diff --git "a/EngageEngine/pipeline.py" "b/EngageEngine/pipeline.py" deleted file mode 100644--- "a/EngageEngine/pipeline.py" +++ /dev/null @@ -1,3631 +0,0 @@ -# Copyright 2024 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, Tuple, Union - -import torch -import torch.nn.functional as F -import PIL -import numpy as np -from torchvision.transforms.functional import to_pil_image - -from transformers import ( - CLIPImageProcessor, - CLIPTextModel, - CLIPTextModelWithProjection, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) - -from diffusers.image_processor import PipelineImageInput, VaeImageProcessor -from diffusers.loaders import ( - FromSingleFileMixin, - IPAdapterMixin, - StableDiffusionXLLoraLoaderMixin, - TextualInversionLoaderMixin, -) - -from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, ControlNetModel -from diffusers.models.attention_processor import ( - AttnProcessor2_0, - FusedAttnProcessor2_0, - LoRAAttnProcessor2_0, - LoRAXFormersAttnProcessor, - XFormersAttnProcessor, -) -from diffusers.models.lora import adjust_lora_scale_text_encoder -from diffusers.schedulers import KarrasDiffusionSchedulers -from diffusers.utils import ( - USE_PEFT_BACKEND, - deprecate, - is_torch_xla_available, - logging, - replace_example_docstring, - scale_lora_layers, - unscale_lora_layers, -) -from diffusers.utils.torch_utils import randn_tensor, is_compiled_module, is_torch_version -from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin -from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput - -from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel - -if is_torch_xla_available(): - import torch_xla.core.xla_model as xm - - XLA_AVAILABLE = True -else: - XLA_AVAILABLE = False - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> import torch - >>> from diffusers import StableDiffusionXLPipeline - - >>> pipe = StableDiffusionXLPipeline.from_pretrained( - ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 - ... ) - >>> pipe = pipe.to("cuda") - - >>> prompt = "a photo of an astronaut riding a horse on mars" - >>> image = pipe(prompt).images[0] - ``` -""" - - -def retrieve_latents( - encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" -): - if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": - return encoder_output.latent_dist.sample(generator) - elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": - return encoder_output.latent_dist.mode() - elif hasattr(encoder_output, "latents"): - return encoder_output.latents - else: - raise AttributeError("Could not access latents of provided encoder_output") - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg -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 - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps -def retrieve_timesteps( - scheduler, - num_inference_steps: Optional[int] = None, - device: Optional[Union[str, torch.device]] = None, - timesteps: Optional[List[int]] = None, - **kwargs, -): - """ - Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles - custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. - - Args: - scheduler (`SchedulerMixin`): - The scheduler to get timesteps from. - num_inference_steps (`int`): - The number of diffusion steps used when generating samples with a pre-trained model. If used, - `timesteps` must be `None`. - device (`str` or `torch.device`, *optional*): - The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. - timesteps (`List[int]`, *optional*): - Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default - timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` - must be `None`. - - Returns: - `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the - second element is the number of inference steps. - """ - if timesteps is not None: - accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) - if not accepts_timesteps: - raise ValueError( - f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" timestep schedules. Please check whether you are using the correct scheduler." - ) - scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) - timesteps = scheduler.timesteps - num_inference_steps = len(timesteps) - else: - scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) - timesteps = scheduler.timesteps - return timesteps, num_inference_steps - - -class EngagePipeline( - DiffusionPipeline, - StableDiffusionMixin, - FromSingleFileMixin, - StableDiffusionXLLoraLoaderMixin, - TextualInversionLoaderMixin, - IPAdapterMixin, -): - r""" - Pipeline for text-to-image generation using Stable Diffusion XL. - - 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.) - - The pipeline also inherits the following loading methods: - - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights - - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights - - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters - - Args: - vae ([`AutoencoderKL`]): - Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. - text_encoder ([`CLIPTextModel`]): - Frozen text-encoder. Stable Diffusion XL uses the text portion of - [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically - the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. - text_encoder_2 ([` CLIPTextModelWithProjection`]): - Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of - [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), - specifically the - [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) - variant. - tokenizer (`CLIPTokenizer`): - Tokenizer of class - [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - tokenizer_2 (`CLIPTokenizer`): - Second Tokenizer of class - [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - unet ([`UNet2DConditionModel`]): Conditional U-Net architecture 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`]. - force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): - Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of - `stabilityai/stable-diffusion-xl-base-1-0`. - add_watermarker (`bool`, *optional*): - Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to - watermark output images. If not defined, it will default to True if the package is installed, otherwise no - watermarker will be used. - """ - - model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae" - _optional_components = [ - "tokenizer", - "tokenizer_2", - "text_encoder", - "text_encoder_2", - "image_encoder", - "feature_extractor", - ] - _callback_tensor_inputs = [ - "latents", - "prompt_embeds", - "negative_prompt_embeds", - "add_text_embeds", - "add_time_ids", - "negative_pooled_prompt_embeds", - "negative_add_time_ids", - ] - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - text_encoder_2: CLIPTextModelWithProjection, - tokenizer: CLIPTokenizer, - tokenizer_2: CLIPTokenizer, - unet: UNet2DConditionModel, - controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], - scheduler: KarrasDiffusionSchedulers, - image_encoder: CLIPVisionModelWithProjection = None, - feature_extractor: CLIPImageProcessor = None, - force_zeros_for_empty_prompt: bool = True, - add_watermarker: Optional[bool] = None, - ): - super().__init__() - - self.register_modules( - vae=vae, - text_encoder=text_encoder, - text_encoder_2=text_encoder_2, - tokenizer=tokenizer, - tokenizer_2=tokenizer_2, - unet=unet, - controlnet=controlnet, - scheduler=scheduler, - image_encoder=image_encoder, - feature_extractor=feature_extractor, - ) - self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) - 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.control_image_processor = VaeImageProcessor( - vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False - ) - self.mask_processor = VaeImageProcessor( - vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True - ) - - self.default_sample_size = self.unet.config.sample_size - - def encode_prompt( - self, - prompt: str, - prompt_2: Optional[str] = None, - device: Optional[torch.device] = None, - num_images_per_prompt: int = 1, - do_classifier_free_guidance: bool = True, - negative_prompt: Optional[str] = None, - negative_prompt_2: Optional[str] = None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_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 - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - 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`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - 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. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled 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. - """ - device = device or self._execution_device - - # 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, StableDiffusionXLLoraLoaderMixin): - self._lora_scale = lora_scale - - # dynamically adjust the LoRA scale - if self.text_encoder is not None: - 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 self.text_encoder_2 is not None: - if not USE_PEFT_BACKEND: - adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) - else: - scale_lora_layers(self.text_encoder_2, lora_scale) - - prompt = [prompt] if isinstance(prompt, str) else prompt - - if prompt is not None: - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - # Define tokenizers and text encoders - tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] - text_encoders = ( - [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] - ) - - if prompt_embeds is None: - prompt_2 = prompt_2 or prompt - prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 - - # textual inversion: process multi-vector tokens if necessary - prompt_embeds_list = [] - prompts = [prompt, prompt_2] - for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): - if isinstance(self, TextualInversionLoaderMixin): - prompt = self.maybe_convert_prompt(prompt, tokenizer) - - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - - text_input_ids = text_inputs.input_ids - untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, 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" {tokenizer.model_max_length} tokens: {removed_text}" - ) - - prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True) - - # We are only ALWAYS interested in the pooled output of the final text encoder - pooled_prompt_embeds = prompt_embeds[0] - if clip_skip is None: - prompt_embeds = prompt_embeds.hidden_states[-2] - else: - # "2" because SDXL always indexes from the penultimate layer. - prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)] - - prompt_embeds_list.append(prompt_embeds) - - prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) - - # get unconditional embeddings for classifier free guidance - zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt - if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: - negative_prompt_embeds = torch.zeros_like(prompt_embeds) - negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) - elif do_classifier_free_guidance and negative_prompt_embeds is None: - negative_prompt = negative_prompt or "" - negative_prompt_2 = negative_prompt_2 or negative_prompt - - # normalize str to list - negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt - negative_prompt_2 = ( - batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 - ) - - uncond_tokens: List[str] - if 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 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, negative_prompt_2] - - negative_prompt_embeds_list = [] - for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): - if isinstance(self, TextualInversionLoaderMixin): - negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) - - max_length = prompt_embeds.shape[1] - uncond_input = tokenizer( - negative_prompt, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - - negative_prompt_embeds = text_encoder( - uncond_input.input_ids.to(device), - output_hidden_states=True, - ) - # We are only ALWAYS interested in the pooled output of the final text encoder - negative_pooled_prompt_embeds = negative_prompt_embeds[0] - negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] - - negative_prompt_embeds_list.append(negative_prompt_embeds) - - negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) - - if self.text_encoder_2 is not None: - prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) - else: - prompt_embeds = prompt_embeds.to(dtype=self.unet.