import torch
from diffusers import DDIMScheduler, CosineDPMSolverMultistepScheduler
from diffusers.schedulers.scheduling_dpmsolver_sde import BrownianTreeNoiseSampler
from diffusers import AudioLDM2Pipeline, StableAudioPipeline
from transformers import RobertaTokenizer, RobertaTokenizerFast, VitsTokenizer
from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
from diffusers.models.embeddings import get_1d_rotary_pos_embed
from typing import Any, Dict, List, Optional, Tuple, Union
import gradio as gr
class PipelineWrapper(torch.nn.Module):
def __init__(self, model_id: str,
device: torch.device,
double_precision: bool = False,
token: Optional[str] = None, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.model_id = model_id
self.device = device
self.double_precision = double_precision
self.token = token
def get_sigma(self, timestep: int) -> float:
sqrt_recipm1_alphas_cumprod = torch.sqrt(1.0 / self.model.scheduler.alphas_cumprod - 1)
return sqrt_recipm1_alphas_cumprod[timestep]
def load_scheduler(self) -> None:
pass
def get_fn_STFT(self) -> torch.nn.Module:
pass
def get_sr(self) -> int:
return 16000
def vae_encode(self, x: torch.Tensor) -> torch.Tensor:
pass
def vae_decode(self, x: torch.Tensor) -> torch.Tensor:
pass
def decode_to_mel(self, x: torch.Tensor) -> torch.Tensor:
pass
def setup_extra_inputs(self, *args, **kwargs) -> None:
pass
def encode_text(self, prompts: List[str], **kwargs
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
pass
def get_variance(self, timestep: torch.Tensor, prev_timestep: torch.Tensor) -> torch.Tensor:
pass
def get_alpha_prod_t_prev(self, prev_timestep: torch.Tensor) -> torch.Tensor:
pass
def get_noise_shape(self, x0: torch.Tensor, num_steps: int) -> Tuple[int, ...]:
variance_noise_shape = (num_steps,
self.model.unet.config.in_channels,
x0.shape[-2],
x0.shape[-1])
return variance_noise_shape
def sample_xts_from_x0(self, x0: torch.Tensor, num_inference_steps: int = 50) -> torch.Tensor:
"""
Samples from P(x_1:T|x_0)
"""
alpha_bar = self.model.scheduler.alphas_cumprod
sqrt_one_minus_alpha_bar = (1-alpha_bar) ** 0.5
variance_noise_shape = self.get_noise_shape(x0, num_inference_steps + 1)
timesteps = self.model.scheduler.timesteps.to(self.device)
t_to_idx = {int(v): k for k, v in enumerate(timesteps)}
xts = torch.zeros(variance_noise_shape).to(x0.device)
xts[0] = x0
for t in reversed(timesteps):
idx = num_inference_steps - t_to_idx[int(t)]
xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0) * sqrt_one_minus_alpha_bar[t]
return xts
def get_zs_from_xts(self, xt: torch.Tensor, xtm1: torch.Tensor, noise_pred: torch.Tensor,
t: torch.Tensor, eta: float = 0, numerical_fix: bool = True, **kwargs
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
# pred of x0
alpha_bar = self.model.scheduler.alphas_cumprod
if self.model.scheduler.config.prediction_type == 'epsilon':
pred_original_sample = (xt - (1 - alpha_bar[t]) ** 0.5 * noise_pred) / alpha_bar[t] ** 0.5
elif self.model.scheduler.config.prediction_type == 'v_prediction':
pred_original_sample = (alpha_bar[t] ** 0.5) * xt - ((1 - alpha_bar[t]) ** 0.5) * noise_pred
# direction to xt
prev_timestep = t - self.model.scheduler.config.num_train_timesteps // \
self.model.scheduler.num_inference_steps
alpha_prod_t_prev = self.get_alpha_prod_t_prev(prev_timestep)
variance = self.get_variance(t, prev_timestep)
if self.model.scheduler.config.prediction_type == 'epsilon':
radom_noise_pred = noise_pred
elif self.model.scheduler.config.prediction_type == 'v_prediction':
radom_noise_pred = (alpha_bar[t] ** 0.5) * noise_pred + ((1 - alpha_bar[t]) ** 0.5) * xt
pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * radom_noise_pred
mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
z = (xtm1 - mu_xt) / (eta * variance ** 0.5)
# correction to avoid error accumulation
if numerical_fix:
xtm1 = mu_xt + (eta * variance ** 0.5)*z
return z, xtm1, None
def reverse_step_with_custom_noise(self, model_output: torch.Tensor, timestep: torch.Tensor, sample: torch.Tensor,
variance_noise: Optional[torch.Tensor] = None, eta: float = 0, **kwargs
) -> torch.Tensor:
# 1. get previous step value (=t-1)
prev_timestep = timestep - self.model.scheduler.config.num_train_timesteps // \
self.model.scheduler.num_inference_steps
# 2. compute alphas, betas
alpha_prod_t = self.model.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.get_alpha_prod_t_prev(prev_timestep)
