diff --git a/examples/community/README.md b/examples/community/README.md index 3858f25d39..87b0ed9151 100755 --- a/examples/community/README.md +++ b/examples/community/README.md @@ -47,6 +47,7 @@ sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint m prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) | - | [Umer H. Adil](https://twitter.com/UmerHAdil) | | Latent Consistency Pipeline | Implementation of [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) | [Latent Consistency Pipeline](#latent-consistency-pipeline) | - | [Simian Luo](https://github.com/luosiallen) | | Latent Consistency Img2img Pipeline | Img2img pipeline for Latent Consistency Models | [Latent Consistency Img2Img Pipeline](#latent-consistency-img2img-pipeline) | - | [Logan Zoellner](https://github.com/nagolinc) | +| Latent Consistency Interpolation Pipeline | Interpolate the latent space of Latent Consistency Models with multiple prompts | [Latent Consistency Interpolation Pipeline](#latent-consistency-interpolation-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pK3NrLWJSiJsBynLns1K1-IDTW9zbPvl?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) | To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly. @@ -2295,3 +2296,50 @@ num_inference_steps = 4 images = pipe(prompt=prompt, image=input_image, strength=strength, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images ``` + + + +### Latent Consistency Interpolation Pipeline + +This pipeline extends the Latent Consistency Pipeline to allow for interpolation of the latent space between multiple prompts. It is similar to the [Stable Diffusion Interpolate](https://github.com/huggingface/diffusers/blob/main/examples/community/interpolate_stable_diffusion.py) and [unCLIP Interpolate](https://github.com/huggingface/diffusers/blob/main/examples/community/unclip_text_interpolation.py) community pipelines. + +```py +import torch +import numpy as np + +from diffusers import DiffusionPipeline + +pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_interpolate") + +# To save GPU memory, torch.float16 can be used, but it may compromise image quality. +pipe.to(torch_device="cuda", torch_dtype=torch.float32) + +prompts = [ + "Self-portrait oil painting, a beautiful cyborg with golden hair, Margot Robbie, 8k", + "Self-portrait oil painting, an extremely strong man, body builder, Huge Jackman, 8k", + "An astronaut floating in space, renaissance art, realistic, high quality, 8k", + "Oil painting of a cat, cute, dream-like", + "Hugging face emoji, cute, realistic" +] +num_inference_steps = 4 +num_interpolation_steps = 60 +seed = 1337 + +torch.manual_seed(seed) +np.random.seed(seed) + +images = pipe( + prompt=prompts, + height=512, + width=512, + num_inference_steps=num_inference_steps, + num_interpolation_steps=num_interpolation_steps, + guidance_scale=8.0, + embedding_interpolation_type="lerp", + latent_interpolation_type="slerp", + process_batch_size=4, # Make it higher or lower based on your GPU memory + generator=torch.Generator(seed), +) + +assert len(images) == (len(prompts) - 1) * num_interpolation_steps +``` diff --git a/examples/community/latent_consistency_interpolate.py b/examples/community/latent_consistency_interpolate.py new file mode 100644 index 0000000000..1c091062e8 --- /dev/null +++ b/examples/community/latent_consistency_interpolate.py @@ -0,0 +1,1050 @@ +import inspect +from typing import Any, Callable, Dict, List, Optional, Union + +import numpy as np +import torch +from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer + +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.pipelines.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker +from diffusers.schedulers import LCMScheduler +from diffusers.utils import ( + USE_PEFT_BACKEND, + deprecate, + logging, + replace_example_docstring, + scale_lora_layers, + unscale_lora_layers, +) +from diffusers.utils.torch_utils import randn_tensor + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> import numpy as np + + >>> from diffusers import DiffusionPipeline + + >>> pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_interpolate") + >>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality. + >>> pipe.to(torch_device="cuda", torch_dtype=torch.float32) + + >>> prompts = ["A cat", "A dog", "A horse"] + >>> num_inference_steps = 4 + >>> num_interpolation_steps = 24 + >>> seed = 1337 + + >>> torch.manual_seed(seed) + >>> np.random.seed(seed) + + >>> images = pipe( + prompt=prompts, + height=512, + width=512, + num_inference_steps=num_inference_steps, + num_interpolation_steps=num_interpolation_steps, + guidance_scale=8.0, + embedding_interpolation_type="lerp", + latent_interpolation_type="slerp", + process_batch_size=4, # Make it higher or lower based on your GPU memory + generator=torch.Generator(seed), + ) + + >>> # Save the images as a video + >>> import imageio + >>> from PIL import Image + + >>> def pil_to_video(images: List[Image.Image], filename: str, fps: int = 60) -> None: + frames = [np.array(image) for image in images] + with imageio.get_writer(filename, fps=fps) as