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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, device=device).repeat( batch_size * num_images_per_prompt, 1 ) style = style.to(device=device).repeat(batch_size * num_images_per_prompt) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue
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# 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 match the 1st dim of latent_model_input t_expand = torch.tensor([t] * latent_model_input.shape[0], device=device).to( dtype=latent_model_input.dtype )
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# 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_embeds_2, text_embedding_mask_t5=prompt_attention_mask_2, image_meta_size=add_time_ids, style=style, image_rotary_emb=image_rotary_emb, return_dict=False, )[0] noise_pred, _ = noise_pred.chunk(2, dim=1) # 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)
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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 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)
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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("prompt_embeds_2", prompt_embeds_2) negative_prompt_embeds_2 = callback_outputs.pop( "negative_prompt_embeds_2", negative_prompt_embeds_2 ) 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()
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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 has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
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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 a particular device, etc.)
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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#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 ([`PixArtTransformer2DModel`]): A text conditioned `PixArtTransformer2DModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. """
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bad_punct_regex = re.compile( r"[" + "#®•©™&@·º½¾¿¡§~" + r"\)" + r"\(" + r"\]" + r"\[" + r"\}" + r"\{" + r"\|" + "\\" + r"\/" + r"\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder"] model_cpu_offload_seq = "text_encoder->transformer->vae" def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, vae: AutoencoderKL, transformer: PixArtTransformer2DModel, scheduler: DPMSolverMultistepScheduler, ): super().__init__() self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler ) 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)
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# 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, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, clean_caption: bool = False, max_sequence_length: int = 120, **kwargs, ): r""" Encodes the prompt into text encoder hidden states.
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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 not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For PixArt-Alpha, this should be "". do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt device: (`torch.device`, *optional*): torch device to place the resulting embeddings on prompt_embeds (`torch.Tensor`, *optional*):
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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 PixArt-Alpha, it's should be the embeddings of the "" string. clean_caption (`bool`, defaults to `False`): If `True`, the function will preprocess and clean the provided caption before encoding. max_sequence_length (`int`, defaults to 120): Maximum sequence length to use for the prompt. """
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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) if device is None: device = self._execution_device # See Section 3.1. of the paper. max_length = max_sequence_length if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_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
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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 input was truncated because T5 can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) prompt_attention_mask = text_inputs.attention_mask prompt_attention_mask = prompt_attention_mask.to(device) prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.transformer is not None: dtype = self.transformer.dtype else: dtype = None
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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_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) prompt_attention_mask = prompt_attention_mask.repeat(1, num_images_per_prompt) prompt_attention_mask = prompt_attention_mask.view(bs_embed * num_images_per_prompt, -1)
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# 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, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) negative_prompt_attention_mask = uncond_input.attention_mask negative_prompt_attention_mask = negative_prompt_attention_mask.to(device)
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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 generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
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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 = None return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask # 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]
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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 def check_inputs( self, prompt, height, width, negative_prompt, callback_steps, prompt_embeds=None, negative_prompt_embeds=None, prompt_attention_mask=None, negative_prompt_attention_mask=None, ): 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}.")
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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)}." )
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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." )
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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`.")
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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}." )
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# 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]
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# 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)
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# 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) #######################################################
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# 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) # &quot; caption = re.sub(r"&quot;?", "", caption) # &amp caption = re.sub(r"&amp", "", 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+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption)
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# "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption))
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caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip()
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caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip()
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# 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, 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." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents
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@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: Optional[int] = None, width: Optional[int] = None, 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, callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
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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.
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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 100): 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
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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`,
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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.
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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-Alpha this negative prompt should be "". If not
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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. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`. callback_steps (`int`, *optional*, defaults to 1):
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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 `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. max_sequence_length (`int` defaults to 120): Maximum sequence length to use with the `prompt`.
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Examples:
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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: 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) # 1. Check inputs. Raise error if not correct height = height or self.transformer.config.sample_size * self.vae_scale_factor width = width or self.transformer.config.sample_size * self.vae_scale_factor if use_resolution_binning: if self.transformer.config.sample_size == 128: aspect_ratio_bin = ASPECT_RATIO_1024_BIN
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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, orig_width = height, width height, width = self.image_processor.classify_height_width_bin(height, width, ratios=aspect_ratio_bin)
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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 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 # 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
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# 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, 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, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
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# 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, prompt_embeds.dtype, 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)
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# 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)]).repeat(batch_size * num_images_per_prompt, 1) resolution = resolution.to(dtype=prompt_embeds.dtype, device=device) aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device) if do_classifier_free_guidance: resolution = torch.cat([resolution, resolution], dim=0) aspect_ratio = torch.cat([aspect_ratio, aspect_ratio], dim=0) added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio} # 7. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
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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)
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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_model_input.device.type == "mps" if isinstance(current_timestep, float): dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device) elif len(current_timestep.shape) == 0: current_timestep = current_timestep[None].to(latent_model_input.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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current_timestep = current_timestep.expand(latent_model_input.shape[0])
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# 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_kwargs=added_cond_kwargs, return_dict=False, )[0] # 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) # 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
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# 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: 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): 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()
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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 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)
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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"\|" + "\\" + r"\/" + r"\*" + r"]{1,}" ) # noqa _optional_components = ["tokenizer", "text_encoder"] model_cpu_offload_seq = "text_encoder->transformer->vae" def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, vae: AutoencoderKL, transformer: PixArtTransformer2DModel, scheduler: KarrasDiffusionSchedulers, ): super().__init__() self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler )
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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->300 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, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, clean_caption: bool = False, max_sequence_length: int = 300, **kwargs, ): r""" Encodes the prompt into text encoder hidden states.
