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| # 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 | |
| from typing import Any, Callable, Dict, List, Optional, Union | |
| import torch | |
| from packaging import version | |
| from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer | |
| from diffusers.configuration_utils import FrozenDict | |
| from diffusers.image_processor import VaeImageProcessor | |
| from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin | |
| from diffusers.models import AutoencoderKL, UNet2DConditionModel | |
| from diffusers.models.lora import adjust_lora_scale_text_encoder | |
| from diffusers.schedulers import KarrasDiffusionSchedulers | |
| from diffusers.utils import ( | |
| deprecate, | |
| logging, | |
| replace_example_docstring, | |
| ) | |
| from diffusers.utils.torch_utils import randn_tensor | |
| from diffusers.pipelines.pipeline_utils import DiffusionPipeline | |
| from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput | |
| from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): | |
| """ | |
| Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and | |
| Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 | |
| """ | |
| std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) | |
| std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) | |
| # rescale the results from guidance (fixes overexposure) | |
| noise_pred_rescaled = noise_cfg * (std_text / std_cfg) | |
| # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images | |
| noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg | |
| return noise_cfg | |
| class RectifiedFlowPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin): | |
| r""" | |
| Pipeline for text-to-image generation using Rectified Flow and Euler discretization. | |
| This customized pipeline is based on StableDiffusionPipeline from the official Diffusers library (0.21.4) | |
| 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.). | |
| The pipeline also inherits the following loading methods: | |
| - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings | |
| - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights | |
| - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights | |
| - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files | |
| Args: | |
| vae ([`AutoencoderKL`]): | |
| Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. | |
| text_encoder ([`~transformers.CLIPTextModel`]): | |
| Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). | |
| tokenizer ([`~transformers.CLIPTokenizer`]): | |
| A `CLIPTokenizer` to tokenize text. | |
| unet ([`UNet2DConditionModel`]): | |
| A `UNet2DConditionModel` to denoise the encoded image latents. | |
| scheduler ([`SchedulerMixin`]): | |
| A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of | |
| [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. | |
| safety_checker ([`StableDiffusionSafetyChecker`]): | |
| Classification module that estimates whether generated images could be considered offensive or harmful. | |
| Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details | |
| about a model's potential harms. | |
| feature_extractor ([`~transformers.CLIPImageProcessor`]): | |
| A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. | |
| """ | |
| model_cpu_offload_seq = "text_encoder->unet->vae" | |
| _optional_components = ["safety_checker", "feature_extractor"] | |
| _exclude_from_cpu_offload = ["safety_checker"] | |
| def __init__( | |
| self, | |
| vae: AutoencoderKL, | |
| text_encoder: CLIPTextModel, | |
| tokenizer: CLIPTokenizer, | |
| unet: UNet2DConditionModel, | |
| scheduler: KarrasDiffusionSchedulers, | |
| safety_checker: StableDiffusionSafetyChecker, | |
| feature_extractor: CLIPImageProcessor, | |
| requires_safety_checker: bool = True, | |
| ): | |
| super().__init__() | |
| if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: | |
| deprecation_message = ( | |
| f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" | |
| f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " | |
| "to update the config accordingly as leaving `steps_offset` might led to incorrect results" | |
| " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," | |
| " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" | |
| " file" | |
| ) | |
| deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) | |
| new_config = dict(scheduler.config) | |
| new_config["steps_offset"] = 1 | |
| scheduler._internal_dict = FrozenDict(new_config) | |
| if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: | |
| deprecation_message = ( | |
| f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." | |
| " `clip_sample` should be set to False in the configuration file. Please make sure to update the" | |
| " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" | |
| " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" | |
| " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" | |
| ) | |
| deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) | |
| new_config = dict(scheduler.config) | |
| new_config["clip_sample"] = False | |
| scheduler._internal_dict = FrozenDict(new_config) | |
| if safety_checker is None and requires_safety_checker: | |
| logger.warning( | |
| f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" | |
| " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" | |
| " results in services or applications open to the public. Both the diffusers team and Hugging Face" | |
| " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" | |
| " it only for use-cases that involve analyzing network behavior or auditing its results. For more" | |
| " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." | |
