# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import inspect import json import math import os from collections.abc import Callable, Iterable from typing import Any import numpy as np import torch from diffusers.image_processor import VaeImageProcessor from diffusers.models import AutoencoderKL from diffusers.schedulers import FlowMatchEulerDiscreteScheduler from diffusers.utils import deprecate, logging from diffusers.utils.torch_utils import randn_tensor from torch import nn from transformers import ( CLIPTextModelWithProjection, CLIPTokenizer, LlamaForCausalLM, PreTrainedTokenizerFast, T5EncoderModel, T5Tokenizer, ) from vllm.model_executor.models.utils import AutoWeightsLoader from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin from vllm_omni.diffusion.distributed.parallel_state import get_classifier_free_guidance_world_size from vllm_omni.diffusion.distributed.utils import get_local_device from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader from vllm_omni.diffusion.model_loader.hub_prefetch import from_pretrained_with_prefetch, prefetch_subfolders from vllm_omni.diffusion.models.hidream_image import HiDreamImageTransformer2DModel from vllm_omni.diffusion.models.progress_bar import ProgressBarMixin from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin from vllm_omni.diffusion.request import OmniDiffusionRequest from vllm_omni.diffusion.utils.tf_utils import get_transformer_config_kwargs from vllm_omni.model_executor.model_loader.weight_utils import download_weights_from_hf_specific logger = logging.get_logger(__name__) def get_hidream_image_post_process_func( od_config: OmniDiffusionConfig, ): model_name = od_config.model if os.path.exists(model_name): model_path = model_name else: model_path = download_weights_from_hf_specific(model_name, None, ["*"]) vae_config_path = os.path.join(model_path, "vae/config.json") if not os.path.exists(vae_config_path): raise FileNotFoundError( f"VAE config not found at {vae_config_path}. " "Please ensure the model path contains a valid VAE configuration." ) with open(vae_config_path) as f: vae_config = json.load(f) vae_scale_factor = 2 ** (len(vae_config["block_out_channels"]) - 1) if "block_out_channels" in vae_config else 8 image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2) def post_process_func( images: torch.Tensor, ): return image_processor.postprocess(images) return post_process_func # Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift def calculate_shift( image_seq_len, base_seq_len: int = 256, max_seq_len: int = 4096, base_shift: float = 0.5, max_shift: float = 1.15, ): m = (max_shift - base_shift) / (max_seq_len - base_seq_len) b = base_shift - m * base_seq_len mu = image_seq_len * m + b return mu # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: int | None = None, device: str | torch.device | None = None, timesteps: list[int] | None = None, sigmas: list[float] | None = None, **kwargs, ): r""" 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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`list[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`. Returns: timesteps (`torch.Tensor`): The timestep schedule from the scheduler. num_inference_steps (`int`): The number of inference steps. """ if timesteps is not None and sigmas is not None: raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") 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) elif sigmas is not None: accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" sigmas schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(sigmas=sigmas, 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 HiDreamImagePipeline(nn.Module, CFGParallelMixin, DiffusionPipelineProfilerMixin, ProgressBarMixin): def __init__( self, *, od_config: OmniDiffusionConfig, prefix: str = "", ): super().