# Copyright 2025 Qwen-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. from mmgp import offload import inspect from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import torch, json import math from diffusers.image_processor import VaeImageProcessor from .transformer_qwenimage import QwenImageTransformer2DModel from diffusers.utils import logging, replace_example_docstring from diffusers.utils.torch_utils import randn_tensor from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, AutoTokenizer from .autoencoder_kl_qwenimage import AutoencoderKLQwenImage from diffusers import FlowMatchEulerDiscreteScheduler from PIL import Image from shared.utils.utils import calculate_new_dimensions, convert_image_to_tensor, convert_tensor_to_image XLA_AVAILABLE = False PREFERRED_QWENIMAGE_RESOLUTIONS = [ (672, 1568), (688, 1504), (720, 1456), (752, 1392), (800, 1328), (832, 1248), (880, 1184), (944, 1104), (1024, 1024), (1104, 944), (1184, 880), (1248, 832), (1328, 800), (1392, 752), (1456, 720), (1504, 688), (1568, 672), ] logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import QwenImagePipeline >>> pipe = QwenImagePipeline.from_pretrained("Qwen/QwenImage-20B", 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, num_inference_steps=4, guidance_scale=0.0).images[0] >>> image.save("qwenimage.png") ``` """ 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: 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 # 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") class QwenImagePipeline(): #DiffusionPipeline r""" The QwenImage pipeline for text-to-image generation. Args: transformer ([`QwenImageTransformer2DModel`]): 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 ([`AutoencoderKL`]): 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 (`QwenTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer). """ model_cpu_offload_seq = "text_encoder->transformer->vae" _callback_tensor_inputs = ["latents", "prompt_embeds"] def __init__( self, vae, text_encoder, tokenizer, transformer, processor, ): self.vae=vae self.text_encoder=text_encoder self.tokenizer=tokenizer self.transformer=transformer self.processor = processor self.latent_channels = self.vae.config.z_dim if getattr(self, "vae", None) else 16 self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8 # QwenImage 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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2) self.tokenizer_max_length = 1024 if processor is not None: # self.prompt_template_encode = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n" self.prompt_template_encode = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" self.prompt_template_encode_start_idx = 64 else: 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|>\n<|im_start|>assistant\n" self.prompt_template_encode_start_idx = 34 self.default_sample_size = 128 def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor): bool_mask = mask.bool() valid_lengths = bool_mask.sum(dim=1) selected = hidden_states[bool_mask] split_result = torch.split(selected, valid_lengths.tolist(), dim=0) return split_result def _get_qwen_prompt_embeds( self, prompt: Union[str, List[str]] = None, image: Optional[torch.Tensor] = None, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ): device = device or self._execution_device dtype = dtype or self.text_encoder.dtype prompt = [prompt] if isinstance(prompt, str) else prompt template = self.prompt_template_encode drop_idx = self.prompt_template_encode_start_idx txt = [template.format(e) for e in prompt] if self.processor is not None and image is not None and len(image) > 0: img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>" if isinstance(image, list): base_img_prompt = "" for i, img in enumerate(image): base_img_prompt += img_prompt_template.format(i + 1) elif image is not None: base_img_prompt = img_prompt_template.format(1) else: base_img_prompt = "" template = self.prompt_template_encode drop_idx = self.prompt_template_encode_start_idx txt = [template.format(base_img_prompt + e) for e in prompt] model_inputs = self.processor( text=txt, images=image, padding=True, return_tensors="pt", ).to(device) outputs = self.text_encoder( input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask, pixel_values=model_inputs.pixel_values, image_grid_thw=model_inputs.image_grid_thw, output_hidden_states=True, ) hidden_states = outputs.hidden_states[-1] split_hidden_states = self._extract_masked_hidden(hidden_states, model_inputs.attention_mask) else: txt_tokens = self.tokenizer( txt, max_length=self.tokenizer_max_length + drop_idx, padding=True, truncation=True, return_tensors="pt" ).to(device) encoder_hidden_states = self.text_encoder( input_ids=txt_tokens.input_ids, attention_mask=txt_tokens.attention_mask, output_hidden_states=True, ) hidden_states = encoder_hidden_states.hidden_states[-1] split_hidden_states = self._extract_masked_hidden(hidden_states, txt_tokens.attention_mask) split_hidden_states = [e[drop_idx:] for e in split_hidden_states] attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states] max_seq_len = max([e.size(0) for e in split_hidden_states]) prompt_embeds = torch.stack( [torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states] ) encoder_attention_mask = torch.stack( [torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list] ) prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) return prompt_embeds, encoder_attention_mask def encode_prompt( self, prompt: Union[str, List[str]], image: Optional[torch.Tensor] = None, device: Optional[torch.device] = None, num_images_per_prompt: int = 1, prompt_embeds: Optional[torch.Tensor] = None, prompt_embeds_mask: Optional[torch.Tensor] = None, max_sequence_length: int = 1024, ): r""" Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded image (`torch.Tensor`, *optional*): image to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt 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. """ device = device or self._execution_device prompt = [prompt] if isinstance(prompt, str) else prompt batch_size = len(prompt) if prompt_embeds is None else prompt_embeds.shape[0] if prompt_embeds is None: prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, image, device) _, 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) return prompt_embeds, prompt_embeds_mask 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, callback_on_step_end_tensor_inputs=None, max_sequence_length=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 max_sequence_length is not None and max_sequence_length > 1024: raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}") @staticmethod def _prepare_latent_image_ids(batch_size, height, width, device, dtype): latent_image_ids = torch.zeros(height, width, 3) latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None] latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :] latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape latent_image_ids = latent_image_ids.reshape( latent_image_id_height * latent_image_id_width, latent_image_id_channels ) return latent_image_ids.to(device=device, dtype=dtype) @staticmethod def _pack_latents(latents): batch_size, num_channels_latents, _, height, width = latents.shape latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2) latents = latents.permute(0, 2, 4, 1, 3, 5) latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4) return latents @staticmethod def _unpack_latents(latents, height, width, vae_scale_factor): batch_size, num_patches, channels = latents.shape # 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) // (vae_scale_factor * 2)) width = 2 * (int(width) // (vae_scale_factor * 2)) latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2) latents = latents.permute(0, 3, 1, 4, 2, 5) latents = latents.reshape(batch_size, channels // (2 * 2), 1, height, width) return latents def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator): if isinstance(generator, list): image_latents = [ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode="argmax") 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, sample_mode="argmax") latents_mean = ( torch.tensor(self.vae.config.latents_mean) .view(1, self.latent_channels, 1, 1, 1) .to(image_latents.device, image_latents.dtype) ) latents_std = ( torch.tensor(self.vae.config.latents_std) .view(1, self.latent_channels, 1, 1, 1) .to(image_latents.device, image_latents.dtype) ) image_latents = (image_latents - latents_mean) / latents_std return image_latents 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. """ 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. """ 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. """ 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. """ self.vae.disable_tiling() def prepare_latents( self, images, batch_size, num_channels_latents, height, width, dtype, device, 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, 1, height, width) image_latents = None if images is not None and len(images ) > 0: if not isinstance(images, list): images = [images] all_image_latents = [] for image in images: image = image.to(device=device, dtype=dtype) if image.shape[1] != self.latent_channels: image_latents = self._encode_vae_image(image=image, generator=generator) else: image_latents = image if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: # expand init_latents for batch_size 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." ) else: image_latents = torch.cat([image_latents], dim=0) image_latents = self._pack_latents(image_latents) all_image_latents.append(image_latents) image_latents = torch.cat(all_image_latents, dim=1) 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." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) latents = self._pack_latents(latents) else: latents = latents.to(device=device, dtype=dtype) return latents, image_latents @property def guidance_scale(self): return self._guidance_scale @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, true_cfg_scale: float = 4.0, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, sigmas: Optional[List[float]] = None, guidance_scale: float = 1.0, 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, 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"], max_sequence_length: int = 512, image = None, image_mask = None, denoising_strength = 0, callback=None, pipeline=None, loras_slists=None, joint_pass= True, lora_inpaint = False, outpainting_dims = None, qwen_edit_plus = False, ): 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, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is not greater than `1`). true_cfg_scale (`float`, *optional*, defaults to 1.0): When > 1.0 and a provided `negative_prompt`, enables true classifier-free 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. guidance_scale (`float`, *optional*, defaults to 3.