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) - - if do_classifier_free_guidance: - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = negative_prompt_embeds.shape[1] - - if self.text_encoder_2 is not None: - negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) - else: - negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.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) - - pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( - bs_embed * num_images_per_prompt, -1 - ) - if do_classifier_free_guidance: - negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( - bs_embed * num_images_per_prompt, -1 - ) - - if self.text_encoder is not None: - if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder, lora_scale) - - if self.text_encoder_2 is not None: - if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder_2, lora_scale) - - return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image - def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None): - dtype = next(self.image_encoder.parameters()).dtype - - if not isinstance(image, torch.Tensor): - image = self.feature_extractor(image, return_tensors="pt").pixel_values - - image = image.to(device=device, dtype=dtype) - if output_hidden_states: - image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] - image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_enc_hidden_states = self.image_encoder( - torch.zeros_like(image), output_hidden_states=True - ).hidden_states[-2] - uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave( - num_images_per_prompt, dim=0 - ) - return image_enc_hidden_states, uncond_image_enc_hidden_states - else: - image_embeds = self.image_encoder(image).image_embeds - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) - uncond_image_embeds = torch.zeros_like(image_embeds) - - return image_embeds, uncond_image_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds - def prepare_ip_adapter_image_embeds( - self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance - ): - if ip_adapter_image_embeds is None: - if not isinstance(ip_adapter_image, list): - ip_adapter_image = [ip_adapter_image] - - if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers): - raise ValueError( - f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." - ) - - image_embeds = [] - for single_ip_adapter_image, image_proj_layer in zip( - ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers - ): - output_hidden_state = not isinstance(image_proj_layer, ImageProjection) - single_image_embeds, single_negative_image_embeds = self.encode_image( - single_ip_adapter_image, device, 1, output_hidden_state - ) - single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0) - single_negative_image_embeds = torch.stack( - [single_negative_image_embeds] * num_images_per_prompt, dim=0 - ) - - if do_classifier_free_guidance: - single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) - single_image_embeds = single_image_embeds.to(device) - - image_embeds.append(single_image_embeds) - else: - repeat_dims = [1] - image_embeds = [] - for single_image_embeds in ip_adapter_image_embeds: - if do_classifier_free_guidance: - single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2) - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) - ) - single_negative_image_embeds = single_negative_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:])) - ) - single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) - else: - single_image_embeds = single_image_embeds.repeat( - num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) - ) - image_embeds.append(single_image_embeds) - - return image_embeds - - # 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, - prompt_2, - height, - width, - callback_steps, - negative_prompt=None, - negative_prompt_2=None, - prompt_embeds=None, - negative_prompt_embeds=None, - pooled_prompt_embeds=None, - negative_pooled_prompt_embeds=None, - ip_adapter_image=None, - ip_adapter_image_embeds=None, - callback_on_step_end_tensor_inputs=None, - - control_image=None, - controlnet_conditioning_scale=1.0, - control_guidance_start=0.0, - control_guidance_end=1.0, - - padding_mask_crop=None, - strength=None, - mask_image=None, - num_inference_steps=None, - - ): - if strength is not None: - if strength < 0 or strength > 1: - raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") - if control_image is 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 padding_mask_crop is not None: - if not isinstance(control_image, PIL.Image.Image): - raise ValueError( - f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(control_image)}." - ) - if not isinstance(mask_image, PIL.Image.Image): - raise ValueError( - f"The mask image should be a PIL image when inpainting mask crop, but is of type" - f" {type(mask_image)}." - ) - - 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_2 is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt_2`: {prompt_2} 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)}") - elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): - raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") - - 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." - ) - elif negative_prompt_2 is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} 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}." - ) - - if prompt_embeds is not None and pooled_prompt_embeds is None: - raise ValueError( - "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`." - ) - - if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: - raise ValueError( - "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." - ) - - # `prompt` needs more sophisticated handling when there are multiple - # conditionings. - if control_image is not None: - if isinstance(self.controlnet, MultiControlNetModel): - if isinstance(prompt, list): - logger.warning( - f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}" - " prompts. The conditionings will be fixed across the prompts." - ) - - # Check `image` - is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( - self.controlnet, torch._dynamo.eval_frame.OptimizedModule - ) - if ( - isinstance(self.controlnet, ControlNetModel) - or is_compiled - and isinstance(self.controlnet._orig_mod, ControlNetModel) - ): - self.check_control_image(control_image, prompt, prompt_embeds) - elif ( - isinstance(self.controlnet, MultiControlNetModel) - or is_compiled - and isinstance(self.controlnet._orig_mod, MultiControlNetModel) - ): - if not isinstance(control_image, list): - raise TypeError("For multiple controlnets: `image` must be type `list`") - - # When `image` is a nested list: - # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]]) - elif any(isinstance(i, list) for i in control_image): - raise ValueError("A single batch of multiple conditionings are supported at the moment.") - elif len(control_image) != len(self.controlnet.nets): - raise ValueError( - f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(control_image)} images and {len(self.controlnet.nets)} ControlNets." - ) - - for image_ in control_image: - self.check_control_image(image_, prompt, prompt_embeds) - else: - assert False - - # Check `controlnet_conditioning_scale` - if ( - isinstance(self.controlnet, ControlNetModel) - or is_compiled - and isinstance(self.controlnet._orig_mod, ControlNetModel) - ): - if not isinstance(controlnet_conditioning_scale, float): - raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") - elif ( - isinstance(self.controlnet, MultiControlNetModel) - or is_compiled - and isinstance(self.controlnet._orig_mod, MultiControlNetModel) - ): - if isinstance(controlnet_conditioning_scale, list): - if any(isinstance(i, list) for i in controlnet_conditioning_scale): - raise ValueError("A single batch of multiple conditionings are supported at the moment.") - elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( - self.controlnet.nets - ): - raise ValueError( - "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have" - " the same length as the number of controlnets" - ) - else: - assert False - - if not isinstance(control_guidance_start, (tuple, list)): - control_guidance_start = [control_guidance_start] - - if not isinstance(control_guidance_end, (tuple, list)): - control_guidance_end = [control_guidance_end] - - if len(control_guidance_start) != len(control_guidance_end): - raise ValueError( - f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list." - ) - - if isinstance(self.controlnet, MultiControlNetModel): - if len(control_guidance_start) != len(self.controlnet.nets): - raise ValueError( - f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}." - ) - - for start, end in zip(control_guidance_start, control_guidance_end): - if start >= end: - raise ValueError( - f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}." - ) - if start < 0.0: - raise ValueError(f"control guidance start: {start} can't be smaller than 0.") - if end > 1.0: - raise ValueError(f"control guidance end: {end} can't be larger than 1.0.") - - if ip_adapter_image is not None and ip_adapter_image_embeds is not None: - raise ValueError( - "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined." - ) - - if ip_adapter_image_embeds is not None: - if not isinstance(ip_adapter_image_embeds, list): - raise ValueError( - f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}" - ) - elif ip_adapter_image_embeds[0].ndim not in [3, 4]: - raise ValueError( - f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D" - ) - - def check_control_image(self, control_image, prompt, prompt_embeds): - image_is_pil = isinstance(control_image, PIL.Image.Image) - image_is_tensor = isinstance(control_image, torch.Tensor) - image_is_np = isinstance(control_image, np.ndarray) - image_is_pil_list = isinstance(control_image, list) and isinstance(control_image[0], PIL.Image.Image) - image_is_tensor_list = isinstance(control_image, list) and isinstance(control_image[0], torch.Tensor) - image_is_np_list = isinstance(control_image, list) and isinstance(control_image[0], np.ndarray) - - if ( - not image_is_pil - and not image_is_tensor - and not image_is_np - and not image_is_pil_list - and not image_is_tensor_list - and not image_is_np_list - ): - raise TypeError( - f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(control_image)}" - ) - - if image_is_pil: - image_batch_size = 1 - else: - image_batch_size = len(control_image) - - if prompt is not None and isinstance(prompt, str): - prompt_batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - prompt_batch_size = len(prompt) - elif prompt_embeds is not None: - prompt_batch_size = prompt_embeds.shape[0] - - if image_batch_size != 1 and image_batch_size != prompt_batch_size: - raise ValueError( - f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" - ) - - def prepare_control_image( - self, - control_image, - width, - height, - batch_size, - num_images_per_prompt, - device, - dtype, - crops_coords=None, - resize_mode="Default", - do_classifier_free_guidance=False, - guess_mode=False, - ): - control_image = self.control_image_processor.preprocess(control_image, - height=height, width=width, - crops_coords=crops_coords, - resize_mode=resize_mode).to(dtype=torch.float32) - control_image_batch_size = control_image.shape[0] - - if control_image_batch_size == 1: - repeat_by = batch_size - else: - # image batch size is the same as prompt batch size - repeat_by = num_images_per_prompt - - control_image = control_image.repeat_interleave(repeat_by, dim=0) - - control_image = control_image.to(device=device, dtype=dtype) - - if do_classifier_free_guidance and not guess_mode: - control_image = torch.cat([control_image] * 2) - - return control_image - - # 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 prepare_inpaint_latents( - self, - batch_size, - num_channels_latents, - height, - width, - dtype, - device, - generator, - latents=None, - image=None, - timestep=None, - is_strength_max=True, - add_noise=True, - return_noise=False, - return_image_latents=False, - ): - 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 (image is None or timestep is None) and not is_strength_max: - raise ValueError( - "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise." - "However, either the image or the noise