text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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.
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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,
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.default_sample_size = 128 | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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:... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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 | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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(
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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 ... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
prompt_embeds=None,
pooled_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
max_sequence_length=None,
):
if height % (self.vae_scale_factor * 2) != 0 or width % (self.... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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_2 is not None and prompt_embeds is not ... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
)
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
latent_image_ids = latent_image_ids.reshape(
latent_image_id_height * latent_image_id_width, latent_image_id_channels
)
return latent_image_ids.to(device=device, dtype=dtype)
@stat... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
# VAE applies 8x compression on images but we must also account for packing which requires
# latent height and width to be divisible by 2.
height = 2 * (int(height) // (vae_scale_factor * 2))
width = 2 * (int(width) // (vae_scale_factor * 2))
latents = latents.view(batch_size, height //... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
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.disable_slicing()
def enable_vae_tiling(self):
r"""
Enable tiled VAE deco... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.prepare_latents
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
):
# VAE applies 8x compression on... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
# Copied from diffusers.pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet.StableDiffusion3ControlNetPipeline.prepare_image
def prepare_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classif... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
@property
def guidance_scale(self):
return self._guidance_scale
@property
def joint_attention_kwargs(self):
return self._joint_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interru... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
control_image: PipelineImageInput = None,
height: Optional[int] = None,
width: Optional[int]... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 512,
):
r"""
Function invoked when calling the pipeline for generation. | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokeni... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,
images must be passed as a list such that each element of the list can be correctly batched for input
to a single ControlNet.
height (`int`, *optional*, defaults to self.unet.co... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
will be used.
guidance_scale (`float`, *optional*, defaults to 7.0):
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generat... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the gener... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dic... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
Examples:
Returns:
[`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
images.
"""
height = height or ... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.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
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
# 4. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels // 8
control_image = self.prepare_image(
image=control_image,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_pr... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
latents, latent_image_ids = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
) | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
# 5. Prepare timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
image_seq_len = latents.shape[1]
mu = calculate_shift(
image_seq_len,
self.scheduler.config.get("base_image_seq_len", 256),
self.schedu... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
# 6. Denoising loop
with self.progress_bar(... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
if callback_on_... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
if XLA_AVAILABLE:
xm.mark_step()
if output_type == "latent":
image = latents
else:
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
... | 174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control.py |
class StableAudioPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-audio generation using StableAudio.
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.... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
Args:
vae ([`AutoencoderOobleck`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.T5EncoderModel`]):
Frozen text-encoder. StableAudio uses the encoder of
[T5](https://huggingface.co/d... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
A `StableAudioDiTModel` to denoise the encoded audio latents.
scheduler ([`EDMDPMSolverMultistepScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded audio latents.
""" | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
model_cpu_offload_seq = "text_encoder->projection_model->transformer->vae"
def __init__(
self,
vae: AutoencoderOobleck,
text_encoder: T5EncoderModel,
projection_model: StableAudioProjectionModel,
tokenizer: Union[T5Tokenizer, T5TokenizerFast],
transformer: StableAudi... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_slicing
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
def encode_prompt(
self,
prompt,
device,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
nega... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
if prompt_embeds is None:
# 1. Tokenize text
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
tex... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
logg... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
if do_classifier_free_guidance and negative_prompt is not None:
uncond_tokens: List[str]
if type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# 1. Tokenize text
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
uncond_input_ids = uncond_input.... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# 3. Project prompt_embeds and negative_prompt_embeds
if do_classifier_free_guidance and negative_prompt_embeds is not None:
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the negative and text embeddings into a single batch
# to avoid... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
prompt_embeds = self.projection_model(
text_hidden_states=prompt_embeds,
).text_hidden_states
if attention_mask is not None:
prompt_embeds = prompt_embeds * attention_mask.unsqueeze(-1).to(prompt_embeds.dtype)
prompt_embeds = prompt_embeds * attention_mask.unsqueeze(-... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# Cast the inputs to floats
audio_start_in_s = [float(x) for x in audio_start_in_s]
audio_start_in_s = torch.tensor(audio_start_in_s).to(device)
audio_end_in_s = [float(x) for x in audio_end_in_s]
audio_end_in_s = torch.tensor(audio_end_in_s).to(device)
projection_output = self... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# For classifier free guidance, we need to do two forward passes.
