# 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 time from math import ceil from typing import Any, Callable, Dict, List, Optional, Union import numpy import torch from diffusers.image_processor import PipelineImageInput from diffusers.models import AsymmetricAutoencoderKL, AutoencoderKL, UNet2DConditionModel from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline, StableDiffusionSafetyChecker from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import retrieve_timesteps from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import deprecate, logging from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from ....transformers.gaudi_configuration import GaudiConfig from ....utils import speed_metrics, warmup_inference_steps_time_adjustment from ..pipeline_utils import GaudiDiffusionPipeline from .pipeline_stable_diffusion import GaudiStableDiffusionPipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name class GaudiStableDiffusionInpaintPipeline(GaudiDiffusionPipeline, StableDiffusionInpaintPipeline): r""" Adapted from: https://github.com/huggingface/diffusers/blob/v0.26.3/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py#L222 - Two `mark_step()` were added to add support for lazy mode - Added support for HPU graphs Pipeline for text-guided image inpainting using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files Args: vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). tokenizer ([`~transformers.CLIPTokenizer`]): A `CLIPTokenizer` to tokenize text. unet ([`UNet2DConditionModel`]): A `UNet2DConditionModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. use_habana (bool, defaults to `False`): Whether to use Gaudi (`True`) or CPU (`False`). use_hpu_graphs (bool, defaults to `False`): Whether to use HPU graphs or not. gaudi_config (Union[str, [`GaudiConfig`]], defaults to `None`): Gaudi configuration to use. Can be a string to download it from the Hub. Or a previously initialized config can be passed. bf16_full_eval (bool, defaults to `False`): Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory compared to fp32/mixed precision but can harm generated images. sdp_on_bf16 (bool, defaults to `False`): Whether to allow PyTorch to use reduced precision in the SDPA math backend. """ _callback_tensor_inputs = ["latents", "prompt_embeds", "mask", "masked_image_latents"] def __init__( self, vae: Union[AutoencoderKL, AsymmetricAutoencoderKL], text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection = None, requires_safety_checker: bool = True, use_habana: bool = True, use_hpu_graphs: bool = True, gaudi_config: Union[str, GaudiConfig] = None, bf16_full_eval: bool = False, sdp_on_bf16: bool = False, ): GaudiDiffusionPipeline.__init__( self, use_habana, use_hpu_graphs, gaudi_config, bf16_full_eval, sdp_on_bf16, ) StableDiffusionInpaintPipeline.__init__( self, vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, image_encoder, requires_safety_checker, ) self.to(self._device) @classmethod def _split_inputs_into_batches( cls, batch_size, latents, prompt_embeds, negative_prompt_embeds, mask, masked_image_latents ): # Use torch.split to generate num_batches batches of size batch_size latents_batches = list(torch.split(latents, batch_size)) prompt_embeds_batches = list(torch.split(prompt_embeds, batch_size)) if negative_prompt_embeds is not None: negative_prompt_embeds_batches = list(torch.split(negative_prompt_embeds, batch_size)) mask_batches = list(torch.split(mask, batch_size)) masked_image_latents_batches = list(torch.split(masked_image_latents, batch_size)) # If the last batch has less samples than batch_size, pad it with dummy samples num_dummy_samples = 0 if latents_batches[-1].shape[0] < batch_size: num_dummy_samples = batch_size - latents_batches[-1].shape[0] # Pad latents_batches sequence_to_stack = (latents_batches[-1],) + tuple( torch.zeros_like(latents_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) latents_batches[-1] = torch.vstack(sequence_to_stack) # Pad prompt_embeds_batches sequence_to_stack = (prompt_embeds_batches[-1],) + tuple( torch.zeros_like(prompt_embeds_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack) # Pad negative_prompt_embeds_batches if necessary if negative_prompt_embeds is not None: sequence_to_stack = (negative_prompt_embeds_batches[-1],) + tuple( torch.zeros_like(negative_prompt_embeds_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) negative_prompt_embeds_batches[-1] = torch.vstack(sequence_to_stack) if mask_batches[-1].shape[0] < batch_size: num_dummy_samples = batch_size - mask_batches[-1].shape[0] # Pad mask_batches sequence_to_stack = (mask_batches[-1],) + tuple( torch.zeros_like(mask_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) mask_batches[-1] = torch.vstack(sequence_to_stack) if masked_image_latents_batches[-1].shape[0] < batch_size: num_dummy_samples = batch_size - masked_image_latents_batches[-1].shape[0] # Pad masked_image_latents_batches sequence_to_stack = (masked_image_latents_batches[-1],) + tuple( torch.zeros_like(masked_image_latents_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) masked_image_latents_batches[-1] = torch.vstack(sequence_to_stack) # Stack batches in the same tensor latents_batches = torch.stack(latents_batches) if negative_prompt_embeds is not None: # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes for i, (negative_prompt_embeds_batch, prompt_embeds_batch) in enumerate( zip(negative_prompt_embeds_batches, prompt_embeds_batches[:]) ): prompt_embeds_batches[i] = torch.cat([negative_prompt_embeds_batch, prompt_embeds_batch]) prompt_embeds_batches = torch.stack(prompt_embeds_batches) mask_batches = torch.stack(mask_batches) masked_image_latents_batches = torch.stack(masked_image_latents_batches) return latents_batches, prompt_embeds_batches, num_dummy_samples, mask_batches, masked_image_latents_batches @torch.no_grad() def __call__( self, prompt: 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 = 1.0, num_inference_steps: int = 50, timesteps: List[int] = None, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, batch_size: 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, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, clip_skip: int = None, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], **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`. image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image`, numpy array or tensor representing an image batch to be inpainted (which parts of the image to be masked out with `mask_image` and repainted according to `prompt`). For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but if passing latents directly it is not encoded again. mask_image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B, H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W, 1)`, or `(H, W)`. 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 irrelevant for inpainting, such as background. strength (`float`, *optional*, defaults to 1.0): Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `image`. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. This parameter is modulated by `strength`. 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. guidance_scale (`float`, *optional*, defaults to 7.5): A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. batch_size (`int`, *optional*, defaults to 1): The number of images in a batch. 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. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. Examples: ```py >>> import PIL >>> import requests >>> import torch >>> from io import BytesIO >>> from diffusers import StableDiffusionInpaintPipeline >>> def download_image(url): ... response = requests.get(url) ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" >>> init_image = download_image(img_url).resize((512, 512)) >>> mask_image = download_image(mask_url).resize((512, 512)) >>> pipe = StableDiffusionInpaintPipeline.from_pretrained( ... "stabilityai/stable-diffusion-2-inpainting", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> prompt = "Face of a yellow cat, high resolution, sitting on a park bench" >>> image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0] ``` Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images and the second element is a list of `bool`s indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. """ callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) if callback is not None: deprecate( "callback", "1.0.0", "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", ) with torch.autocast(device_type="hpu", dtype=torch.bfloat16, enabled=self.gaudi_config.use_torch_autocast): # 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, image, mask_image, height, width, strength, callback_steps, output_type, negative_prompt, prompt_embeds, negative_prompt_embeds, callback_on_step_end_tensor_inputs, padding_mask_crop, ) self._guidance_scale = guidance_scale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs self._interrupt = False # 2. Define call parameters if prompt is not None and isinstance(prompt, str): num_prompts = 1 elif prompt is not None and isinstance(prompt, list): num_prompts = len(prompt) else: num_prompts = prompt_embeds.shape[0] num_batches = ceil((num_images_per_prompt * num_prompts) / batch_size) logger.info( f"{num_prompts} prompt(s) received, {num_images_per_prompt} generation(s) per