text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
) | 157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
a... | 157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
encoder_hidden_states_1 = (
encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states
)
encoder_attention_mask_1 = (
encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask
)
for i in range(len(s... | 157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
if cross_attention_dim is not None and idx <= 1:
forward_encoder_hidden_states = encoder_hid... | 157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
hidden_states,
forward_encoder_hidden_states,
None, # timestep
None, # class_labels
cross_attention_kwargs,
attention_mask,
forward_encoder_attention_mask,
... | 157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
elif cross_attention_dim is not None and idx > 1:
forward_encoder_hidden_states = encoder_hidden_states_1
forward_encoder_attention_mask = encoder_attention_mask_1
else:
forward_encoder_hidden_states = None
... | 157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
hidden_states = self.resnets[i + 1](hidden_states, temb)
return hidden_states | 157 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
class CrossAttnUpBlock2D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
transformer_layers_per_block: int = 1,
resnet_eps: float = 1e... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,)
if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:
raise ValueError(
"Only up to 4 cross-attention layers are supported. Ensure that the length of cross-... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
double_self_attention=True if cross_attention_dim[j] is None else Fals... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
if add_upsample:
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
res_hidden_states_... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
encoder_hidden_states_1 = (
encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states
)
encoder_attention_mask_1 = (
encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask
)
for i in range(num_l... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
return custom_forward | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.resnets[i]),
hidden_states,
temb,
**ckpt_kwargs... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
forward_encoder_attention_mask = None
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.attentions[i * num_attention_per_layer + idx], return_dict=False),
hidden_states,
forward_encoder_hidden_states,... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
forward_encoder_attention_mask = encoder_attention_mask
elif cross_attention_dim is not None and idx > 1:
forward_encoder_hidden_states = encoder_hidden_states_1
forward_encoder_attention_mask = encoder_attention_mask_1
else:
... | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states | 158 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/modeling_audioldm2.py |
class AudioLDM2Pipeline(DiffusionPipeline):
r"""
Pipeline for text-to-audio generation using AudioLDM2.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.ClapModel`]):
First frozen text-encoder. AudioLDM2 uses the joint audio-text embedding model
[CLAP](https:... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
[google/flan-t5-large](https://huggingface.co/google/flan-t5-large) variant. Second frozen text-encoder use
for TTS. AudioLDM2 uses the encoder of
[Vits](https://huggingface.co/docs/transformers/model_doc/vits#transformers.VitsModel).
projection_model ([`AudioLDM2ProjectionModel`]):
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Tokenizer to tokenize text for the first frozen text-encoder.
tokenizer_2 ([`~transformers.T5Tokenizer`, `~transformers.VitsTokenizer`]):
Tokenizer to tokenize text for the second frozen text-encoder.
feature_extractor ([`~transformers.ClapFeatureExtractor`]):
Feature extractor t... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: ClapModel,
text_encoder_2: Union[T5EncoderModel, VitsModel],
projection_model: AudioLDM2ProjectionModel,
language_model: GPT2Model,
tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
tokenizer_2:... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
projection_model=projection_model,
language_model=language_model,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
feature_extractor=feat... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_slicing
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
r"""
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to `enable_sequential_cpu_offload`, this method moves one whole model at... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
if gpu_id is not None and device_index is not None:
raise ValueError(
f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}"
f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the ... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
model_sequence = [
self.text_encoder.text_model,
self.text_encoder.text_projection,
self.text_encoder_2,
self.projection_model,
self.language_model,
self.unet,
self.vae,
self.vocoder,
self.text_encoder,
]... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Parameters:
inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
The sequence used as a prompt for the generation.
max_new_tokens (`int`):
Number of new tokens to generate.
model_kwargs (`Dict[str, Any]`, *optional*):
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Return:
`inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
The sequence of generated hidden-states.