video_writer: + for frame in frames: + video_writer.append_data(frame) + + >>> pil_to_video(images, "lcm_interpolate.mp4", fps=24) + ``` +""" + + +def lerp( + v0: Union[torch.Tensor, np.ndarray], + v1: Union[torch.Tensor, np.ndarray], + t: Union[float, torch.Tensor, np.ndarray], +) -> Union[torch.Tensor, np.ndarray]: + """ + Linearly interpolate between two vectors/tensors. + + Args: + v0 (`torch.Tensor` or `np.ndarray`): First vector/tensor. + v1 (`torch.Tensor` or `np.ndarray`): Second vector/tensor. + t: (`float`, `torch.Tensor`, or `np.ndarray`): + Interpolation factor. If float, must be between 0 and 1. If np.ndarray or + torch.Tensor, must be one dimensional with values between 0 and 1. + + Returns: + Union[torch.Tensor, np.ndarray] + Interpolated vector/tensor between v0 and v1. + """ + inputs_are_torch = False + t_is_float = False + + if isinstance(v0, torch.Tensor): + inputs_are_torch = True + input_device = v0.device + v0 = v0.cpu().numpy() + v1 = v1.cpu().numpy() + + if isinstance(t, torch.Tensor): + inputs_are_torch = True + input_device = t.device + t = t.cpu().numpy() + elif isinstance(t, float): + t_is_float = True + t = np.array([t]) + + t = t[..., None] + v0 = v0[None, ...] + v1 = v1[None, ...] + v2 = (1 - t) * v0 + t * v1 + + if t_is_float and v0.ndim > 1: + assert v2.shape[0] == 1 + v2 = np.squeeze(v2, axis=0) + if inputs_are_torch: + v2 = torch.from_numpy(v2).to(input_device) + + return v2 + + +def slerp( + v0: Union[torch.Tensor, np.ndarray], + v1: Union[torch.Tensor, np.ndarray], + t: Union[float, torch.Tensor, np.ndarray], + DOT_THRESHOLD=0.9995, +) -> Union[torch.Tensor, np.ndarray]: + """ + Spherical linear interpolation between two vectors/tensors. + + Args: + v0 (`torch.Tensor` or `np.ndarray`): First vector/tensor. + v1 (`torch.Tensor` or `np.ndarray`): Second vector/tensor. + t: (`float`, `torch.Tensor`, or `np.ndarray`): + Interpolation factor. If float, must be between 0 and 1. If np.ndarray or + torch.Tensor, must be one dimensional with values between 0 and 1. + DOT_THRESHOLD (`float`, *optional*, default=0.9995): + Threshold for when to use linear interpolation instead of spherical interpolation. + + Returns: + `torch.Tensor` or `np.ndarray`: + Interpolated vector/tensor between v0 and v1. + """ + inputs_are_torch = False + t_is_float = False + + if isinstance(v0, torch.Tensor): + inputs_are_torch = True + input_device = v0.device + v0 = v0.cpu().numpy() + v1 = v1.cpu().numpy() + + if isinstance(t, torch.Tensor): + inputs_are_torch = True + input_device = t.device + t = t.cpu().numpy() + elif isinstance(t, float): + t_is_float = True + t = np.array([t], dtype=v0.dtype) + + dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1))) + if np.abs(dot) > DOT_THRESHOLD: + # v1 and v2 are close to parallel + # Use linear interpolation instead + v2 = lerp(v0, v1, t) + else: + theta_0 = np.arccos(dot) + sin_theta_0 = np.sin(theta_0) + theta_t = theta_0 * t + sin_theta_t = np.sin(theta_t) + s0 = np.sin(theta_0 - theta_t) / sin_theta_0 + s1 = sin_theta_t / sin_theta_0 + s0 = s0[..., None] + s1 = s1[..., None] + v0 = v0[None, ...] + v1 = v1[None, ...] + v2 = s0 * v0 + s1 * v1 + + if t_is_float and v0.ndim > 1: + assert v2.shape[0] == 1 + v2 = np.squeeze(v2, axis=0) + if inputs_are_torch: + v2 = torch.from_numpy(v2).to(input_device) + + return v2 + + +class LatentConsistencyModelWalkPipeline( + DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin +): + r""" + Pipeline for text-to-image generation using a latent consistency model. + + 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. Currently only + supports [`LCMScheduler`]. + 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`. + requires_safety_checker (`bool`, *optional*, defaults to `True`): + Whether the pipeline requires a safety checker component. + """ + model_cpu_offload_seq = "text_encoder->unet->vae" + _optional_components = ["safety_checker", "feature_extractor"] + _exclude_from_cpu_offload = ["safety_checker"] + _callback_tensor_inputs = ["latents", "denoised", "prompt_embeds", "w_embedding"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: LCMScheduler, + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPImageProcessor, + requires_safety_checker: bool = True, + ): + super().__init__() + + 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." + ) + + 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) + + # 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 enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling + 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() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_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() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu + def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): + r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497. + + The suffixes after the scaling factors represent the stages where they are being applied. + + Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values + that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. + + Args: + s1 (`float`): + Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to + mitigate "oversmoothing effect" in the enhanced denoising process. + s2 (`float`): + Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to + mitigate "oversmoothing effect" in the enhanced denoising process. + b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. + b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. + """ + if not hasattr(self, "unet"): + raise ValueError("The pipeline must have `unet` for using FreeU.") + self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu + def disable_freeu(self): + """Disables the FreeU mechanism if enabled.""" + self.unet.disable_freeu() + + # 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, + clip_skip: Optional[int] = 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. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + """ + # 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 + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) + else: + scale_lora_layers(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 + + if clip_skip is None: + prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask) + prompt_embeds = prompt_embeds[0] + else: + prompt_embeds = self.text_encoder( + text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True + ) + # Access the `hidden_states` first, that contains a tuple of + # all the hidden states from the encoder layers. Then index into + # the tuple to access the hidden states from the desired layer. + prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)] + # We also need to apply the final LayerNorm here to not mess with the + # representations. The `last_hidden_states` that we typically use for + # obtaining the final prompt representations passes through the LayerNorm + # layer. + prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) + + 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) + + if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder, lora_scale) + + return prompt_embeds, negative_prompt_embeds + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker + 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 + + # 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, 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 get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32): + """ + See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 + + Args: + timesteps (`torch.Tensor`): + generate embedding vectors at these timesteps + embedding_dim (`int`, *optional*, defaults to 512): + dimension of the embeddings to generate + dtype: + data type of the generated embeddings + + Returns: + `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` + """ + assert len(w.shape) == 1 + w = w * 1000.0 + + half_dim = embedding_dim // 2 + emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) + emb = w.to(dtype)[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1)) + assert emb.shape == (w.shape[0], embedding_dim) + return emb + + # 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 + + # Currently StableDiffusionPipeline.check_inputs with negative prompt stuff removed + def check_inputs( + self, + prompt: Union[str, List[str]], + height: int, + width: int, + callback_steps: int, + prompt_embeds: Optional[torch.FloatTensor] = None, + callback_on_step_end_tensor_inputs=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 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 callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + @torch.no_grad() + def interpolate_embedding( + self, + start_embedding: torch.FloatTensor, + end_embedding: torch.FloatTensor, + num_interpolation_steps: Union[int, List[int]], + interpolation_type: str, + ) -> torch.FloatTensor: + if interpolation_type == "lerp": + interpolation_fn = lerp + elif interpolation_type == "slerp": + interpolation_fn = slerp + else: + raise ValueError( + f"embedding_interpolation_type must be one of ['lerp', 'slerp'], got {interpolation_type}." + ) + + embedding = torch.cat([start_embedding, end_embedding]) + steps = torch.linspace(0, 1, num_interpolation_steps, dtype=embedding.dtype).cpu().numpy() + steps = np.expand_dims(steps, axis=tuple(range(1, embedding.ndim))) + interpolations = [] + + # Interpolate between text embeddings + # TODO(aryan): Think of a better way of doing this + # See if it can be done parallelly instead + for i in range(embedding.shape[0] - 1): + interpolations.append(interpolation_fn(embedding[i], embedding[i + 1], steps).squeeze(dim=1)) + + interpolations = torch.cat(interpolations) + return interpolations + + @torch.no_grad() + def interpolate_latent( + self, + start_latent: torch.FloatTensor, + end_latent: torch.FloatTensor, + num_interpolation_steps: Union[int, List[int]], + interpolation_type: str, + ) -> torch.FloatTensor: + if interpolation_type == "lerp": + interpolation_fn = lerp + elif interpolation_type == "slerp": + interpolation_fn = slerp + + latent = torch.cat([start_latent, end_latent]) + steps = torch.linspace(0, 