# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright 2025 Alibaba Z-Image Team and 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 json import os from collections.abc import Callable, Iterable from typing import Any, ClassVar import PIL.Image import torch import torch.nn as nn from diffusers.image_processor import VaeImageProcessor from diffusers.schedulers import FlowMatchEulerDiscreteScheduler from diffusers.utils import logging from diffusers.utils.torch_utils import randn_tensor from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer from vllm.model_executor.models.utils import AutoWeightsLoader from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig from vllm_omni.diffusion.distributed.autoencoders.autoencoder_kl import DistributedAutoencoderKL 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 prefetch_subfolders from vllm_omni.diffusion.models.interface import SupportsComponentDiscovery from vllm_omni.diffusion.models.utils import create_transformers_model from vllm_omni.diffusion.models.z_image.z_image_transformer import ( ZImageTransformer2DModel, ) from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch from vllm_omni.model_executor.model_loader.weight_utils import ( download_weights_from_hf_specific, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name def get_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") 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, do_convert_rgb=True) 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 def retrieve_latents( encoder_output: torch.Tensor, generator: torch.Generator | None = None, sample_mode: str = "sample" ): if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": return encoder_output.latent_dist.sample(generator) elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": return encoder_output.latent_dist.mode() elif hasattr(encoder_output, "latents"): return encoder_output.latents else: raise AttributeError("Could not access latents of provided encoder_output") # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.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, ) -> tuple[torch.Tensor, int]: 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: `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 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 ZImagePipeline(nn.Module, DiffusionPipelineProfilerMixin, SupportsComponentDiscovery): supports_request_batch = False _dit_modules: ClassVar[list[str]] = ["transformer"] _encoder_modules: ClassVar[list[str]] = ["text_encoder"] _vae_modules: ClassVar[list[str]] = ["vae"] def __init__( self, *, od_config: OmniDiffusionConfig, prefix: str = "", ): super().__init__() self.od_config = od_config self.weights_sources = [ DiffusersPipelineLoader.ComponentSource( model_or_path=od_config.model, subfolder="text_encoder", revision=od_config.revision, prefix="text_encoder.", ), DiffusersPipelineLoader.ComponentSource( model_or_path=od_config.model, subfolder="transformer", revision=od_config.revision, prefix="transformer.", fall_back_to_pt=True, ), DiffusersPipelineLoader.ComponentSource( model_or_path=od_config.model, subfolder="vae", revision=od_config.revision, prefix="vae.", ), ] self._execution_device = get_local_device() model = od_config.model local_files_only = os.path.exists(model) # See ``hub_prefetch.py`` for the transformers v5 subfolder race. prefetch_subfolders( model, ["scheduler", "text_encoder", "vae", "tokenizer"], local_files_only=local_files_only, ) self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained( model, subfolder="scheduler", local_files_only=local_files_only ) text_encoder_config = AutoConfig.from_pretrained( model, subfolder="text_encoder", local_files_only=local_files_only ) self.text_encoder = create_transformers_model( AutoModelForCausalLM, od_config, hf_config=text_encoder_config, ).to(self._execution_device) if text_encoder_config.tie_word_embeddings: self.text_encoder.lm_head.weight = self.text_encoder.get_input_embeddings().weight vae_config = DistributedAutoencoderKL.load_config(model, subfolder="vae", local_files_only=local_files_only) self.vae = DistributedAutoencoderKL.from_config(vae_config).to(self._execution_device) self.transformer = ZImageTransformer2DModel(quant_config=od_config.quantization_config) self.tokenizer = AutoTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only) # Note: Context parallelism is applied centrally in registry.initialize_model() # following