timestep has not been provided." - ) - - if return_image_latents or (latents is None and not is_strength_max): - image = image.to(device=device, dtype=dtype) - - if image.shape[1] == 4: - image_latents = image - else: - image_latents = self._encode_vae_image(image=image, generator=generator) - image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1) - - if latents is None and add_noise: - noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) - # if strength is 1. then initialise the latents to noise, else initial to image + noise - latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep) - # if pure noise then scale the initial latents by the Scheduler's init sigma - latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents - elif add_noise: - noise = latents.to(device) - latents = noise * self.scheduler.init_noise_sigma - else: - noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) - latents = image_latents.to(device) - - outputs = (latents,) - - if return_noise: - outputs += (noise,) - - if return_image_latents: - outputs += (image_latents,) - - return outputs - - def prepare_mask_latents( - self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance - ): - # resize the mask to latents shape as we concatenate the mask to the latents - # we do that before converting to dtype to avoid breaking in case we're using cpu_offload - # and half precision - mask = torch.nn.functional.interpolate( - mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) - ) - mask = mask.to(device=device, dtype=dtype) - - # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method - if mask.shape[0] < batch_size: - if not batch_size % mask.shape[0] == 0: - raise ValueError( - "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" - f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" - " of masks that you pass is divisible by the total requested batch size." - ) - mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1) - - mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask - - masked_image_latents = None - if masked_image is not None: - masked_image = masked_image.to(device=device, dtype=dtype) - masked_image_latents = self._encode_vae_image(masked_image, generator=generator) - if masked_image_latents.shape[0] < batch_size: - if not batch_size % masked_image_latents.shape[0] == 0: - raise ValueError( - "The passed images and the required batch size don't match. Images are supposed to be duplicated" - f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." - " Make sure the number of images that you pass is divisible by the total requested batch size." - ) - masked_image_latents = masked_image_latents.repeat( - batch_size // masked_image_latents.shape[0], 1, 1, 1 - ) - - masked_image_latents = ( - torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents - ) - - # aligning device to prevent device errors when concating it with the latent model input - masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) - - return mask, masked_image_latents - - def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator): - dtype = image.dtype - if self.vae.config.force_upcast: - image = image.float() - self.vae.to(dtype=torch.float32) - - if isinstance(generator, list): - image_latents = [ - retrieve_latents(self.vae.encode(image[i: i + 1]), generator=generator[i]) - for i in range(image.shape[0]) - ] - image_latents = torch.cat(image_latents, dim=0) - else: - image_latents = retrieve_latents(self.vae.encode(image), generator=generator) - - if self.vae.config.force_upcast: - self.vae.to(dtype) - - image_latents = image_latents.to(dtype) - image_latents = self.vae.config.scaling_factor * image_latents - - return image_latents - - def _get_add_time_ids( - self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None - ): - add_time_ids = list(original_size + crops_coords_top_left + target_size) - - passed_add_embed_dim = ( - self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim - ) - expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features - - if expected_add_embed_dim != passed_add_embed_dim: - raise ValueError( - f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." - ) - - add_time_ids = torch.tensor([add_time_ids], dtype=dtype) - return add_time_ids - - def _get_inpaint_add_time_ids( - self, - original_size, - crops_coords_top_left, - target_size, - aesthetic_score, - negative_aesthetic_score, - dtype, - text_encoder_projection_dim=None, - ): - - add_time_ids = list(original_size + crops_coords_top_left + target_size) - add_neg_time_ids = list(original_size + crops_coords_top_left + target_size) - - passed_add_embed_dim = ( - self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim - ) - expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features - - if ( - expected_add_embed_dim > passed_add_embed_dim - and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim - ): - raise ValueError( - f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model." - ) - elif ( - expected_add_embed_dim < passed_add_embed_dim - and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim - ): - raise ValueError( - f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model." - ) - elif expected_add_embed_dim != passed_add_embed_dim: - raise ValueError( - f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." - ) - - add_time_ids = torch.tensor([add_time_ids], dtype=dtype) - add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype) - - return add_time_ids, add_neg_time_ids - - def upcast_vae(self): - dtype = self.vae.dtype - self.vae.to(dtype=torch.float32) - use_torch_2_0_or_xformers = isinstance( - self.vae.decoder.mid_block.attentions[0].processor, - ( - AttnProcessor2_0, - XFormersAttnProcessor, - LoRAXFormersAttnProcessor, - LoRAAttnProcessor2_0, - FusedAttnProcessor2_0, - ), - ) - # if xformers or torch_2_0 is used attention block does not need - # to be in float32 which can save lots of memory - if use_torch_2_0_or_xformers: - self.vae.post_quant_conv.to(dtype) - self.vae.decoder.conv_in.to(dtype) - self.vae.decoder.mid_block.to(dtype) - - # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding - 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_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps - def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None): - # get the original timestep using init_timestep - if denoising_start is None: - init_timestep = min(int(num_inference_steps * strength), num_inference_steps) - t_start = max(num_inference_steps - init_timestep, 0) - else: - t_start = 0 - - timesteps = self.scheduler.timesteps[t_start * self.scheduler.order:] - - # Strength is irrelevant if we directly request a timestep to start at; - # that is, strength is determined by the denoising_start instead. - if denoising_start is not None: - discrete_timestep_cutoff = int( - round( - self.scheduler.config.num_train_timesteps - - (denoising_start * self.scheduler.config.num_train_timesteps) - ) - ) - - num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item() - if self.scheduler.order == 2 and num_inference_steps % 2 == 0: - # if the scheduler is a 2nd order scheduler we might have to do +1 - # because `num_inference_steps` might be even given that every timestep - # (except the highest one) is duplicated. If `num_inference_steps` is even it would - # mean that we cut the timesteps in the middle of the denoising step - # (between 1st and 2nd devirative) which leads to incorrect results. By adding 1 - # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler - num_inference_steps = num_inference_steps + 1 - - # because t_n+1 >= t_n, we slice the timesteps starting from the end - timesteps = timesteps[-num_inference_steps:] - return timesteps, num_inference_steps - - return timesteps, num_inference_steps - t_start - - @property - def guidance_scale(self): - return self._guidance_scale - - @property - def guidance_rescale(self): - return self._guidance_rescale - - @property - def clip_skip(self): - return self._clip_skip - - # 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. - @property - def do_classifier_free_guidance(self): - return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None - - @property - def cross_attention_kwargs(self): - return self._cross_attention_kwargs - - @property - def denoising_end(self): - return self._denoising_end - - @property - def num_timesteps(self): - return self._num_timesteps - - @property - def interrupt(self): - return self._interrupt - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def __call__( - self, - prompt: Union[str, List[str]] = None, - prompt_2: Optional[Union[str, List[str]]] = None, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - timesteps: List[int] = None, - denoising_end: Optional[float] = None, - guidance_scale: float = 5.0, - negative_prompt: Optional[Union[str, List[str]]] = None, - negative_prompt_2: 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, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - ip_adapter_image: Optional[PipelineImageInput] = None, - ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - guidance_rescale: float = 0.0, - original_size: Optional[Tuple[int, int]] = None, - crops_coords_top_left: Tuple[int, int] = (0, 0), - target_size: Optional[Tuple[int, int]] = None, - negative_original_size: Optional[Tuple[int, int]] = None, - negative_crops_coords_top_left: Tuple[int, int] = (0, 0), - negative_target_size: Optional[Tuple[int, int]] = 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"], - gradio_progress=None, - exposure=0.0, - **kwargs, - ): - r""" - 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. - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The height in pixels of the generated image. This is set to 1024 by default for the best results. - Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The width in pixels of the generated image. This is set to 1024 by default for the best results. - Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - 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. - timesteps (`List[int]`, *optional*): - Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument - in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is - passed will be used. Must be in descending order. - denoising_end (`float`, *optional*): - When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be - completed before it is intentionally prematurely terminated. As a result, the returned sample will - still retain a substantial amount of noise as determined by the discrete timesteps selected by the - scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a - "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image - Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) - guidance_scale (`float`, *optional*, defaults to 5.0): - 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`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - 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. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` - input argument. - ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. - ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): - Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. - Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding - if `do_classifier_free_guidance` is set to `True`. - If not provided, embeddings are computed from the `ip_adapter_image` 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_xl.StableDiffusionXLPipelineOutput`] instead - of a plain tuple. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - guidance_rescale (`float`, *optional*, defaults to 0.0): - Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are - Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of - [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. - original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. - `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as - explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position - `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting - `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - For most cases, `target_size` should be set to the desired height and width of the generated image. If - not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in - section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a specific image resolution. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a target image resolution. It should be as same - as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - 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 pipeline class. - - Examples: - - Returns: - [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a - `tuple`. When returning a tuple, the first element is a list with the generated images. - """ - - 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.default_sample_size * self.vae_scale_factor - width = width or self.default_sample_size * self.vae_scale_factor - - original_size = original_size or (height, width) - target_size = target_size or (height, width) - - # 1. Check inputs. Raise error if not correct - self.check_inputs( - prompt, - prompt_2, - height, - width, - callback_steps, - negative_prompt, - negative_prompt_2, - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ip_adapter_image, - ip_adapter_image_embeds, - callback_on_step_end_tensor_inputs, - ) - - self._guidance_scale = guidance_scale - self._guidance_rescale = guidance_rescale - self._clip_skip = clip_skip - self._cross_attention_kwargs = cross_attention_kwargs - self._denoising_end = denoising_end - self._interrupt = False - - # 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 - - # 3. Encode input prompt - lora_scale = ( - self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None - ) - - ( - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ) = self.encode_prompt( - prompt=prompt, - prompt_2=prompt_2, - device=device, - num_images_per_prompt=num_images_per_prompt, - do_classifier_free_guidance=self.do_classifier_free_guidance, - negative_prompt=negative_prompt, - negative_prompt_2=negative_prompt_2, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - pooled_prompt_embeds=pooled_prompt_embeds, - negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, - lora_scale=lora_scale, - clip_skip=self.clip_skip, - ) - - # 4. Prepare timesteps - timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) - - # 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) - - # 7. Prepare added time ids & embeddings - add_text_embeds = pooled_prompt_embeds - if self.text_encoder_2 is None: - text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) - else: - text_encoder_projection_dim = self.text_encoder_2.config.projection_dim - - add_time_ids = self._get_add_time_ids( - original_size, - crops_coords_top_left, - target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - if negative_original_size is not None and negative_target_size is not None: - negative_add_time_ids = self._get_add_time_ids( - negative_original_size, - negative_crops_coords_top_left, - negative_target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - else: - negative_add_time_ids = add_time_ids - - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) - add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) - - prompt_embeds = prompt_embeds.to(device) - add_text_embeds = add_text_embeds.to(device) - add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) - - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - image_embeds = self.prepare_ip_adapter_image_embeds( - ip_adapter_image, - ip_adapter_image_embeds, - device, - batch_size * num_images_per_prompt, - self.do_classifier_free_guidance, - ) - - # 8. Denoising loop - num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) - - # 8.1 Apply denoising_end - if ( - self.denoising_end is not None - and isinstance(self.denoising_end, float) - and self.denoising_end > 0 - and self.denoising_end < 1 - ): - discrete_timestep_cutoff = int( - round( - self.scheduler.config.num_train_timesteps - - (self.denoising_end * self.scheduler.config.num_train_timesteps) - ) - ) - num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) - timesteps = timesteps[:num_inference_steps] - - # 9. Optionally get Guidance Scale Embedding - timestep_cond = None - if self.unet.config.time_cond_proj_dim is not None: - guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) - timestep_cond = self.get_guidance_scale_embedding( - guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim - ).to(device=device, dtype=latents.dtype) - - latents = latents + exposure - - self._num_timesteps = len(timesteps) - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - if self.interrupt: - continue - - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents - - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - added_cond_kwargs["image_embeds"] = image_embeds - noise_pred = self.unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - timestep_cond=timestep_cond, - cross_attention_kwargs=self.cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - if self.do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) - - if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: - # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) - - latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) - add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) - negative_pooled_prompt_embeds = callback_outputs.pop( - "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds - ) - add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) - negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids) - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) - - if gradio_progress is not None: - gradio_progress((i + 1) / int(self._num_timesteps), - "Brainstorming...", - total=int(self._num_timesteps)) - - if XLA_AVAILABLE: - xm.mark_step() - - if not output_type == "latent": - # make sure the VAE is in float32 mode, as it overflows in float16 - needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast - - if needs_upcasting: - self.upcast_vae() - latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) - - # unscale/denormalize the latents - # denormalize with the mean and std if available and not None - has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None - has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None - if has_latents_mean and has_latents_std: - latents_mean = ( - torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents_std = ( - torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean - else: - latents = latents / self.vae.config.scaling_factor - - image = self.vae.decode(latents, return_dict=False)[0] - - # cast back to fp16 if needed - if needs_upcasting: - self.vae.to(dtype=torch.float16) - else: - image = latents - - if not output_type == "latent": - # apply watermark if available - image = self.image_processor.postprocess(image, output_type=output_type) - - # Offload all models - self.maybe_free_model_hooks() - - if not return_dict: - return (image,) - - return StableDiffusionXLPipelineOutput(images=image) - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def control_sketch( - self, - prompt: Union[str, List[str]] = None, - prompt_2: Optional[Union[str, List[str]]] = None, - control_image: PipelineImageInput = None, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - denoising_end: Optional[float] = None, - guidance_scale: float = 5.0, - negative_prompt: Optional[Union[str, List[str]]] = None, - negative_prompt_2: 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, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - ip_adapter_image: Optional[PipelineImageInput] = None, - ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - controlnet_conditioning_scale: Union[float, List[float]] = 1.0, - guess_mode: bool = False, - control_guidance_start: Union[float, List[float]] = 0.0, - control_guidance_end: Union[float, List[float]] = 1.0, - original_size: Tuple[int, int] = None, - crops_coords_top_left: Tuple[int, int] = (0, 0), - target_size: Tuple[int, int] = None, - negative_original_size: Optional[Tuple[int, int]] = None, - negative_crops_coords_top_left: Tuple[int, int] = (0, 0), - negative_target_size: Optional[Tuple[int, int]] = 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"], - gradio_progress=None, - **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`. - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders. - image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: - `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): - The ControlNet input condition to provide guidance to the `unet` for generation. If the type is - specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be - accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height - and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in - `init`, images must be passed as a list such that each element of the list can be correctly batched for - input to a single ControlNet. - height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): - The height in pixels of the generated image. Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): - The width in pixels of the generated image. Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - 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. - denoising_end (`float`, *optional*): - When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be - completed before it is intentionally prematurely terminated. As a result, the returned sample will - still retain a substantial amount of noise as determined by the discrete timesteps selected by the - scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a - "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image - Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) - guidance_scale (`float`, *optional*, defaults to 5.0): - 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`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2` - and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders. - 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. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If - not provided, pooled text embeddings are generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt - weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input - argument. - ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. - ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): - Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. - Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding - if `do_classifier_free_guidance` is set to `True`. - If not provided, embeddings are computed from the `ip_adapter_image` 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). - controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): - The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added - to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set - the corresponding scale as a list. - guess_mode (`bool`, *optional*, defaults to `False`): - The ControlNet encoder tries to recognize the content of the input image even if you remove all - prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended. - control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): - The percentage of total steps at which the ControlNet starts applying. - control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): - The percentage of total steps at which the ControlNet stops applying. - original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. - `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as - explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position - `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting - `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - For most cases, `target_size` should be set to the desired height and width of the generated image. If - not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in - section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a specific image resolution. