# Copyright 2025 Hunyuan-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 re from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import torch from transformers import ByT5Tokenizer, Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, T5EncoderModel from ...guiders import AdaptiveProjectedMixGuidance from ...image_processor import VaeImageProcessor from ...models import AutoencoderKLHunyuanImage, HunyuanImageTransformer2DModel from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import is_torch_xla_available, logging, replace_example_docstring from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline from .pipeline_output import HunyuanImagePipelineOutput if is_torch_xla_available(): import torch_xla.core.xla_model as xm XLA_AVAILABLE = True else: XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import HunyuanImagePipeline >>> pipe = HunyuanImagePipeline.from_pretrained( ... "hunyuanvideo-community/HunyuanImage-2.1-Diffusers", torch_dtype=torch.bfloat16 ... ) >>> pipe.to("cuda") >>> prompt = "A cat holding a sign that says hello world" >>> # Depending on the variant being used, the pipeline call will slightly vary. >>> # Refer to the pipeline documentation for more details. >>> image = pipe(prompt, negative_prompt="", num_inference_steps=50).images[0] >>> image.save("hunyuanimage.png") ``` """ def extract_glyph_text(prompt: str): """ Extract text enclosed in quotes for glyph rendering. Finds text in single quotes, double quotes, and Chinese quotes, then formats it for byT5 processing. Args: prompt: Input text prompt Returns: Formatted glyph text string or None if no quoted text found """ text_prompt_texts = [] pattern_quote_single = r"\'(.*?)\'" pattern_quote_double = r"\"(.*?)\"" pattern_quote_chinese_single = r"‘(.*?)’" pattern_quote_chinese_double = r"“(.*?)”" matches_quote_single = re.findall(pattern_quote_single, prompt) matches_quote_double = re.findall(pattern_quote_double, prompt) matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, prompt) matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, prompt) text_prompt_texts.extend(matches_quote_single) text_prompt_texts.extend(matches_quote_double) text_prompt_texts.extend(matches_quote_chinese_single) text_prompt_texts.extend(matches_quote_chinese_double) if text_prompt_texts: glyph_text_formatted = ". ".join([f'Text "{text}"' for text in text_prompt_texts]) + ". " else: glyph_text_formatted = None return glyph_text_formatted # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, sigmas: Optional[List[float]] = 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: `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 HunyuanImagePipeline(DiffusionPipeline): r""" The HunyuanImage pipeline for text-to-image generation. Args: transformer ([`HunyuanImageTransformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKLHunyuanImage`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`Qwen2.5-VL-7B-Instruct`]): [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), specifically the [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) variant. tokenizer (`Qwen2Tokenizer`): Tokenizer of class [Qwen2Tokenizer]. text_encoder_2 ([`T5EncoderModel`]): [T5EncoderModel](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel) variant. tokenizer_2 (`ByT5Tokenizer`): Tokenizer of class [ByT5Tokenizer] guider ([`AdaptiveProjectedMixGuidance`]): [AdaptiveProjectedMixGuidance]to be used to guide the image generation. ocr_guider ([`AdaptiveProjectedMixGuidance`], *optional*): [AdaptiveProjectedMixGuidance] to be used to guide the image generation when text rendering is needed. """ model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae" _callback_tensor_inputs = ["latents", "prompt_embeds"] _optional_components = ["ocr_guider", "guider"] def __init__( self, scheduler: FlowMatchEulerDiscreteScheduler, vae: AutoencoderKLHunyuanImage, text_encoder: Qwen2_5_VLForConditionalGeneration, tokenizer: Qwen2Tokenizer, text_encoder_2: T5EncoderModel, tokenizer_2: ByT5Tokenizer, transformer: HunyuanImageTransformer2DModel, guider: Optional[AdaptiveProjectedMixGuidance] = None, ocr_guider: Optional[AdaptiveProjectedMixGuidance] = None, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, text_encoder_2=text_encoder_2, tokenizer_2=tokenizer_2, transformer=transformer, scheduler=scheduler, guider=guider, ocr_guider=ocr_guider, ) self.vae_scale_factor = self.vae.config.spatial_compression_ratio if getattr(self, "vae", None) else 32 self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.tokenizer_max_length = 1000 self.tokenizer_2_max_length = 128 self.prompt_template_encode = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>" self.prompt_template_encode_start_idx = 34 self.default_sample_size = 64 def _get_qwen_prompt_embeds( self, tokenizer: Qwen2Tokenizer, text_encoder: Qwen2_5_VLForConditionalGeneration, prompt: Union[str, List[str]] = None, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, tokenizer_max_length: int = 1000, template: str = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>", drop_idx: int = 34, hidden_state_skip_layer: int = 2, ): device = device or self._execution_device dtype = dtype or text_encoder.dtype prompt = [prompt] if isinstance(prompt, str) else prompt txt = [template.format(e) for e in prompt] txt_tokens = tokenizer( txt, max_length=tokenizer_max_length + drop_idx, padding="max_length", truncation=True, return_tensors="pt" ).to(device) encoder_hidden_states = text_encoder( input_ids=txt_tokens.input_ids, attention_mask=txt_tokens.attention_mask, output_hidden_states=True, ) prompt_embeds = encoder_hidden_states.hidden_states[-(hidden_state_skip_layer + 1)] prompt_embeds = prompt_embeds[:, drop_idx:] encoder_attention_mask = txt_tokens.attention_mask[:, drop_idx:] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) encoder_attention_mask = encoder_attention_mask.to(device=device) return prompt_embeds, encoder_attention_mask def _get_byt5_prompt_embeds( self, tokenizer: ByT5Tokenizer, text_encoder: T5EncoderModel, prompt: str, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, tokenizer_max_length: int = 128, ): device = device or self._execution_device dtype = dtype or text_encoder.dtype if isinstance(prompt, list): raise ValueError("byt5 prompt should be a string") elif prompt is None: raise ValueError("byt5 prompt should not be None") txt_tokens = tokenizer( prompt, padding="max_length", max_length=tokenizer_max_length, truncation=True, add_special_tokens=True, return_tensors="pt", ).to(device) prompt_embeds = text_encoder( input_ids=txt_tokens.input_ids, attention_mask=txt_tokens.attention_mask.float(), )[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) encoder_attention_mask = txt_tokens.attention_mask.to(device=device) return prompt_embeds, encoder_attention_mask def encode_prompt( self, prompt: Union[str, List[str]], device: Optional[torch.device] = None, batch_size: int = 1, num_images_per_prompt: int = 1, prompt_embeds: Optional[torch.Tensor] = None, prompt_embeds_mask: Optional[torch.Tensor] = None, prompt_embeds_2: Optional[torch.Tensor] = None, prompt_embeds_mask_2: Optional[torch.Tensor] = None, ): r""" Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device batch_size (`int`): batch size of prompts, defaults to 1 num_images_per_prompt (`int`): number of images that should be generated per prompt prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. If not provided, text embeddings will be generated from `prompt` input argument. prompt_embeds_mask (`torch.Tensor`, *optional*): Pre-generated text mask. If not provided, text mask will be generated from `prompt` input argument. prompt_embeds_2 (`torch.Tensor`, *optional*): Pre-generated glyph text embeddings from ByT5. If not provided, will be generated from `prompt` input argument using self.tokenizer_2 and self.text_encoder_2. prompt_embeds_mask_2 (`torch.Tensor`, *optional*): Pre-generated glyph text mask from ByT5. If not provided, will be generated from `prompt` input argument using self.tokenizer_2 and self.text_encoder_2. """ device = device or self._execution_device if prompt is None: prompt = [""] * batch_size prompt = [prompt] if isinstance(prompt, str) else prompt if prompt_embeds is None: prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds( tokenizer=self.tokenizer, text_encoder=self.text_encoder, prompt=prompt, device=device, tokenizer_max_length=self.tokenizer_max_length, template=self.prompt_template_encode, drop_idx=self.prompt_template_encode_start_idx, ) if prompt_embeds_2 is None: prompt_embeds_2_list = [] prompt_embeds_mask_2_list = [] glyph_texts = [extract_glyph_text(p) for p in prompt] for glyph_text in glyph_texts: if glyph_text is None: glyph_text_embeds = torch.zeros( (1, self.tokenizer_2_max_length, self.text_encoder_2.config.d_model), device=device ) glyph_text_embeds_mask = torch.zeros( (1, self.tokenizer_2_max_length), device=device, dtype=torch.int64 ) else: glyph_text_embeds, glyph_text_embeds_mask = self._get_byt5_prompt_embeds( tokenizer=self.tokenizer_2, text_encoder=self.text_encoder_2, prompt=glyph_text, device=device, tokenizer_max_length=self.tokenizer_2_max_length, ) prompt_embeds_2_list.append(glyph_text_embeds) prompt_embeds_mask_2_list.append(glyph_text_embeds_mask) prompt_embeds_2 = torch.cat(prompt_embeds_2_list, dim=0) prompt_embeds_mask_2 = torch.cat(prompt_embeds_mask_2_list, dim=0) _, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1) prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len) _, seq_len_2, _ = prompt_embeds_2.shape prompt_embeds_2 = prompt_embeds_2.repeat(1, num_images_per_prompt, 1) prompt_embeds_2 = prompt_embeds_2.view(batch_size * num_images_per_prompt, seq_len_2, -1) prompt_embeds_mask_2 = prompt_embeds_mask_2.repeat(1, num_images_per_prompt, 1) prompt_embeds_mask_2 = prompt_embeds_mask_2.view(batch_size * num_images_per_prompt, seq_len_2) return prompt_embeds, prompt_embeds_mask, prompt_embeds_2, prompt_embeds_mask_2 def check_inputs( self, prompt, height, width, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, prompt_embeds_mask=None, negative_prompt_embeds_mask=None, prompt_embeds_2=None, prompt_embeds_mask_2=None, negative_prompt_embeds_2=None, negative_prompt_embeds_mask_2=None, callback_on_step_end_tensor_inputs=None, ): if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0: logger.warning( f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}. Dimensions will be resized accordingly" ) if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` 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)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and prompt_embeds_mask is None: raise ValueError( "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`." ) if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None: raise ValueError( "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`." ) if prompt is None and prompt_embeds_2 is None: raise ValueError( "Provide either `prompt` or `prompt_embeds_2`. Cannot leave both `prompt` and `prompt_embeds_2` undefined." ) if prompt_embeds_2 is not None and prompt_embeds_mask_2 is None: raise ValueError( "If `prompt_embeds_2` are provided, `prompt_embeds_mask_2` also have to be passed. Make sure to generate `prompt_embeds_mask_2` from the same text encoder that was used to generate `prompt_embeds_2`." ) if negative_prompt_embeds_2 is not None and negative_prompt_embeds_mask_2 is None: raise ValueError( "If `negative_prompt_embeds_2` are provided, `negative_prompt_embeds_mask_2` also have to be passed. Make sure to generate `negative_prompt_embeds_mask_2` from the same text encoder that was used to generate `negative_prompt_embeds_2`." ) def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, ): height = int(height) // self.vae_scale_factor width = int(width) // self.vae_scale_factor shape = (batch_size, num_channels_latents, height, width) if latents is not None: return latents.to(device=device, dtype=dtype) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) return latents @property def attention_kwargs(self): return self._attention_kwargs @property def num_timesteps(self): return self._num_timesteps @property def current_timestep(self): return self._current_timestep @property def interrupt(self): return self._interrupt @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, negative_prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, distilled_guidance_scale: Optional[float] = 3.25, sigmas: Optional[List[float]] = None, num_images_per_prompt: int = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, prompt_embeds_mask: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds_mask: Optional[torch.Tensor] = None, prompt_embeds_2: Optional[torch.Tensor] = None, prompt_embeds_mask_2: Optional[torch.Tensor] = None, negative_prompt_embeds_2: Optional[torch.Tensor] = None, negative_prompt_embeds_mask_2: Optional[torch.Tensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, attention_kwargs: Optional[Dict[str, Any]] = None, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], ): 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. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined and negative_prompt_embeds is not provided, will use an empty negative prompt. Ignored when not using guidance. ). height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. 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. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. distilled_guidance_scale (`float`, *optional*, defaults to None): A guidance scale value for guidance distilled models. Unlike the traditional classifier-free guidance where the guidance scale is applied during inference through noise prediction rescaling, guidance distilled models take the guidance scale directly as an input parameter during forward pass. Guidance is enabled by setting `distilled_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. For guidance distilled models, this parameter is required. For non-distilled models, this parameter will be ignored. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. 