| ) | |
| if safety_checker is not None and feature_extractor is None: | |
| raise ValueError( | |
| "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" | |
| " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." | |
| ) | |
| is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( | |
| version.parse(unet.config._diffusers_version).base_version | |
| ) < version.parse("0.9.0.dev0") | |
| is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 | |
| if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: | |
| deprecation_message = ( | |
| "The configuration file of the unet has set the default `sample_size` to smaller than" | |
| " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" | |
| " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" | |
| " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" | |
| " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" | |
| " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" | |
| " in the config might lead to incorrect results in future versions. If you have downloaded this" | |
| " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" | |
| " the `unet/config.json` file" | |
| ) | |
| deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) | |
| new_config = dict(unet.config) | |
| new_config["sample_size"] = 64 | |
| unet._internal_dict = FrozenDict(new_config) | |
| self.register_modules( | |
| vae=vae, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| unet=unet, | |
| scheduler=scheduler, | |
| safety_checker=safety_checker, | |
| feature_extractor=feature_extractor, | |
| ) | |
| self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
| self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | |
| self.register_to_config(requires_safety_checker=requires_safety_checker) | |
| 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() | |
| def disable_vae_slicing(self): | |
| r""" | |
| Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to | |
| computing decoding in one step. | |
| """ | |
| self.vae.disable_slicing() | |
| def enable_vae_tiling(self): | |
| r""" | |
| Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to | |
| compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow | |
| processing larger images. | |
| """ | |
| self.vae.enable_tiling() | |
| def disable_vae_tiling(self): | |
| r""" | |
| Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to | |
| computing decoding in one step. | |
| """ | |
| self.vae.disable_tiling() | |
| 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, | |
| ): | |
| deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple." | |
| deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False) | |
| prompt_embeds_tuple = 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=lora_scale, | |
| ) | |
| # concatenate for backwards comp | |
| prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]]) | |
| return prompt_embeds | |
| 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 | |
| # dynamically adjust the LoRA scale | |
| adjust_lora_scale_text_encoder(self.text_encoder, 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] | |
| if self.text_encoder is not None: | |
| prompt_embeds_dtype = self.text_encoder.dtype | |
| elif self.unet is not None: | |
| prompt_embeds_dtype = self.unet.dtype | |
| else: | |
| prompt_embeds_dtype = prompt_embeds.dtype | |
| prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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=prompt_embeds_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) | |
| return prompt_embeds, negative_prompt_embeds | |
| def run_safety_checker(self, image, device, dtype): | |
| if self.safety_checker is None: | |
| has_nsfw_concept = None | |
| else: | |
| if torch.is_tensor(image): | |
| feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") | |
| else: | |
| feature_extractor_input = self.image_processor.numpy_to_pil(image) | |
| safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) | |
| image, has_nsfw_concept = self.safety_checker( | |
| images=image, clip_input=safety_checker_input.pixel_values.to(dtype) | |
| ) | |
| return image, has_nsfw_concept | |
| def decode_latents(self, latents): | |
| deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead" | |
| deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False) | |
| 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 | |
| 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, | |
| 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( | |
| f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" | |
| " only forward one of the two." | |
| ) | |
| elif prompt is None and prompt_embeds is None: | |
| raise ValueError( | |
| "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." | |
| ) | |
| elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): | |
| raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | |
| if negative_prompt is not None and negative_prompt_embeds is not None: | |
| raise ValueError( | |
| f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" | |
| f" {negative_prompt_embeds}. Please make sure to only forward one of the two." | |
| ) | |
| 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}." | |
| ) | |
| def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): | |
| shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, 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) | |
| # scale the initial noise by the standard deviation required by the scheduler | |
| latents = latents * self.scheduler.init_noise_sigma | |
| return latents | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]] = None, | |
| height: Optional[int] = None, | |
| width: Optional[int] = None, | |