"""
max_new_tokens = max_new_tokens if max_new_tokens is not None else self.language_model.config.max_new_tokens
model_kwargs = self... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# Update generated hidden states, model inputs, and length for next step
model_kwargs = self.language_model._update_model_kwargs_for_generation(output, model_kwargs)
return inputs_embeds[:, -max_new_tokens:, :]
def encode_prompt(
self,
prompt,
device,
num_wavefo... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
transcription (`str` or `List[str]`):
transcription of text to speech
device (`torch.device`):
torch device
num_waveforms_per_prompt (`int`):
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
prompt weighting. If not provided, text embeddings will be computed from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-computed negative text embeddings from the Flan T5 model. Can be used to easily tweak text inputs,
*e.g.* prompt weightin... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from
`negative_prompt` input argument.
attention_mask (`torch.LongTensor`, *optional*):
Pre-computed attention mask to be applied to the `prompt_embeds`. If not provided, attention mask ... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Attention mask to be applied to the `prompt_embeds`.
generated_prompt_embeds (`torch.Tensor`):
Text embeddings generated from the GPT2 langauge model. | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Example:
```python
>>> import scipy
>>> import torch
>>> from diffusers import AudioLDM2Pipeline
>>> repo_id = "cvssp/audioldm2"
>>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> # Get text e... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
>>> # save generated audio sample
>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
```"""
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
use_prompt = isinstance(
tokenizer, (RobertaTokenizer, RobertaTokenizerFast, T5Tokenizer, T5TokenizerFast)
)
text_inputs = tokenizer(
prompt if use_prompt else transcrip... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
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])
logger.warning(
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
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_si... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
text_attention_mask[idx] = 1
break
prompt_embeds = text_encoder(
text_input_ids, attention_mask=attention_mask, padding_mask=attention_mask.unsqueeze(-1)
)
prompt_embeds = prompt_embeds[0]
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
prompt_embeds_list.append(prompt_embeds)
attention_mask_list.append(attention_mask)
projection_output = self.projection_model(
hidden_states=prompt_embeds_list[0],
hidden_states_1=prompt_embeds_list[1],
attention_mask=attention_mask_list[0],
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
attention_mask = (
attention_mask.to(device=device)
if attention_mask is not None
else torch.ones(prompt_embeds.shape[:2], dtype=torch.long, device=device)
)
generated_prompt_embe... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
bs_embed, seq_len, hidden_size = generated_prompt_embeds.shape
# duplicate generated embeddings for each generation per prompt, using mps friendly method
generated_prompt_embeds = generated_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1)
generated_prompt_embeds = generated_prompt_embeds.vie... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt)... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
else:
uncond_tokens = negative_prompt | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
negative_prompt_embeds_list = []
negative_attention_mask_list = []
max_length = prompt_embeds.shape[1]
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
uncond_input = tokenizer(
uncond_tokens,
padding="max_length",... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
if text_encoder.config.model_type == "clap":
negative_prompt_embeds = text_encoder.get_text_features(
uncond_input_ids,
attention_mask=negative_attention_mask,
)
# append the seq-len dim: (bs, hidden_size) -> (bs... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
negative_attention_mask = torch.zeros(batch_size, tokenizer.model_max_length).to(
dtype=self.text_encoder_2.dtype, device=device
)
else:
negative_prompt_embeds = text_encoder(
uncond_input_ids,
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
negative_prompt_embeds_list.append(negative_prompt_embeds)
negative_attention_mask_list.append(negative_attention_mask)
projection_output = self.projection_model(
hidden_states=negative_prompt_embeds_list[0],
hidden_states_1=negative_prompt_embeds_list[1],
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
negative_attention_mask = (
negative_attention_mask.to(device=device)
if negative_attention_mask is not None
else torch.ones(negative_prompt_embeds.shape[:2], dt... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# duplicate unconditional attention mask for each generation per prompt
negative_attention_mask = negative_attention_mask.repeat(1, num_waveforms_per_prompt)
negative_attention_mask = negative_attention_mask.view(batch_size * num_waveforms_per_prompt, seq_len)
# duplicate unconditio... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
attention_mask = torch.... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
waveform = self.vocoder(mel_spectrogram)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
waveform = waveform.cpu().float()
return waveform | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
def score_waveforms(self, text, audio, num_waveforms_per_prompt, device, dtype):
if not is_librosa_available():
logger.info(
"Automatic scoring of the generated audio waveforms against the input prompt text requires the "
"`librosa` package to resample the generated w... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# compute the audio-text similarity score using the CLAP model
logits_per_text = self.text_encoder(**inputs).logits_per_text
# sort by the highest matching generations per prompt
indices = torch.argsort(logits_per_text, dim=1, descending=True)[:, :num_waveforms_per_prompt]