1, num_interpolation_steps, dtype=latent.dtype).cpu().numpy() + steps = np.expand_dims(steps, axis=tuple(range(1, latent.ndim))) + interpolations = [] + + # Interpolate between latents + # TODO: Think of a better way of doing this + # See if it can be done parallelly instead + for i in range(latent.shape[0] - 1): + interpolations.append(interpolation_fn(latent[i], latent[i + 1], steps).squeeze(dim=1)) + + return torch.cat(interpolations) + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def cross_attention_kwargs(self): + return self._cross_attention_kwargs + + @property + def clip_skip(self): + return self._clip_skip + + @property + def num_timesteps(self): + return self._num_timesteps + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 4, + num_interpolation_steps: int = 8, + original_inference_steps: int = None, + guidance_scale: float = 8.5, + num_images_per_prompt: Optional[int] = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + clip_skip: Optional[int] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + embedding_interpolation_type: str = "lerp", + latent_interpolation_type: str = "slerp", + process_batch_size: int = 4, + **kwargs, + ): + 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. + original_inference_steps (`int`, *optional*): + The original number of inference steps use to generate a linearly-spaced timestep schedule, from which + we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule, + following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the + scheduler's `original_inference_steps` attribute. + 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`. + Note that the original latent consistency models paper uses a different CFG formulation where the + guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale > + 0`). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + 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. + 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. + 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). + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeine class. + embedding_interpolation_type (`str`, *optional*, defaults to `"lerp"`): + The type of interpolation to use for interpolating between text embeddings. Choose between `"lerp"` and `"slerp"`. + latent_interpolation_type (`str`, *optional*, defaults to `"slerp"`): + The type of interpolation to use for interpolating between latents. Choose between `"lerp"` and `"slerp"`. + process_batch_size (`int`, *optional*, defaults to 4): + The batch size to use for processing the images. This is useful when generating a large number of images + and you want to avoid running out of memory. + + 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. + """ + + callback = kwargs.pop("callback", None) + callback_steps = kwargs.pop("callback_steps", None) + + if callback is not None: + deprecate( + "callback", + "1.0.0", + "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", + ) + if callback_steps is not None: + deprecate( + "callback_steps", + "1.0.0", + "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", + ) + + # 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, prompt_embeds, callback_on_step_end_tensor_inputs) + self._guidance_scale = guidance_scale + self._clip_skip = clip_skip + self._cross_attention_kwargs = cross_attention_kwargs + + # 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] + if batch_size < 2: + raise ValueError(f"`prompt` must have length of atleast 2 but found {batch_size}") + if num_images_per_prompt != 1: + raise ValueError("`num_images_per_prompt` must be `1` as no other value is supported yet") + if prompt_embeds is not None: + raise ValueError("`prompt_embeds` must be None since it is not supported yet") + if latents is not None: + raise ValueError("`latents` must be None since it is not supported yet") + + device = self._execution_device + # do_classifier_free_guidance = guidance_scale > 1.0 + + lora_scale = ( + self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None + ) + + self.scheduler.set_timesteps(num_inference_steps, device, original_inference_steps=original_inference_steps) + timesteps = self.scheduler.timesteps + num_channels_latents = self.unet.config.in_channels + # bs = batch_size * num_images_per_prompt + + # 3. Encode initial input prompt + prompt_embeds_1, _ = self.encode_prompt( + prompt[:1], + device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=False, + negative_prompt=None, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=None, + lora_scale=lora_scale, + clip_skip=self.clip_skip, + ) + + # 4. Prepare initial latent variables + latents_1 = self.prepare_latents( + 1, + num_channels_latents, + height, + width, + prompt_embeds_1.dtype, + device, + generator, + latents, + ) + + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None) + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + self._num_timesteps = len(timesteps) + images = [] + + # 5. Iterate over prompts and perform latent walk. Note that we do this two prompts at a time + # otherwise the memory usage ends up being too high. + with