text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = No... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`b... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
lora... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = sel... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.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 prompt is not None and type(prompt) is no... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
negative... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feat... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3,... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.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:
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.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... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstan... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
"""
See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps (`torch.Tensor`):
generate embedding vectors at these tim... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero ... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
timesteps: List[int] = None,
sigmas: List[fl... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
The call function to the pipeline for generation. | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images c... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https:/... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are gen... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
guidance_rescale (`float`, *optional*, defaults to 0.0):
Guida... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the ge... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
callback_on_step_end_tensor_inputs,
)
s... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_prompt,
self.do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if ip_adapter_image is not None:
output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
image_embeds, negative_image_embeds = self.encode_image(
ip_adapter_image, device, num_images_per_prompt, output_hidden_state
)
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 6.1 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are ... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
if self.do_classifier_free_guidance and s... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = ... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = laten... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
class TransformationModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
text_embeds (`torch.Tensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
... | 369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.Tensor` (one for each layer) of shape `(batch_... | 369 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py |
class RobertaSeriesConfig(XLMRobertaConfig):
def __init__(
self,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
project_dim=512,
pooler_fn="cls",
learn_encoder=False,
use_attention_mask=True,
**kwargs,
):
super().__init__(pad_token... | 370 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py |
class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler", r"logit_scale"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
base_model_prefix = "roberta"
config_class = RobertaSeriesConfig
def __init__(self, c... | 371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optiona... | 371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py |
outputs = self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
... | 371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py |
return TransformationModelOutput(
projection_state=projection_state2,
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
projection_state = self.transform... | 371 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/modeling_roberta_series.py |
class AudioDiffusionPipeline(DiffusionPipeline):
"""
Pipeline for audio diffusion.
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.).
Parameters:
... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
def __init__(
self,
vqvae: AutoencoderKL,
unet: UNet2DConditionModel,
mel: Mel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)
def get_default_steps(self) -... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
audio_file: str = None,
raw_audio: np.ndarray = None,
slice: int = 0,
start_step: int = 0,
steps: int = None,
generator: torch.Generator = None,
mask_start_secs: float = 0,
mask_... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
Args:
batch_size (`int`):
Number of samples to generate.
audio_file (`str`):
An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.
raw_audio (`np.ndarray`):
The raw audio file as a NumPy array.
... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
step_generator (`torch.Generator`):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) used to denoise.
None
eta (`float`):
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applie... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
Examples:
For audio diffusion:
```py
import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
```py
output = pipe(
raw_audio=output.audios[0, 0],
start_step=int(pipe.get_default_steps() / 2),
mask_start_secs=1,
mask_end_secs=1,
)
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
```
... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
steps = steps or self.get_default_steps()
self.scheduler.set_timesteps(steps)
step_generator = step_generator or generator
# For backwards compatibility
if isinstance(self.unet.config.sample_size, int):
self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.c... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(audio_file, raw_audio)
input_image = self.mel.audio_slice_to_image(slice)
input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape(
(input_image.height, input_image.width)
... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
pixels_per_second = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
mask_start = int(mask_start_secs * pixels_per_second)
mask_end = int(mask_end_secs * pixels_per_second)
mask = self.scheduler.add_no... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
if isinstance(self.scheduler, DDIMScheduler):
images = self.scheduler.step(
model_output=model_output,
timestep=t,
sample=images,
eta=eta,
generator=step_generator,
)["prev_sample"]
... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
images = 1 / self.vqvae.config.scaling_factor * images
images = self.vqvae.decode(images)["sample"]
images = (images / 2 + 0.5).clamp(0, 1)
images = images.cpu().permute(... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
@torch.no_grad()
def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
"""
Reverse the denoising step process to recover a noisy image from the generated image.