diffusers' pattern of enable_parallelism() at model loading time self.vae_scale_factor = ( 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8 ) self.setup_diffusion_pipeline_profiler( enable_diffusion_pipeline_profiler=self.od_config.enable_diffusion_pipeline_profiler ) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2, do_convert_rgb=True) def encode_prompt( self, prompt: str | list[str], device: torch.device | None = None, do_classifier_free_guidance: bool = True, negative_prompt: str | list[str] | None = None, prompt_embeds: list[torch.FloatTensor] | None = None, negative_prompt_embeds: torch.FloatTensor | None = None, max_sequence_length: int = 512, ): prompt = [prompt] if isinstance(prompt, str) else prompt prompt_embeds = self._encode_prompt( prompt=prompt, device=device, prompt_embeds=prompt_embeds, max_sequence_length=max_sequence_length, ) if do_classifier_free_guidance: if negative_prompt is None: negative_prompt = ["" for _ in prompt] else: negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt assert len(prompt) == len(negative_prompt) negative_prompt_embeds = self._encode_prompt( prompt=negative_prompt, device=device, prompt_embeds=negative_prompt_embeds, max_sequence_length=max_sequence_length, ) else: negative_prompt_embeds = [] return prompt_embeds, negative_prompt_embeds def _encode_prompt( self, prompt: str | list[str], device: torch.device | None = None, prompt_embeds: list[torch.FloatTensor] | None = None, max_sequence_length: int = 512, ) -> list[torch.FloatTensor]: device = device or self._execution_device if prompt_embeds is not None: return prompt_embeds if isinstance(prompt, str): prompt = [prompt] for i, prompt_item in enumerate(prompt): messages = [ {"role": "user", "content": prompt_item}, ] prompt_item = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True, ) prompt[i] = prompt_item text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_sequence_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids.to(device) prompt_masks = text_inputs.attention_mask.to(device).bool() prompt_embeds = self.text_encoder( input_ids=text_input_ids, attention_mask=prompt_masks, output_hidden_states=True, ).hidden_states[-2] embeddings_list = [] for i in range(len(prompt_embeds)): embeddings_list.append(prompt_embeds[i][prompt_masks[i]]) return embeddings_list def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, image=None, timestep=None, ): 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 image is not None: if latents is not None: return latents.to(device=device, dtype=dtype) image = image.to(device=device, dtype=dtype) if image.shape[1] != num_channels_latents: if isinstance(generator, list): image_latents = [ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i]) for i in range(image.shape[0]) ] image_latents = torch.cat(image_latents, dim=0) else: image_latents = retrieve_latents(self.vae.encode(image), generator=generator) image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor else: image_latents = image if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: additional_image_per_prompt = batch_size // image_latents.shape[0] image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." ) noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) latents = self.scheduler.scale_noise(image_latents, timestep, noise) return latents if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: if latents.shape != shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") latents = latents.to(device) return latents def get_timesteps(self, num_inference_steps, strength, device): init_timestep = min(num_inference_steps * strength, num_inference_steps) t_start = int(max(num_inference_steps - init_timestep, 0)) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] if hasattr(self.scheduler, "set_begin_index"): self.scheduler.set_begin_index(t_start * self.scheduler.order) return timesteps, num_inference_steps - t_start @property def guidance_scale(self): return self._guidance_scale @property def do_classifier_free_guidance(self): return self._guidance_scale > 0 @property def joint_attention_kwargs(self): return self._joint_attention_kwargs @property def num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt def forward(self, req: DiffusionRequestBatch) -> DiffusionOutput: # TODO: In online mode, sometimes it receives [{"negative_prompt": None}, {...