text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1)
negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)
else:
negative_prompt_embeds = None
negative_prompt_attention_mask = None
if self.text_... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
def check_inputs(
self,
prompt,
height,
width,
callback_on_step_end_tensor_inputs=None,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
prompt_attention_mask=None,
negative_prompt_attention_mask=None,
):
i... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.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:
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.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."
)
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.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... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.w... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = ... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
def prepare_latents(self, batch_size, num_channels_late... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
return latents
@property
def guidance_scale(self):
return self._guidance_scale
@property
def attention_kwargs(self):
return self._attention_kwargs
@property
def do_classifier_free_guidance(s... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
negative_prompt: str = "",
num_inference_steps: int = 20,
timesteps: List[int] = None,
sigmas: List[float] = None,
guidance_scale: flo... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 300,
complex_human_instruction: List[str] = [
"Given a user prom... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
"- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.",
"- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.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.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide t... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.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.
sigmas (`List[float]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` ar... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
height (`int`, *optional*, defaults to self.unet.config.sample_size):
The height in pixels of the generated image.... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
latents (`torch.Tensor`, *optional*):
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 samp... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
negative_prompt_attention_mask (`torch.Tensor`, *optional*):
Pre-generated attention mask for negative text embeddings.
output_type (`str`, *optional*, defaults to `"pil"`):
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.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... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
Examples:
Returns:
[`~pipelines.sana.pipeline_output.SanaPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.sana.pipeline_output.SanaPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated ... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# 1. Check inputs. Raise error if not correct
if use_resolution_binning:
if self.transformer.config.sample_size == 128:
aspect_ratio_bin = ASPECT_RATIO_4096_BIN
elif self.transformer.config.sample_size == 64:
aspect_ratio_bin = ASPECT_RATIO_2048_BIN
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
self.check_inputs(
prompt,
height,
width,
callback_on_step_end_tensor_inputs,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
)
self... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# 3. Encode input prompt
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt,
self.do_classifier_free_guidance,
negative_prompt=negative_prompt,
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler, num_inference_steps, device, timesteps, sigmas
)
# 5. Prepare latents.
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_in... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
# perform guidance
if self.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)
# learned sigma
if self.transf... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# call the callback, if provided
... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
if output_type == "latent":
image = latents
else:
latents = latents.to(self.vae.dtype)
try:
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
except torch.cuda.OutOfMemoryError as e:
warnings.wa... | 238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/sana/pipeline_sana.py |
class AudioLDMPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for text-to-audio generation using AudioLDM.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particu... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.ClapTextModelWithProjection`]):
Frozen text-encoder (`ClapTextModelWithProjection`, specifically the
[laio... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
def __init__(
self,
vae: AutoencoderKL,
text_encoder: ClapTextModelWithProjection,
tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
vo... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
def _encode_prompt(
self,
prompt,
device,
num_waveforms_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
):
r"""
Encodes ... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device (`torch.device`):
torch device
num_waveforms_per_prompt (`int`):
number of waveforms that should be generated per prompt
do_classifier_free_guidanc... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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.
"""
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
if prompt_embeds is None:
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... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
(
bs_embed,
seq_len,
) = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt)
prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_pro... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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)... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
else:
uncond_tokens = negative_prompt | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_input_ids = uncond_input.inpu... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len)
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipe... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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,
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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 "
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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:
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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,
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
audio_length_in_s: Optional[float] = None,
num_inference_steps: int = 10,
guidance_scale: float = 2.5,
negative_prompt: Optional[Union[str, Li... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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_length_in_s (`int`, *optional*, defaults to 5.12):
The length of the generated audio sample in seconds.
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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 is generated by sampling using the supplied random `generator`.
prom... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps during inference. The function is called with the
following arguments: `callback(step: int, timestep: ... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
The output format of the generated image. Choose between `"np"` to return a NumPy `np.ndarray` or
`"pt"` to return a PyTorch `torch.Tensor` object. | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
Examples:
Returns:
[`~pipelines.AudioPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated audio.