Args:
images (`List[PIL Image]`):
List of images to encode.
steps (`... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):
prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
alpha_prod_t = self.scheduler.alphas_cumprod[t]
alpha_prod_t_prev = (
self.scheduler.alphas_cump... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
Args:
x0 (`torch.Tensor`):
The first tensor to interpolate between.
x1 (`torch.Tensor`):
Second tensor to interpolate between.
alpha (`float`):
Interpolation between 0 and 1
Returns:
`torch.Tensor`:
... | 372 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/pipeline_audio_diffusion.py |
class Mel(ConfigMixin, SchedulerMixin):
"""
Parameters:
x_res (`int`):
x resolution of spectrogram (time).
y_res (`int`):
y resolution of spectrogram (frequency bins).
sample_rate (`int`):
Sample rate of audio.
n_fft (`int`):
Number... | 373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py |
@register_to_config
def __init__(
self,
x_res: int = 256,
y_res: int = 256,
sample_rate: int = 22050,
n_fft: int = 2048,
hop_length: int = 512,
top_db: int = 80,
n_iter: int = 32,
):
self.hop_length = hop_length
self.sr = sample_rat... | 373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py |
def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None):
"""Load audio.
Args:
audio_file (`str`):
An audio file that must be on disk due to [Librosa](https://librosa.org/) limitation.
raw_audio (`np.ndarray`):
The raw audio file... | 373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py |
def get_audio_slice(self, slice: int = 0) -> np.ndarray:
"""Get slice of audio.
Args:
slice (`int`):
Slice number of audio (out of `get_number_of_slices()`).
Returns:
`np.ndarray`:
The audio slice as a NumPy array.
"""
ret... | 373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py |
Returns:
`PIL Image`:
A grayscale image of `x_res x y_res`.
"""
S = librosa.feature.melspectrogram(
y=self.get_audio_slice(slice), sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels
)
log_S = librosa.power_to_db(S, ref=np.... | 373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py |
Returns:
audio (`np.ndarray`):
The audio as a NumPy array.
"""
bytedata = np.frombuffer(image.tobytes(), dtype="uint8").reshape((image.height, image.width))
log_S = bytedata.astype("float") * self.top_db / 255 - self.top_db
S = librosa.db_to_power(log_S)
... | 373 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py |
class LDMPipeline(DiffusionPipeline):
r"""
Pipeline for unconditional image generation using latent diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, e... | 374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py |
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
eta: float = 0.0,
num_inference_steps: int = 50,
output_type: Optional[str] = "pil",
return_dict: bool = True,
**kwargs... | 374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py |
Args:
batch_size (`int`, *optional*, defaults to 1):
Number of images to generate.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
... | 374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py |
>>> # load model and scheduler
>>> pipe = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
>>> # run pipeline in inference (sample random noise and denoise)
>>> image = pipe().images[0]
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
... | 374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_kwargs = {}
if accepts_eta:
extra_kwargs["eta"] = eta
for t in self.progress_bar(self.s... | 374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py |
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 374 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py |
class SpectrogramNotesEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):
@register_to_config
def __init__(
self,
max_length: int,
vocab_size: int,
d_model: int,
dropout_rate: float,
num_layers: int,
num_heads: int,
d_kv: int,
d_ff: int,
... | 375 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/notes_encoder.py |
self.encoders = nn.ModuleList()
for lyr_num in range(num_layers):
lyr = T5Block(t5config)
self.encoders.append(lyr)
self.layer_norm = T5LayerNorm(d_model)
self.dropout_post = nn.Dropout(p=dropout_rate)
def forward(self, encoder_input_tokens, encoder_inputs_mask):
... | 375 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/notes_encoder.py |
class SpectrogramContEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):
@register_to_config
def __init__(
self,
input_dims: int,
targets_context_length: int,
d_model: int,
dropout_rate: float,
num_layers: int,
num_heads: int,
d_kv: int,
d_... | 376 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/continuous_encoder.py |
t5config = T5Config(
d_model=d_model,
num_heads=num_heads,
d_kv=d_kv,
d_ff=d_ff,
feed_forward_proj=feed_forward_proj,
dropout_rate=dropout_rate,
is_decoder=is_decoder,
is_encoder_decoder=False,
)
self.encoder... | 376 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/continuous_encoder.py |
seq_lens = encoder_inputs_mask.sum(-1)
input_positions = torch.roll(input_positions.unsqueeze(0), tuple(seq_lens.tolist()), dims=0)
x += self.position_encoding(input_positions)
x = self.dropout_pre(x)
# inverted the attention mask
input_shape = encoder_inputs.size()
ext... | 376 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/continuous_encoder.py |
class SpectrogramDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for unconditional audio generation.