}], so cannot use .get("...", "") # TODO: May be some data formatting operations on the API side. Hack for now. prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts] if all(isinstance(p, str) or p.get("negative_prompt") is None for p in req.prompts): negative_prompt = None elif req.prompts: negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts] prompt_embeds = None negative_prompt_embeds = None image = None if req.prompts: if len(req.prompts) > 1: logger.warning( "This model only supports a single prompt for img2img, not a batched request. " "Taking only the first image for now." ) first_prompt = req.prompts[0] if not isinstance(first_prompt, str): raw_image = first_prompt.get("multi_modal_data", {}).get("image") if raw_image is not None: if isinstance(raw_image, list): image = [PIL.Image.open(im) if isinstance(im, str) else raw_image[0] for im in raw_image[:1]] else: image = PIL.Image.open(raw_image) if isinstance(raw_image, str) else raw_image explicit_strength = req.sampling_params.strength is not None strength = req.sampling_params.strength if explicit_strength else 0.6 if explicit_strength and image is None: logger.warning( "strength parameter (%.2f) is only applicable for image-to-image (I2I) generation. " "It will be ignored for text-to-image (T2I) generation.", strength, ) strength = None if image is not None and strength is not None and (strength < 0 or strength > 1): raise ValueError(f"The value of strength should be in [0.0, 1.0] but is {strength}") height = req.sampling_params.height or 1024 width = req.sampling_params.width or 1024 num_inference_steps = req.sampling_params.num_inference_steps or 50 generator = req.sampling_params.generator sigmas = req.sampling_params.sigmas max_sequence_length = req.sampling_params.max_sequence_length or 512 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 1 ) latents = req.sampling_params.latents cfg_normalization = req.sampling_params.cfg_normalize cfg_truncation = req.sampling_params.extra_args.get("cfg_truncation", 1.0) joint_attention_kwargs: dict[str, Any] | None = None callback_on_step_end: Callable[[int, int, dict], None] | None = None callback_on_step_end_tensor_inputs = ["latents"] output_type = req.sampling_params.output_type or "pil" vae_scale = self.vae_scale_factor * 2 if height % vae_scale != 0: raise ValueError( f"Height must be divisible by {vae_scale} (got {height}). " f"Please adjust the height to a multiple of {vae_scale}." ) if width % vae_scale != 0: raise ValueError( f"Width must be divisible by {vae_scale} (got {width}). " f"Please adjust the width to a multiple of {vae_scale}." ) device = self._execution_device self._guidance_scale = guidance_scale self._joint_attention_kwargs = joint_attention_kwargs self._interrupt = False self._cfg_normalization = cfg_normalization self._cfg_truncation = cfg_truncation # 2. Define call parameters batch_size = len(prompt) ( prompt_embeds, negative_prompt_embeds, ) = self.encode_prompt( prompt=prompt, negative_prompt=negative_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, device=device, max_sequence_length=max_sequence_length, ) # 4. Prepare latent variables num_channels_latents = self.transformer.in_channels # img2img mode: prepare latents from input image if image is not None: # Handle image list - take first image if isinstance(image, list): image = image[0] # Prepare image for VAE encoding using image_processor if not isinstance(image, torch.Tensor): init_image = self.image_processor.preprocess(image, height, width) image = init_image.to(dtype=torch.float32, device=device) # Initialize scheduler kwargs for img2img mu = calculate_shift( (height // self.vae_scale_factor // 2) * (width // self.vae_scale_factor // 2), 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), ) self.scheduler.sigma_min = 0.0 scheduler_kwargs = {"mu": mu} # First initialize timesteps in scheduler timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, sigmas=sigmas, **scheduler_kwargs, ) # Then adjust timesteps based on strength timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) if num_inference_steps < 1: raise ValueError( f"After adjusting the num_inference_steps by strength parameter: " f"{strength}, the number of pipeline steps is {num_inference_steps} " f"which is < 1 and not appropriate for this pipeline." ) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds[0].dtype, device, generator, latents, image, latent_timestep, ) else: latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, torch.float32, device, generator, latents, ) # Repeat