self.progress_bar(total=batch_size - 1) as prompt_progress_bar: + for i in range(1, batch_size): + # 6. Encode current prompt + prompt_embeds_2, _ = self.encode_prompt( + prompt[i : i + 1], + device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=False, + negative_prompt=None, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=None, + lora_scale=lora_scale, + clip_skip=self.clip_skip, + ) + + # 7. Prepare current latent variables + latents_2 = self.prepare_latents( + 1, + num_channels_latents, + height, + width, + prompt_embeds_2.dtype, + device, + generator, + latents, + ) + + # 8. Interpolate between previous and current prompt embeddings and latents + inference_embeddings = self.interpolate_embedding( + start_embedding=prompt_embeds_1, + end_embedding=prompt_embeds_2, + num_interpolation_steps=num_interpolation_steps, + interpolation_type=embedding_interpolation_type, + ) + inference_latents = self.interpolate_latent( + start_latent=latents_1, + end_latent=latents_2, + num_interpolation_steps=num_interpolation_steps, + interpolation_type=latent_interpolation_type, + ) + next_prompt_embeds = inference_embeddings[-1:].detach().clone() + next_latents = inference_latents[-1:].detach().clone() + bs = num_interpolation_steps + + # 9. Perform inference in batches. Note the use of `process_batch_size` to control the batch size + # of the inference. This is useful for reducing memory usage and can be configured based on the + # available GPU memory. + with self.progress_bar( + total=(bs + process_batch_size - 1) // process_batch_size + ) as batch_progress_bar: + for batch_index in range(0, bs, process_batch_size): + batch_inference_latents = inference_latents[batch_index : batch_index + process_batch_size] + batch_inference_embedddings = inference_embeddings[ + batch_index : batch_index + process_batch_size + ] + + self.scheduler.set_timesteps( + num_inference_steps, device, original_inference_steps=original_inference_steps + ) + timesteps = self.scheduler.timesteps + + current_bs = batch_inference_embedddings.shape[0] + w = torch.tensor(self.guidance_scale - 1).repeat(current_bs) + w_embedding = self.get_guidance_scale_embedding( + w, embedding_dim=self.unet.config.time_cond_proj_dim + ).to(device=device, dtype=latents_1.dtype) + + # 10. Perform inference for current batch + with self.progress_bar(total=num_inference_steps) as progress_bar: + for index, t in enumerate(timesteps): + batch_inference_latents = batch_inference_latents.to(batch_inference_embedddings.dtype) + + # model prediction (v-prediction, eps, x) + model_pred = self.unet( + batch_inference_latents, + t, + timestep_cond=w_embedding, + encoder_hidden_states=batch_inference_embedddings, + cross_attention_kwargs=self.cross_attention_kwargs, + return_dict=False, + )[0] + + # compute the previous noisy sample x_t -> x_t-1 + batch_inference_latents, denoised = self.scheduler.step( + model_pred, t, batch_inference_latents, **extra_step_kwargs, return_dict=False + ) + 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, index, t, callback_kwargs) + + batch_inference_latents = callback_outputs.pop("latents", batch_inference_latents) + batch_inference_embedddings = callback_outputs.pop( + "prompt_embeds", batch_inference_embedddings + ) + w_embedding = callback_outputs.pop("w_embedding", w_embedding) + denoised = callback_outputs.pop("denoised", denoised) + + # call the callback, if provided + if index == len(timesteps) - 1 or ( + (index + 1) > num_warmup_steps and (index + 1) % self.scheduler.order == 0 + ): + progress_bar.update() + if callback is not None and index % callback_steps == 0: + step_idx = index // getattr(self.scheduler, "order", 1) + callback(step_idx, t, batch_inference_latents) + + denoised = denoised.to(batch_inference_embedddings.dtype) + + # Note: This is not supported because you would get black images in your latent walk if + # NSFW concept is detected + # if not output_type == "latent": + # image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0] + # image, has_nsfw_concept = self.run_safety_checker(image, device, inference_embeddings.dtype) + # else: + # image = denoised + # 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.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0] + do_denormalize = [True] * image.shape[0] + has_nsfw_concept = None + + image = self.image_processor.postprocess( + image, output_type=output_type, do_denormalize=do_denormalize + ) + images.append(image) + + batch_progress_bar.update() + + prompt_embeds_1 = next_prompt_embeds + latents_1 = next_latents + + prompt_progress_bar.update() + + # 11. Determine what should be returned + if output_type == "pil": + images = [image for image_list in images for image in image_list] + elif output_type == "np": + images = np.concatenate(images) + elif output_type == "pt": + images = torch.cat(images) + else: + raise ValueError("`output_type` must be one of 'pil', 'np' or 'pt'.") + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (images, has_nsfw_concept) + + return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)