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.Tensor`, *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 be generated by sampling using the supplied random `generator`. prompt_embeds (`torch.Tensor`, *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. prompt_embeds_mask (`torch.Tensor`, *optional*): Pre-generated text embeddings mask. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings mask will be generated from `prompt` input argument. prompt_embeds_2 (`torch.Tensor`, *optional*): Pre-generated text embeddings for ocr. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings for ocr will be generated from `prompt` input argument. prompt_embeds_mask_2 (`torch.Tensor`, *optional*): Pre-generated text embeddings mask for ocr. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings mask for ocr will be generated from `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *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. negative_prompt_embeds_mask (`torch.Tensor`, *optional*): Pre-generated negative text embeddings mask. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative text embeddings mask will be generated from `negative_prompt` input argument. negative_prompt_embeds_2 (`torch.Tensor`, *optional*): Pre-generated negative text embeddings for ocr. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative text embeddings for ocr will be generated from `negative_prompt` input argument. negative_prompt_embeds_mask_2 (`torch.Tensor`, *optional*): Pre-generated negative text embeddings mask for ocr. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative text embeddings mask for ocr 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 [`~pipelines.qwenimage.QwenImagePipelineOutput`] instead of a plain tuple. attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. Examples: Returns: [`~pipelines.hunyuan_image.HunyuanImagePipelineOutput`] or `tuple`: [`~pipelines.hunyuan_image.HunyuanImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ height = height or self.default_sample_size * self.vae_scale_factor width = width or self.default_sample_size * self.vae_scale_factor # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, prompt_embeds_mask=prompt_embeds_mask, negative_prompt_embeds_mask=negative_prompt_embeds_mask, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, prompt_embeds_2=prompt_embeds_2, prompt_embeds_mask_2=prompt_embeds_mask_2, negative_prompt_embeds_2=negative_prompt_embeds_2, negative_prompt_embeds_mask_2=negative_prompt_embeds_mask_2, ) self._attention_kwargs = attention_kwargs self._current_timestep = None 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) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # 3. prepare prompt embeds prompt_embeds, prompt_embeds_mask, prompt_embeds_2, prompt_embeds_mask_2 = self.encode_prompt( prompt=prompt, prompt_embeds=prompt_embeds, prompt_embeds_mask=prompt_embeds_mask, device=device, batch_size=batch_size, num_images_per_prompt=num_images_per_prompt, prompt_embeds_2=prompt_embeds_2, prompt_embeds_mask_2=prompt_embeds_mask_2, ) prompt_embeds = prompt_embeds.to(self.transformer.dtype) prompt_embeds_2 = prompt_embeds_2.to(self.transformer.dtype) # select guider if not torch.all(prompt_embeds_2 == 0) and self.ocr_guider is not None: # prompt contains ocr and pipeline has a guider for ocr guider = self.ocr_guider elif self.guider is not None: guider = self.guider # distilled model does not use guidance method, use default guider with enabled=False else: guider = AdaptiveProjectedMixGuidance(enabled=False) if guider._enabled and guider.num_conditions > 1: ( negative_prompt_embeds, negative_prompt_embeds_mask, negative_prompt_embeds_2, negative_prompt_embeds_mask_2, ) = self.encode_prompt( prompt=negative_prompt, prompt_embeds=negative_prompt_embeds, prompt_embeds_mask=negative_prompt_embeds_mask, device=device, batch_size=batch_size, num_images_per_prompt=num_images_per_prompt, prompt_embeds_2=negative_prompt_embeds_2, prompt_embeds_mask_2=negative_prompt_embeds_mask_2, ) negative_prompt_embeds = negative_prompt_embeds.to(self.transformer.dtype) negative_prompt_embeds_2 = negative_prompt_embeds_2.to(self.transformer.dtype) # 4. Prepare latent variables num_channels_latents = self.transformer.config.in_channels latents = self.prepare_latents( batch_size=batch_size * num_images_per_prompt, num_channels_latents=num_channels_latents, height=height, width=width, dtype=prompt_embeds.dtype, device=device, generator=generator, latents=latents, ) # 5. Prepare timesteps sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) # handle guidance (for guidance-distilled model) if self.transformer.config.guidance_embeds and distilled_guidance_scale is None: raise ValueError("`distilled_guidance_scale` is