# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from transformers.models.clip.modeling_clip import CLIPTextModelOutput from ...image_processor import VaeImageProcessor from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin from ...models import AutoencoderKL, PriorTransformer, UNet2DConditionModel from ...models.embeddings import get_timestep_embedding from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableUnCLIPPipeline >>> pipe = StableUnCLIPPipeline.from_pretrained( ... "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16 ... ) # TODO update model path >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> images = pipe(prompt).images >>> images[0].save("astronaut_horse.png") ``` """ class StableUnCLIPPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): """ Pipeline for text-to-image generation using stable unCLIP. 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.) Args: prior_tokenizer ([`CLIPTokenizer`]): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). prior_text_encoder ([`CLIPTextModelWithProjection`]): Frozen text-encoder. prior ([`PriorTransformer`]): The canonincal unCLIP prior to approximate the image embedding from the text embedding. prior_scheduler ([`KarrasDiffusionSchedulers`]): Scheduler used in the prior denoising process. image_normalizer ([`StableUnCLIPImageNormalizer`]): Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image embeddings after the noise has been applied. image_noising_scheduler ([`KarrasDiffusionSchedulers`]): Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined by `noise_level` in `StableUnCLIPPipeline.__call__`. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). text_encoder ([`CLIPTextModel`]): Frozen text-encoder. unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`KarrasDiffusionSchedulers`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. """ _exclude_from_cpu_offload = ["prior", "image_normalizer"] # prior components prior_tokenizer: CLIPTokenizer prior_text_encoder: CLIPTextModelWithProjection prior: PriorTransformer prior_scheduler: KarrasDiffusionSchedulers # image noising components image_normalizer: StableUnCLIPImageNormalizer image_noising_scheduler: KarrasDiffusionSchedulers # regular denoising components tokenizer: CLIPTokenizer text_encoder: CLIPTextModel unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers vae: AutoencoderKL def __init__( self, # prior components prior_tokenizer: CLIPTokenizer, prior_text_encoder: CLIPTextModelWithProjection, prior: PriorTransformer, prior_scheduler: KarrasDiffusionSchedulers, # image noising components image_normalizer: StableUnCLIPImageNormalizer, image_noising_scheduler: KarrasDiffusionSchedulers, # regular denoising components tokenizer: CLIPTokenizer, text_encoder: CLIPTextModelWithProjection, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, # vae vae: AutoencoderKL, ): super().__init__() self.register_modules( prior_tokenizer=prior_tokenizer, prior_text_encoder=prior_text_encoder, prior=prior, prior_scheduler=prior_scheduler, image_normalizer=image_normalizer, image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, vae=vae, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_model_cpu_offload(self, gpu_id=0): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. """ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") device = torch.device(f"cuda:{gpu_id}") if self.device.type != "cpu": self.to("cpu", silence_dtype_warnings=True) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) hook = None for cpu_offloaded_model in [self.text_encoder, self.prior_text_encoder, self.unet, self.vae]: _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) # We'll offload the last model manually. self.final_offload_hook = hook # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._encode_prompt with _encode_prompt->_encode_prior_prompt, tokenizer->prior_tokenizer, text_encoder->prior_text_encoder def _encode_prior_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None, text_attention_mask: Optional[torch.Tensor] = None, ): if text_model_output is None: batch_size = len(prompt) if isinstance(prompt, list) else 1 # get prompt text embeddings text_inputs = self.prior_tokenizer( prompt, padding="max_length", max_length=self.prior_tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids text_mask = text_inputs.attention_mask.bool().to(device) untruncated_ids = self.prior_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.prior_tokenizer.batch_decode( untruncated_ids[:, self.prior_tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.prior_tokenizer.model_max_length} tokens: {removed_text}" ) text_input_ids = text_input_ids[:, : self.prior_tokenizer.model_max_length] prior_text_encoder_output = self.prior_text_encoder(text_input_ids.to(device)) prompt_embeds = prior_text_encoder_output.text_embeds prior_text_encoder_hidden_states = prior_text_encoder_output.last_hidden_state else: batch_size = text_model_output[0].shape[0] prompt_embeds, prior_text_encoder_hidden_states = text_model_output[0], text_model_output[1] text_mask = text_attention_mask prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0) prior_text_encoder_hidden_states = prior_text_encoder_hidden_states.repeat_interleave( num_images_per_prompt, dim=0 ) text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0) if do_classifier_free_guidance: uncond_tokens = [""] * batch_size uncond_input = self.prior_tokenizer( uncond_tokens, padding="max_length", max_length=self.prior_tokenizer.model_max_length, truncation=True, return_tensors="pt", ) uncond_text_mask = uncond_input.attention_mask.bool().to(device) negative_prompt_embeds_prior_text_encoder_output = self.prior_text_encoder( uncond_input.input_ids.to(device) ) negative_prompt_embeds = negative_prompt_embeds_prior_text_encoder_output.text_embeds uncond_prior_text_encoder_hidden_states = ( negative_prompt_embeds_prior_text_encoder_output.last_hidden_state ) # 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.repeat(1, num_images_per_prompt) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len) seq_len = uncond_prior_text_encoder_hidden_states.shape[1] uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.repeat( 1, num_images_per_prompt, 1 ) uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.view( batch_size * num_images_per_prompt, seq_len, -1 ) uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0) # done duplicates # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prior_text_encoder_hidden_states = torch.cat( [uncond_prior_text_encoder_hidden_states, prior_text_encoder_hidden_states] ) text_mask = torch.cat([uncond_text_mask, text_mask]) return prompt_embeds, prior_text_encoder_hidden_states, text_mask # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the 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`). prompt_embeds (`torch.FloatTensor`, *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. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. """ # set lora scale so that monkey patched LoRA # function of text encoder can correctly access it if lora_scale is not None and isinstance(self, LoraLoaderMixin): self._lora_scale = lora_scale 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] if prompt_embeds is None: # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, ) prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=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=self.text_encoder.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents def decode_latents(self, latents): warnings.warn( "The decode_latents method is deprecated and will be removed in a future version. Please" " use VaeImageProcessor instead", FutureWarning, ) latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.decode(latents, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs with prepare_extra_step_kwargs->prepare_prior_extra_step_kwargs, scheduler->prior_scheduler def prepare_prior_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the prior_scheduler step, since not all prior_schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other prior_schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.prior_scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the prior_scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.prior_scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, height, width, callback_steps, noise_level, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=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}.") 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)}." ) if prompt is not None and prompt_embeds is not None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Please make sure to define only one of the two." ) if prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) if prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( "Provide either `negative_prompt` or `negative_prompt_embeds`. Cannot leave both `negative_prompt` and `negative_prompt_embeds` undefined." ) if prompt is not None and negative_prompt is not None: if type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) 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 noise_level < 0 or noise_level >= self.image_noising_scheduler.config.num_train_timesteps: raise ValueError( f"`noise_level` must be between 0 and {self.image_noising_scheduler.config.num_train_timesteps - 1}, inclusive." ) # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents def prepare_latents(self, shape, dtype, device, generator, latents, scheduler): if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) latents = latents * scheduler.init_noise_sigma return latents def noise_image_embeddings( self, image_embeds: torch.Tensor, noise_level: int, noise: Optional[torch.FloatTensor] = None, generator: Optional[torch.Generator] = None, ): """ Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher `noise_level` increases the variance in the final un-noised images. The noise is applied in two ways 1. A noise schedule is applied directly to the embeddings 2. A vector of sinusoidal time embeddings are appended to the output. In both cases, the amount of noise is controlled by the same `noise_level`. The embeddings are normalized before the noise is applied and un-normalized after the noise is applied. """ if noise is None: noise = randn_tensor( image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype ) noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device) self.image_normalizer.to(image_embeds.device) image_embeds = self.image_normalizer.scale(image_embeds) image_embeds = self.image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise) image_embeds = self.image_normalizer.unscale(image_embeds) noise_level = get_timestep_embedding( timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0 ) # `get_timestep_embeddings` does not contain any weights and will always return f32 tensors, # but we might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. noise_level = noise_level.to(image_embeds.dtype) image_embeds = torch.cat((image_embeds, noise_level), 1) return image_embeds @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, # regular denoising process args prompt: Optional[Union[str, List[str]]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 20, guidance_scale: float = 10.