# Here we repeat the audio hidden states to avoid doing two forward passes
if do_classifier_free_guidance:
seconds_start_hidden_states = torch.cat([seconds_start_hidden_states, seconds_start_hidden_states], dim=0)
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.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)... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
def check_inputs(
self,
prompt,
audio_start_in_s,
audio_end_in_s,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
attention_mask=None,
negative_attention_mask=None,
initial_audio_waveforms=None... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
if (
audio_end_in_s < self.projection_model.config.min_value
or audio_end_in_s > self.projection_model.config.max_value
):
raise ValueError(
f"`audio_end_in_s` must be greater than or equal to {self.projection_model.config.min_value}, and lower than or equal t... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.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):
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.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."
) | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.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... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
if initial_audio_sampling_rate is None and initial_audio_waveforms is not None:
raise ValueError(
"`initial_audio_waveforms' is provided but the sampling rate is not. Make sure to pass `initial_audio_sampling_rate`."
)
if initial_audio_sampling_rate is not None and initi... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
def prepare_latents(
self,
batch_size,
num_channels_vae,
sample_size,
dtype,
device,
generator,
latents=None,
initial_audio_waveforms=None,
num_waveforms_per_prompt=None,
audio_channels=None,
):
shape = (batch_size, num_... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# encode the initial audio for use by the model
if initial_audio_waveforms is not None:
# check dimension
if initial_audio_waveforms.ndim == 2:
initial_audio_waveforms = initial_audio_waveforms.unsqueeze(1)
elif initial_audio_waveforms.ndim != 3:
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# check num_channels
if initial_audio_waveforms.shape[1] == 1 and audio_channels == 2:
initial_audio_waveforms = initial_audio_waveforms.repeat(1, 2, 1)
elif initial_audio_waveforms.shape[1] == 2 and audio_channels == 1:
initial_audio_waveforms = initial_audio_wav... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# crop or pad
audio_length = initial_audio_waveforms.shape[-1]
if audio_length < audio_vae_length:
logger.warning(
f"The provided input waveform is shorter ({audio_length}) than the required audio length ({audio_vae_length}) of the model and will thus be padde... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
encoded_audio = self.vae.encode(audio).latent_dist.sample(generator)
encoded_audio = encoded_audio.repeat((num_waveforms_per_prompt, 1, 1))
latents = encoded_audio + latents
return latents | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
audio_end_in_s: Optional[float] = None,
audio_start_in_s: Optional[float] = 0.0,
num_inference_steps: int = 100,
guidance_scale: float = 7.0,
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: Optional[int] = 1,
output_type: Optional[str] = "pt",
):
r"""
The call function to the pipeline for generation. | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.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`.
audio_end_in_s (`float`, *optional*, defaults to 47.55):
Audio end index in seconds.