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.model.scheduler.config.prediction_type == 'epsilon':
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
elif self.model.scheduler.config.prediction_type == 'v_prediction':
pred_original_sample = (alpha_prod_t ** 0.5) * sample - (beta_prod_t ** 0.5) * model_output
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
# variance = self.scheduler._get_variance(timestep, prev_timestep)
variance = self.get_variance(timestep, prev_timestep)
# std_dev_t = eta * variance ** (0.5)
# Take care of asymetric reverse process (asyrp)
if self.model.scheduler.config.prediction_type == 'epsilon':
model_output_direction = model_output
elif self.model.scheduler.config.prediction_type == 'v_prediction':
model_output_direction = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
# pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * model_output_direction
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
# 8. Add noice if eta > 0
if eta > 0:
if variance_noise is None:
variance_noise = torch.randn(model_output.shape, device=self.device)
sigma_z = eta * variance ** (0.5) * variance_noise
prev_sample = prev_sample + sigma_z
return prev_sample
def unet_forward(self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
replace_h_space: Optional[torch.Tensor] = None,
replace_skip_conns: Optional[Dict[int, torch.Tensor]] = None,
return_dict: bool = True,
zero_out_resconns: Optional[Union[int, List]] = None) -> Tuple:
pass
class AudioLDM2Wrapper(PipelineWrapper):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
if self.double_precision:
self.model = AudioLDM2Pipeline.from_pretrained(self.model_id, torch_dtype=torch.float64, token=self.token
).to(self.device)
else:
try:
self.model = AudioLDM2Pipeline.from_pretrained(self.model_id, local_files_only=True, token=self.token
).to(self.device)
except FileNotFoundError:
self.model = AudioLDM2Pipeline.from_pretrained(self.model_id, local_files_only=False, token=self.token
).to(self.device)
def load_scheduler(self) -> None:
self.model.scheduler = DDIMScheduler.from_pretrained(self.model_id, subfolder="scheduler")
def get_fn_STFT(self) -> torch.nn.Module:
from audioldm.audio import TacotronSTFT
return TacotronSTFT(
filter_length=1024,
hop_length=160,
win_length=1024,
n_mel_channels=64,
sampling_rate=16000,
mel_fmin=0,
mel_fmax=8000,
)
def vae_encode(self, x: torch.Tensor) -> torch.Tensor:
# self.model.vae.disable_tiling()
if x.shape[2] % 4:
x = torch.nn.functional.pad(x, (0, 0, 4 - (x.shape[2] % 4), 0))
return (self.model.vae.encode(x).latent_dist.mode() * self.model.vae.config.scaling_factor).float()
# return (self.encode_no_tiling(x).latent_dist.mode() * self.model.vae.config.scaling_factor).float()
def vae_decode(self, x: torch.Tensor) -> torch.Tensor:
return self.model.vae.decode(1 / self.model.vae.config.scaling_factor * x).sample
def decode_to_mel(self, x: torch.Tensor) -> torch.Tensor:
if self.double_precision:
tmp = self.model.mel_spectrogram_to_waveform(x[:, 0].detach().double()).detach()
tmp = self.model.mel_spectrogram_to_waveform(x[:, 0].detach().float()).detach()
if len(tmp.shape) == 1:
tmp = tmp.unsqueeze(0)
return tmp
def encode_text(self, prompts: List[str], negative: bool = False,
save_compute: bool = False, cond_length: int = 0, **kwargs
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
tokenizers = [self.model.tokenizer, self.model.tokenizer_2]
text_encoders = [self.model.text_encoder, self.model.text_encoder_2]
prompt_embeds_list = []
attention_mask_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
text_inputs = tokenizer(
prompts,
padding="max_length" if (save_compute and negative) or isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast))
else True,
max_length=tokenizer.model_max_length
if (not save_compute) or ((not negative) or isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast, VitsTokenizer)))
else cond_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
attention_mask = text_inputs.attention_mask
untruncated_ids = tokenizer(prompts, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] \
and not torch.equal(text_input_ids, untruncated_ids):
removed_text = tokenizer.batch_decode(
untruncated_ids[:, tokenizer.model_max_length - 1: -1])
print(f"The following part of your input was truncated because {text_encoder.config.model_type} can "
f"only handle sequences up to {tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids.to(self.device)