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a target image resolution. It should be as same - as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - 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. - - Examples: - - Returns: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, - otherwise a `tuple` is returned containing the output images. - """ - - 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 using `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 using `callback_on_step_end`", - ) - - controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet - - # align format for control guidance - if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): - control_guidance_start = len(control_guidance_end) * [control_guidance_start] - elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): - control_guidance_end = len(control_guidance_start) * [control_guidance_end] - elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): - mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1 - control_guidance_start, control_guidance_end = ( - mult * [control_guidance_start], - mult * [control_guidance_end], - ) - - # 1. Check inputs. Raise error if not correct - self.check_inputs( - prompt, - prompt_2, - None, - None, - callback_steps, - negative_prompt, - negative_prompt_2, - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ip_adapter_image, - ip_adapter_image_embeds, - callback_on_step_end_tensor_inputs, - - control_image, - controlnet_conditioning_scale, - control_guidance_start, - control_guidance_end, - ) - - self._guidance_scale = guidance_scale - self._clip_skip = clip_skip - self._cross_attention_kwargs = cross_attention_kwargs - self._denoising_end = denoising_end - - # 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 - - if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): - controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) - - global_pool_conditions = ( - controlnet.config.global_pool_conditions - if isinstance(controlnet, ControlNetModel) - else controlnet.nets[0].config.global_pool_conditions - ) - guess_mode = guess_mode or global_pool_conditions - - # 3.1 Encode input prompt - text_encoder_lora_scale = ( - self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None - ) - ( - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ) = self.encode_prompt( - prompt, - prompt_2, - device, - num_images_per_prompt, - self.do_classifier_free_guidance, - negative_prompt, - negative_prompt_2, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - pooled_prompt_embeds=pooled_prompt_embeds, - negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, - lora_scale=text_encoder_lora_scale, - clip_skip=self.clip_skip, - ) - - # 3.2 Encode ip_adapter_image - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - image_embeds = self.prepare_ip_adapter_image_embeds( - ip_adapter_image, - ip_adapter_image_embeds, - device, - batch_size * num_images_per_prompt, - self.do_classifier_free_guidance, - ) - - # 4. Prepare image - if isinstance(controlnet, ControlNetModel): - control_image = self.prepare_control_image( - control_image=control_image, - width=width, - height=height, - batch_size=batch_size * num_images_per_prompt, - num_images_per_prompt=num_images_per_prompt, - device=device, - dtype=controlnet.dtype, - do_classifier_free_guidance=self.do_classifier_free_guidance, - guess_mode=guess_mode, - ) - height, width = control_image.shape[-2:] - elif isinstance(controlnet, MultiControlNetModel): - control_images = [] - - for control_image_ in control_image: - control_image_ = self.prepare_control_image( - control_image=control_image_, - width=width, - height=height, - batch_size=batch_size * num_images_per_prompt, - num_images_per_prompt=num_images_per_prompt, - device=device, - dtype=controlnet.dtype, - do_classifier_free_guidance=self.do_classifier_free_guidance, - guess_mode=guess_mode, - ) - - control_images.append(control_image_) - - control_image = control_images - height, width = control_image[0].shape[-2:] - else: - assert False - - # 5. Prepare timesteps - self.scheduler.set_timesteps(num_inference_steps, device=device) - timesteps = self.scheduler.timesteps - self._num_timesteps = len(timesteps) - - # 6. 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, - ) - - # 6.5 Optionally get Guidance Scale Embedding - timestep_cond = None - if self.unet.config.time_cond_proj_dim is not None: - guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) - timestep_cond = self.get_guidance_scale_embedding( - guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim - ).to(device=device, dtype=latents.dtype) - - # 7. 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) - - # 7.1 Create tensor stating which controlnets to keep - controlnet_keep = [] - for i in range(len(timesteps)): - keeps = [ - 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) - for s, e in zip(control_guidance_start, control_guidance_end) - ] - controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps) - - # 7.2 Prepare added time ids & embeddings - if isinstance(control_image, list): - original_size = original_size or control_image[0].shape[-2:] - else: - original_size = original_size or control_image.shape[-2:] - target_size = target_size or (height, width) - - add_text_embeds = pooled_prompt_embeds - if self.text_encoder_2 is None: - text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) - else: - text_encoder_projection_dim = self.text_encoder_2.config.projection_dim - - add_time_ids = self._get_add_time_ids( - original_size, - crops_coords_top_left, - target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - - if negative_original_size is not None and negative_target_size is not None: - negative_add_time_ids = self._get_add_time_ids( - negative_original_size, - negative_crops_coords_top_left, - negative_target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - else: - negative_add_time_ids = add_time_ids - - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) - add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) - - prompt_embeds = prompt_embeds.to(device) - add_text_embeds = add_text_embeds.to(device) - add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) - - # 8. Denoising loop - num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order - - # 8.1 Apply denoising_end - if ( - self.denoising_end is not None - and isinstance(self.denoising_end, float) - and self.denoising_end > 0 - and self.denoising_end < 1 - ): - discrete_timestep_cutoff = int( - round( - self.scheduler.config.num_train_timesteps - - (self.denoising_end * self.scheduler.config.num_train_timesteps) - ) - ) - num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) - timesteps = timesteps[:num_inference_steps] - - is_unet_compiled = is_compiled_module(self.unet) - is_controlnet_compiled = is_compiled_module(self.controlnet) - is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1") - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # Relevant thread: - # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428 - if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1: - torch._inductor.cudagraph_mark_step_begin() - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} - - # controlnet(s) inference - if guess_mode and self.do_classifier_free_guidance: - # Infer ControlNet only for the conditional batch. - control_model_input = latents - control_model_input = self.scheduler.scale_model_input(control_model_input, t) - controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] - controlnet_added_cond_kwargs = { - "text_embeds": add_text_embeds.chunk(2)[1], - "time_ids": add_time_ids.chunk(2)[1], - } - else: - control_model_input = latent_model_input - controlnet_prompt_embeds = prompt_embeds - controlnet_added_cond_kwargs = added_cond_kwargs - - if isinstance(controlnet_keep[i], list): - cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] - else: - controlnet_cond_scale = controlnet_conditioning_scale - if isinstance(controlnet_cond_scale, list): - controlnet_cond_scale = controlnet_cond_scale[0] - cond_scale = controlnet_cond_scale * controlnet_keep[i] - - down_block_res_samples, mid_block_res_sample = self.controlnet( - control_model_input, - t, - encoder_hidden_states=controlnet_prompt_embeds, - controlnet_cond=control_image, - conditioning_scale=cond_scale, - guess_mode=guess_mode, - added_cond_kwargs=controlnet_added_cond_kwargs, - return_dict=False, - ) - - if guess_mode and self.do_classifier_free_guidance: - # Infered ControlNet only for the conditional batch. - # To apply the output of ControlNet to both the unconditional and conditional batches, - # add 0 to the unconditional batch to keep it unchanged. - down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] - mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) - - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - added_cond_kwargs["image_embeds"] = image_embeds - - # predict the noise residual - noise_pred = self.unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - timestep_cond=timestep_cond, - cross_attention_kwargs=self.cross_attention_kwargs, - down_block_additional_residuals=down_block_res_samples, - mid_block_additional_residual=mid_block_res_sample, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - if self.do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) - - latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) - - if gradio_progress is not None: - gradio_progress((i + 1) / int(self._num_timesteps), - "Brainstorming...", - total=int(self._num_timesteps)) - - if not output_type == "latent": - # make sure the VAE is in float32 mode, as it overflows in float16 - needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast - - if needs_upcasting: - self.upcast_vae() - latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) - - # unscale/denormalize the latents - # denormalize with the mean and std if available and not None - has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None - has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None - if has_latents_mean and has_latents_std: - latents_mean = ( - torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents_std = ( - torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean - else: - latents = latents / self.vae.config.scaling_factor - - image = self.vae.decode(latents, return_dict=False)[0] - - # cast back to fp16 if needed - if needs_upcasting: - self.vae.to(dtype=torch.float16) - else: - image = latents - - if not output_type == "latent": - # apply watermark if available - - image = self.image_processor.postprocess(image, output_type=output_type) - - # Offload all models - self.maybe_free_model_hooks() - - if not return_dict: - return (image,) - - return StableDiffusionXLPipelineOutput(images=image) - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def control_inpaint_sketch( - self, - prompt: Union[str, List[str]] = None, - prompt_2: Optional[Union[str, List[str]]] = None, - image: PipelineImageInput = None, - mask_image: PipelineImageInput = None, - control_image: Union[ - PipelineImageInput, - List[PipelineImageInput], - ] = None, - height: Optional[int] = None, - width: Optional[int] = None, - padding_mask_crop: Optional[int] = None, - strength: float = 0.9999, - num_inference_steps: int = 50, - denoising_start: Optional[float] = None, - denoising_end: Optional[float] = None, - guidance_scale: float = 5.0, - negative_prompt: Optional[Union[str, List[str]]] = None, - negative_prompt_2: 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, - ip_adapter_image: Optional[PipelineImageInput] = None, - ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - controlnet_conditioning_scale: Union[float, List[float]] = 1.0, - guess_mode: bool = False, - control_guidance_start: Union[float, List[float]] = 0.0, - control_guidance_end: Union[float, List[float]] = 1.0, - guidance_rescale: float = 0.0, - original_size: Tuple[int, int] = None, - crops_coords_top_left: Tuple[int, int] = (0, 0), - target_size: Tuple[int, int] = None, - aesthetic_score: float = 6.0, - negative_aesthetic_score: float = 2.5, - 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"], - gradio_progress=None, - **kwargs, - ): - r""" - 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. - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - image (`PIL.Image.Image`): - `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will - be masked out with `mask_image` and repainted according to `prompt`. - mask_image (`PIL.Image.Image`): - `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be - repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted - to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) - instead of 3, so the expected shape would