prompt_embeds for num_images_per_prompt if num_images_per_prompt > 1: prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)] if self.do_classifier_free_guidance and negative_prompt_embeds: negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)] actual_batch_size = batch_size * num_images_per_prompt # 5. Prepare timesteps if image is None: image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2) 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), ) self.scheduler.sigma_min = 0.0 scheduler_kwargs = {"mu": mu} timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, sigmas=sigmas, **scheduler_kwargs, ) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) # Precompute normalized timesteps once to avoid per-step GPU->CPU sync (.item() causes cudaStreamSynchronize) if isinstance(timesteps, torch.Tensor): timesteps_tensor = timesteps.to(device=device, dtype=torch.float32) else: timesteps_tensor = torch.as_tensor(timesteps, device=device, dtype=torch.float32) norm_timesteps = (1000 - timesteps_tensor) / 1000 t_norm_list = norm_timesteps.cpu().tolist() if not isinstance(t_norm_list, list): t_norm_list = [t_norm_list] # 6. Denoising loop for i, t in enumerate(timesteps): if self.interrupt: continue # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latents.shape[0]) timestep = (1000 - timestep) / 1000 # Normalized time for time-aware config (0 at start, 1 at end); # use precomputed to avoid .item() sync per step t_norm = t_norm_list[i] # Handle cfg truncation current_guidance_scale = self.guidance_scale if ( self.do_classifier_free_guidance and self._cfg_truncation is not None and float(self._cfg_truncation) <= 1 ): if t_norm > self._cfg_truncation: current_guidance_scale = 0.0 # Run CFG only if configured AND scale is non-zero apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0 latents_typed = latents.to(self.od_config.dtype) if apply_cfg: latent_model_input = latents_typed.repeat(2, 1, 1, 1) prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds timestep_model_input = timestep.repeat(2) else: latent_model_input = latents_typed prompt_embeds_model_input = prompt_embeds timestep_model_input = timestep latent_model_input = latent_model_input.unsqueeze(2) latent_model_input_list = list(latent_model_input.unbind(dim=0)) model_out_list = self.transformer( latent_model_input_list, timestep_model_input, prompt_embeds_model_input, )[0] if apply_cfg: # Perform CFG pos_out = model_out_list[:actual_batch_size] neg_out = model_out_list[actual_batch_size:] noise_pred = [] for j in range(actual_batch_size): pos = pos_out[j].float() neg = neg_out[j].float() pred = pos + current_guidance_scale * (pos - neg) # Renormalization (torch.where avoids GPU->CPU sync from Python if/scalar comparison) if self._cfg_normalization and float(self._cfg_normalization) > 0.0: ori_pos_norm = torch.linalg.vector_norm(pos) new_pos_norm = torch.linalg.vector_norm(pred) max_new_norm = ori_pos_norm * float(self._cfg_normalization) scale = torch.where( new_pos_norm > max_new_norm, (max_new_norm / new_pos_norm.clamp(min=1e-12)).to(pred.dtype), pred.new_tensor(1.0), ) pred = pred * scale noise_pred.append(pred) noise_pred = torch.stack(noise_pred, dim=0) else: noise_pred = torch.stack([t.float() for t in model_out_list], dim=0) noise_pred = noise_pred.squeeze(2) noise_pred = -noise_pred # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0] assert latents.dtype == torch.float32 if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) if output_type == "latent": image = latents else: latents = latents.to(self.vae.dtype) latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor image = self.vae.decode(latents, return_dict=False)[0] # image = self.image_processor.postprocess(image, output_type=output_type) stage_durations = self.stage_durations if hasattr(self, "stage_durations") else None return DiffusionOutput(output=image, stage_durations=stage_durations) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) loaded_weights = loader.load_weights(weights) # Record components loaded by diffusers submodules to satisfy strict checks. loaded_weights |= {f"vae.{name}" for name, _ in self.vae.named_parameters()} # downstream pipelines (e.g. MingImagePipeline) may set ``self.text_encoder = None`` when they # bring their own conditioning path. if self.text_encoder is not None: loaded_weights |= {f"text_encoder.{name}" for name, _ in self.text_encoder.named_parameters()} return loaded_weights