = [u.to(self.param_dtype) for u in all_text_context]
# Get empty text condition encoding (for CFG)
text_null_list = [""] * batch_size
text_null_context = self.encode_text_with_cache(text_null_list, device)
text_null_context = [u.to(self.param_dtype) for u in text_null_context]
# print(len(all_text_context), len(text_null_context))
commit_index = 0
# Progressively advance from t=0 to t=max_t
for step in range(total_steps):
# Current time step
t = step * dt
start_index = max(0, int(self.chunk_size * (t - 1)) + 1)
end_index = int(self.chunk_size * t) + 1
time_steps = torch.full((batch_size,), t, device=device)
# Calculate current noise schedule
noise_level = self._get_noise_levels(
device, seq_len + self.chunk_size, time_steps
) # (B, T)
# Predict noise through WanModel
noisy_input = []
for i in range(batch_size):
noisy_input.append(generated[i, :, :end_index, ...])
predicted_result = self.model(
noisy_input,
noise_level * self.time_embedding_scale,
all_text_context,
seq_len + self.chunk_size,
y=None,
) # (B, C, T, 1, 1)
# Adjust using CFG
if self.cfg_scale != 1.0:
predicted_result_null = self.model(
noisy_input,
noise_level * self.time_embedding_scale,
text_null_context,
seq_len + self.chunk_size,
y=None,
) # (B, C, T, 1, 1)
predicted_result = [
self.cfg_scale * pv - (self.cfg_scale - 1) * pvn
for pv, pvn in zip(predicted_result, predicted_result_null)
]
for i in range(batch_size):
predicted_result_i = predicted_result[i] # (C, input_length, 1, 1)
if self.prediction_type == "vel":
predicted_vel = predicted_result_i[:, start_index:end_index, ...]
generated[i, :, start_index:end_index, ...] += predicted_vel * dt
elif self.prediction_type == "x0":
predicted_vel = (
predicted_result_i[:, start_index:end_index, ...]
- generated[i, :, start_index:end_index, ...]
) / (
noise_level[i, start_index:end_index]
.unsqueeze(0)
.unsqueeze(-1)
.unsqueeze(-1)
)
generated[i, :, start_index:end_index, ...] += predicted_vel * dt
elif self.prediction_type == "noise":
predicted_vel = (
generated[i, :, start_index:end_index, ...]
- predicted_result_i[:, start_index:end_index, ...]
) / (
1
+ dt
- noise_level[i, start_index:end_index]
.unsqueeze(0)
.unsqueeze(-1)
.unsqueeze(-1)
)
generated[i, :, start_index:end_index, ...] += predicted_vel * dt
if commit_index < start_index:
output = generated[:, :, commit_index:start_index, ...]
output = self.postprocess(output) # (B, T, C)
y_hat_out = []
for i in range(batch_size):
if commit_index < generated_length[i]:
y_hat_out.append(
output[i, : generated_length[i] - commit_index, ...]
)
else:
y_hat_out.append(None)
out = {}
out["generated"] = y_hat_out
yield out
commit_index = start_index
output = generated[:, :, commit_index:, ...]
output = self.postprocess(output) # (B, T_remain, C)
y_hat_out = []
for i in range(batch_size):
if commit_index < generated_length[i]:
y_hat_out.append(output[i, : generated_length[i] - commit_index, ...])
else:
y_hat_out.append(None)
out = {}
out["generated"] = y_hat_out
yield out
def init_generated(self, seq_len, batch_size=1, num_denoise_steps=None):
self.seq_len = seq_len
self.batch_size = batch_size
if num_denoise_steps is None:
self.num_denoise_steps = self.noise_steps
else:
self.num_denoise_steps = num_denoise_steps
assert self.num_denoise_steps % self.chunk_size == 0
self.dt = 1 / self.num_denoise_steps
self.current_step = 0
self.text_condition_list = [[] for _ in range(self.batch_size)]
self.generated = torch.randn(
self.batch_size, self.seq_len * 2 + self.chunk_size, self.input_dim
)
self.generated = self.preprocess(self.generated) # (B, C, T, 1, 1)
self.commit_index = 0
@torch.no_grad()
def stream_generate_step(self, x, first_chunk=True):
"""
Streaming generation step - Diffusion Forcing inference
Uses triangular noise schedule, progressively generating from left to right
Generation process:
1. Start from t=0, gradually increase t
2. Each t corresponds to a noise schedule: clean on left, noisy on right, gradient in middle
3. After each denoising step, t increases slightly and continues
"""
device = next(self.parameters()).device
if first_chunk:
self.generated = self.generated.to(device)
# Encode text condition (using cache)
if self.use_text_cond and "text" in x:
text_list = x["text"] # List[str]
new_text_context = self.encode_text_with_cache(text_list, device)