"""
# 0. Convert ... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_waveforms_p... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
# 7. 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
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.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
... | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
audio = self.mel_spectrogram_to_waveform(mel_spectrogram)
audio = audio[:, :original_waveform_length]
if output_type == "np":
audio = audio.numpy()
if not return_dict:
return (audio,)
return AudioPipelineOutput(audios=audio) | 239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/audioldm/pipeline_audioldm.py |
class ConsisIDPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
r"""
Pipeline for image-to-video generation using ConsisID.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading o... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. ConsisID uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transfo... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
_optional_components = []
model_cpu_offload_seq = "text_encoder->transformer->vae"
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
vae: ... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
vae=vae,
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor_spatial = (
2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "v... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.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[:, max_sequence_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because `max... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
negative_prompt: Optional[Union[str, List[str]]] = None,
do_classifier_free_guidance: bool = True,
num_videos_per_prompt: int ... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ign... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
provided, text embeddings will be generated from `prompt` input argument.
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 wil... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
prompt_embeds = self._get_t5_prompt_embeds(
prompt=prompt,
... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif batch_size != len(negative_pro... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
def prepare_latents(
self,
image: torch.Tensor,
batch_size: int = 1,
num_channels_latents: int = 16,
num_frames: int = 13,
height: int = 60,
width: int = 90,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
genera... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
num_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
shape = (
batch_size,
num_frames,
num_channels_latents,
height // self.vae_scale_factor_spatial,
width // self.vae_scale_factor_spatial,
)
image = image.unsqueeze(2) ... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
if isinstance(generator, list):
image_latents = [
retrieve_latents(self.vae.encode(image[i].unsqueeze(0)), generator[i]) for i in range(batch_size)
]
if kps_cond is not None:
kps_cond = kps_cond.unsqueeze(2)
kps_cond_latents = [
... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
if kps_cond is not None:
kps_cond_latents = torch.cat(kps_cond_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W]
kps_cond_latents = self.vae_scaling_factor_image * kps_cond_latents
padding_shape = (
batch_size,
num_frames - 2,
... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
latent_padding = torch.zeros(padding_shape, device=device, dtype=dtype)
if kps_cond is not None:
image_latents = torch.cat([image_latents, kps_cond_latents, latent_padding], dim=1)
else:
image_latents = torch.cat([image_latents, latent_padding], dim=1)
if latents is None... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
frames = self.vae.decode(latents).sample
return frames
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, timesteps, strength, device):
# get the original timestep using init_t... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
def check_inputs(
self,
image,
prompt,
height,
width,
negative_prompt,
callback_on_step_end_tensor_inputs,
latents=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (
not isinstance(image, torch.Tensor)
... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
if prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if negative_prompt is ... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.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... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
def _prepare_rotary_positional_embeddings(
self,
height: int,
width: int,
num_frames: int,
device: torch.device,
) -> Tuple[torch.Tensor, torch.Tensor]:
grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
grid_width = w... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
grid_crops_coords = get_resize_crop_region_for_grid(
(grid_height, grid_width), base_size_width, base_size_height
)
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
embed_dim=self.transformer.config.attention_head_dim,
crops_coords=grid_crops_coords,
grid_s... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: PipelineImageInput,
prompt: Optional[Union[str, List[str]]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: int = 480,
width: int = 720,
num... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 226,
id_vit_hidden: Optional[torch.Tensor] = None,
id_cond: Optional[torch.Tensor] = None,
k... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
Args:
image (`PipelineImageInput`):
The input image to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to p... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
The width in pixels of the generated image. This is set to 720 by default for the best results.
num_frames (`int`, defaults to `49`):
Number of frames to generate. Mu... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.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... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
more faithful image generation, while later steps reduce it for more diverse and natural results.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of videos to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. ... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/att... | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
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