audio = torch.... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step)... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
def check_inputs(
self,
prompt,
audio_length_in_s,
vocoder_upsample_factor,
callback_steps,
transcription=None,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
generated_prompt_embeds=None,
negative_generated_... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0:
raise ValueError(
f"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the "
f"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of "
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and (prompt_embeds is None or g... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
if transcription is None:
if self.text_encoder_2.config.model_type == "vits":
raise ValueError("Cannot forward without transcription. Please make sure to" " have transcription")
elif transcription is not None and (
not isinstance(transcription, str) and not isinstance(tra... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
if generated_prompt_embeds is not None and negative_generated_prompt_embeds is not None:
if generated_prompt_embeds.shape != negative_generated_prompt_embeds.shape:
raise ValueError(
"`generated_prompt_embeds` and `negative_generated_prompt_embeds` must have the same shap... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
) | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim
def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):
shape = (
batch_size,
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
transcription: Union[str, List[str]] = None,
audio_length_in_s: Optional[float] = None,
num_inference_steps: int = 200,
guidance_scale: float ... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
max_new_tokens: Optional[int] = None,
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
output_type: Optional[str] = "np",
):
r"""
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`.
transcription (`str` or `List[str]`, *optional*):\
The transcript for text to speech.
audio_length_in_s ... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in audio generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_waveforms_per_prompt (`in... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents samp... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
generated_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embe... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Pre-computed attention mask to be applied to the `prompt_embeds`. If not provided, attention mask will
be computed from `prompt` input argument.
negative_attention_mask (`torch.LongTensor`, *optional*):
Pre-computed attention mask to be applied to the `negative_prompt_embeds`... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` func... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
model (LDM) output. | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with ... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate)
if height % self.vae_scale_factor != 0:
height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor
logger.info(
f"Audio length in seconds {audio_length_in_s} is increa... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# 3. Encode input prompt
prompt_embeds, attention_mask, generated_prompt_embeds = self.encode_prompt(
prompt,
device,
num_waveforms_per_prompt,
do_classifier_free_guidance,
transcription,
negative_prompt,
prompt_embeds=prompt_em... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_waveforms_per_prompt,
num_channels_latents,
height,
prompt_embeds.dtype,
device,
generator,
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=generated_prompt_embeds,
encoder_hidden_states_1=prompt_embeds,
encoder_attention_mask_1=attention_mask,
... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.sch... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
# 9. Automatic scoring
if num_waveforms_per_prompt > 1 and prompt is not None:
audio = self.score_waveforms(
text=prompt,
audio=audio,
num_waveforms_per_prompt=num_waveforms_per_prompt,
device=device,
dtype=prompt_embeds... | 159 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py |
class FluxControlInpaintPipeline(
DiffusionPipeline,
FluxLoraLoaderMixin,
FromSingleFileMixin,
TextualInversionLoaderMixin,
):
r"""
The Flux pipeline for image inpainting using Flux-dev-Depth/Canny.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Toke... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
_optional_components = []
_callback_tensor_inputs = ["latents", "prompt_embeds"]
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(se... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
do_binarize=True,
do_convert_grayscale=True,
)
self.tokenizer_max_length = (
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.default_sample_size = 128 | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 512,
device: Optional[torch.device] = None,
dtype:... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
text_inputs = self.tokenizer_2(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.inp... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
dtype = self.text_encoder_2.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer_max_length,
truncation=True,
return_overflowing_tokens=False,
return_length=False,
return_tensors="pt",
)
text_input_ids = text_input... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
# Use pooled output of CLIPTextModel
prompt_embeds = prompt_embeds.pooler_output
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, n... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
device = device or self._execution_device | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
# We only use the pooled prompt output from the CLIPTextModel
pooled_prompt_embeds = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
)
prompt_embeds = self._get_t5_prompt_embeds(
... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
if self.text_encoder_2 is not None:
if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder_2, lora_scale)
dtype = self.text_encoder.dtype if self.text_encoder ... | 160 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py |
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