prompt," f" {batch_size} sample(s) per batch, {num_batches} total batch(es)." ) if num_batches < 3: logger.warning("The first two iterations are slower so it is recommended to feed more batches.") device = self._execution_device # 3. Encode input prompt text_encoder_lora_scale = ( cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None ) prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, device, num_images_per_prompt, self.do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=self.clip_skip, ) if ip_adapter_image is not None: image_embeds = self.prepare_ip_adapter_image_embeds( ip_adapter_image, device, num_prompts * num_images_per_prompt, ) # 4. set timesteps timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) timesteps, num_inference_steps = self.get_timesteps( num_inference_steps=num_inference_steps, strength=strength, device=device ) # 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(num_prompts * 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) # 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 latents_outputs = self.prepare_latents( num_prompts * 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, 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_condition = self.mask_processor.preprocess( mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords ) if masked_image_latents is None: masked_image = init_image * (mask_condition < 0.5) else: masked_image = masked_image_latents mask, masked_image_latents = self.prepare_mask_latents( mask_condition, masked_image, num_prompts * 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}." ) # 9. 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) # 9.1 Add image embeds for IP-Adapter added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None # 9.2 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) 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) # 10. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order throughput_warmup_steps = kwargs.get("throughput_warmup_steps", 3) use_warmup_inference_steps = ( num_batches <= throughput_warmup_steps and num_inference_steps > throughput_warmup_steps ) self._num_timesteps = len(timesteps) # 11. Split into batches (HPU-specific step) latents_batches, prompt_embeds_batches, num_dummy_samples, mask_batches, masked_image_latents_batches = ( self._split_inputs_into_batches( batch_size, latents, prompt_embeds, negative_prompt_embeds, mask, masked_image_latents, ) ) outputs = { "images": [], "has_nsfw_concept": [], } t0 = time.time() t1 = t0 for j in self.progress_bar(range(num_batches)): # The throughput is calculated from the 3rd iteration # because compilation occurs in the first two iterations if j == throughput_warmup_steps: t1 = time.time() if use_warmup_inference_steps: t0_inf = time.time() latents_batch = latents_batches[0] latents_batches = torch.roll(latents_batches, shifts=-1, dims=0) prompt_embeds_batch = prompt_embeds_batches[0] prompt_embeds_batches = torch.roll(prompt_embeds_batches, shifts=-1, dims=0) mask_batch = mask_batches[0] mask_batches = torch.roll(mask_batches, shifts=-1, dims=0) masked_image_latents_batch = masked_image_latents_batches[0] masked_image_latents_batches = torch.roll(masked_image_latents_batches, shifts=-1, dims=0) for i in range(len(timesteps)): if use_warmup_inference_steps and i == throughput_warmup_steps: t1_inf = time.time() t1 += t1_inf - t0_inf if self.interrupt: continue timestep = timesteps[0] timesteps = torch.roll(timesteps, shifts=-1, dims=0) # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([latents_batch] * 2) if self.do_classifier_free_guidance else latents_batch ) mask_batch_input = torch.cat([mask_batch] * 2) if self.do_classifier_free_guidance else mask_batch masked_image_latents_batch_input = ( torch.cat([masked_image_latents_batch] * 2) if self.do_classifier_free_guidance else masked_image_latents_batch ) # concat latents, mask, masked_image_latents in the channel dimension latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep) if num_channels_unet == 9: latent_model_input = torch.cat( [latent_model_input, mask_batch_input, masked_image_latents_batch_input], dim=1 ) # predict the noise residual noise_pred = self.unet_hpu( latent_model_input, timestep, encoder_hidden_states=prompt_embeds_batch, timestep_cond=timestep_cond, cross_attention_kwargs=self.cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, ) noise_pred.to(torch.float) # 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) # compute the previous noisy sample x_t -> x_t-1 latents_batch = self.scheduler.step( noise_pred, timestep, latents_batch, **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_batch.chunk(2) else: init_mask = mask_batch if i < len(timesteps) - 1: noise_timestep = timesteps[1] init_latents_proper = self.scheduler.add_noise( init_latents_proper, noise, torch.tensor([noise_timestep]) ) latents_batch = (1 - init_mask) * init_latents_proper + init_mask * latents_batch if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: k_batch = k + "_batch" callback_kwargs[k] = locals()[k_batch] callback_outputs = callback_on_step_end(self, i, timestep, callback_kwargs) latents_batch = callback_outputs.pop("latents", latents_batch) prompt_embeds_batch = callback_outputs.pop("prompt_embeds", prompt_embeds_batch) mask_batch = callback_outputs.pop("mask", mask_batch) masked_image_latents_batch = callback_outputs.pop( "masked_image_latents", masked_image_latents_batch ) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, timestep, latents_batch) if use_warmup_inference_steps: t1 = warmup_inference_steps_time_adjustment( t1, t1_inf, num_inference_steps, throughput_warmup_steps ) if not output_type == "latent": condition_kwargs = {} if isinstance(self.vae, AsymmetricAutoencoderKL): init_image = init_image.to(device=device, dtype=masked_image_latents.dtype) init_image_condition = init_image.clone() init_image = self._encode_vae_image(init_image, generator=generator) mask_condition = mask_condition.to(device=device, dtype=masked_image_latents.dtype) condition_kwargs = {"image": init_image_condition, "mask": mask_condition} image = self.vae.decode( latents_batch / self.vae.config.scaling_factor, return_dict=False, generator=generator, **condition_kwargs, )[0] else: image = latents_batch outputs["images"].append(image) if not self.use_hpu_graphs: self.htcore.mark_step() # Remove dummy generations if needed if num_dummy_samples > 0: outputs["images"][-1] = outputs["images"][-1][:-num_dummy_samples] speed_metrics_prefix = "inpainting" speed_measures = speed_metrics( split=speed_metrics_prefix, start_time=t0, num_samples=num_batches * batch_size if t1 == t0 or use_warmup_inference_steps else (num_batches - throughput_warmup_steps) * batch_size, num_steps=num_batches * batch_size * num_inference_steps, start_time_after_warmup=t1, ) logger.info(f"Speed metrics: {speed_measures}") # Process generated images for i, image in enumerate(outputs["images"][:]): if i == 0: outputs["images"].clear() if output_type == "latent": has_nsfw_concept = None else: image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if padding_mask_crop is not None: image = [ self.image_processor.apply_overlay(mask_image, original_image, j, crops_coords) for j in image ] if output_type == "pil" and isinstance(image, list): outputs["images"] += image elif output_type in ["np", "numpy"] and isinstance(image, numpy.ndarray): if len(outputs["images"]) == 0: outputs["images"] = image else: outputs["images"] = numpy.concatenate((outputs["images"], image), axis=0) else: if len(outputs["images"]) == 0: outputs["images"] = image else: outputs["images"] = torch.cat((outputs["images"], image), 0) if has_nsfw_concept is not None: outputs["has_nsfw_concept"] += has_nsfw_concept else: outputs["has_nsfw_concept"] = None # Offload all models self.maybe_free_model_hooks() if not return_dict: return (outputs["images"], outputs["has_nsfw_concept"]) return GaudiStableDiffusionPipelineOutput( images=outputs["images"], nsfw_content_detected=outputs["has_nsfw_concept"], throughput=speed_measures[f"{speed_metrics_prefix}_samples_per_second"], ) @torch.no_grad() def unet_hpu( self, latent_model_input, timestep, encoder_hidden_states, timestep_cond, cross_attention_kwargs, added_cond_kwargs, return_dict=False, ): if self.use_hpu_graphs: return self.capture_replay(latent_model_input, timestep, encoder_hidden_states) else: return self.unet( latent_model_input, timestep, encoder_hidden_states=encoder_hidden_states, timestep_cond=timestep_cond, cross_attention_kwargs=cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] @torch.no_grad() def capture_replay(self, latent_model_input, timestep, encoder_hidden_states): inputs = [latent_model_input, timestep, encoder_hidden_states, False] h = self.ht.hpu.graphs.input_hash(inputs) cached = self.cache.get(h) if cached is None: # Capture the graph and cache it with self.ht.hpu.stream(self.hpu_stream): graph = self.ht.hpu.HPUGraph() graph.capture_begin() outputs = self.unet(inputs[0], inputs[1], inputs[2], inputs[3])[0] graph.capture_end() graph_inputs = inputs graph_outputs = outputs self.cache[h] = self.ht.hpu.graphs.CachedParams(graph_inputs, graph_outputs, graph) return outputs # Replay the cached graph with updated inputs self.ht.hpu.graphs.copy_to(cached.graph_inputs, inputs) cached.graph.replay() self.ht.core.hpu.default_stream().synchronize() return cached.graph_outputs