attention_mask = attention_mask.to(self.device)
with torch.no_grad():
if text_encoder.config.model_type == "clap":
prompt_embeds = text_encoder.get_text_features(
text_input_ids,
attention_mask=attention_mask,
)
# append the seq-len dim: (bs, hidden_size) -> (bs, seq_len, hidden_size)
prompt_embeds = prompt_embeds[:, None, :]
# make sure that we attend to this single hidden-state
attention_mask = attention_mask.new_ones((len(prompts), 1))
else:
prompt_embeds = text_encoder(
text_input_ids,
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds_list.append(prompt_embeds)
attention_mask_list.append(attention_mask)
# print(f'prompt[0].shape: {prompt_embeds_list[0].shape}')
# print(f'prompt[1].shape: {prompt_embeds_list[1].shape}')
# print(f'attn[0].shape: {attention_mask_list[0].shape}')
# print(f'attn[1].shape: {attention_mask_list[1].shape}')
projection_output = self.model.projection_model(
hidden_states=prompt_embeds_list[0],
hidden_states_1=prompt_embeds_list[1],
attention_mask=attention_mask_list[0],
attention_mask_1=attention_mask_list[1],
)
projected_prompt_embeds = projection_output.hidden_states
projected_attention_mask = projection_output.attention_mask
generated_prompt_embeds = self.model.generate_language_model(
projected_prompt_embeds,
attention_mask=projected_attention_mask,
max_new_tokens=None,
)
prompt_embeds = prompt_embeds.to(dtype=self.model.text_encoder_2.dtype, device=self.device)
attention_mask = (
attention_mask.to(device=self.device)
if attention_mask is not None
else torch.ones(prompt_embeds.shape[:2], dtype=torch.long, device=self.device)
)
generated_prompt_embeds = generated_prompt_embeds.to(dtype=self.model.language_model.dtype, device=self.device)
return generated_prompt_embeds, prompt_embeds, attention_mask
def get_variance(self, timestep: torch.Tensor, prev_timestep: torch.Tensor) -> torch.Tensor:
alpha_prod_t = self.model.scheduler.alphas_cumprod[timestep]
alpha_prod_t_prev = self.get_alpha_prod_t_prev(prev_timestep)
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
return variance
def get_alpha_prod_t_prev(self, prev_timestep: torch.Tensor) -> torch.Tensor:
return self.model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 \
else self.model.scheduler.final_alpha_cumprod
def unet_forward(self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
timestep_cond: Optional[torch.Tensor] = None,
class_labels: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
replace_h_space: Optional[torch.Tensor] = None,
replace_skip_conns: Optional[Dict[int, torch.Tensor]] = None,
zero_out_resconns: Optional[Union[int, List]] = None) -> Tuple:
# Translation
encoder_hidden_states_1 = class_labels
class_labels = None
encoder_attention_mask_1 = encoder_attention_mask
encoder_attention_mask = None
# return self.model.unet(sample, timestep,
# encoder_hidden_states=generated_prompt_embeds,
# encoder_hidden_states_1=encoder_hidden_states_1,
# encoder_attention_mask_1=encoder_attention_mask_1,
# ), None, None
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2 ** self.model.unet.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
# print("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
# expects mask of shape:
# [batch, key_tokens]
# adds singleton query_tokens dimension:
# [batch, 1, key_tokens]
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
if attention_mask is not None:
# assume that mask is expressed as:
# (1 = keep, 0 = discard)
# convert mask into a bias that can be added to attention scores:
# (keep = +0, discard = -10000.0)
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# convert encoder_attention_mask to a bias the same way we do for attention_mask
if encoder_attention_mask is not None:
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
if encoder_attention_mask_1 is not None:
encoder_attention_mask_1 = (1 - encoder_attention_mask_1.to(sample.dtype)) * -10000.0
encoder_attention_mask_1 = encoder_attention_mask_1.unsqueeze(1)
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.model.unet.time_proj(timesteps)
# `Timesteps` does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=sample.dtype)
emb = self.model.unet.time_embedding(t_emb, timestep_cond)
aug_emb = None
if self.model.unet.class_embedding is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when num_class_embeds > 0")
if self.model.unet.config.class_embed_type == "timestep":
class_labels = self.model.unet.time_proj(class_labels)
