# coding=utf-8 # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import time from dataclasses import dataclass from math import ceil from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL import torch from diffusers.image_processor import PipelineImageInput from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline, StableDiffusionSafetyChecker from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import BaseOutput, deprecate from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from optimum.utils import logging from ....transformers.gaudi_configuration import GaudiConfig from ....utils import HabanaProfile, speed_metrics, warmup_inference_steps_time_adjustment from ...models.unet_2d_condition import set_default_attn_processor_hpu from ..pipeline_utils import GaudiDiffusionPipeline logger = logging.get_logger(__name__) @dataclass class GaudiStableDiffusionPipelineOutput(BaseOutput): images: Union[List[PIL.Image.Image], np.ndarray] nsfw_content_detected: Optional[List[bool]] throughput: float 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="cpu", **kwargs) timesteps = scheduler.timesteps.to(device) num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device="cpu", **kwargs) timesteps = scheduler.timesteps.to(device) # Handles the case where the scheduler cannot implement reset_timestep_dependent_params() # Example: UniPCMultiStepScheduler used for inference in ControlNet training as it has non-linear accesses to timestep dependent parameter: sigma. if hasattr(scheduler, "reset_timestep_dependent_params") and callable(scheduler.reset_timestep_dependent_params): scheduler.reset_timestep_dependent_params() return timesteps, num_inference_steps class GaudiStableDiffusionPipeline(GaudiDiffusionPipeline, StableDiffusionPipeline): """ Adapted from: https://github.com/huggingface/diffusers/blob/v0.23.1/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L73 - Generation is performed by batches - Two `mark_step()` were added to add support for lazy mode - Added support for HPU graphs Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder ([`~transformers.CLIPTextModel`]): Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). tokenizer (`~transformers.CLIPTokenizer`): A `CLIPTokenizer` to tokenize text. unet ([`UNet2DConditionModel`]): A `UNet2DConditionModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the [model card](https://huggingface.co/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. """ def __init__( self, vae: AutoencoderKL, 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 = False, use_hpu_graphs: bool = False, 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, ) # Workaround for Synapse 1.11 for full bf16 if bf16_full_eval: unet.conv_in.float() StableDiffusionPipeline.__init__( self, vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, image_encoder, requires_safety_checker, ) self.unet.set_default_attn_processor = set_default_attn_processor_hpu self.to(self._device) def prepare_latents(self, num_images, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (num_images, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != num_images: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective number" f" of images of {num_images}. Make sure the number of images matches the length of the generators." ) if latents is None: # torch.randn is broken on HPU so running it on CPU rand_device = "cpu" if device.type == "hpu" else device if isinstance(generator, list): shape = (1,) + shape[1:] latents = [ torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) for i in range(num_images) ] latents = torch.cat(latents, dim=0).to(device) else: latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") 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 @classmethod def _split_inputs_into_batches(cls, batch_size, latents, prompt_embeds, negative_prompt_embeds): # 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)) # 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) # 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) return latents_batches, prompt_embeds_batches, num_dummy_samples @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, 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, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, 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"], profiling_warmup_steps: Optional[int] = 0, profiling_steps: Optional[int] = 0, **kwargs, ): r""" The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated images. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated images. 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. 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 [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] 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). guidance_rescale (`float`, *optional*, defaults to 0.0): Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when using zero terminal SNR. 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. profiling_warmup_steps (`int`, *optional*): Number of steps to ignore for profling. profiling_steps (`int`, *optional*): Number of steps to be captured when enabling profiling. Returns: [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~diffusers.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 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`", ) 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 # to deal with lora scaling and other possible forward hooks # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_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._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 lora_scale = ( self.cross_attention_kwargs.get("scale", None) if self.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=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. 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( num_prompts * 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) # 6.1 Add image embeds for IP-Adapter added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None # 6.2 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 * 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. Split into batches (HPU-specific step) latents_batches, text_embeddings_batches, num_dummy_samples = self._split_inputs_into_batches( batch_size, latents, prompt_embeds, negative_prompt_embeds, ) outputs = { "images": [], "has_nsfw_concept": [], } t0 = time.time() t1 = t0 self._num_timesteps = len(timesteps) hb_profiler = HabanaProfile( warmup=profiling_warmup_steps, active=profiling_steps, record_shapes=False, ) hb_profiler.start() # 8. Denoising loop 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 ) 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) text_embeddings_batch = text_embeddings_batches[0] text_embeddings_batches = torch.roll(text_embeddings_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 ) latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep) # predict the noise residual noise_pred = self.unet_hpu( latent_model_input, timestep, text_embeddings_batch, timestep_cond, self.cross_attention_kwargs, added_cond_kwargs, ) # 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_batch = self.scheduler.step( noise_pred, timestep, latents_batch, **extra_step_kwargs, return_dict=False )[0] if not self.use_hpu_graphs: self.htcore.mark_step() 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, timestep, callback_kwargs) latents_batch = callback_outputs.pop("latents", latents_batch) text_embeddings_batch = callback_outputs.pop("prompt_embeds", text_embeddings_batch) # negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) # call the callback, if provided if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, timestep, latents_batch) hb_profiler.step() 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": # 8. Post-processing image = self.vae.decode( latents_batch / self.vae.config.scaling_factor, return_dict=False, generator=generator )[0] else: image = latents_batch outputs["images"].append(image) if not self.use_hpu_graphs: self.htcore.mark_step() hb_profiler.stop() speed_metrics_prefix = "generation" if t1 == t0 or use_warmup_inference_steps: num_samples = num_batches * batch_size num_steps = num_inference_steps * num_batches * batch_size else: num_samples = (num_batches - throughput_warmup_steps) * batch_size num_steps = (num_batches - throughput_warmup_steps) * num_inference_steps * batch_size speed_measures = speed_metrics( split=speed_metrics_prefix, start_time=t0, num_samples=num_samples, num_steps=num_steps, start_time_after_warmup=t1, ) logger.info(f"Speed metrics: {speed_measures}") # Remove dummy generations if needed if num_dummy_samples > 0: outputs["images"][-1] = outputs["images"][-1][:-num_dummy_samples] # 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 output_type == "pil" and isinstance(image, list): outputs["images"] += image elif output_type in ["np", "numpy"] and isinstance(image, np.ndarray): if len(outputs["images"]) == 0: outputs["images"] = image else: outputs["images"] = np.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, ): 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