audio_start_in_s (`... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
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 (`int`, *optional*, defaults to 1):
The number of wavefo... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for audio
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
init... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
Pre-computed text embeddings from the text encoder model. Can be used to easily tweak text inputs,
*e.g.* prompt weighting. If not provided, text embeddings will be computed from `prompt` input
argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps durin... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.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 ... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
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 than the model maximum possible length ({max_audio_length_in_s}). Make sure that 'audio_end_in_s-audio_start_in_s<={max_audio_leng... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.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
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# Encode duration
seconds_start_hidden_states, seconds_end_hidden_states = self.encode_duration(
audio_start_in_s,
audio_end_in_s,
device,
do_classifier_free_guidance and (negative_prompt is not None or negative_prompt_embeds is not None),
batch_size,
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# In case of classifier free guidance without negative prompt, we need to create unconditional embeddings and
# to concatenate it to the embeddings
if do_classifier_free_guidance and negative_prompt_embeds is None and negative_prompt is None:
negative_text_audio_duration_embeds = torch.zeros... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
bs_embed, seq_len, hidden_size = text_audio_duration_embeds.shape
# duplicate audio_duration_embeds and text_audio_duration_embeds for each generation per prompt, using mps friendly method
text_audio_duration_embeds = text_audio_duration_embeds.repeat(1, num_waveforms_per_prompt, 1)
text_audio_d... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# 5. Prepare latent variables
num_channels_vae = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_waveforms_per_prompt,
num_channels_vae,
waveform_length,
text_audio_duration_embeds.dtype,
device,
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
# 9. Post-processing
if not output_type == "latent":
audio = self.vae.decode(latents).sample
else:
return AudioPipelineOutput(audios=latents)
audio = audio[:, :, waveform_start:waveform_end]
if output_type == "np":
audio = audio.cpu().float().numpy()... | 175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py |
class StableAudioPositionalEmbedding(nn.Module):
"""Used for continuous time"""
def __init__(self, dim: int):
super().__init__()
assert (dim % 2) == 0
half_dim = dim // 2
self.weights = nn.Parameter(torch.randn(half_dim))
def forward(self, times: torch.Tensor) -> torch.Tens... | 176 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/modeling_stable_audio.py |
class StableAudioProjectionModelOutput(BaseOutput):
"""
Args:
Class for StableAudio projection layer's outputs.
text_hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states obtained by linearly projecting the hidden-stat... | 177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/modeling_stable_audio.py |
class StableAudioNumberConditioner(nn.Module):
"""
A simple linear projection model to map numbers to a latent space.
Args:
number_embedding_dim (`int`):
Dimensionality of the number embeddings.
min_value (`int`):
The minimum value of the seconds number conditioning ... | 178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/modeling_stable_audio.py |
self.number_embedding_dim = number_embedding_dim
self.min_value = min_value
self.max_value = max_value
def forward(
self,
floats: torch.Tensor,
):
floats = floats.clamp(self.min_value, self.max_value)
normalized_floats = (floats - self.min_value) / (self.max_val... | 178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/modeling_stable_audio.py |
class StableAudioProjectionModel(ModelMixin, ConfigMixin):
"""
A simple linear projection model to map the conditioning values to a shared latent space.
Args:
text_encoder_dim (`int`):
Dimensionality of the text embeddings from the text encoder (T5).
conditioning_dim (`int`):
... | 179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/modeling_stable_audio.py |
@register_to_config
def __init__(self, text_encoder_dim, conditioning_dim, min_value, max_value):
super().__init__()
self.text_projection = (
nn.Identity() if conditioning_dim == text_encoder_dim else nn.Linear(text_encoder_dim, conditioning_dim)
)
self.start_number_condi... | 179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/modeling_stable_audio.py |
def forward(
self,
text_hidden_states: Optional[torch.Tensor] = None,
start_seconds: Optional[torch.Tensor] = None,
end_seconds: Optional[torch.Tensor] = None,
):
text_hidden_states = (
text_hidden_states if text_hidden_states is None else self.text_projection(tex... | 179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_audio/modeling_stable_audio.py |
class KandinskyV22Pipeline(DiffusionPipeline):
"""
Pipeline for text-to-image generation using Kandinsky
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a p... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
self.register_modules(
unet=unet,
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, ... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
@property
def num_timesteps(self):
return self._num_timesteps
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image_embeds: Union[torch.Tensor, List[torch.Tensor]],
negative_image_embeds: Union[torch.Tensor, List[torch.Tensor]],
... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
Args:
image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for text prompt, that will be used to condition the image generation.
negative_image_embeds (`torch.Tensor` or `List[torch.Tensor]`):
The clip image embeddings for negative text... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Highe... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
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`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specifie... | 180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py |
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