new_text_context = [u.to(self.param_dtype) for u in new_text_context]
else:
new_text_context = [""] * self.batch_size
new_text_context = self.encode_text_with_cache(new_text_context, device)
new_text_context = [u.to(self.param_dtype) for u in new_text_context]
# Get empty text condition encoding (for CFG)
text_null_list = [""] * self.batch_size
text_null_context = self.encode_text_with_cache(text_null_list, device)
text_null_context = [u.to(self.param_dtype) for u in text_null_context]
for i in range(self.batch_size):
if first_chunk:
self.text_condition_list[i].extend(
[new_text_context[i]] * self.chunk_size
)
else:
self.text_condition_list[i].extend([new_text_context[i]])
end_step = (
(self.commit_index + self.chunk_size)
* self.num_denoise_steps
/ self.chunk_size
)
while self.current_step < end_step:
current_time = self.current_step * self.dt
start_index = max(0, int(self.chunk_size * (current_time - 1)) + 1)
end_index = int(self.chunk_size * current_time) + 1
time_steps = torch.full((self.batch_size,), current_time, device=device)
noise_level = self._get_noise_levels(device, end_index, time_steps)[
:, -self.seq_len :
] # (B, T)
# Predict noise through WanModel
noisy_input = []
for i in range(self.batch_size):
noisy_input.append(
self.generated[i, :, :end_index, ...][:, -self.seq_len :]
) # (C, T, 1, 1)
text_condition = []
for i in range(self.batch_size):
text_condition.extend(
self.text_condition_list[i][:end_index][-self.seq_len :]
) # (T, D, 4096)
# print("////////////////////")
# print("current step: ", self.current_step)
# print("chunk size: ", self.chunk_size)
# print("start_index: ", start_index)
# print("end_index: ", end_index)
# print("noisy_input shape: ", noisy_input[0].shape)
# print("noise_level: ", noise_level[0, start_index:end_index])
# print("text_condition shape: ", len(text_condition))
# print("commit_index: ", self.commit_index)
# print("////////////////////")
predicted_result = self.model(
noisy_input,
noise_level * self.time_embedding_scale,
text_condition,
min(end_index, self.seq_len),
y=None,
) # (B, C, T, 1, 1)
# Adjust using CFG
if self.cfg_scale != 1.0:
predicted_result_null = self.model(
noisy_input,
noise_level * self.time_embedding_scale,
text_null_context,
min(end_index, self.seq_len),
y=None,
) # (B, C, T, 1, 1)
predicted_result = [
self.cfg_scale * pv - (self.cfg_scale - 1) * pvn
for pv, pvn in zip(predicted_result, predicted_result_null)
]
for i in range(self.batch_size):
predicted_result_i = predicted_result[i] # (C, input_length, 1, 1)
if end_index > self.seq_len:
predicted_result_i = torch.cat(
[
torch.zeros(
predicted_result_i.shape[0],
end_index - self.seq_len,
predicted_result_i.shape[2],
predicted_result_i.shape[3],
device=device,
),
predicted_result_i,
],
dim=1,
)
if self.prediction_type == "vel":
predicted_vel = predicted_result_i[:, start_index:end_index, ...]
self.generated[i, :, start_index:end_index, ...] += (
predicted_vel * self.dt
)
elif self.prediction_type == "x0":
predicted_vel = (
predicted_result_i[:, start_index:end_index, ...]
- self.generated[i, :, start_index:end_index, ...]
) / (
noise_level[i, start_index:end_index]
.unsqueeze(0)
.unsqueeze(-1)
.unsqueeze(-1)
)
self.generated[i, :, start_index:end_index, ...] += (
predicted_vel * self.dt
)
elif self.prediction_type == "noise":
predicted_vel = (
self.generated[i, :, start_index:end_index, ...]
- predicted_result_i[:, start_index:end_index, ...]
) / (
1
+ self.dt
- noise_level[i, start_index:end_index]
.unsqueeze(0)
.unsqueeze(-1)
.unsqueeze(-1)
)
self.generated[i, :, start_index:end_index, ...] += (
predicted_vel * self.dt
)
self.current_step += 1
output = self.generated[:, :, self.commit_index : self.commit_index + 1, ...]
output = self.postprocess(output) # (B, 1, C)
out = {}
out["generated"] = output
self.commit_index += 1
if self.commit_index == self.seq_len * 2:
self.generated = torch.cat(
[
self.generated[:, :, self.seq_len :, ...],
torch.randn(
self.batch_size,
self.input_dim,
self.seq_len,
1,
1,
device=device,
),
],
dim=2,
)
self.current_step -= self.seq_len * self.num_denoise_steps / self.chunk_size
self.commit_index -= self.seq_len
for i in range(self.batch_size):
self.text_condition_list[i] = self.text_condition_list[i][
self.seq_len :
]
return out
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