__init__() self.od_config = od_config dtype = getattr(od_config, "dtype", torch.bfloat16) llama_path = self.od_config.extras["auxiliary_text_encoder"] if llama_path is None: logger.warning( f"auxiliary_text_encoder is not provided. " f"Attempting to load default LLaMA model: {self.od_config.extras['default_llama_model_id']}" ) llama_path = self.od_config.extras["default_llama_model_id"] logger.info(f"Loading LLAMA model from: {llama_path}") self.weights_sources = [ DiffusersPipelineLoader.ComponentSource( model_or_path=od_config.model, subfolder="transformer", revision=None, prefix="transformer.", fall_back_to_pt=True, ), ] self.device = get_local_device() model = od_config.model # Check if model is a local path local_files_only = os.path.exists(model) # See ``hub_prefetch.py`` for the transformers v5 multi-worker subfolder # race; prefetch the in-repo component set before any from_pretrained # (``text_encoder_4`` lives in a separate Llama repo and is unaffected). hidream_subfolders = [ "scheduler", "vae", "text_encoder", "tokenizer", "text_encoder_2", "tokenizer_2", "text_encoder_3", "tokenizer_3", ] prefetch_subfolders(model, hidream_subfolders, local_files_only=local_files_only) self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained( model, subfolder="scheduler", local_files_only=local_files_only ) self.vae = from_pretrained_with_prefetch( AutoencoderKL.from_pretrained, model, subfolder="vae", prefetch_list=hidream_subfolders, local_files_only=local_files_only, ).to(self.device) self.text_encoder = from_pretrained_with_prefetch( CLIPTextModelWithProjection.from_pretrained, model, subfolder="text_encoder", prefetch_list=hidream_subfolders, local_files_only=local_files_only, ) self.tokenizer = CLIPTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only) self.text_encoder_2 = from_pretrained_with_prefetch( CLIPTextModelWithProjection.from_pretrained, model, subfolder="text_encoder_2", prefetch_list=hidream_subfolders, local_files_only=local_files_only, ) self.tokenizer_2 = CLIPTokenizer.from_pretrained( model, subfolder="tokenizer_2", local_files_only=local_files_only ) self.text_encoder_3 = from_pretrained_with_prefetch( T5EncoderModel.from_pretrained, model, subfolder="text_encoder_3", prefetch_list=hidream_subfolders, local_files_only=local_files_only, ) self.tokenizer_3 = T5Tokenizer.from_pretrained( model, subfolder="tokenizer_3", local_files_only=local_files_only ) self.text_encoder_4 = LlamaForCausalLM.from_pretrained(llama_path, output_hidden_states=True, dtype=dtype).to( self.device ) self.tokenizer_4 = PreTrainedTokenizerFast.from_pretrained(llama_path, use_fast=False) transformer_kwargs = get_transformer_config_kwargs(od_config.tf_model_config, HiDreamImageTransformer2DModel) self.transformer = HiDreamImageTransformer2DModel( od_config=od_config, quant_config=od_config.quantization_config, **transformer_kwargs ) self.stage = None self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 # HiDreamImage latents are turned into 2x2 patches and packed. # This means the latent width and height has to be divisible # by the patch size. So the vae scale factor is multiplied by the patch size to account for this self.default_sample_size = 128 if getattr(self, "tokenizer_4", None) is not None: self.tokenizer_4.pad_token = self.tokenizer_4.eos_token self.setup_diffusion_pipeline_profiler( enable_diffusion_pipeline_profiler=self.od_config.enable_diffusion_pipeline_profiler ) def check_inputs( self, prompt, prompt_2, prompt_3, prompt_4, negative_prompt=None, negative_prompt_2=None, negative_prompt_3=None, negative_prompt_4=None, prompt_embeds_t5=None, prompt_embeds_llama3=None, negative_prompt_embeds_t5=None, negative_prompt_embeds_llama3=None, pooled_prompt_embeds=None, negative_pooled_prompt_embeds=None, callback_on_step_end_tensor_inputs=None, ): if prompt is not None and pooled_prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `pooled_prompt_embeds`: {pooled_prompt_embeds}. " "Please make sure to only forward one of the two." ) elif prompt_2 is not None and pooled_prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt_2`: {prompt_2} and `pooled_prompt_embeds`: {pooled_prompt_embeds}." "Please make sure to only forward one of the two." ) elif prompt_3 is not None and prompt_embeds_t5 is not None: raise ValueError( f"Cannot forward both `prompt_3`: {prompt_3} and `prompt_embeds_t5`: {prompt_embeds_t5}." "Please make sure to only forward one of the two." ) elif prompt_4 is not None and prompt_embeds_llama3 is not None: raise ValueError( f"Cannot forward both `prompt_4`: {prompt_4} and `prompt_embeds_llama3`: {prompt_embeds_llama3}." "Please make sure to only forward one of the two." ) elif prompt is None and pooled_prompt_embeds is None: raise ValueError( "Provide either `prompt` or `pooled_prompt_embeds`." "Cannot leave both `prompt` and `pooled_prompt_embeds` undefined." ) elif prompt is None and prompt_embeds_t5 is None: raise ValueError( "Provide either `prompt` or `prompt_embeds_t5`." "Cannot leave both `prompt` and `prompt_embeds_t5` undefined." ) elif prompt is None and prompt_embeds_llama3 is None: raise ValueError( "Provide either `prompt` or `prompt_embeds_llama3`." "Cannot leave both `prompt` and `prompt_embeds_llama3` 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)}") elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)): raise ValueError(f"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}") elif prompt_4 is not None and (not isinstance(prompt_4, str) and not isinstance(prompt_4, list)): raise ValueError(f"`prompt_4` has to be of type `str` or `list` but is {type(prompt_4)}") if negative_prompt is not None and negative_pooled_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_pooled_prompt_embeds`:" f" {negative_pooled_prompt_embeds}. Please make sure to only forward one of the two." ) elif negative_prompt_2 is not None and negative_pooled_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_pooled_prompt_embeds`:" f" {negative_pooled_prompt_embeds}. Please make sure to only forward one of the two." ) elif negative_prompt_3 is not None and negative_prompt_embeds_t5 is not None: raise ValueError( f"Cannot forward both `negative_prompt_3`: {negative_prompt_3} and `negative_prompt_embeds_t5`:" f" {negative_prompt_embeds_t5}. Please make sure to only forward one of the two." ) elif negative_prompt_4 is not None and negative_prompt_embeds_llama3 is not None: raise ValueError( f"Cannot forward both `negative_prompt_4`: {negative_prompt_4} and `negative_prompt_embeds_llama3`:" f" {negative_prompt_embeds_llama3}. Please make sure to only forward one of the two." ) if pooled_prompt_embeds is not None and negative_pooled_prompt_embeds is not None: if pooled_prompt_embeds.shape != negative_pooled_prompt_embeds.shape: raise ValueError( "`pooled_prompt_embeds` and `negative_pooled_prompt_embeds`" " must have the same shape when passed directly, but" f" got: `pooled_prompt_embeds` {pooled_prompt_embeds.shape} != `negative_pooled_prompt_embeds`" f" {negative_pooled_prompt_embeds.shape}." ) if prompt_embeds_t5 is not None and negative_prompt_embeds_t5 is not None: if prompt_embeds_t5.shape != negative_prompt_embeds_t5.shape: raise ValueError( "`prompt_embeds_t5` and `negative_prompt_embeds_t5`" " must have the same shape when passed directly, but" f" got: `prompt_embeds_t5` {prompt_embeds_t5.shape} != `negative_prompt_embeds_t5`" f" {negative_prompt_embeds_t5.shape}." ) if prompt_embeds_llama3 is not None and negative_prompt_embeds_llama3 is not None: if prompt_embeds_llama3.shape != negative_prompt_embeds_llama3.shape: raise ValueError( "`prompt_embeds_llama3` and `negative_prompt_embeds_llama3`" " must have the same shape when passed directly, but" f" got: `prompt_embeds_llama3` {prompt_embeds_llama3.shape} != `negative_prompt_embeds_llama3`" f" {negative_prompt_embeds_llama3.shape}." ) def _get_t5_prompt_embeds( self, prompt: str | list[str] = None, max_sequence_length: int = 128, dtype: torch.dtype | None = None, ): dtype = dtype or self.text_encoder_3.dtype prompt = [prompt] if isinstance(prompt, str) else prompt text_inputs = self.tokenizer_3( prompt, padding="max_length", max_length=min(max_sequence_length, self.tokenizer_3.model_max_length), truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids attention_mask = text_inputs.attention_mask untruncated_ids = self.tokenizer_3(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer_3.batch_decode( untruncated_ids[:, min(max_sequence_length, self.tokenizer_3.model_max_length) - 1 : -1] ) logger.warning( "The following part of your input was truncated because `max_sequence_length` is set to " f" {min(max_sequence_length, self.tokenizer_3.model_max_length)} tokens: {removed_text}" ) prompt_embeds = self.text_encoder_3( text_input_ids.to(self.device), attention_mask=attention_mask.to(self.device) )[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=self.device) return prompt_embeds def _get_clip_prompt_embeds( self, tokenizer, text_encoder, prompt: str | list[str], max_sequence_length: int = 128, dtype: torch.dtype | None = None, ): dtype = dtype or text_encoder.dtype prompt = [prompt] if isinstance(prompt, str) else prompt model_max_length = self.tokenizer.model_max_length text_inputs = tokenizer( prompt, padding="max_length", max_length=min(max_sequence_length, 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[:, model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {model_max_length} tokens: {removed_text}" ) prompt_embeds = text_encoder(text_input_ids.to(self.device), output_hidden_states=True) # Use pooled output of CLIPTextModel prompt_embeds = prompt_embeds[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=self.device) return prompt_embeds def _get_llama3_prompt_embeds( self, prompt: str | list[str] = None, max_sequence_length: int = 128, dtype: torch.dtype | None = None, ): dtype = dtype or self.text_encoder_4.dtype prompt = [prompt] if isinstance(prompt, str) else prompt text_inputs = self.tokenizer_4( prompt, padding="max_length", max_length=min(max_sequence_length, self.tokenizer_4.model_max_length), truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids attention_mask = text_inputs.attention_mask untruncated_ids = self.tokenizer_4(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer_4.batch_decode( untruncated_ids[:, min(max_sequence_length, self.tokenizer_4.model_max_length) - 1 : -1] ) logger.warning( "The following part of your input was truncated because `max_sequence_length` is set to " f" {min(max_sequence_length, self.tokenizer_4.model_max_length)} tokens: {removed_text}" ) outputs = self.text_encoder_4( text_input_ids.to(self.device), attention_mask=attention_mask.to(self.device), output_hidden_states=True, ) prompt_embeds = outputs.hidden_states[1:] prompt_embeds = torch.stack(prompt_embeds, dim=0) return prompt_embeds def encode_prompt( self, prompt: str | list[str] | None = None, prompt_2: str | list[str] | None = None, prompt_3: str | list[str] | None = None, prompt_4: str | list[str] | None = None, dtype: torch.dtype | None = None, num_images_per_prompt: int = 1, do_classifier_free_guidance: bool = True, negative_prompt: str | list[str] | None = None, negative_prompt_2: str | list[str] | None = None, negative_prompt_3: str | list[str] | None = None, negative_prompt_4: str | list[str] | None = None, prompt_embeds_t5: list[torch.FloatTensor] | None = None, prompt_embeds_llama3: list[torch.FloatTensor] | None = None, negative_prompt_embeds_t5: list[torch.FloatTensor] | None = None, negative_prompt_embeds_llama3: list[torch.FloatTensor] | None = None, pooled_prompt_embeds: torch.FloatTensor | None = None, negative_pooled_prompt_embeds: torch.FloatTensor | None = None, max_sequence_length: int = 128, lora_scale: float | None = None, ): prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: batch_size = len(prompt) else: batch_size = pooled_prompt_embeds.shape[0] if pooled_prompt_embeds is None: pooled_prompt_embeds_1 = self._get_clip_prompt_embeds( self.tokenizer, self.text_encoder, prompt, max_sequence_length, dtype ) if do_classifier_free_guidance and negative_pooled_prompt_embeds is None: negative_prompt = negative_prompt or "" negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt if len(negative_prompt) > 1 and len(negative_prompt) != batch_size: raise ValueError(f"negative_prompt must be of length 1 or {batch_size}") negative_pooled_prompt_embeds_1 = self._get_clip_prompt_embeds( self.tokenizer, self.text_encoder, negative_prompt, max_sequence_length, dtype ) if negative_pooled_prompt_embeds_1.shape[0] == 1 and batch_size > 1: negative_pooled_prompt_embeds_1 = negative_pooled_prompt_embeds_1.repeat(batch_size, 1) if pooled_prompt_embeds is None: prompt_2 = prompt_2 or prompt prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 if len(prompt_2) > 1 and len(prompt_2) != batch_size: raise ValueError(f"prompt_2 must be of length 1 or {batch_size}") pooled_prompt_embeds_2 = self._get_clip_prompt_embeds( self.tokenizer_2, self.text_encoder_2, prompt_2, max_sequence_length, dtype ) if pooled_prompt_embeds_2.shape[0] == 1 and batch_size > 1: pooled_prompt_embeds_2 = pooled_prompt_embeds_2.repeat(batch_size, 1) if do_classifier_free_guidance and negative_pooled_prompt_embeds is None: negative_prompt_2 = negative_prompt_2 or negative_prompt negative_prompt_2 = [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 if len(negative_prompt_2) > 1 and len(negative_prompt_2) != batch_size: raise ValueError(f"negative_prompt_2 must be of length 1 or {batch_size}") negative_pooled_prompt_embeds_2 = self._get_clip_prompt_embeds( self.tokenizer_2, self.text_encoder_2, negative_prompt_2, max_sequence_length, dtype ) if negative_pooled_prompt_embeds_2.shape[0] == 1 and batch_size > 1: negative_pooled_prompt_embeds_2 = negative_pooled_prompt_embeds_2.repeat(batch_size, 1) if pooled_prompt_embeds is None: pooled_prompt_embeds = torch.cat([pooled_prompt_embeds_1, pooled_prompt_embeds_2], dim=-1) if do_classifier_free_guidance and negative_pooled_prompt_embeds is None: negative_pooled_prompt_embeds = torch.cat( [negative_pooled_prompt_embeds_1, negative_pooled_prompt_embeds_2], dim=-1 ) if prompt_embeds_t5 is None: prompt_3 = prompt_3 or prompt prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3 if len(prompt_3) > 1 and len(prompt_3) != batch_size: raise ValueError(f"prompt_3 must be of length 1 or {batch_size}") prompt_embeds_t5 = self._get_t5_prompt_embeds(prompt_3, max_sequence_length, dtype) if prompt_embeds_t5.shape[0] == 1 and batch_size > 1: prompt_embeds_t5 = prompt_embeds_t5.repeat(batch_size, 1, 1) if do_classifier_free_guidance and negative_prompt_embeds_t5 is None: negative_prompt_3 = negative_prompt_3 or negative_prompt negative_prompt_3 = [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3 if len(negative_prompt_3) > 1 and len(negative_prompt_3) != batch_size: raise ValueError(f"negative_prompt_3 must be of length 1 or {batch_size}") negative_prompt_embeds_t5 = self._get_t5_prompt_embeds(negative_prompt_3, max_sequence_length, dtype) if negative_prompt_embeds_t5.shape[0] == 1 and batch_size > 1: negative_prompt_embeds_t5 = negative_prompt_embeds_t5.repeat(batch_size, 1, 1) if prompt_embeds_llama3 is None: prompt_4 = prompt_4 or prompt prompt_4 = [prompt_4] if isinstance(prompt_4, str) else prompt_4 if len(prompt_4) > 1 and len(prompt_4) != batch_size: raise ValueError(f"prompt_4 must be of length 1 or {batch_size}") prompt_embeds_llama3 = self._get_llama3_prompt_embeds(prompt_4, max_sequence_length, dtype) if prompt_embeds_llama3.shape[0] == 1 and batch_size > 1: prompt_embeds_llama3 = prompt_embeds_llama3.repeat(1, batch_size, 1, 1) if do_classifier_free_guidance and negative_prompt_embeds_llama3 is None: negative_prompt_4 = negative_prompt_4 or negative_prompt negative_prompt_4 = [negative_prompt_4] if isinstance(negative_prompt_4, str) else negative_prompt_4 if len(negative_prompt_4) > 1 and len(negative_prompt_4) != batch_size: raise ValueError(f"negative_prompt_4 must be of length 1 or {batch_size}") negative_prompt_embeds_llama3 = self._get_llama3_prompt_embeds( negative_prompt_4, max_sequence_length, dtype ) if negative_prompt_embeds_llama3.shape[0] == 1 and batch_size > 1: negative_prompt_embeds_llama3 = negative_prompt_embeds_llama3.repeat(1, batch_size, 1, 1) # duplicate pooled_prompt_embeds for each generation per prompt pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt) pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1) # duplicate t5_prompt_embeds for batch_size and num_images_per_prompt bs_embed, seq_len, _ = prompt_embeds_t5.shape if bs_embed == 1 and batch_size > 1: prompt_embeds_t5 = prompt_embeds_t5.repeat(batch_size, 1, 1) elif bs_embed > 1 and bs_embed != batch_size: raise ValueError(f"cannot duplicate prompt_embeds_t5 of batch size {bs_embed}") prompt_embeds_t5 = prompt_embeds_t5.repeat(1, num_images_per_prompt, 1) prompt_embeds_t5 = prompt_embeds_t5.view(batch_size * num_images_per_prompt, seq_len, -1) # duplicate llama3_prompt_embeds for batch_size and num_images_per_prompt _, bs_embed, seq_len, dim = prompt_embeds_llama3.shape if bs_embed == 1 and batch_size > 1: prompt_embeds_llama3 = prompt_embeds_llama3.repeat(1, batch_size, 