diffsynth/pipelines/qwen_image.py

import torch
from PIL import Image
from typing import Union
from PIL import Image
from tqdm import tqdm
from einops import rearrange
import numpy as np

from ..models import ModelManager, load_state_dict
from ..models.qwen_image_dit import QwenImageDiT
from ..models.qwen_image_text_encoder import QwenImageTextEncoder
from ..models.qwen_image_vae import QwenImageVAE
from ..models.qwen_image_controlnet import QwenImageBlockWiseControlNet
from ..schedulers import FlowMatchScheduler
from ..utils import BasePipeline, ModelConfig, PipelineUnitRunner, PipelineUnit
from ..lora import GeneralLoRALoader
from .flux_image_new import ControlNetInput

from ..vram_management import gradient_checkpoint_forward, enable_vram_management, AutoWrappedModule, AutoWrappedLinear


class QwenImageBlockwiseMultiControlNet(torch.nn.Module):
    def __init__(self, models: list[QwenImageBlockWiseControlNet]):
        super().__init__()
        if not isinstance(models, list):
            models = [models]
        self.models = torch.nn.ModuleList(models)

    def preprocess(self, controlnet_inputs: list[ControlNetInput], conditionings: list[torch.Tensor], **kwargs):
        processed_conditionings = []
        for controlnet_input, conditioning in zip(controlnet_inputs, conditionings):
            conditioning = rearrange(conditioning, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
            model_output = self.models[controlnet_input.controlnet_id].process_controlnet_conditioning(conditioning)
            processed_conditionings.append(model_output)
        return processed_conditionings

    def blockwise_forward(self, image, conditionings: list[torch.Tensor], controlnet_inputs: list[ControlNetInput], progress_id, num_inference_steps, block_id, **kwargs):
        res = 0
        for controlnet_input, conditioning in zip(controlnet_inputs, conditionings):
            progress = (num_inference_steps - 1 - progress_id) / max(num_inference_steps - 1, 1)
            if progress > controlnet_input.start + (1e-4) or progress < controlnet_input.end - (1e-4):
                continue
            model_output = self.models[controlnet_input.controlnet_id].blockwise_forward(image, conditioning, block_id)
            res = res + model_output * controlnet_input.scale
        return res


class QwenImagePipeline(BasePipeline):

    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
        super().__init__(
            device=device, torch_dtype=torch_dtype,
            height_division_factor=16, width_division_factor=16,
        )
        from transformers import Qwen2Tokenizer, Qwen2VLProcessor
        
        self.scheduler = FlowMatchScheduler(sigma_min=0, sigma_max=1, extra_one_step=True, exponential_shift=True, exponential_shift_mu=0.8, shift_terminal=0.02)
        self.text_encoder: QwenImageTextEncoder = None
        self.dit: QwenImageDiT = None
        self.vae: QwenImageVAE = None
        self.blockwise_controlnet: QwenImageBlockwiseMultiControlNet = None
        self.tokenizer: Qwen2Tokenizer = None
        self.processor: Qwen2VLProcessor = None
        self.unit_runner = PipelineUnitRunner()
        self.in_iteration_models = ("dit", "blockwise_controlnet")
        self.units = [
            QwenImageUnit_ShapeChecker(),
            QwenImageUnit_NoiseInitializer(),
            QwenImageUnit_InputImageEmbedder(),
            QwenImageUnit_Inpaint(),
            QwenImageUnit_EditImageEmbedder(),
            QwenImageUnit_ContextImageEmbedder(),
            QwenImageUnit_PromptEmbedder(),
            QwenImageUnit_EntityControl(),
            QwenImageUnit_BlockwiseControlNet(),
        ]
        self.model_fn = model_fn_qwen_image
        
        
    def load_lora(
        self,
        module: torch.nn.Module,
        lora_config: Union[ModelConfig, str] = None,
        alpha=1,
        hotload=False,
        state_dict=None,
    ):
        if state_dict is None:
            if isinstance(lora_config, str):
                lora = load_state_dict(lora_config, torch_dtype=self.torch_dtype, device=self.device)
            else:
                lora_config.download_if_necessary()
                lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device)
        else:
            lora = state_dict
        if hotload:
            for name, module in module.named_modules():
                if isinstance(module, AutoWrappedLinear):
                    lora_a_name = f'{name}.lora_A.default.weight'
                    lora_b_name = f'{name}.lora_B.default.weight'
                    if lora_a_name in lora and lora_b_name in lora:
                        module.lora_A_weights.append(lora[lora_a_name] * alpha)
                        module.lora_B_weights.append(lora[lora_b_name])
        else:
            loader = GeneralLoRALoader(torch_dtype=self.torch_dtype, device=self.device)
            loader.load(module, lora, alpha=alpha)
            
