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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_encoder is not None:
if isinstance(self, SanaLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder, lora_scale)
return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask | 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 with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
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).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs | 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,
):
if height % 32 != 0 or width % 32 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
) | 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:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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."
) | 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."
)
if prompt_embeds is not None and prompt_attention_mask is None:
raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") | 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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
raise ValueError(
"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
f" {negative_prompt_attention_mask.shape}."
) | 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.warning("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warning("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text] | 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:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption) | 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 = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", 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 = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# "
caption = re.sub(r""?", "", caption)
# &
caption = re.sub(r"&", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", 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)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(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 = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip() | 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_latents, height, width, dtype, device, generator, latents=None):
if latents is not None:
return latents.to(device=device, dtype=dtype)
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
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."
) | 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(self):
return self._guidance_scale > 1.0
@property
def num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interrupt | 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: float = 4.5,
num_images_per_prompt: Optional[int] = 1,
height: int = 1024,
width: int = 1024,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_attention_mask: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
clean_caption: bool = False,
use_resolution_binning: bool = True, | 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 prompt, generate an 'Enhanced prompt' that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:",
"- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.",
"- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.",
"Here are examples of how to transform or refine prompts:", | 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, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.",
"Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:",
"User Prompt: ",
],
) -> Union[SanaPipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation. | 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 the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_inference_steps (`int`, *optional*, defaults to 20):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument | 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` 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 4.5):
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`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, | 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.
width (`int`, *optional*, defaults to self.unet.config.sample_size):
The width in pixels of the generated image.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
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. | 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 sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
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.
prompt_attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask for text embeddings.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not | 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"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
attention_kwargs:
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/attention_processor.py). | 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.
use_resolution_binning (`bool` defaults to `True`):
If set to `True`, the requested height and width are first mapped to the closest resolutions using
`ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to
the requested resolution. Useful for generating non-square images.
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, | 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 specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to `300`):
Maximum sequence length to use with the `prompt`.
complex_human_instruction (`List[str]`, *optional*):
Instructions for complex human attention:
https://github.com/NVlabs/Sana/blob/main/configs/sana_app_config/Sana_1600M_app.yaml#L55. | 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 images
"""
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs | 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
elif self.transformer.config.sample_size == 32:
aspect_ratio_bin = ASPECT_RATIO_1024_BIN
elif self.transformer.config.sample_size == 16:
aspect_ratio_bin = ASPECT_RATIO_512_BIN
else:
raise ValueError("Invalid sample size")
orig_height, orig_width = height, width
height, width = self.image_processor.classify_height_width_bin(height, width, ratios=aspect_ratio_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._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._interrupt = False
# 2. Default height and width to transformer
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
lora_scale = self.attention_kwargs.get("scale", None) if self.attention_kwargs is not None else None | 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,
num_images_per_prompt=num_images_per_prompt,
device=device,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
prompt_attention_mask=prompt_attention_mask,
negative_prompt_attention_mask=negative_prompt_attention_mask,
clean_caption=clean_caption,
max_sequence_length=max_sequence_length,
complex_human_instruction=complex_human_instruction,
lora_scale=lora_scale,
)
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) | 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_size * num_images_per_prompt,
latent_channels,
height,
width,
torch.float32,
device,
generator,
latents,
)
# 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)
# 7. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps) | 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_input = latent_model_input.to(prompt_embeds.dtype)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latent_model_input.shape[0]).to(latents.dtype)
# predict noise model_output
noise_pred = self.transformer(
latent_model_input,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
timestep=timestep,
return_dict=False,
attention_kwargs=self.attention_kwargs,
)[0]
noise_pred = noise_pred.float() | 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.transformer.config.out_channels // 2 == latent_channels:
noise_pred = noise_pred.chunk(2, dim=1)[0]
else:
noise_pred = noise_pred
# compute previous image: x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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) | 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
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step() | 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.warn(
f"{e}. \n"
f"Try to use VAE tiling for large images. For example: \n"
f"pipe.vae.enable_tiling(tile_sample_min_width=512, tile_sample_min_height=512)"
)
if use_resolution_binning:
image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height)
if not output_type == "latent":
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return SanaPipelineOutput(images=image) | 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 particular device, etc.). | 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
[laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant.
tokenizer ([`PreTrainedTokenizer`]):
A [`~transformers.RobertaTokenizer`] to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded audio latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
vocoder ([`~transformers.SpeechT5HifiGan`]):
Vocoder of class `SpeechT5HifiGan`.
""" | 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,
vocoder: SpeechT5HifiGan,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
vocoder=vocoder,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 | 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 the prompt into text encoder hidden states. | 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_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the audio generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
prompt_embeds (`torch.Tensor`, *optional*):
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. | 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.
"""
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] | 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_ids
attention_mask = text_inputs.attention_mask
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 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]
)
logger.warning(
"The following part of your input was truncated because CLAP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask.to(device),
)
prompt_embeds = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
prompt_embeds = F.normalize(prompt_embeds, dim=-1)
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) | 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_prompt, seq_len) | 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):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `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.input_ids.to(device)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input_ids,
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
negative_prompt_embeds = F.normalize(negative_prompt_embeds, dim=-1)
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1] | 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)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents
mel_spectrogram = self.vae.decode(latents).sample
return mel_spectrogram
def mel_spectrogram_to_waveform(self, mel_spectrogram):
if mel_spectrogram.dim() == 4:
mel_spectrogram = mel_spectrogram.squeeze(1) | 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.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 with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta | 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,
audio_length_in_s,
vocoder_upsample_factor,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor
if audio_length_in_s < min_audio_length_in_s:
raise ValueError(
f"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but "
f"is {audio_length_in_s}."
) | 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 "
f"{self.vae_scale_factor}."
)
if (callback_steps is None) or (
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)}."