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.).
... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
def __init__(
self,
notes_encoder: SpectrogramNotesEncoder,
continuous_encoder: SpectrogramContEncoder,
decoder: T5FilmDecoder,
scheduler: DDPMScheduler,
melgan: OnnxRuntimeModel if is_onnx_available() else Any,
) -> None:
super().__init__()
# From ME... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
def scale_features(self, features, output_range=(-1.0, 1.0), clip=False):
"""Linearly scale features to network outputs range."""
min_out, max_out = output_range
if clip:
features = torch.clip(features, self.min_value, self.max_value)
# Scale to [0, 1].
zero_one = (fe... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
def encode(self, input_tokens, continuous_inputs, continuous_mask):
tokens_mask = input_tokens > 0
tokens_encoded, tokens_mask = self.notes_encoder(
encoder_input_tokens=input_tokens, encoder_inputs_mask=tokens_mask
)
continuous_encoded, continuous_mask = self.continuous_enc... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps * torch.ones(input_tokens.shape[0], dtype=timesteps.dtype, device=timesteps.device)
logits = self.decoder(
encodings_and_masks=encodings_and_masks, decoder_input_tokens=input_tokens, decoder_no... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
@torch.no_grad()
def __call__(
self,
input_tokens: List[List[int]],
generator: Optional[torch.Generator] = None,
num_inference_steps: int = 100,
return_dict: bool = True,
output_type: str = "np",
callback: Optional[Callable[[int, int, torch.Tensor], None]] = N... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
Args:
input_tokens (`List[List[int]]`):
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
num_inference_steps (`... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
Example:
```py
>>> from diffusers import SpectrogramDiffusionPipeline, MidiProcessor
>>> pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
>>> pipe = pipe.to("cuda")
>>> processor = MidiProcessor()
>>> # Download MIDI from: wget ... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
pred_mel = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims], dtype=np.float32)
full_pred_mel = np.zeros([1, 0, self.n_dims], np.float32)
ones = torch.ones((1, TARGET_FEATURE_LENGTH), dtype=bool, device=self.device)
for i, encoder_input_tokens in enumerate(input_tokens):
if i == 0:
... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
encoder_continuous_inputs = self.scale_features(
encoder_continuous_inputs, output_range=[-1.0, 1.0], clip=True
)
encodings_and_masks = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device),
continuous_inputs=enc... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
output = self.decode(
encodings_and_masks=encodings_and_masks,
input_tokens=x,
noise_time=t / self.scheduler.config.num_train_timesteps, ... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
if output_type == "np" and not is_onnx_available():
raise ValueError(
"Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."
)
elif output_type == "np" and self.melgan is None:
raise Valu... | 377 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/pipeline_spectrogram_diffusion.py |
class NoteRepresentationConfig:
"""Configuration note representations."""
onsets_only: bool
include_ties: bool | 378 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
class NoteEventData:
pitch: int
velocity: Optional[int] = None
program: Optional[int] = None
is_drum: Optional[bool] = None
instrument: Optional[int] = None | 379 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
class NoteEncodingState:
"""Encoding state for note transcription, keeping track of active pitches."""
# velocity bin for active pitches and programs
active_pitches: MutableMapping[Tuple[int, int], int] = dataclasses.field(default_factory=dict) | 380 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
class EventRange:
type: str
min_value: int
max_value: int | 381 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/spectrogram_diffusion/midi_utils.py |
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