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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. 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. 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. 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. max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`. Examples: Returns: [`~pipelines.qwenimage.QwenImagePipelineOutput`] or `tuple`: [`~pipelines.qwenimage.QwenImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ kwargs = {'pipeline': pipeline, 'callback': callback} if callback != None: callback(-1, None, True) height = height or self.default_sample_size * self.vae_scale_factor width = width or self.default_sample_size * self.vae_scale_factor multiple_of = self.vae_scale_factor * 2 width = width // multiple_of * multiple_of height = height // multiple_of * multiple_of # 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, max_sequence_length=max_sequence_length, ) self._guidance_scale = guidance_scale 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 = "cuda" condition_images = [] vae_image_sizes = [] vae_images = [] image_mask_latents = None ref_size = 1024 ref_text_encoder_size = 384 if qwen_edit_plus else 1024 if image is not None: if not isinstance(image, list): image = [image] if height * width < ref_size * ref_size: ref_size = round(math.sqrt(height * width)) for ref_no, img in enumerate(image): image_width, image_height = img.size any_mask = ref_no == 0 and image_mask is not None if (image_height * image_width > ref_size * ref_size) and not any_mask: vae_height, vae_width =calculate_new_dimensions(ref_size, ref_size, image_height, image_width, False, block_size=multiple_of) else: vae_height, vae_width = image_height, image_width vae_width = vae_width // multiple_of * multiple_of vae_height = vae_height // multiple_of * multiple_of vae_image_sizes.append((vae_width, vae_height)) condition_height, condition_width =calculate_new_dimensions(ref_text_encoder_size, ref_text_encoder_size, image_height, image_width, False, block_size=multiple_of) condition_images.append(img.resize((condition_width, condition_height), resample=Image.Resampling.LANCZOS) ) if img.size != (vae_width, vae_height): img = img.resize((vae_width, vae_height), resample=Image.Resampling.LANCZOS) if any_mask : if lora_inpaint: image_mask_rebuilt = torch.where(convert_image_to_tensor(image_mask)>-0.5, 1., 0. )[0:1] img = convert_image_to_tensor(img) green = torch.tensor([-1.0, 1.0, -1.0]).to(img) green_image = green[:, None, None] .expand_as(img) img = torch.where(image_mask_rebuilt > 0, green_image, img) img = convert_tensor_to_image(img) else: image_mask_latents = convert_image_to_tensor(image_mask.resize((vae_width // 8, vae_height // 8), resample=Image.Resampling.LANCZOS)) image_mask_latents = torch.where(image_mask_latents>-0.5, 1., 0. )[0:1] image_mask_rebuilt = image_mask_latents.repeat_interleave(8, dim=-1).repeat_interleave(8, dim=-2).unsqueeze(0) # convert_tensor_to_image( image_mask_rebuilt.squeeze(0).repeat(3,1,1)).save("mmm.png") image_mask_latents = image_mask_latents.to(device).unsqueeze(0).unsqueeze(0).repeat(1,16,1,1,1) image_mask_latents = self._pack_latents(image_mask_latents) # img.save("nnn.png") vae_images.append( convert_image_to_tensor(img).unsqueeze(0).unsqueeze(2) ) has_neg_prompt = negative_prompt is not None or ( negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None ) do_true_cfg = true_cfg_scale > 1 and has_neg_prompt prompt_embeds, prompt_embeds_mask = self.encode_prompt( image=condition_images, prompt=prompt, prompt_embeds=prompt_embeds, prompt_embeds_mask=prompt_embeds_mask, device=device, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, ) if do_true_cfg: negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt( image=condition_images, prompt=negative_prompt, prompt_embeds=negative_prompt_embeds, prompt_embeds_mask=negative_prompt_embeds_mask, device=device, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, ) dtype = torch.bfloat16 prompt_embeds = prompt_embeds.to(dtype) if do_true_cfg: negative_prompt_embeds = negative_prompt_embeds.to(dtype) # 4. Prepare latent variables num_channels_latents = self.transformer.in_channels // 4 latents, image_latents = self.prepare_latents( vae_images, batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) original_image_latents = None if image_latents is None else image_latents.clone() if image is not None: img_shapes = [ [ (1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2), # (1, image_height // self.vae_scale_factor // 2, image_width // self.vae_scale_factor // 2), *[ (1, vae_height // self.vae_scale_factor // 2, vae_width // self.vae_scale_factor // 2) for vae_width, vae_height in vae_image_sizes ], ] ] * batch_size else: img_shapes = [(1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2)] * batch_size # 5. Prepare timesteps sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas image_seq_len = latents.shape[1] 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, device, sigmas=sigmas, mu=mu, ) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) original_timesteps = timesteps # handle guidance if self.transformer.guidance_embeds: guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32) guidance = guidance.expand(latents.shape[0]) else: guidance = None if self.attention_kwargs is None: self._attention_kwargs = {} txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist() if prompt_embeds_mask is not None else None negative_txt_seq_lens = ( negative_prompt_embeds_mask.sum(dim=1).tolist() if negative_prompt_embeds_mask is not None else None ) morph, first_step = False, 0 lanpaint_proc = None if image_mask_latents is not None: randn = torch.randn_like(original_image_latents) if denoising_strength < 1.: first_step = int(len(timesteps) * (1. - denoising_strength)) if not morph: latent_noise_factor = timesteps[first_step]/1000 # latents = original_image_latents * (1.0 - latent_noise_factor) + torch.randn_like(original_image_latents) * latent_noise_factor latents = original_image_latents * (1.0 - latent_noise_factor) + randn * latent_noise_factor timesteps = timesteps[first_step:] self.scheduler.timesteps = timesteps self.scheduler.sigmas= self.scheduler.sigmas[first_step:] # from shared.inpainting.lanpaint import LanPaint # lanpaint_proc = LanPaint() # 6. Denoising loop self.scheduler.set_begin_index(0) updated_num_steps= len(timesteps) if callback != None: from shared.utils.loras_mutipliers import update_loras_slists update_loras_slists(self.transformer, loras_slists, len(original_timesteps)) callback(-1, None, True, override_num_inference_steps = updated_num_steps) for i, t in enumerate(timesteps): offload.set_step_no_for_lora(self.transformer, first_step + i) 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 image_mask_latents is not None and denoising_strength <1. and i == first_step and morph: latent_noise_factor = t/1000 latents = original_image_latents * (1.0 - latent_noise_factor) + latents * latent_noise_factor latents_dtype = latents.dtype # latent_model_input = latents def denoise(latent_model_input, true_cfg_scale): if image_latents is not None: latent_model_input = torch.cat([latents, image_latents], dim=1) do_true_cfg = true_cfg_scale > 1 if do_true_cfg and joint_pass: noise_pred, neg_noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep / 1000, guidance=guidance, #!!!! encoder_hidden_states_mask_list=[prompt_embeds_mask,negative_prompt_embeds_mask], encoder_hidden_states_list=[prompt_embeds, negative_prompt_embeds], img_shapes=img_shapes, txt_seq_lens_list=[txt_seq_lens, negative_txt_seq_lens], attention_kwargs=self.attention_kwargs, **kwargs ) if noise_pred == None: return None, None noise_pred = noise_pred[:, : latents.size(1)] neg_noise_pred = neg_noise_pred[:, : latents.size(1)] else: neg_noise_pred = None noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep / 1000, guidance=guidance, encoder_hidden_states_mask_list=[prompt_embeds_mask], encoder_hidden_states_list=[prompt_embeds], img_shapes=img_shapes, txt_seq_lens_list=[txt_seq_lens], attention_kwargs=self.attention_kwargs, **kwargs )[0] if noise_pred == None: return None, None noise_pred = noise_pred[:, : latents.size(1)] if do_true_cfg: neg_noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep / 1000, guidance=guidance, encoder_hidden_states_mask_list=[negative_prompt_embeds_mask], encoder_hidden_states_list=[negative_prompt_embeds], img_shapes=img_shapes, txt_seq_lens_list=[negative_txt_seq_lens], attention_kwargs=self.attention_kwargs, **kwargs )[0] if neg_noise_pred == None: return None, None neg_noise_pred = neg_noise_pred[:, : latents.size(1)] return noise_pred, neg_noise_pred def cfg_predictions( noise_pred, neg_noise_pred, guidance, t): if do_true_cfg: comb_pred = neg_noise_pred + guidance * (noise_pred - neg_noise_pred) if comb_pred == None: return None cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True) noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True) noise_pred = comb_pred * (cond_norm / noise_norm) return noise_pred if lanpaint_proc is not None and i<=3: latents = lanpaint_proc(denoise, cfg_predictions, true_cfg_scale, 1., latents, original_image_latents, randn, t/1000, image_mask_latents, height=height , width= width, vae_scale_factor= 8) if latents is None: return None noise_pred, neg_noise_pred = denoise(latents, true_cfg_scale) if noise_pred == None: return None noise_pred = cfg_predictions(noise_pred, neg_noise_pred, true_cfg_scale, t) neg_noise_pred = None # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] noise_pred = None if image_mask_latents is not None: if lanpaint_proc is not None: latents = original_image_latents * (1-image_mask_latents) + image_mask_latents * latents else: next_t = timesteps[i+1] if i