) | 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:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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."
) | 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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
) | 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,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(self.vocoder.config.model_in_dim) // self.vae_scale_factor,
)
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."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device) | 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, List[str]]] = None,
num_waveforms_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: Optional[int] = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
output_type: Optional[str] = "np",
):
r"""
The call function to the pipeline for generation. | 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.
num_inference_steps (`int`, *optional*, defaults to 10):
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 2.5):
A higher guidance scale value encourages the model to generate audio that is closely linked to the text
`prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*): | 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 waveforms to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
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://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*): | 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`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
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 generated from the `negative_prompt` input argument.
return_dict (`bool`, *optional*, defaults to `True`): | 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: 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.
cross_attention_kwargs (`dict`, *optional*):
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).
output_type (`str`, *optional*, defaults to `"np"`): | 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 audio input length from seconds to spectrogram height
vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate
if audio_length_in_s is None:
audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor
height = int(audio_length_in_s / vocoder_upsample_factor) | 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 increased to {height * vocoder_upsample_factor} "
f"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the "
f"denoising process."
)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
audio_length_in_s,
vocoder_upsample_factor,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
) | 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
# 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.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_waveforms_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
) | 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_per_prompt,
num_channels_latents,
height,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | 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
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=None,
class_labels=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample | 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
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
# 8. Post-processing
mel_spectrogram = self.decode_latents(latents) | 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 or saving, running on a particular device, etc.) | 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#transformers.T5EncoderModel); specifically the
[t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
tokenizer (`T5Tokenizer`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
transformer ([`ConsisIDTransformer3DModel`]):
A text conditioned `ConsisIDTransformer3DModel` to denoise the encoded video latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
""" | 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: AutoencoderKLCogVideoX,
transformer: ConsisIDTransformer3DModel,
scheduler: CogVideoXDPMScheduler,
):
super().__init__() | 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, "vae") and self.vae is not None else 8
)
self.vae_scale_factor_temporal = (
self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
)
self.vae_scaling_factor_image = (
self.vae.config.scaling_factor if hasattr(self, "vae") and self.vae is not None else 0.7
)
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) | 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,
dtype: Optional[torch.dtype] = None,
):
device = device or self._execution_device
dtype = dtype or self.text_encoder.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | 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_sequence_length` is set to "
f" {max_sequence_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
return prompt_embeds | 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 = 1,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
Whether to use classifier free guidance or not.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not | 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 will be generated from `negative_prompt` input
argument.
device: (`torch.device`, *optional*):
torch device
dtype: (`torch.dtype`, *optional*):
torch dtype
"""
device = device or self._execution_device | 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,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
if do_classifier_free_guidance and negative_prompt_embeds is None:
negative_prompt = negative_prompt or ""
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_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_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
negative_prompt_embeds = self._get_t5_prompt_embeds(
prompt=negative_prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
return prompt_embeds, negative_prompt_embeds | 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,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.Tensor] = None,
kps_cond: Optional[torch.Tensor] = None,
):
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."
) | 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) # [B, C, F, H, W] | 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 = [
retrieve_latents(self.vae.encode(kps_cond[i].unsqueeze(0)), generator[i])
for i in range(batch_size)
]
else:
image_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator) for img in image]
if kps_cond is not None:
kps_cond = kps_cond.unsqueeze(2)
kps_cond_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator) for img in kps_cond]
image_latents = torch.cat(image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W]
image_latents = self.vae_scaling_factor_image * image_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,
num_channels_latents,
height // self.vae_scale_factor_spatial,
width // self.vae_scale_factor_spatial,
)
else:
padding_shape = (
batch_size,
num_frames - 1,
num_channels_latents,
height // self.vae_scale_factor_spatial,
width // self.vae_scale_factor_spatial,
) | 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:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents, image_latents
# Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_latents
def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]
latents = 1 / self.vae_scaling_factor_image * latents | 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_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start | 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 with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
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).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs | 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)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
f" {type(image)}"
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | 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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
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:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): | 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 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."
) | 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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
) | 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 = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
base_size_width = self.transformer.config.sample_width // self.transformer.config.patch_size
base_size_height = self.transformer.config.sample_height // self.transformer.config.patch_size | 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_size=(grid_height, grid_width),
temporal_size=num_frames,
device=device,
)
return freqs_cos, freqs_sin
@property
def guidance_scale(self):
return self._guidance_scale
@property
def num_timesteps(self):
return self._num_timesteps
@property
def attention_kwargs(self):
return self._attention_kwargs
@property
def interrupt(self):
return self._interrupt | 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_frames: int = 49,
num_inference_steps: int = 50,
guidance_scale: float = 6.0,
use_dynamic_cfg: bool = False,
num_videos_per_prompt: int = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: str = "pil",
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[ | 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,
kps_cond: Optional[torch.Tensor] = None,
) -> Union[ConsisIDPipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation. | 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 pass `prompt_embeds`.
instead.
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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
height (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial):
The height in pixels of the generated image. This is set to 480 by default for the best results. | 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. Must be divisible by self.vae_scale_factor_temporal. Generated video will
contain 1 extra frame because ConsisID is conditioned with (num_seconds * fps + 1) frames where
num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
needs to be satisfied is that of divisibility mentioned above.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 6): | 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`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
use_dynamic_cfg (`bool`, *optional*, defaults to `False`):
If True, dynamically adjusts the guidance scale during inference. This allows the model to use a
progressive guidance scale, improving the balance between text-guided generation and image quality over
the course of the inference steps. Typically, early inference steps use a higher guidance scale for | 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*):
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-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`.
prompt_embeds (`torch.FloatTensor`, *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. 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.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
of a plain tuple. | 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/attention_processor.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: 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 specified in the list | 240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/consisid/pipeline_consisid.py |
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