# 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 from typing import Any, Callable, Dict, List, Optional, Union import torch from transformers import AutoTokenizer, PreTrainedModel from ...image_processor import PipelineImageInput, VaeImageProcessor from ...loaders import FromSingleFileMixin, ZImageLoraLoaderMixin from ...models.autoencoders import AutoencoderKL from ...models.transformers import ZImageTransformer2DModel from ...pipelines.pipeline_utils import DiffusionPipeline from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import logging, replace_example_docstring from ...utils.torch_utils import randn_tensor from .pipeline_output import ZImagePipelineOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import ZImageImg2ImgPipeline >>> from diffusers.utils import load_image >>> pipe = ZImageImg2ImgPipeline.from_pretrained("Z-a-o/Z-Image-Turbo", torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" >>> init_image = load_image(url).resize((1024, 1024)) >>> prompt = "A fantasy landscape with mountains and a river, detailed, vibrant colors" >>> image = pipe( ... prompt, ... image=init_image, ... strength=0.6, ... num_inference_steps=9, ... guidance_scale=0.0, ... generator=torch.Generator("cuda").manual_seed(42), ... ).images[0] >>> image.save("zimage_img2img.png") ``` """ # 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_img2img.retrieve_latents def retrieve_latents( encoder_output: torch.Tensor, generator: Optional[torch.Generator] = 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: 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 ZImageImg2ImgPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFileMixin): r""" The ZImage pipeline for image-to-image generation. Args: scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`PreTrainedModel`]): A text encoder model to encode text prompts. tokenizer ([`AutoTokenizer`]): A tokenizer to tokenize text prompts. transformer ([`ZImageTransformer2DModel`]): A ZImage transformer model to denoise the encoded image latents. """ model_cpu_offload_seq = "text_encoder->transformer->vae" _optional_components = [] _callback_tensor_inputs = ["latents", "prompt_embeds"] def __init__( self, scheduler: FlowMatchEulerDiscreteScheduler, vae: AutoencoderKL, text_encoder: PreTrainedModel, tokenizer: AutoTokenizer, transformer: ZImageTransformer2DModel, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler, transformer=transformer, ) 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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2) # Copied from diffusers.pipelines.z_image.pipeline_z_image.ZImagePipeline.encode_prompt def encode_prompt( self, prompt: Union[str, List[str]], device: Optional[torch.device] = None, do_classifier_free_guidance: bool = True, negative_prompt: Optional[Union[str, List[str]]] = None, prompt_embeds: Optional[List[torch.FloatTensor]] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = 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 # Copied from diffusers.pipelines.z_image.pipeline_z_image.ZImagePipeline._encode_prompt def _encode_prompt( self, prompt: Union[str, List[str]], device: Optional[torch.device] = None, prompt_embeds: Optional[List[torch.FloatTensor]] = 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 # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_img2img.StableDiffusion3Img2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep 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 def prepare_latents( self, image, timestep, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=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 latents is not None: return latents.to(device=device, dtype=dtype) # Encode the input image 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) # Apply scaling (inverse of decoding: decode does latents/scaling_factor + shift_factor) image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor else: image_latents = image # Handle batch size expansion 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." ) # Add noise using flow matching scale_noise noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) latents = self.scheduler.scale_noise(image_latents, timestep, noise) 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 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 @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, image: PipelineImageInput = None, strength: float = 0.6, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, sigmas: Optional[List[float]] = None, guidance_scale: float = 5.0, cfg_normalization: bool = False, cfg_truncation: float = 1.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[List[torch.FloatTensor]] = None, negative_prompt_embeds: Optional[List[torch.FloatTensor]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, joint_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"], max_sequence_length: int = 512, ): r""" Function invoked when calling the pipeline for image-to-image generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`): `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]`. If it's a tensor or a list of tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`. strength (`float`, *optional*, defaults to 0.6): Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `image`. height (`int`, *optional*, defaults to 1024): The height in pixels of the generated image. If not provided, uses the input image height. width (`int`, *optional*, defaults to 1024): The width in pixels of the generated image. If not provided, uses the input image width. 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. guidance_scale (`float`, *optional*, defaults to 5.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. cfg_normalization (`bool`, *optional*, defaults to False): Whether to apply configuration normalization. cfg_truncation (`float`, *optional*, defaults to 1.0): The truncation value for configuration. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will be generated by sampling using the supplied random `generator`. prompt_embeds (`List[torch.FloatTensor]`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`List[torch.FloatTensor]`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.ZImagePipelineOutput`] instead of a plain tuple. joint_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. max_sequence_length (`int`, *optional*, defaults to 512): Maximum sequence length to use with the `prompt`. Examples: Returns: [`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ # 1. Check inputs and validate strength if strength < 0 or strength > 1: raise ValueError(f"The value of strength should be in [0.0, 1.0] but is {strength}") # 2. Preprocess image init_image = self.image_processor.preprocess(image) init_image = init_image.to(dtype=torch.float32) # Get dimensions from the preprocessed image if not specified if height is None: height = init_image.shape[-2] if width is None: width = init_image.shape[-1] 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 # 3. 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 = len(prompt_embeds) # If prompt_embeds is provided and prompt is None, skip encoding if prompt_embeds is not None and prompt is None: if self.do_classifier_free_guidance and negative_prompt_embeds is None: raise ValueError( "When `prompt_embeds` is provided without `prompt`, " "`negative_prompt_embeds` must also be provided for classifier-free guidance." ) else: ( 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 # 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 # Calculate latent dimensions for image_seq_len latent_height = 2 * (int(height) // (self.vae_scale_factor * 2)) latent_width = 2 * (int(width) // (self.vae_scale_factor * 2)) image_seq_len = (latent_height // 2) * (latent_width // 2) # 5. Prepare timesteps 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, ) # 6. 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: {strength}, the number of pipeline " f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline." ) latent_timestep = timesteps[:1].repeat(actual_batch_size) # 7. Prepare latents from image latents = self.prepare_latents( init_image, latent_timestep, actual_batch_size, num_channels_latents, height, width, prompt_embeds[0].dtype, device, generator, latents, ) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) # 8. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: 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) t_norm = timestep[0].item() # 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 if apply_cfg: latents_typed = latents.to(self.transformer.dtype) 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.to(self.transformer.dtype) 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 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) if new_pos_norm > max_new_norm: pred = pred * (max_new_norm / new_pos_norm) 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) # 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 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) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image,) return ZImagePipelineOutput(images=image)