# 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 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.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipelines import StableDiffusionUpscalePipeline from diffusers.schedulers import DDPMScheduler, KarrasDiffusionSchedulers from diffusers.utils import BaseOutput from diffusers.utils.torch_utils import randn_tensor from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from optimum.utils import logging from ....transformers.gaudi_configuration import GaudiConfig from ....utils import speed_metrics, warmup_inference_steps_time_adjustment from ..pipeline_utils import GaudiDiffusionPipeline logger = logging.get_logger(__name__) PipelineImageInput = Union[ PIL.Image.Image, np.ndarray, torch.FloatTensor, List[PIL.Image.Image], List[np.ndarray], List[torch.FloatTensor] ] @dataclass class GaudiStableDiffusionPipelineOutput(BaseOutput): images: Union[List[PIL.Image.Image], np.ndarray] nsfw_content_detected: Optional[List[bool]] throughput: float class GaudiStableDiffusionUpscalePipeline(GaudiDiffusionPipeline, StableDiffusionUpscalePipeline): """ Pipeline for text-guided image super-resolution using Stable Diffusion 2. Adapted from: https://github.com/huggingface/diffusers/blob/v0.23.1/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py#L70 - 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 ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion 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. tokenizer (`CLIPTokenizer`): 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. low_res_scheduler ([`SchedulerMixin`]): A scheduler used to add initial noise to the low resolution conditioning image. It must be an instance of [`DDPMScheduler`]. 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 details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for 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, low_res_scheduler: DDPMScheduler, scheduler: KarrasDiffusionSchedulers, safety_checker: Optional[Any] = None, feature_extractor: Optional[CLIPImageProcessor] = None, watermarker: Optional[Any] = None, max_noise_level: int = 350, 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() StableDiffusionUpscalePipeline.__init__( self, vae, text_encoder, tokenizer, unet, low_res_scheduler, scheduler, safety_checker, feature_extractor, watermarker, max_noise_level, ) self.to(self._device) def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height, width) 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(batch_size) ] 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, text_embeddings, uncond_embeddings, image, noise_level): # Use torch.split to generate num_batches batches of size batch_size latents_batches = list(torch.split(latents, batch_size)) text_embeddings_batches = list(torch.split(text_embeddings, batch_size)) image_batches = list(torch.split(image, batch_size)) noise_level_batches = list(torch.split(noise_level.view(-1, 1), batch_size)) if uncond_embeddings is not None: uncond_embeddings_batches = list(torch.split(uncond_embeddings, 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 image_batches sequence_to_stack = (image_batches[-1],) + tuple( torch.zeros_like(image_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) image_batches[-1] = torch.vstack(sequence_to_stack) # Pad noise_level_batches sequence_to_stack = (noise_level_batches[-1],) + tuple( torch.zeros_like(noise_level_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) noise_level_batches[-1] = torch.vstack(sequence_to_stack) # Pad text_embeddings_batches sequence_to_stack = (text_embeddings_batches[-1],) + tuple( torch.zeros_like(text_embeddings_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) text_embeddings_batches[-1] = torch.vstack(sequence_to_stack) # Pad uncond_embeddings_batches if necessary if uncond_embeddings is not None: sequence_to_stack = (uncond_embeddings_batches[-1],) + tuple( torch.zeros_like(uncond_embeddings_batches[-1][0][None, :]) for _ in range(num_dummy_samples) ) uncond_embeddings_batches[-1] = torch.vstack(sequence_to_stack) # Stack batches in the same tensor latents_batches = torch.stack(latents_batches) if uncond_embeddings 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, (uncond_embeddings_batch, text_embeddings_batch) in enumerate( zip(uncond_embeddings_batches, text_embeddings_batches[:]) ): text_embeddings_batches[i] = torch.cat([uncond_embeddings_batch, text_embeddings_batch]) text_embeddings_batches = torch.stack(text_embeddings_batches) image_batches = torch.stack(image_batches) noise_level_batches = torch.stack(noise_level_batches).squeeze(-1) return latents_batches, text_embeddings_batches, image_batches, noise_level_batches, num_dummy_samples @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, image: PipelineImageInput = None, num_inference_steps: int = 75, guidance_scale: float = 9.0, noise_level: int = 20, 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, 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, clip_skip: int = 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. image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image` or tensor representing an image batch to be upscaled. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): 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`). 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) 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 randomly. 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. 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 [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that will be called every `callback_steps` steps during inference. The function will be called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function will be called. If not specified, the callback will be called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.