text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
prompt_embeds = prompt_embeds.to(device=device)
prompt_attention_mask = prompt_attention_mask.to(device=device)
prompt_embeds_2 = prompt_embeds_2.to(device=device)
prompt_attention_mask_2 = prompt_attention_mask_2.to(device=device)
add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype... | 328 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py |
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# expand scalar t to 1-D tensor to ma... | 328 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py |
# predict the noise residual
noise_pred = self.transformer(
latent_model_input,
t_expand,
encoder_hidden_states=prompt_embeds,
text_embedding_mask=prompt_attention_mask,
encoder_hidden_states_t5=prompt_em... | 328 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py |
if self.do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1... | 328 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.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)
prompt_embeds_2 = callback_outputs.pop("prom... | 328 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if ... | 328 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py |
class PixArtAlphaPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using PixArt-Alpha.
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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. PixArt-Alpha uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#tra... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
bad_punct_regex = re.compile(
r"["
+ "#®•©™&@·º½¾¿¡§~"
+ r"\)"
+ r"\("
+ r"\]"
+ r"\["
+ r"\}"
+ r"\{"
+ r"\|"
+ "\\"
+ r"\/"
+ r"\*"
+ r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder"]... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
# Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
negative_prompt: str = "",
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
instead. Ignored when n... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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.Tensor`, *optional*):
Pre-generated negative text embeddings. For P... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
if "mask_feature" in kwargs:
deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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_length - 1 : -1])
logger.warning(
"The following part of your... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embed... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens = [negative_prompt] * bs_embed if isinstance(negative_prompt, str) else negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, cle... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each gen... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(1, num_images_per_prompt)
negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed * num_images_per_prompt, -1)
else:
negative_prompt_embeds = None
negative_prompt_attention_mask = Non... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step)... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
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)}."
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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:
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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."
)
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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:
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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)... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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 = ... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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() | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
callback_steps: int = 1,
clean_caption: bool = True,
use_resolution_binning: bool = True,
max_sequence_length: int = 120,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation. | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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.... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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"`):
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `f... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
Examples: | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
if "mask_feature" in kwargs:
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
elif self.transformer.config.sample_size == 64:
aspect_ratio_bin = ASPECT_RATIO_512_BIN
elif self.transformer.config.sample_size == 32:
aspect_ratio_bin = ASPECT_RATIO_256_BIN
else:
raise ValueError("Invalid sample size")
orig_height, o... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
self.check_inputs(
prompt,
height,
width,
negative_prompt,
callback_steps,
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
)
# 2. Default height and ... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
# 3. Encode input prompt
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt,
do_classifier_free_guidance,
negative_prompt=negative_prompt,
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.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_... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
# 6.1 Prepare micro-conditions.
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
if self.transformer.config.sample_size == 128:
resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1)
aspect_ratio = torch.tensor([float(height / width... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
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) | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
current_timestep = t
if not torch.is_tensor(current_timestep):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_m... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
current_timestep = current_timestep.expand(latent_model_input.shape[0]) | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
# predict noise model_output
noise_pred = self.transformer(
latent_model_input,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
timestep=current_timestep,
added_cond_kwa... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
# compute previous image: x_t -> x_t-1
if num_inference_steps == 1:
# For DMD one step sampling: https://arxiv.org/abs/2311.18828
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).pred_original_sample
else:
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
if use_resolution_binning:
image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height)
else:
image = latents
... | 329 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py |
class PixArtSigmaPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using PixArt-Sigma.
"""
bad_punct_regex = re.compile(
r"["
+ "#®•©™&@·º½¾¿¡§~"
+ r"\)"
+ r"\("
+ r"\]"
+ r"\["
+ r"\}"
+ r"\{"
+ r"\|"
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = PixArtImageProcessor(vae_scale_factor=self.vae_scale_factor)
# Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.encode_prompt with 120... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
instead. Ignored when n... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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.Tensor`, *optional*):
Pre-generated negative text embeddings. For P... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
if "mask_feature" in kwargs:
deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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_length - 1 : -1])
logger.warning(
"The following part of your... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embed... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens = [negative_prompt] * bs_embed if isinstance(negative_prompt, str) else negative_prompt
uncond_tokens = self._text_preprocessing(uncond_tokens, cle... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each gen... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(1, num_images_per_prompt)
negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed * num_images_per_prompt, -1)
else:
negative_prompt_embeds = None
negative_prompt_attention_mask = Non... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step)... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
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)}."
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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:
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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."
)
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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:
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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)... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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 = ... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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() | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
callback_steps: int = 1,
clean_caption: bool = True,
use_resolution_binning: bool = True,
max_sequence_length: int = 300,
**kwargs,
) -> Union[ImagePipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation. | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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.... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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"`):
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `f... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
Examples: | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
# 1. Check inputs. Raise error i... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
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) | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
self.check_inputs(
prompt,
height,
width,
negative_prompt,
callback_steps,
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
)
# 2. Default height and ... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
# 3. Encode input prompt
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt,
do_classifier_free_guidance,
negative_prompt=negative_prompt,
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.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_... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
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) | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
current_timestep = t
if not torch.is_tensor(current_timestep):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_m... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
current_timestep = current_timestep.expand(latent_model_input.shape[0]) | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
# predict noise model_output
noise_pred = self.transformer(
latent_model_input,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
timestep=current_timestep,
added_cond_kwa... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
# compute previous image: x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
... | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
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 ImagePipelineOutput(images=image) | 330 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py |
class DDPMPipeline(DiffusionPipeline):
r"""
Pipeline for image generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
unet ... | 331 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py |
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
num_inference_steps: int = 1000,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tupl... | 331 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py |
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic... | 331 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/ddpm/pipeline_ddpm.py |
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