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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 not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For PixArt-Alpha, this should be "". do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): whether to use classifier free guidance or not num_images_per_prompt (`int`, *optional*, defaults to 1): number of images that should be generated per prompt device: (`torch.device`, *optional*): torch device to place the resulting embeddings on prompt_embeds (`torch.Tensor`, *optional*):
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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 PixArt-Alpha, it's should be the embeddings of the "" string. clean_caption (`bool`, defaults to `False`): If `True`, the function will preprocess and clean the provided caption before encoding. max_sequence_length (`int`, defaults to 300): Maximum sequence length to use for the prompt. """
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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) if device is None: device = self._execution_device # See Section 3.1. of the paper. max_length = max_sequence_length if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_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
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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 input was truncated because T5 can only handle sequences up to" f" {max_length} tokens: {removed_text}" ) prompt_attention_mask = text_inputs.attention_mask prompt_attention_mask = prompt_attention_mask.to(device) prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask) prompt_embeds = prompt_embeds[0] if self.text_encoder is not None: dtype = self.text_encoder.dtype elif self.transformer is not None: dtype = self.transformer.dtype else: dtype = None
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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_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) prompt_attention_mask = prompt_attention_mask.repeat(1, num_images_per_prompt) prompt_attention_mask = prompt_attention_mask.view(bs_embed * num_images_per_prompt, -1)
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# 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, clean_caption=clean_caption) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) negative_prompt_attention_mask = uncond_input.attention_mask negative_prompt_attention_mask = negative_prompt_attention_mask.to(device)
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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 generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
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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 = None return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask # 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]
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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 # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.check_inputs def check_inputs( self, prompt, height, width, negative_prompt, callback_steps, prompt_embeds=None, negative_prompt_embeds=None, prompt_attention_mask=None, negative_prompt_attention_mask=None, ): 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}.")
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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)}." )
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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." )
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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`.")
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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}." )
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# 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]
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# 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)
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# 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) #######################################################
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# 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) # &quot; caption = re.sub(r"&quot;?", "", caption) # &amp caption = re.sub(r"&amp", "", 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+$", "", caption) # \n caption = re.sub(r"\\n", " ", caption)
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# "#123" caption = re.sub(r"#\d{1,3}\b", "", caption) # "#12345.." caption = re.sub(r"#\d{5,}\b", "", caption) # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat regex2 = re.compile(r"(?:\-|\_)") if len(re.findall(regex2, caption)) > 3: caption = re.sub(regex2, " ", caption) caption = ftfy.fix_text(caption) caption = html.unescape(html.unescape(caption))
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caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) caption = re.sub(r"\b\s+\:\s+", r": ", caption) caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) caption = re.sub(r"\s+", " ", caption) caption.strip()
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caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) caption = re.sub(r"^\.\S+$", "", caption) return caption.strip()
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# 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, 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." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents
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@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: Optional[int] = None, width: Optional[int] = None, 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, callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
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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.
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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 100): 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
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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`,
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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.
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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
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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. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`. callback_steps (`int`, *optional*, defaults to 1):
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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 `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. max_sequence_length (`int` defaults to 300): Maximum sequence length to use with the `prompt`.
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Examples:
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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 if not correct height = height or self.transformer.config.sample_size * self.vae_scale_factor width = width or self.transformer.config.sample_size * self.vae_scale_factor if use_resolution_binning: if self.transformer.config.sample_size == 256: aspect_ratio_bin = ASPECT_RATIO_2048_BIN elif self.transformer.config.sample_size == 128: aspect_ratio_bin = ASPECT_RATIO_1024_BIN 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
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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)
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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 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 # 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
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# 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, 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, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
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# 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, prompt_embeds.dtype, 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) # 6.1 Prepare micro-conditions. added_cond_kwargs = {"resolution": None, "aspect_ratio": None} # 7. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
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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)
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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_model_input.device.type == "mps" if isinstance(current_timestep, float): dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device) elif len(current_timestep.shape) == 0: current_timestep = current_timestep[None].to(latent_model_input.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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current_timestep = current_timestep.expand(latent_model_input.shape[0])
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# 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_kwargs=added_cond_kwargs, return_dict=False, )[0] # 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) # 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
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# 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): 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() 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
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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)
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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 ([`UNet2DModel`]): A `UNet2DModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of [`DDPMScheduler`], or [`DDIMScheduler`]. """ model_cpu_offload_seq = "unet" def __init__(self, unet, scheduler): super().__init__() self.register_modules(unet=unet, scheduler=scheduler)
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@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, Tuple]: r""" The call function to the pipeline for generation.
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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. num_inference_steps (`int`, *optional*, defaults to 1000): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. Example: ```py >>> from diffusers import DDPMPipeline
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