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. Returns: [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] or `tuple`: [`~diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.GaudiStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. Examples: ```py >>> import requests #TODO to test? >>> from PIL import Image >>> from io import BytesIO >>> from optimum.habana.diffusers import GaudiStableDiffusionUpscalePipeline >>> import torch >>> # load model and scheduler >>> model_id = "stabilityai/stable-diffusion-x4-upscaler" >>> pipeline = GaudiStableDiffusionUpscalePipeline.from_pretrained( ... model_id, revision="fp16", torch_dtype=torch.bfloat16 ... ) >>> pipeline = pipeline.to("cuda") >>> # let's download an image >>> url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png" >>> response = requests.get(url) >>> low_res_img = Image.open(BytesIO(response.content)).convert("RGB") >>> low_res_img = low_res_img.resize((128, 128)) >>> prompt = "a white cat" >>> upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] >>> upscaled_image.save("upsampled_cat.png") ``` """ with torch.autocast(device_type="hpu", dtype=torch.bfloat16, enabled=self.gaudi_config.use_torch_autocast): # 0. Check inputs. Raise error if not correct self.check_inputs( prompt, image, noise_level, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) if image is None: raise ValueError("`image` input cannot be undefined.") # 1. 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 # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 2. Encode input prompt text_encoder_lora_scale = ( cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None ) prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=clip_skip, ) # 3. Preprocess image image = self.image_processor.preprocess(image) image = image.to(dtype=prompt_embeds.dtype, device=device) # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device="cpu") timesteps = self.scheduler.timesteps.to(device) self.scheduler.reset_timestep_dependent_params() # 5. Add noise to image noise_level = torch.tensor([noise_level], dtype=torch.long, device=device) noise = randn_tensor(image.shape, generator=generator, device=device, dtype=prompt_embeds.dtype) image = self.low_res_scheduler.add_noise(image, noise, noise_level) image = torch.cat([image] * num_images_per_prompt) noise_level = torch.cat([noise_level] * image.shape[0]) # 6. Prepare latent variables height, width = image.shape[2:] num_channels_latents = self.vae.config.latent_channels latents = self.prepare_latents( num_prompts * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 7. Check that sizes of image and latents match num_channels_image = image.shape[1] if num_channels_latents + num_channels_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_image`: {num_channels_image} " f" = {num_channels_latents + num_channels_image}. Please verify the config of" " `pipeline.unet` or your `image` input." ) # 8. 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. Split into batches (HPU-specific step) ( latents_batches, text_embeddings_batches, image_batches, noise_level_batches, num_dummy_samples, ) = self._split_inputs_into_batches( batch_size, latents, prompt_embeds, negative_prompt_embeds, image, noise_level ) outputs = {"images": [], "has_nsfw_concept": []} t0 = time.time() t1 = t0 # 10. 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) image_batch = image_batches[0] image_batches = torch.roll(image_batches, shifts=-1, dims=0) noise_level_batch = noise_level_batches[0] noise_level_batches = torch.roll(noise_level_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 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 do_classifier_free_guidance else latents_batch ) # latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep) #TODO why this has been removed? image_input = torch.cat([image_batch] * 2) if do_classifier_free_guidance else image_batch noise_level_input = ( torch.cat([noise_level_batch] * 2) if do_classifier_free_guidance else noise_level_batch ) latent_model_input = torch.cat([latent_model_input, image_input], dim=1) # predict the noise residual noise_pred = self.unet_hpu( latent_model_input, timestep, text_embeddings_batch, cross_attention_kwargs, class_labels=noise_level_input, ) # perform guidance if 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_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() # 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) 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 # make sure the VAE is in float32 mode, as it overflows in bfloat16 needs_upcasting = self.vae.dtype == torch.bfloat16 and self.vae.config.force_upcast if needs_upcasting: self.upcast_vae() # Ensure latents are always the same type as the VAE latents_batch = latents_batch.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) image = self.vae.decode(latents_batch / self.vae.config.scaling_factor, return_dict=False)[0] # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.bfloat16) image, has_nsfw_concept, _ = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents_batch outputs["images"].append(image) if not self.use_hpu_graphs: self.htcore.mark_step() speed_metrics_prefix = "generation" 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}") # 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): # Apply watermark if self.watermarker is not None: image = self.watermarker.apply_watermark(image) 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 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, cross_attention_kwargs, class_labels): if self.use_hpu_graphs: return self.capture_replay(latent_model_input, timestep, encoder_hidden_states, class_labels) else: return self.unet( latent_model_input, timestep, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, class_labels=class_labels, )[0] @torch.no_grad() def capture_replay(self, latent_model_input, timestep, encoder_hidden_states, class_labels): inputs = [latent_model_input, timestep, encoder_hidden_states, False, class_labels] 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], timestep=inputs[1], encoder_hidden_states=inputs[2], return_dict=inputs[3], class_labels=inputs[4], )[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