# `Timesteps` does not contain any weights and will always return f32 tensors
# there might be better ways to encapsulate this.
class_labels = class_labels.to(dtype=sample.dtype)
class_emb = self.model.unet.class_embedding(class_labels).to(dtype=sample.dtype)
if self.model.unet.config.class_embeddings_concat:
emb = torch.cat([emb, class_emb], dim=-1)
else:
emb = emb + class_emb
emb = emb + aug_emb if aug_emb is not None else emb
if self.model.unet.time_embed_act is not None:
emb = self.model.unet.time_embed_act(emb)
# 2. pre-process
sample = self.model.unet.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.model.unet.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
encoder_hidden_states_1=encoder_hidden_states_1,
encoder_attention_mask_1=encoder_attention_mask_1,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# 4. mid
if self.model.unet.mid_block is not None:
sample = self.model.unet.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
encoder_hidden_states_1=encoder_hidden_states_1,
encoder_attention_mask_1=encoder_attention_mask_1,
)
if replace_h_space is None:
h_space = sample.clone()
else:
h_space = replace_h_space
sample = replace_h_space.clone()
if mid_block_additional_residual is not None:
sample = sample + mid_block_additional_residual
extracted_res_conns = {}
# 5. up
for i, upsample_block in enumerate(self.model.unet.up_blocks):
is_final_block = i == len(self.model.unet.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets):]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
if replace_skip_conns is not None and replace_skip_conns.get(i):
res_samples = replace_skip_conns.get(i)
if zero_out_resconns is not None:
if (type(zero_out_resconns) is int and i >= (zero_out_resconns - 1)) or \
type(zero_out_resconns) is list and i in zero_out_resconns:
res_samples = [torch.zeros_like(x) for x in res_samples]
# down_block_res_samples = [torch.zeros_like(x) for x in down_block_res_samples]
extracted_res_conns[i] = res_samples
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
upsample_size=upsample_size,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
encoder_hidden_states_1=encoder_hidden_states_1,
encoder_attention_mask_1=encoder_attention_mask_1,
)
else:
sample = upsample_block(
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
)
# 6. post-process
if self.model.unet.conv_norm_out:
sample = self.model.unet.conv_norm_out(sample)
sample = self.model.unet.conv_act(sample)
sample = self.model.unet.conv_out(sample)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample), h_space, extracted_res_conns
class StableAudWrapper(PipelineWrapper):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
try:
self.model = StableAudioPipeline.from_pretrained(self.model_id, token=self.token, local_files_only=True
).to(self.device)
except FileNotFoundError:
self.model = StableAudioPipeline.from_pretrained(self.model_id, token=self.token, local_files_only=False
).to(self.device)
self.model.transformer.eval()
self.model.vae.eval()
if self.double_precision:
self.model = self.model.to(torch.float64)
def load_scheduler(self) -> None:
self.model.scheduler = CosineDPMSolverMultistepScheduler.from_pretrained(
self.model_id, subfolder="scheduler", token=self.token)
def encode_text(self, prompts: List[str], negative: bool = False, **kwargs) -> Tuple[torch.Tensor, None, torch.Tensor]:
text_inputs = self.model.tokenizer(
prompts,
padding="max_length",
max_length=self.model.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(self.device)
attention_mask = text_inputs.attention_mask.to(self.device)
self.model.text_encoder.eval()
with torch.no_grad():
prompt_embeds = self.model.text_encoder(text_input_ids, attention_mask=attention_mask)[0]
if negative and attention_mask is not None: # set the masked tokens to the null embed
prompt_embeds = torch.where(attention_mask.to(torch.bool).unsqueeze(2), prompt_embeds, 0.0)
prompt_embeds = self.model.projection_model(text_hidden_states=prompt_embeds).text_hidden_states
if attention_mask is None:
raise gr.Error("Shouldn't reach here. Please raise an issue if you do.")