1, 1) elif bs_embed > 1 and bs_embed != batch_size: raise ValueError(f"cannot duplicate prompt_embeds_llama3 of batch size {bs_embed}") prompt_embeds_llama3 = prompt_embeds_llama3.repeat(1, 1, num_images_per_prompt, 1) prompt_embeds_llama3 = prompt_embeds_llama3.view(-1, batch_size * num_images_per_prompt, seq_len, dim) if do_classifier_free_guidance: # duplicate negative_pooled_prompt_embeds for batch_size and num_images_per_prompt bs_embed, seq_len = negative_pooled_prompt_embeds.shape if bs_embed == 1 and batch_size > 1: negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(batch_size, 1) elif bs_embed > 1 and bs_embed != batch_size: raise ValueError(f"cannot duplicate negative_pooled_prompt_embeds of batch size {bs_embed}") negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt) negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1) # duplicate negative_t5_prompt_embeds for batch_size and num_images_per_prompt bs_embed, seq_len, _ = negative_prompt_embeds_t5.shape if bs_embed == 1 and batch_size > 1: negative_prompt_embeds_t5 = negative_prompt_embeds_t5.repeat(batch_size, 1, 1) elif bs_embed > 1 and bs_embed != batch_size: raise ValueError(f"cannot duplicate negative_prompt_embeds_t5 of batch size {bs_embed}") negative_prompt_embeds_t5 = negative_prompt_embeds_t5.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds_t5 = negative_prompt_embeds_t5.view(batch_size * num_images_per_prompt, seq_len, -1) # duplicate negative_prompt_embeds_llama3 for batch_size and num_images_per_prompt _, bs_embed, seq_len, dim = negative_prompt_embeds_llama3.shape if bs_embed == 1 and batch_size > 1: negative_prompt_embeds_llama3 = negative_prompt_embeds_llama3.repeat(1, batch_size, 1, 1) elif bs_embed > 1 and bs_embed != batch_size: raise ValueError(f"cannot duplicate negative_prompt_embeds_llama3 of batch size {bs_embed}") negative_prompt_embeds_llama3 = negative_prompt_embeds_llama3.repeat(1, 1, num_images_per_prompt, 1) negative_prompt_embeds_llama3 = negative_prompt_embeds_llama3.view( -1, batch_size * num_images_per_prompt, seq_len, dim ) return ( prompt_embeds_t5, negative_prompt_embeds_t5, prompt_embeds_llama3, negative_prompt_embeds_llama3, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ depr_message = ( f"Calling `enable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this" " method will be removed in a future version. Please use `pipe.vae.enable_slicing()`." ) deprecate( "enable_vae_slicing", "0.40.0", depr_message, ) self.vae.enable_slicing() def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. """ depr_message = ( f"Calling `disable_vae_slicing()` on a `{self.__class__.__name__}` is deprecated and this" " method will be removed in a future version. Please use `pipe.vae.disable_slicing()`." ) deprecate( "disable_vae_slicing", "0.40.0", depr_message, ) self.vae.disable_slicing() def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. """ depr_message = ( f"Calling `enable_vae_tiling()` on a `{self.__class__.__name__}`" " is deprecated and this method will be removed in a future version. Please use `pipe.vae.enable_tiling()`." ) deprecate( "enable_vae_tiling", "0.40.0", depr_message, ) self.vae.enable_tiling() def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step. """ depr_message = ( f"Calling `disable_vae_tiling()` on a `{self.__class__.__name__}` is" " deprecated and this method will be removed in a future version. Please use `pipe.vae.disable_tiling()`." ) deprecate( "disable_vae_tiling", "0.40.0", depr_message, ) self.vae.disable_tiling() def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None, ): # VAE applies 8x compression on images but we must also account for packing which requires # latent height and width to be divisible by 2. height = 2 * (int(height) // (self.vae_scale_factor * 2)) width = 2 * (int(width) // (self.vae_scale_factor * 2)) shape = (batch_size, num_channels_latents, height, width) if latents is None: latents = randn_tensor(shape, generator=generator, device=self.device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(self.device) return latents def prepare_timesteps(self, num_inference_steps, sigmas, image_seq_len): sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas mu = calculate_shift( image_seq_len, self.scheduler.config.get("base_image_seq_len", 256), self.scheduler.config.get("max_image_seq_len", 4096), self.scheduler.config.get("base_shift", 0.5), self.scheduler.config.get("max_shift", 1.15), ) timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, self.device, sigmas=sigmas, mu=mu, ) return timesteps, num_inference_steps def _extract_prompts(self, prompts): """Extract prompt and negative_prompt from OmniPromptType list.""" prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in prompts] or None if all(isinstance(p, str) or p.get("negative_prompt") is None for p in prompts): negative_prompt = None elif prompts: negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in prompts] else: negative_prompt = None return prompt, negative_prompt def check_cfg_parallel_validity(self, true_cfg_scale: float, has_neg_prompt: bool): if get_classifier_free_guidance_world_size() == 1: return True if true_cfg_scale <= 1: logger.warning("CFG parallel is NOT working correctly when true_cfg_scale <= 1.") return False if not has_neg_prompt: logger.warning( "CFG parallel is NOT working correctly when there is no negative prompt or negative prompt embeddings." ) return False return True def diffuse( self, prompt_embeds_t5: torch.Tensor, prompt_embeds_llama3: torch.Tensor, pooled_prompt_embeds: torch.Tensor, latents: torch.Tensor, timesteps: torch.Tensor, do_true_cfg: bool, ) -> torch.Tensor: with self.progress_bar(total=len(timesteps)) as pbar: 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 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) noise_pred = self.transformer( hidden_states=latent_model_input, timesteps=timestep, encoder_hidden_states_t5=prompt_embeds_t5, encoder_hidden_states_llama3=prompt_embeds_llama3, pooled_embeds=pooled_prompt_embeds, return_dict=False, )[0] noise_pred = -noise_pred # TODO: Modify CFG guidance # 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 = self.scheduler_step_maybe_with_cfg(noise_pred, t, latents, do_true_cfg) pbar.update() return latents @property def guidance_scale(self): return self._guidance_scale @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 @property def attention_kwargs(self): return self._attention_kwargs @property def num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt def forward( self, req: OmniDiffusionRequest, prompt: str | list[str] = None, prompt_2: str | list[str] | None = None, prompt_3: str | list[str] | None = None, prompt_4: str | list[str] | None = None, height: int | None = None, width: int | None = None, num_inference_steps: int = 50, sigmas: list[float] | None = None, guidance_scale: float = 5.0, negative_prompt: str | list[str] | None = None, negative_prompt_2: str | list[str] | None = None, negative_prompt_3: str | list[str] | None = None, negative_prompt_4: str | list[str] | None = None, num_images_per_prompt: int | None = 1, generator: torch.Generator | list[torch.Generator] | None = None, latents: torch.FloatTensor | None = None, prompt_embeds_t5: torch.FloatTensor | None = None, prompt_embeds_llama3: torch.FloatTensor | None = None, negative_prompt_embeds_t5: torch.FloatTensor | None = None, negative_prompt_embeds_llama3: torch.FloatTensor | None = None, pooled_prompt_embeds: torch.FloatTensor | None = None, negative_pooled_prompt_embeds: torch.FloatTensor | None = None, output_type: str | None = "pil", return_dict: bool = True, attention_kwargs: dict[str, Any] | None = None, callback_on_step_end: Callable[[int, int], None] | None = None, callback_on_step_end_tensor_inputs: list[str] = ["latents"], max_sequence_length: int = 128, **kwargs, ): extracted_prompt, negative_prompt = self._extract_prompts(req.prompts) prompt = extracted_prompt or prompt height = req.sampling_params.height or self.default_sample_size * self.vae_scale_factor width = req.sampling_params.width or self.default_sample_size * self.vae_scale_factor num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps sigmas = req.sampling_params.sigmas or sigmas max_sequence_length = req.sampling_params.max_sequence_length or max_sequence_length generator = req.sampling_params.generator or generator true_cfg_scale = req.sampling_params.true_cfg_scale or guidance_scale if req.sampling_params.guidance_scale_provided: guidance_scale = req.sampling_params.guidance_scale num_images_per_prompt = ( req.sampling_params.num_outputs_per_prompt if req.sampling_params.num_outputs_per_prompt > 0 else num_images_per_prompt ) prompt_embeds = kwargs.get("prompt_embeds", None) negative_prompt_embeds = kwargs.get("negative_prompt_embeds", None) if prompt_embeds is not None: deprecation_message = ( "The `prompt_embeds` argument is deprecated." " Please use `prompt_embeds_t5` and `prompt_embeds_llama3` instead." ) deprecate("prompt_embeds", "0.35.0", deprecation_message) prompt_embeds_t5 = prompt_embeds[0] prompt_embeds_llama3 = prompt_embeds[1] if negative_prompt_embeds is not None: deprecation_message = ( "The `negative_prompt_embeds` argument is deprecated." "Please use `negative_prompt_embeds_t5` and `negative_prompt_embeds_llama3` instead." ) deprecate("negative_prompt_embeds", "0.35.0", deprecation_message) negative_prompt_embeds_t5 = negative_prompt_embeds[0] negative_prompt_embeds_llama3 = negative_prompt_embeds[1] division = self.vae_scale_factor * 2 S_max = (self.default_sample_size * self.vae_scale_factor) ** 2 scale = S_max / (width * height) scale = math.sqrt(scale) width, height = int(width * scale // division * division), int(height * scale // division * division) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, prompt_2, prompt_3, prompt_4, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, negative_prompt_3=negative_prompt_3, negative_prompt_4=negative_prompt_4, prompt_embeds_t5=prompt_embeds_t5, prompt_embeds_llama3=prompt_embeds_llama3, negative_prompt_embeds_t5=negative_prompt_embeds_t5, negative_prompt_embeds_llama3=negative_prompt_embeds_llama3, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._attention_kwargs = attention_kwargs 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) elif pooled_prompt_embeds is not None: batch_size = pooled_prompt_embeds.shape[0] # TODO: CFG guidance configuration has_neg_prompt = negative_prompt is not None or ( negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None ) do_true_cfg = true_cfg_scale > 1 and has_neg_prompt self.check_cfg_parallel_validity(true_cfg_scale, has_neg_prompt) # 3. Encode prompt lora_scale = self.attention_kwargs.get("scale", None) if self.attention_kwargs is not None else None ( prompt_embeds_t5, negative_prompt_embeds_t5, prompt_embeds_llama3, negative_prompt_embeds_llama3, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = self.encode_prompt( prompt=prompt, prompt_2=prompt_2, prompt_3=prompt_3, prompt_4=prompt_4, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, negative_prompt_3=negative_prompt_3, negative_prompt_4=negative_prompt_4, do_classifier_free_guidance=self.do_classifier_free_guidance, prompt_embeds_t5=prompt_embeds_t5, prompt_embeds_llama3=prompt_embeds_llama3, negative_prompt_embeds_t5=negative_prompt_embeds_t5, negative_prompt_embeds_llama3=negative_prompt_embeds_llama3, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, lora_scale=lora_scale, ) if self.do_classifier_free_guidance: prompt_embeds_t5 = torch.cat([negative_prompt_embeds_t5, prompt_embeds_t5], dim=0) prompt_embeds_llama3 = torch.cat([negative_prompt_embeds_llama3, prompt_embeds_llama3], dim=1) pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0) # 4. Prepare latent variables num_channels_latents = self.transformer.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, pooled_prompt_embeds.dtype, generator, latents, ) # 5. Prepare timesteps timesteps, num_inference_steps = self.prepare_timesteps(num_inference_steps, sigmas, latents.shape[1]) self._num_timesteps = len(timesteps) # 6. Denoising loop latents = self.diffuse( prompt_embeds_t5, prompt_embeds_llama3, pooled_prompt_embeds, latents, timesteps, do_true_cfg, ) if output_type == "latent": image = latents else: latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor image = self.vae.decode(latents, return_dict=False)[0] return DiffusionOutput( output=image, stage_durations=self.stage_durations if hasattr(self, "stage_durations") else None ) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) return loader.load_weights(weights)