            
    def clear_lora(self):
        for name, module in self.named_modules():
            if isinstance(module, AutoWrappedLinear): 
                if hasattr(module, "lora_A_weights"):
                    module.lora_A_weights.clear()
                if hasattr(module, "lora_B_weights"):
                    module.lora_B_weights.clear()
                    
    
    def enable_lora_magic(self):
        if self.dit is not None:
            if not (hasattr(self.dit, "vram_management_enabled") and self.dit.vram_management_enabled):
                dtype = next(iter(self.dit.parameters())).dtype
                enable_vram_management(
                    self.dit,
                    module_map = {
                        torch.nn.Linear: AutoWrappedLinear,
                    },
                    module_config = dict(
                        offload_dtype=dtype,
                        offload_device=self.device,
                        onload_dtype=dtype,
                        onload_device=self.device,
                        computation_dtype=self.torch_dtype,
                        computation_device=self.device,
                    ),
                    vram_limit=None,
                )
    
    
    def training_loss(self, **inputs):
        timestep_id = torch.randint(0, self.scheduler.num_train_timesteps, (1,))
        timestep = self.scheduler.timesteps[timestep_id].to(dtype=self.torch_dtype, device=self.device)
        
        noise = torch.randn_like(inputs["input_latents"])
        inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], noise, timestep)
        training_target = self.scheduler.training_target(inputs["input_latents"], noise, timestep)
        
        noise_pred = self.model_fn(**inputs, timestep=timestep)
        
        loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
        loss = loss * self.scheduler.training_weight(timestep)
        return loss
    
    
    def direct_distill_loss(self, **inputs):
        self.scheduler.set_timesteps(inputs["num_inference_steps"])
        models = {name: getattr(self, name) for name in self.in_iteration_models}
        for progress_id, timestep in enumerate(self.scheduler.timesteps):
            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
            noise_pred = self.model_fn(**models, **inputs, timestep=timestep, progress_id=progress_id)
            inputs["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs)
        loss = torch.nn.functional.mse_loss(inputs["latents"].float(), inputs["input_latents"].float())
        return loss
    
    
    def _enable_fp8_lora_training(self, dtype):
        from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm, Qwen2_5_VisionPatchEmbed, Qwen2_5_VisionRotaryEmbedding
        from ..models.qwen_image_dit import RMSNorm
        from ..models.qwen_image_vae import QwenImageRMS_norm
        module_map = {
            RMSNorm: AutoWrappedModule,
            torch.nn.Linear: AutoWrappedLinear,
            torch.nn.Conv3d: AutoWrappedModule,
            torch.nn.Conv2d: AutoWrappedModule,
            torch.nn.Embedding: AutoWrappedModule,
            Qwen2_5_VLRotaryEmbedding: AutoWrappedModule,
            Qwen2RMSNorm: AutoWrappedModule,
            Qwen2_5_VisionPatchEmbed: AutoWrappedModule,
            Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule,
            QwenImageRMS_norm: AutoWrappedModule,
        }
        model_config = dict(
            offload_dtype=dtype,
            offload_device="cuda",
            onload_dtype=dtype,
            onload_device="cuda",
            computation_dtype=self.torch_dtype,
            computation_device="cuda",
        )
        if self.text_encoder is not None:
            enable_vram_management(self.text_encoder, module_map=module_map, module_config=model_config)
        if self.dit is not None:
            enable_vram_management(self.dit, module_map=module_map, module_config=model_config)
        if self.vae is not None:
            enable_vram_management(self.vae, module_map=module_map, module_config=model_config)
    
    
    def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5, auto_offload=True, enable_dit_fp8_computation=False):
        self.vram_management_enabled = True
        if vram_limit is None and auto_offload:
            vram_limit = self.get_vram()
        if vram_limit is not None:
            vram_limit = vram_limit - vram_buffer
        