| num_inference_steps: int = 50, | |
| guidance_scale: float = 7.5, | |
| negative_prompt: Optional[Union[str, List[str]]] = None, | |
| num_images_per_prompt: Optional[int] = 1, | |
| eta: float = 0.0, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| latents: Optional[torch.FloatTensor] = None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| output_type: 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, | |
| guidance_rescale: float = 0.0, | |
| ): | |
| r""" | |
| The call function to the pipeline for generation. | |
| Args: | |
| prompt (`str` or `List[str]`, *optional*): | |
| The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. | |
| 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 50): | |
| The number of denoising steps. More denoising steps usually lead to a higher quality image at the | |
| expense of slower inference. | |
| guidance_scale (`float`, *optional*, defaults to 7.5): | |
| A higher guidance scale value encourages the model to generate images closely linked to the text | |
| `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. | |
| negative_prompt (`str` or `List[str]`, *optional*): | |
| The prompt or prompts to guide what to not include in image generation. If not defined, you need to | |
| pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies | |
| to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. | |
| generator (`torch.Generator` or `List[torch.Generator]`, *optional*): | |
| A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make | |
| generation deterministic. | |
| latents (`torch.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 is 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 (prompt weighting). If not | |
| provided, text embeddings are generated from the `prompt` input argument. | |
| negative_prompt_embeds (`torch.FloatTensor`, *optional*): | |
| Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If | |
| not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. | |
| 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.stable_diffusion.StableDiffusionPipelineOutput`] instead of a | |
| plain tuple. | |
| callback (`Callable`, *optional*): | |
| A function that calls every `callback_steps` steps during inference. The function is called with the | |
| following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. | |
| callback_steps (`int`, *optional*, defaults to 1): | |
| The frequency at which the `callback` function is called. If not specified, the callback is called at | |
| every step. | |
| cross_attention_kwargs (`dict`, *optional*): | |
| A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in | |
| [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | |
| guidance_rescale (`float`, *optional*, defaults to 0.7): | |
| Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are | |
| Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when | |
| using zero terminal SNR. | |
| Examples: | |
| Returns: | |
| [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: | |
| If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, | |
| otherwise a `tuple` is returned where the first element is a list with the generated images and the | |
| second element is a list of `bool`s indicating whether the corresponding generated image contains | |
| "not-safe-for-work" (nsfw) content. | |
| """ | |
| # 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, height, width, callback_steps, negative_prompt, 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] | |
| device = self._execution_device | |
| # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
| # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
| # corresponds to doing no classifier free guidance. | |
| do_classifier_free_guidance = guidance_scale > 1.0 | |
| # 3. Encode input prompt | |
| text_encoder_lora_scale = ( | |
| cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None | |
| ) | |
| prompt_embeds, negative_prompt_embeds = self.encode_prompt( | |
| prompt, | |
| device, | |
| num_images_per_prompt, | |
| do_classifier_free_guidance, | |
| negative_prompt, | |
| prompt_embeds=prompt_embeds, | |
| negative_prompt_embeds=negative_prompt_embeds, | |
| lora_scale=text_encoder_lora_scale, | |
| ) | |
| # 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 | |
| if do_classifier_free_guidance: | |
| prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) | |
| # 4. Prepare timesteps | |
| timesteps = [(1. - i/num_inference_steps) * 1000. for i in range(num_inference_steps)] | |
| # 5. Prepare latent variables | |
| num_channels_latents = self.unet.config.in_channels | |
| latents = self.prepare_latents( | |
| batch_size * num_images_per_prompt, | |
| num_channels_latents, | |
| 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) | |
| dt = 1.0 / num_inference_steps | |
| # 7. Denoising loop of Euler discretization from t = 0 to t = 1 | |
| with self.progress_bar(total=num_inference_steps) as progress_bar: | |
| for i, t in enumerate(timesteps): | |
| # expand the latents if we are doing classifier free guidance | |
| latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
| vec_t = torch.ones((latent_model_input.shape[0],), device=latents.device) * t | |
| v_pred = self.unet(latent_model_input, vec_t, encoder_hidden_states=prompt_embeds).sample | |
| # perform guidance | |
| if do_classifier_free_guidance: | |
| v_pred_neg, v_pred_text = v_pred.chunk(2) | |
| v_pred = v_pred_neg + guidance_scale * (v_pred_text - v_pred_neg) | |
| latents = latents + dt * v_pred | |
| # call the callback, if provided | |
| if i == len(timesteps) - 1 or ((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 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) | |