"""prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if attention_mask is not None and negative_attention_mask is None:
negative_attention_mask = torch.ones_like(attention_mask)
elif attention_mask is None and negative_attention_mask is not None:
attention_mask = torch.ones_like(negative_attention_mask)"""
if prompts == [""]: # empty
return torch.zeros_like(prompt_embeds, device=prompt_embeds.device), None, None
prompt_embeds = prompt_embeds * attention_mask.unsqueeze(-1).to(prompt_embeds.dtype)
prompt_embeds = prompt_embeds * attention_mask.unsqueeze(-1).to(prompt_embeds.dtype)
return prompt_embeds, None, attention_mask
def get_fn_STFT(self) -> torch.nn.Module:
from audioldm.audio import TacotronSTFT
return TacotronSTFT(
filter_length=1024,
hop_length=160,
win_length=1024,
n_mel_channels=64,
sampling_rate=44100,
mel_fmin=0,
mel_fmax=22050,
)
def vae_encode(self, x: torch.Tensor) -> torch.Tensor:
x = x.unsqueeze(0)
audio_vae_length = int(self.model.transformer.config.sample_size * self.model.vae.hop_length)
audio_shape = (1, self.model.vae.config.audio_channels, audio_vae_length)
# check num_channels
if x.shape[1] == 1 and self.model.vae.config.audio_channels == 2:
x = x.repeat(1, 2, 1)
audio_length = x.shape[-1]
audio = x.new_zeros(audio_shape)
audio[:, :, : min(audio_length, audio_vae_length)] = x[:, :, :audio_vae_length]
encoded_audio = self.model.vae.encode(audio.to(self.device)).latent_dist
encoded_audio = encoded_audio.sample()
return encoded_audio
def vae_decode(self, x: torch.Tensor) -> torch.Tensor:
torch.cuda.empty_cache()
# return self.model.vae.decode(1 / self.model.vae.config.scaling_factor * x).sample
aud = self.model.vae.decode(x).sample
return aud[:, :, self.waveform_start:self.waveform_end]
def setup_extra_inputs(self, x: torch.Tensor, init_timestep: torch.Tensor,
extra_info: Optional[Any] = None,
audio_start_in_s: float = 0, audio_end_in_s: Optional[float] = None,
save_compute: bool = False) -> None:
max_audio_length_in_s = self.model.transformer.config.sample_size * self.model.vae.hop_length / \
self.model.vae.config.sampling_rate
if audio_end_in_s is None:
audio_end_in_s = max_audio_length_in_s
if audio_end_in_s - audio_start_in_s > max_audio_length_in_s:
raise ValueError(
f"The total audio length requested ({audio_end_in_s-audio_start_in_s}s) is longer "
f"than the model maximum possible length ({max_audio_length_in_s}). "
f"Make sure that 'audio_end_in_s-audio_start_in_s<={max_audio_length_in_s}'."
)
self.waveform_start = int(audio_start_in_s * self.model.vae.config.sampling_rate)
self.waveform_end = int(audio_end_in_s * self.model.vae.config.sampling_rate)
self.seconds_start_hidden_states, self.seconds_end_hidden_states = self.model.encode_duration(
audio_start_in_s, audio_end_in_s, self.device, False, 1)
if save_compute:
self.seconds_start_hidden_states = torch.cat([self.seconds_start_hidden_states, self.seconds_start_hidden_states], dim=0)
self.seconds_end_hidden_states = torch.cat([self.seconds_end_hidden_states, self.seconds_end_hidden_states], dim=0)
self.audio_duration_embeds = torch.cat([self.seconds_start_hidden_states,
self.seconds_end_hidden_states], dim=2)
# 7. Prepare rotary positional embedding
self.rotary_embedding = get_1d_rotary_pos_embed(
self.model.rotary_embed_dim,
x.shape[2] + self.audio_duration_embeds.shape[1],
use_real=True,
repeat_interleave_real=False,
)
self.model.scheduler._init_step_index(init_timestep)
# fix lower_order_nums for the reverse step - Option 1: only start from first order
# self.model.scheduler.lower_order_nums = 0
# self.model.scheduler.model_outputs = [None] * self.model.scheduler.config.solver_order
# fix lower_order_nums for the reverse step - Option 2: start from the correct order with history
t_to_idx = {float(v): k for k, v in enumerate(self.model.scheduler.timesteps)}