required for guidance-distilled model.") if self.transformer.config.guidance_embeds: guidance = ( torch.tensor( [distilled_guidance_scale] * latents.shape[0], dtype=self.transformer.dtype, device=device ) * 1000.0 ) else: guidance = None if self.attention_kwargs is None: self._attention_kwargs = {} # 6. Denoising loop self.scheduler.set_begin_index(0) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue self._current_timestep = t # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latents.shape[0]).to(latents.dtype) if self.transformer.config.use_meanflow: if i == len(timesteps) - 1: timestep_r = torch.tensor([0.0], device=device) else: timestep_r = timesteps[i + 1] timestep_r = timestep_r.expand(latents.shape[0]).to(latents.dtype) else: timestep_r = None # Step 1: Collect model inputs needed for the guidance method # conditional inputs should always be first element in the tuple guider_inputs = { "encoder_hidden_states": (prompt_embeds, negative_prompt_embeds), "encoder_attention_mask": (prompt_embeds_mask, negative_prompt_embeds_mask), "encoder_hidden_states_2": (prompt_embeds_2, negative_prompt_embeds_2), "encoder_attention_mask_2": (prompt_embeds_mask_2, negative_prompt_embeds_mask_2), } # Step 2: Update guider's internal state for this denoising step guider.set_state(step=i, num_inference_steps=num_inference_steps, timestep=t) # Step 3: Prepare batched model inputs based on the guidance method # The guider splits model inputs into separate batches for conditional/unconditional predictions. # For CFG with guider_inputs = {"encoder_hidden_states": (prompt_embeds, negative_prompt_embeds)}: # you will get a guider_state with two batches: # guider_state = [ # {"encoder_hidden_states": prompt_embeds, "__guidance_identifier__": "pred_cond"}, # conditional batch # {"encoder_hidden_states": negative_prompt_embeds, "__guidance_identifier__": "pred_uncond"}, # unconditional batch # ] # Other guidance methods may return 1 batch (no guidance) or 3+ batches (e.g., PAG, APG). guider_state = guider.prepare_inputs(guider_inputs) # Step 4: Run the denoiser for each batch # Each batch in guider_state represents a different conditioning (conditional, unconditional, etc.). # We run the model once per batch and store the noise prediction in guider_state_batch.noise_pred. for guider_state_batch in guider_state: guider.prepare_models(self.transformer) # Extract conditioning kwargs for this batch (e.g., encoder_hidden_states) cond_kwargs = { input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys() } # e.g. "pred_cond"/"pred_uncond" context_name = getattr(guider_state_batch, guider._identifier_key) with self.transformer.cache_context(context_name): # Run denoiser and store noise prediction in this batch guider_state_batch.noise_pred = self.transformer( hidden_states=latents, timestep=timestep, timestep_r=timestep_r, guidance=guidance, attention_kwargs=self.attention_kwargs, return_dict=False, **cond_kwargs, )[0] # Cleanup model (e.g., remove hooks) guider.cleanup_models(self.transformer) # Step 5: Combine predictions using the guidance method # The guider takes all noise predictions from guider_state and combines them according to the guidance algorithm. # Continuing the CFG example, the guider receives: # guider_state = [ # {"encoder_hidden_states": prompt_embeds, "noise_pred": noise_pred_cond, "__guidance_identifier__": "pred_cond"}, # batch 0 # {"encoder_hidden_states": negative_prompt_embeds, "noise_pred": noise_pred_uncond, "__guidance_identifier__": "pred_uncond"}, # batch 1 # ] # And extracts predictions using the __guidance_identifier__: # pred_cond = guider_state[0]["noise_pred"] # extracts noise_pred_cond # pred_uncond = guider_state[1]["noise_pred"] # extracts noise_pred_uncond # Then applies CFG formula: # noise_pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond) # Returns GuiderOutput(pred=noise_pred, pred_cond=pred_cond, pred_uncond=pred_uncond) noise_pred = guider(guider_state)[0] # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] if latents.dtype != latents_dtype: if torch.backends.mps.is_available(): # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 latents = latents.to(latents_dtype) 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) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if XLA_AVAILABLE: xm.mark_step() self._current_timestep = None if output_type == "latent": image = latents else: latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor image = self.vae.decode(latents, return_dict=False)[0] image = self.image_processor.postprocess(image, output_type=output_type) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image,) return HunyuanImagePipelineOutput(images=image)