be `(B, H, W, 1)`. - 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. - padding_mask_crop (`int`, *optional*, defaults to `None`): - The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If - `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and - contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on - the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large - and contain information inreleant for inpainging, such as background. - strength (`float`, *optional*, defaults to 0.9999): - Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be - between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the - `strength`. The number of denoising steps depends on the amount of noise initially added. When - `strength` is 1, added noise will be maximum and the denoising process will run for the full number of - iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked - portion of the reference `image`. Note that in the case of `denoising_start` being declared as an - integer, the value of `strength` will be ignored. - 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. - denoising_start (`float`, *optional*): - When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be - bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and - it is assumed that the passed `image` is a partly denoised image. Note that when this is specified, - strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline - is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image - Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output). - denoising_end (`float`, *optional*): - When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be - completed before it is intentionally prematurely terminated. As a result, the returned sample will - still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be - denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the - final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline - forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image - Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output). - guidance_scale (`float`, *optional*, defaults to 7.5): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - 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`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - 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. - ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. - ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): - Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. - Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding - if `do_classifier_free_guidance` is set to `True`. - If not provided, embeddings are computed from the `ip_adapter_image` input argument. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` - input argument. - 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`, *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`. - 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. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. - `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as - explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position - `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting - `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - For most cases, `target_size` should be set to the desired height and width of the generated image. If - not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in - section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - aesthetic_score (`float`, *optional*, defaults to 6.0): - Used to simulate an aesthetic score of the generated image by influencing the positive text condition. - Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - negative_aesthetic_score (`float`, *optional*, defaults to 2.5): - Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to - simulate an aesthetic score of the generated image by influencing the negative text condition. - 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. - - Examples: - - Returns: - [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a - `tuple. `tuple. When returning a tuple, the first element is a list with the generated images. - """ - - 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 using `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 using `callback_on_step_end`", - ) - - controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet - - # align format for control guidance - if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): - control_guidance_start = len(control_guidance_end) * [control_guidance_start] - elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): - control_guidance_end = len(control_guidance_start) * [control_guidance_end] - elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): - mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1 - control_guidance_start, control_guidance_end = ( - mult * [control_guidance_start], - mult * [control_guidance_end], - ) - - # # 0.0 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 - - # 0.1 align format for control guidance - if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): - control_guidance_start = len(control_guidance_end) * [control_guidance_start] - elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): - control_guidance_end = len(control_guidance_start) * [control_guidance_end] - elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): - mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1 - control_guidance_start, control_guidance_end = ( - mult * [control_guidance_start], - mult * [control_guidance_end], - ) - - # 1. Check inputs - self.check_inputs( - prompt, - prompt_2, - None, - None, - callback_steps, - negative_prompt, - negative_prompt_2, - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ip_adapter_image, - ip_adapter_image_embeds, - callback_on_step_end_tensor_inputs, - - control_image, - controlnet_conditioning_scale, - control_guidance_start, - control_guidance_end, - - padding_mask_crop, - strength, - mask_image, - num_inference_steps - ) - - 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] - - device = self._execution_device - - if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): - controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) - - # 3. Encode input prompt - text_encoder_lora_scale = ( - self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None - ) - - ( - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ) = self.encode_prompt( - prompt=prompt, - prompt_2=prompt_2, - device=device, - num_images_per_prompt=num_images_per_prompt, - do_classifier_free_guidance=self.do_classifier_free_guidance, - negative_prompt=negative_prompt, - negative_prompt_2=negative_prompt_2, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - pooled_prompt_embeds=pooled_prompt_embeds, - negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, - lora_scale=text_encoder_lora_scale, - clip_skip=self.clip_skip, - ) - - # 3.1 Encode ip_adapter_image - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - image_embeds = self.prepare_ip_adapter_image_embeds( - ip_adapter_image, - ip_adapter_image_embeds, - device, - batch_size * num_images_per_prompt, - self.do_classifier_free_guidance, - ) - - # 4. set timesteps - def denoising_value_valid(dnv): - return isinstance(dnv, float) and 0 < dnv < 1 - - self.scheduler.set_timesteps(num_inference_steps, device=device) - timesteps, num_inference_steps = self.get_timesteps( - num_inference_steps, - strength, - device, - denoising_start=denoising_start if denoising_value_valid(denoising_start) else None, - ) - # check that number of inference steps is not < 1 - as this doesn't make sense - if num_inference_steps < 1: - raise ValueError( - f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline" - f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline." - ) - # at which timestep to set the initial noise (n.b. 50% if strength is 0.5) - latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) - # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise - is_strength_max = strength == 1.0 - self._num_timesteps = len(timesteps) - - # 5. Preprocess mask and image - resizes image and mask w.r.t height and width - # 5.1 Prepare init image - if padding_mask_crop is not None: - height, width = self.image_processor.get_default_height_width(image, height, width) - crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop) - resize_mode = "fill" - else: - crops_coords = None - resize_mode = "default" - - original_image = image - init_image = self.image_processor.preprocess( - image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode - ) - init_image = init_image.to(dtype=torch.float32) - - # 5.2 Prepare control images - if isinstance(controlnet, ControlNetModel): - control_image = self.prepare_control_image( - control_image=control_image, - width=width, - height=height, - batch_size=batch_size * num_images_per_prompt, - num_images_per_prompt=num_images_per_prompt, - device=device, - dtype=controlnet.dtype, - crops_coords=crops_coords, - resize_mode=resize_mode, - do_classifier_free_guidance=self.do_classifier_free_guidance, - guess_mode=guess_mode, - ) - elif isinstance(controlnet, MultiControlNetModel): - control_images = [] - - for control_image_ in control_image: - control_image_ = self.prepare_control_image( - control_image=control_image_, - width=width, - height=height, - batch_size=batch_size * num_images_per_prompt, - num_images_per_prompt=num_images_per_prompt, - device=device, - dtype=controlnet.dtype, - crops_coords=crops_coords, - resize_mode=resize_mode, - do_classifier_free_guidance=self.do_classifier_free_guidance, - guess_mode=guess_mode, - ) - - control_images.append(control_image_) - - control_image = control_images - else: - raise ValueError(f"{controlnet.__class__} is not supported.") - - # 5.3 Prepare mask - mask = self.mask_processor.preprocess( - mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords - ) - - masked_image = init_image * (mask < 0.5) - _, _, height, width = init_image.shape - - # 6. Prepare latent variables - num_channels_latents = self.vae.config.latent_channels - num_channels_unet = self.unet.config.in_channels - return_image_latents = num_channels_unet == 4 - - add_noise = True if denoising_start is None else False - latents_outputs = self.prepare_inpaint_latents( - batch_size * num_images_per_prompt, - num_channels_latents, - height, - width, - prompt_embeds.dtype, - device, - generator, - latents, - image=init_image, - timestep=latent_timestep, - is_strength_max=is_strength_max, - add_noise=add_noise, - return_noise=True, - return_image_latents=return_image_latents, - ) - - if return_image_latents: - latents, noise, image_latents = latents_outputs - else: - latents, noise = latents_outputs - - # 7. Prepare mask latent variables - mask, masked_image_latents = self.prepare_mask_latents( - mask, - masked_image, - batch_size * num_images_per_prompt, - height, - width, - prompt_embeds.dtype, - device, - generator, - self.do_classifier_free_guidance, - ) - - # 8. Check that sizes of mask, masked image and latents match - if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting - num_channels_mask = mask.shape[1] - num_channels_masked_image = masked_image_latents.shape[1] - if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: - raise ValueError( - f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" - f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" - f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" - f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of" - " `pipeline.unet` or your `mask_image` or `image` input." - ) - elif num_channels_unet != 4: - raise ValueError( - f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}." - ) - # 8.1 Prepare extra step kwargs. - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # 8.2 Create tensor stating which controlnets to keep - controlnet_keep = [] - for i in range(len(timesteps)): - keeps = [ - 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) - for s, e in zip(control_guidance_start, control_guidance_end) - ] - if isinstance(self.controlnet, MultiControlNetModel): - controlnet_keep.append(keeps) - else: - controlnet_keep.append(keeps[0]) - - # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline - height, width = latents.shape[-2:] - height = height * self.vae_scale_factor - width = width * self.vae_scale_factor - - original_size = original_size or (height, width) - target_size = target_size or (height, width) - - # 10. Prepare added time ids & embeddings - add_text_embeds = pooled_prompt_embeds - if self.text_encoder_2 is None: - text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) - else: - text_encoder_projection_dim = self.text_encoder_2.config.projection_dim - - add_time_ids, add_neg_time_ids = self._get_inpaint_add_time_ids( - original_size, - crops_coords_top_left, - target_size, - aesthetic_score, - negative_aesthetic_score, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1) - - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) - add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) - add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) - - prompt_embeds = prompt_embeds.to(device) - add_text_embeds = add_text_embeds.to(device) - add_time_ids = add_time_ids.to(device) - - # 11. Denoising loop - num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) - - if ( - denoising_end is not None - and denoising_start is not None - and denoising_value_valid(denoising_end) - and denoising_value_valid(denoising_start) - and denoising_start >= denoising_end - ): - raise ValueError( - f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: " - + f" {denoising_end} when using type float." - ) - elif denoising_end is not None and denoising_value_valid(denoising_end): - discrete_timestep_cutoff = int( - round( - self.scheduler.config.num_train_timesteps - - (denoising_end * self.scheduler.config.num_train_timesteps) - ) - ) - num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) - timesteps = timesteps[:num_inference_steps] - - 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 self.do_classifier_free_guidance else latents - - # concat latents, mask, masked_image_latents in the channel dimension - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} - - # controlnet(s) inference - if guess_mode and self.do_classifier_free_guidance: - # Infer ControlNet only for the conditional batch. - control_model_input = latents - control_model_input = self.scheduler.scale_model_input(control_model_input, t) - controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] - controlnet_added_cond_kwargs = { - "text_embeds": add_text_embeds.chunk(2)[1], - "time_ids": add_time_ids.chunk(2)[1], - } - else: - control_model_input = latent_model_input - controlnet_prompt_embeds = prompt_embeds - controlnet_added_cond_kwargs = added_cond_kwargs - - if isinstance(controlnet_keep[i], list): - cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] - else: - controlnet_cond_scale = controlnet_conditioning_scale - if isinstance(controlnet_cond_scale, list): - controlnet_cond_scale = controlnet_cond_scale[0] - cond_scale = controlnet_cond_scale * controlnet_keep[i] - - # # Resize control_image to match the size of the input to the controlnet - # if control_image.shape[-2:] != control_model_input.shape[-2:]: - # control_image = F.interpolate(control_image, size=control_model_input.shape[-2:], mode="bilinear", align_corners=False) - - down_block_res_samples, mid_block_res_sample = self.controlnet( - control_model_input, - t, - encoder_hidden_states=controlnet_prompt_embeds, - controlnet_cond=control_image, - conditioning_scale=cond_scale, - guess_mode=guess_mode, - added_cond_kwargs=controlnet_added_cond_kwargs, - return_dict=False, - ) - - if guess_mode and self.do_classifier_free_guidance: - # Infered ControlNet only for the conditional batch. - # To apply the output of ControlNet to both the unconditional and conditional batches, - # add 0 to the unconditional batch to keep it unchanged. - down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] - mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) - - if ip_adapter_image is not None: - added_cond_kwargs["image_embeds"] = image_embeds - - if num_channels_unet == 9: - latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) - - # predict the noise residual - noise_pred = self.unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - cross_attention_kwargs=self.cross_attention_kwargs, - down_block_additional_residuals=down_block_res_samples, - mid_block_additional_residual=mid_block_res_sample, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - if self.do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - - if self.do_classifier_free_guidance and guidance_rescale > 0.0: - # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - - if num_channels_unet == 4: - init_latents_proper = image_latents - if self.do_classifier_free_guidance: - init_mask, _ = mask.chunk(2) - else: - init_mask = mask - - if i < len(timesteps) - 1: - noise_timestep = timesteps[i + 1] - init_latents_proper = self.scheduler.add_noise( - init_latents_proper, noise, torch.tensor([noise_timestep]) - ) - - latents = (1 - init_mask) * init_latents_proper + init_mask * latents - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) - - latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) - - if gradio_progress is not None: - gradio_progress((i + 1) / int(self._num_timesteps), - "Brainstorming...", - total=int(self._num_timesteps)) - - # make sure the VAE is in float32 mode, as it overflows in float16 - if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: - self.upcast_vae() - latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) - - # If we do sequential model offloading, let's offload unet and controlnet - # manually for max memory savings - if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: - self.unet.to("cpu") - self.controlnet.to("cpu") - torch.cuda.empty_cache() - - if not output_type == "latent": - image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] - else: - return StableDiffusionXLPipelineOutput(images=latents) - - image = self.image_processor.postprocess(image, output_type=output_type) - - if padding_mask_crop is not None: - image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image] - else: - mask_image = to_pil_image(mask_image[0].cpu()) - original_image = to_pil_image(original_image[0].cpu()) - image = [self.image_processor.apply_overlay(mask_image, original_image, i) for i in image] - - # Offload all models - self.maybe_free_model_hooks() - - if not return_dict: - return image - - return StableDiffusionXLPipelineOutput(images=image) - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def inpaint( - self, - prompt: Union[str, List[str]] = None, - prompt_2: Optional[Union[str, List[str]]] = None, - image: PipelineImageInput = None, - mask_image: PipelineImageInput = None, - masked_image_latents: torch.FloatTensor = None, - height: Optional[int] = None, - width: Optional[int] = None, - padding_mask_crop: Optional[int] = None, - strength: float = 0.9999, - num_inference_steps: int = 50, - timesteps: List[int] = None, - denoising_start: Optional[float] = None, - denoising_end: Optional[float] = None, - guidance_scale: float = 7.5, - negative_prompt: Optional[Union[str, List[str]]] = None, - negative_prompt_2: 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, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - ip_adapter_image: Optional[PipelineImageInput] = None, - ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - guidance_rescale: float = 0.0, - original_size: Tuple[int, int] = None, - crops_coords_top_left: Tuple[int, int] = (0, 0), - target_size: Tuple[int, int] = None, - negative_original_size: Optional[Tuple[int, int]] = None, - negative_crops_coords_top_left: Tuple[int, int] = (0, 0), - negative_target_size: Optional[Tuple[int, int]] = None, - aesthetic_score: float = 6.0, - negative_aesthetic_score: float = 2.5, - 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"], - gradio_progress=None, - **kwargs, - ): - r""" - 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. - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - image (`PIL.Image.Image`): - `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will - be masked out with `mask_image` and repainted according to `prompt`. - mask_image (`PIL.Image.Image`): - `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be - repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted - to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) - instead of 3, so the expected shape would be `(B, H, W, 1)`. - height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The height in pixels of the generated image. This is set to 1024 by default for the best results. - Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The width in pixels of the generated image. This is set to 1024 by default for the best results. - Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - padding_mask_crop (`int`, *optional*, defaults to `None`): - The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If - `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and - contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on - the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large - and contain information inreleant for inpainging, such as background. - strength (`float`, *optional*, defaults to 0.9999): - Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be - between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the - `strength`. The number of denoising steps depends on the amount of noise initially added. When - `strength` is 1, added noise will be maximum and the denoising process will run for the full number of - iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked - portion of the reference `image`. Note that in the case of `denoising_start` being declared as an - integer, the value of `strength` will be ignored. - 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. - timesteps (`List[int]`, *optional*): - Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument - in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is - passed will be used. Must be in descending order. - denoising_start (`float`, *optional*): - When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be - bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and - it is assumed that the passed `image` is a partly denoised image. Note that when this is specified, - strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline - is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image - Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output). - denoising_end (`float`, *optional*): - When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be - completed before it is intentionally prematurely terminated. As a result, the returned sample will - still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be - denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the - final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline - forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image - Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output). - guidance_scale (`float`, *optional*, defaults to 7.5): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - 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`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - 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. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` - input argument. - ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. - ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): - Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. - Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding - if `do_classifier_free_guidance` is set to `True`. - If not provided, embeddings are computed from the `ip_adapter_image` input argument. - 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`, *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`. - 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. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. - `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as - explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position - `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting - `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - For most cases, `target_size` should be set to the desired height and width of the generated image. If - not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in - section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a specific image resolution. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a target image resolution. It should be as same - as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - aesthetic_score (`float`, *optional*, defaults to 6.0): - Used to simulate an aesthetic score of the generated image by influencing the positive text condition. - Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - negative_aesthetic_score (`float`, *optional*, defaults to 2.5): - Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to - simulate an aesthetic score of the generated image by influencing the negative text condition. - 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 pipeline class. - - Examples: - - Returns: - [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a - `tuple. `tuple. When returning a tuple, the first element is a list with the generated images. - """ - - 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 - self.check_inputs( - prompt, - prompt_2, - height, - width, - callback_steps, - negative_prompt, - negative_prompt_2, - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ip_adapter_image, - ip_adapter_image_embeds, - callback_on_step_end_tensor_inputs, - - None, - None, - None, - None, - padding_mask_crop, - - strength, - mask_image, - None - ) - - self._guidance_scale = guidance_scale - self._guidance_rescale = guidance_rescale - self._clip_skip = clip_skip - self._cross_attention_kwargs = cross_attention_kwargs - self._denoising_end = denoising_end - self._denoising_start = denoising_start - self._interrupt = False - - # 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 - - # 3. Encode input prompt - text_encoder_lora_scale = ( - self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None - ) - - ( - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ) = self.encode_prompt( - prompt=prompt, - prompt_2=prompt_2, - device=device, - num_images_per_prompt=num_images_per_prompt, - do_classifier_free_guidance=self.do_classifier_free_guidance, - negative_prompt=negative_prompt, - negative_prompt_2=negative_prompt_2, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - pooled_prompt_embeds=pooled_prompt_embeds, - negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, - lora_scale=text_encoder_lora_scale, - clip_skip=self.clip_skip, - ) - - # 4. set timesteps - def denoising_value_valid(dnv): - return isinstance(dnv, float) and 0 < dnv < 1 - - timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) - timesteps, num_inference_steps = self.get_timesteps( - num_inference_steps, - strength, - device, - denoising_start=denoising_start if denoising_value_valid(denoising_start) else None, - ) - # check that number of inference steps is not < 1 - as this doesn't make sense - if num_inference_steps < 1: - raise ValueError( - f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline" - f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline." - ) - # at which timestep to set the initial noise (n.b. 50% if strength is 0.5) - latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) - # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise - is_strength_max = strength == 1.0 - - # 5. Preprocess mask and image - if padding_mask_crop is not None: - crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop) - resize_mode = "fill" - else: - crops_coords = None - resize_mode = "default" - - original_image = image - init_image = self.image_processor.preprocess( - image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode - ) - init_image = init_image.to(dtype=torch.float32) - - mask = self.mask_processor.preprocess( - mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords - ) - - if masked_image_latents is not None: - masked_image = masked_image_latents - elif init_image.shape[1] == 4: - # if images are in latent space, we can't mask it - masked_image = None - else: - masked_image = init_image * (mask < 0.5) - - # 6. Prepare latent variables - num_channels_latents = self.vae.config.latent_channels - num_channels_unet = self.unet.config.in_channels - return_image_latents = num_channels_unet == 4 - - add_noise = True if denoising_start is None else False - latents_outputs = self.prepare_inpaint_latents( - batch_size * num_images_per_prompt, - num_channels_latents, - height, - width, - prompt_embeds.dtype, - device, - generator, - latents, - image=init_image, - timestep=latent_timestep, - is_strength_max=is_strength_max, - add_noise=add_noise, - return_noise=True, - return_image_latents=return_image_latents, - ) - - if return_image_latents: - latents, noise, image_latents = latents_outputs - else: - latents, noise = latents_outputs - - # 7. Prepare mask latent variables - mask, masked_image_latents = self.prepare_mask_latents( - mask, - masked_image, - batch_size * num_images_per_prompt, - height, - width, - prompt_embeds.dtype, - device, - generator, - self.do_classifier_free_guidance, - ) - - # 8. Check that sizes of mask, masked image and latents match - if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting - num_channels_mask = mask.shape[1] - num_channels_masked_image = masked_image_latents.shape[1] - if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: - raise ValueError( - f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" - f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" - f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" - f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of" - " `pipeline.unet` or your `mask_image` or `image` input." - ) - elif num_channels_unet != 4: - raise ValueError( - f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}." - ) - # 8.1 Prepare extra step kwargs. - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline - height, width = latents.shape[-2:] - height = height * self.vae_scale_factor - width = width * self.vae_scale_factor - - original_size = original_size or (height, width) - target_size = target_size or (height, width) - - # 10. Prepare added time ids & embeddings - if negative_original_size is None: - negative_original_size = original_size - if negative_target_size is None: - negative_target_size = target_size - - add_text_embeds = pooled_prompt_embeds - if self.text_encoder_2 is None: - text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) - else: - text_encoder_projection_dim = self.text_encoder_2.config.projection_dim - - add_time_ids, add_neg_time_ids = self._get_inpaint_add_time_ids( - original_size, - crops_coords_top_left, - target_size, - aesthetic_score, - negative_aesthetic_score, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1) - - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) - add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) - add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) - - prompt_embeds = prompt_embeds.to(device) - add_text_embeds = add_text_embeds.to(device) - add_time_ids = add_time_ids.to(device) - - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - image_embeds = self.prepare_ip_adapter_image_embeds( - ip_adapter_image, - ip_adapter_image_embeds, - device, - batch_size * num_images_per_prompt, - self.do_classifier_free_guidance, - ) - - # 11. Denoising loop - num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) - - if ( - self.denoising_end is not None - and self.denoising_start is not None - and denoising_value_valid(self.denoising_end) - and denoising_value_valid(self.denoising_start) - and self.denoising_start >= self.denoising_end - ): - raise ValueError( - f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: " - + f" {self.denoising_end} when using type float." - ) - elif self.denoising_end is not None and denoising_value_valid(self.denoising_end): - discrete_timestep_cutoff = int( - round( - self.scheduler.config.num_train_timesteps - - (self.denoising_end * self.scheduler.config.num_train_timesteps) - ) - ) - num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) - timesteps = timesteps[:num_inference_steps] - - # 11.1 Optionally get Guidance Scale Embedding - timestep_cond = None - if self.unet.config.time_cond_proj_dim is not None: - guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) - timestep_cond = self.get_guidance_scale_embedding( - guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim - ).to(device=device, dtype=latents.dtype) - - self._num_timesteps = len(timesteps) - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - if self.interrupt: - continue - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents - - # concat latents, mask, masked_image_latents in the channel dimension - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - if num_channels_unet == 9: - latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) - - # predict the noise residual - added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} - if ip_adapter_image is not None or ip_adapter_image_embeds is not None: - added_cond_kwargs["image_embeds"] = image_embeds - noise_pred = self.unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - timestep_cond=timestep_cond, - cross_attention_kwargs=self.cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - if self.do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) - - if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: - # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - - if num_channels_unet == 4: - init_latents_proper = image_latents - if self.do_classifier_free_guidance: - init_mask, _ = mask.chunk(2) - else: - init_mask = mask - - if i < len(timesteps) - 1: - noise_timestep = timesteps[i + 1] - init_latents_proper = self.scheduler.add_noise( - init_latents_proper, noise, torch.tensor([noise_timestep]) - ) - - latents = (1 - init_mask) * init_latents_proper + init_mask * latents - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) - - latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) - add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) - negative_pooled_prompt_embeds = callback_outputs.pop( - "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds - ) - add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) - add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids) - mask = callback_outputs.pop("mask", mask) - masked_image_latents = callback_outputs.pop("masked_image_latents", masked_image_latents) - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) - - if gradio_progress is not None: - gradio_progress((i + 1) / int(self._num_timesteps), - "Brainstorming...", - total=int(self._num_timesteps)) - - if XLA_AVAILABLE: - xm.mark_step() - - if not output_type == "latent": - # make sure the VAE is in float32 mode, as it overflows in float16 - needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast - - if needs_upcasting: - self.upcast_vae() - latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) - - # unscale/denormalize the latents - # denormalize with the mean and std if available and not None - has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None - has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None - if has_latents_mean and has_latents_std: - latents_mean = ( - torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents_std = ( - torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) - ) - latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean - else: - latents = latents / self.vae.config.scaling_factor - - image = self.vae.decode(latents, return_dict=False)[0] - - # cast back to fp16 if needed - if needs_upcasting: - self.vae.to(dtype=torch.float16) - else: - return StableDiffusionXLPipelineOutput(images=latents) - - image = self.image_processor.postprocess(image, output_type=output_type) - - if padding_mask_crop is not None: - image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image] - else: - mask_image = to_pil_image(mask_image[0].cpu()) - original_image = to_pil_image(original_image[0].cpu()) - image = [self.image_processor.apply_overlay(mask_image, original_image, i) for i in image] - - # Offload all models - self.maybe_free_model_hooks() - - if not return_dict: - return image - - return StableDiffusionXLPipelineOutput(images=image)