idx = len(self.model.scheduler.timesteps) - t_to_idx[float(init_timestep)] - 1
self.model.scheduler.model_outputs = [None, extra_info[idx] if extra_info is not None else None]
self.model.scheduler.lower_order_nums = min(self.model.scheduler.step_index,
self.model.scheduler.config.solver_order)
# if rand check:
# x *= self.model.scheduler.init_noise_sigma
# return x
def sample_xts_from_x0(self, x0: torch.Tensor, num_inference_steps: int = 50) -> torch.Tensor:
"""
Samples from P(x_1:T|x_0)
"""
sigmas = self.model.scheduler.sigmas
shapes = self.get_noise_shape(x0, num_inference_steps + 1)
xts = torch.zeros(shapes).to(x0.device)
xts[0] = x0
timesteps = self.model.scheduler.timesteps.to(self.device)
t_to_idx = {float(v): k for k, v in enumerate(timesteps)}
for t in reversed(timesteps):
# idx = t_to_idx[int(t)]
idx = num_inference_steps - t_to_idx[float(t)]
n = torch.randn_like(x0)
xts[idx] = x0 + n * sigmas[t_to_idx[float(t)]]
return xts
def get_zs_from_xts(self, xt: torch.Tensor, xtm1: torch.Tensor, data_pred: torch.Tensor,
t: torch.Tensor, numerical_fix: bool = True, first_order: bool = False, **kwargs
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
# pred of x0
sigmas = self.model.scheduler.sigmas
timesteps = self.model.scheduler.timesteps
solver_order = self.model.scheduler.config.solver_order
if self.model.scheduler.step_index is None:
self.model.scheduler._init_step_index(t)
curr_step_index = self.model.scheduler.step_index
# Improve numerical stability for small number of steps
lower_order_final = (curr_step_index == len(timesteps) - 1) and (
self.model.scheduler.config.euler_at_final
or (self.model.scheduler.config.lower_order_final and len(timesteps) < 15)
or self.model.scheduler.config.final_sigmas_type == "zero")
lower_order_second = ((curr_step_index == len(timesteps) - 2) and
self.model.scheduler.config.lower_order_final and len(timesteps) < 15)
data_pred = self.model.scheduler.convert_model_output(data_pred, sample=xt)
for i in range(solver_order - 1):
self.model.scheduler.model_outputs[i] = self.model.scheduler.model_outputs[i + 1]
self.model.scheduler.model_outputs[-1] = data_pred
# instead of brownian noise, here we calculate the noise ourselves
if (curr_step_index == len(timesteps) - 1) and self.model.scheduler.config.final_sigmas_type == "zero":
z = torch.zeros_like(xt)
elif first_order or solver_order == 1 or self.model.scheduler.lower_order_nums < 1 or lower_order_final:
sigma_t, sigma_s = sigmas[curr_step_index + 1], sigmas[curr_step_index]
h = torch.log(sigma_s) - torch.log(sigma_t)
z = (xtm1 - (sigma_t / sigma_s * torch.exp(-h)) * xt - (1 - torch.exp(-2.0 * h)) * data_pred) \
/ (sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)))
elif solver_order == 2 or self.model.scheduler.lower_order_nums < 2 or lower_order_second:
sigma_t = sigmas[curr_step_index + 1]
sigma_s0 = sigmas[curr_step_index]
sigma_s1 = sigmas[curr_step_index - 1]
m0, m1 = self.model.scheduler.model_outputs[-1], self.model.scheduler.model_outputs[-2]
h, h_0 = torch.log(sigma_s0) - torch.log(sigma_t), torch.log(sigma_s1) - torch.log(sigma_s0)
r0 = h_0 / h
D0, D1 = m0, (1.0 / r0) * (m0 - m1)
# sde-dpmsolver++
z = (xtm1 - (sigma_t / sigma_s0 * torch.exp(-h)) * xt
- (1 - torch.exp(-2.0 * h)) * D0
- 0.5 * (1 - torch.exp(-2.0 * h)) * D1) \
/ (sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)))
# correction to avoid error accumulation
if numerical_fix:
if first_order or solver_order == 1 or self.model.scheduler.lower_order_nums < 1 or lower_order_final:
xtm1 = self.model.scheduler.dpm_solver_first_order_update(data_pred, sample=xt, noise=z)
elif solver_order == 2 or self.model.scheduler.lower_order_nums < 2 or lower_order_second:
xtm1 = self.model.scheduler.multistep_dpm_solver_second_order_update(
self.model.scheduler.model_outputs, sample=xt, noise=z)