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, noise_level: int = 0, # prior args prior_num_inference_steps: int = 25, prior_guidance_scale: float = 4.0, prior_latents: Optional[torch.FloatTensor] = None, ): """ Function invoked when calling the pipeline for generation. 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. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 20): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 10.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. 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_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. 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. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] 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.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). noise_level (`int`, *optional*, defaults to `0`): The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in the final un-noised images. See `StableUnCLIPPipeline.noise_image_embeddings` for details. prior_num_inference_steps (`int`, *optional*, defaults to 25): The number of denoising steps in the prior denoising process. More denoising steps usually lead to a higher quality image at the expense of slower inference. prior_guidance_scale (`float`, *optional*, defaults to 4.0): Guidance scale for the prior denoising process as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `prior_guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. prior_latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image embedding generation in the prior denoising process. 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`. Examples: Returns: [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt=prompt, height=height, width=width, callback_steps=callback_steps, noise_level=noise_level, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, ) # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] batch_size = batch_size * num_images_per_prompt 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. prior_do_classifier_free_guidance = prior_guidance_scale > 1.0 # 3. Encode input prompt prior_prompt_embeds, prior_text_encoder_hidden_states, prior_text_mask = self._encode_prior_prompt( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=prior_do_classifier_free_guidance, ) # 4. Prepare prior timesteps self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device) prior_timesteps_tensor = self.prior_scheduler.timesteps # 5. Prepare prior latent variables embedding_dim = self.prior.config.embedding_dim prior_latents = self.prepare_latents( (batch_size, embedding_dim), prior_prompt_embeds.dtype, device, generator, prior_latents, self.prior_scheduler, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline prior_extra_step_kwargs = self.prepare_prior_extra_step_kwargs(generator, eta) # 7. Prior denoising loop for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([prior_latents] * 2) if prior_do_classifier_free_guidance else prior_latents latent_model_input = self.prior_scheduler.scale_model_input(latent_model_input, t) predicted_image_embedding = self.prior( latent_model_input, timestep=t, proj_embedding=prior_prompt_embeds, encoder_hidden_states=prior_text_encoder_hidden_states, attention_mask=prior_text_mask, ).predicted_image_embedding if prior_do_classifier_free_guidance: predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2) predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * ( predicted_image_embedding_text - predicted_image_embedding_uncond ) prior_latents = self.prior_scheduler.step( predicted_image_embedding, timestep=t, sample=prior_latents, **prior_extra_step_kwargs, return_dict=False, )[0] if callback is not None and i % callback_steps == 0: callback(i, t, prior_latents) prior_latents = self.prior.post_process_latents(prior_latents) image_embeds = prior_latents # done prior # 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 # 8. Encode input prompt text_encoder_lora_scale = ( cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None ) prompt_embeds = self._encode_prompt( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, ) # 9. Prepare image embeddings image_embeds = self.noise_image_embeddings( image_embeds=image_embeds, noise_level=noise_level, generator=generator, ) if do_classifier_free_guidance: negative_prompt_embeds = torch.zeros_like(image_embeds) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes image_embeds = torch.cat([negative_prompt_embeds, image_embeds]) # 10. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps # 11. Prepare latent variables num_channels_latents = self.unet.config.in_channels shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) latents = self.prepare_latents( shape=shape, dtype=prompt_embeds.dtype, device=device, generator=generator, latents=latents, scheduler=self.scheduler, ) # 12. 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) # 13. Denoising loop for i, t in enumerate(self.progress_bar(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) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, class_labels=image_embeds, cross_attention_kwargs=cross_attention_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) # 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 is not None and i % callback_steps == 0: callback(i, t, latents) if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) # Offload last model to CPU if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (image,) return ImagePipelineOutput(images=image)