# If not perfect recon - maybe TODO fix self.model.scheduler.model_outputs as well?
if self.model.scheduler.lower_order_nums < solver_order:
self.model.scheduler.lower_order_nums += 1
# upon completion increase step index by one
self.model.scheduler._step_index += 1
return z, xtm1, self.model.scheduler.model_outputs[-2]
def get_sr(self) -> int:
return self.model.vae.config.sampling_rate
def get_noise_shape(self, x0: torch.Tensor, num_steps: int) -> Tuple[int, int, int]:
variance_noise_shape = (num_steps,
self.model.transformer.config.in_channels,
int(self.model.transformer.config.sample_size))
return variance_noise_shape
def reverse_step_with_custom_noise(self, model_output: torch.Tensor, timestep: torch.Tensor, sample: torch.Tensor,
variance_noise: Optional[torch.Tensor] = None,
first_order: bool = False, **kwargs
) -> torch.Tensor:
if self.model.scheduler.step_index is None:
self.model.scheduler._init_step_index(timestep)
# Improve numerical stability for small number of steps
lower_order_final = (self.model.scheduler.step_index == len(self.model.scheduler.timesteps) - 1) and (
self.model.scheduler.config.euler_at_final
or (self.model.scheduler.config.lower_order_final and len(self.model.scheduler.timesteps) < 15)
or self.model.scheduler.config.final_sigmas_type == "zero"
)
lower_order_second = (
(self.model.scheduler.step_index == len(self.model.scheduler.timesteps) - 2) and
self.model.scheduler.config.lower_order_final and len(self.model.scheduler.timesteps) < 15
)
model_output = self.model.scheduler.convert_model_output(model_output, sample=sample)
for i in range(self.model.scheduler.config.solver_order - 1):
self.model.scheduler.model_outputs[i] = self.model.scheduler.model_outputs[i + 1]
self.model.scheduler.model_outputs[-1] = model_output
if variance_noise is None:
if self.model.scheduler.noise_sampler is None:
self.model.scheduler.noise_sampler = BrownianTreeNoiseSampler(
model_output, sigma_min=self.model.scheduler.config.sigma_min,
sigma_max=self.model.scheduler.config.sigma_max, seed=None)
variance_noise = self.model.scheduler.noise_sampler(
self.model.scheduler.sigmas[self.model.scheduler.step_index],
self.model.scheduler.sigmas[self.model.scheduler.step_index + 1]).to(model_output.device)
if first_order or self.model.scheduler.config.solver_order == 1 or \
self.model.scheduler.lower_order_nums < 1 or lower_order_final:
prev_sample = self.model.scheduler.dpm_solver_first_order_update(
model_output, sample=sample, noise=variance_noise)
elif self.model.scheduler.config.solver_order == 2 or \
self.model.scheduler.lower_order_nums < 2 or lower_order_second:
prev_sample = self.model.scheduler.multistep_dpm_solver_second_order_update(
self.model.scheduler.model_outputs, sample=sample, noise=variance_noise)
if self.model.scheduler.lower_order_nums < self.model.scheduler.config.solver_order:
self.model.scheduler.lower_order_nums += 1
# upon completion increase step index by one
self.model.scheduler._step_index += 1
return prev_sample
def unet_forward(self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
**kwargs) -> Tuple:
# Create text_audio_duration_embeds and audio_duration_embeds
embeds = torch.cat([encoder_hidden_states, self.seconds_start_hidden_states, self.seconds_end_hidden_states],
dim=1)
if encoder_attention_mask is None:
# handle the batched case
if embeds.shape[0] > 1:
embeds[0] = torch.zeros_like(embeds[0], device=embeds.device)
else:
embeds = torch.zeros_like(embeds, device=embeds.device)
noise_pred = self.model.transformer(sample,
timestep.unsqueeze(0),
encoder_hidden_states=embeds,
global_hidden_states=self.audio_duration_embeds,
rotary_embedding=self.rotary_embedding)
if not return_dict:
return (noise_pred.sample,)
return noise_pred, None, None
def load_model(model_id: str, device: torch.device,
double_precision: bool = False, token: Optional[str] = None) -> PipelineWrapper:
if 'audioldm2' in model_id:
ldm_stable = AudioLDM2Wrapper(model_id=model_id, device=device, double_precision=double_precision, token=token)
elif 'stable-audio' in model_id:
ldm_stable = StableAudWrapper(model_id=model_id, device=device, double_precision=double_precision, token=token)
ldm_stable.load_scheduler()
torch.cuda.empty_cache()
return ldm_stable