        if self.text_encoder is not None:
            from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm, Qwen2_5_VisionPatchEmbed, Qwen2_5_VisionRotaryEmbedding
            dtype = next(iter(self.text_encoder.parameters())).dtype
            enable_vram_management(
                self.text_encoder,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Embedding: AutoWrappedModule,
                    Qwen2_5_VLRotaryEmbedding: AutoWrappedModule,
                    Qwen2RMSNorm: AutoWrappedModule,
                    Qwen2_5_VisionPatchEmbed: AutoWrappedModule,
                    Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device="cpu",
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                vram_limit=vram_limit,
            )
        if self.dit is not None:
            from ..models.qwen_image_dit import RMSNorm
            dtype = next(iter(self.dit.parameters())).dtype
            device = "cpu" if vram_limit is not None else self.device
            if not enable_dit_fp8_computation:
                enable_vram_management(
                    self.dit,
                    module_map = {
                        RMSNorm: AutoWrappedModule,
                        torch.nn.Linear: AutoWrappedLinear,
                    },
                    module_config = dict(
                        offload_dtype=dtype,
                        offload_device="cpu",
                        onload_dtype=dtype,
                        onload_device=device,
                        computation_dtype=self.torch_dtype,
                        computation_device=self.device,
                    ),
                    vram_limit=vram_limit,
                )
            else:
                enable_vram_management(
                    self.dit,
                    module_map = {
                        RMSNorm: AutoWrappedModule,
                    },
                    module_config = dict(
                        offload_dtype=dtype,
                        offload_device="cpu",
                        onload_dtype=dtype,
                        onload_device=device,
                        computation_dtype=self.torch_dtype,
                        computation_device=self.device,
                    ),
                    vram_limit=vram_limit,
                )
                enable_vram_management(
                    self.dit,
                    module_map = {
                        torch.nn.Linear: AutoWrappedLinear,
                    },
                    module_config = dict(
                        offload_dtype=dtype,
                        offload_device="cpu",
                        onload_dtype=dtype,
                        onload_device=device,
                        computation_dtype=dtype,
                        computation_device=self.device,
                    ),
                    vram_limit=vram_limit,
                )
        if self.vae is not None:
            from ..models.qwen_image_vae import QwenImageRMS_norm
            dtype = next(iter(self.vae.parameters())).dtype
            enable_vram_management(
                self.vae,
                module_map = {
                    torch.nn.Linear: AutoWrappedLinear,
                    torch.nn.Conv3d: AutoWrappedModule,
                    torch.nn.Conv2d: AutoWrappedModule,
                    QwenImageRMS_norm: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device="cpu",
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                vram_limit=vram_limit,
            )
        if self.blockwise_controlnet is not None:
            enable_vram_management(
                self.blockwise_controlnet,
                module_map = {
                    RMSNorm: AutoWrappedModule,
                    torch.nn.Linear: AutoWrappedLinear,
                },
                module_config = dict(
                    offload_dtype=dtype,
                    offload_device="cpu",
                    onload_dtype=dtype,
                    onload_device=device,
                    computation_dtype=self.torch_dtype,
                    computation_device=self.device,
                ),
                vram_limit=vram_limit,
            )
    
    
    @staticmethod
    def from_pretrained(
        torch_dtype: torch.dtype = torch.bfloat16,
        device: Union[str, torch.device] = "cuda",
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
        processor_config: ModelConfig = None,
    ):
        # Download and load models
        model_manager = ModelManager()
        for model_config in model_configs:
            model_config.download_if_necessary()
            model_manager.load_model(
                model_config.path,
                device=model_config.offload_device or device,
                torch_dtype=model_config.offload_dtype or torch_dtype
            )
        
        # Initialize pipeline
        pipe = QwenImagePipeline(device=device, torch_dtype=torch_dtype)
        pipe.text_encoder = model_manager.fetch_model("qwen_image_text_encoder")
        pipe.dit = model_manager.fetch_model("qwen_image_dit")
        pipe.vae = model_manager.fetch_model("qwen_image_vae")
        pipe.blockwise_controlnet = QwenImageBlockwiseMultiControlNet(model_manager.fetch_model("qwen_image_blockwise_controlnet", index="all"))
        if tokenizer_config is not None and pipe.text_encoder is not None:
            tokenizer_config.download_if_necessary()
            from transformers import Qwen2Tokenizer
            pipe.tokenizer = Qwen2Tokenizer.from_pretrained(tokenizer_config.path)
        if processor_config is not None:
            processor_config.download_if_necessary()
            from transformers import Qwen2VLProcessor
            pipe.processor = Qwen2VLProcessor.from_pretrained(processor_config.path)
        return pipe
    
    
    @torch.no_grad()
    def __call__(
        self,
        # Prompt
        prompt: str,
        negative_prompt: str = "",
        cfg_scale: float = 4.0,
        # Image
        input_image: Image.Image = None,
        denoising_strength: float = 1.0,
        # Inpaint
        inpaint_mask: Image.Image = None,
        inpaint_blur_size: int = None,
        inpaint_blur_sigma: float = None,
        # Shape
        height: int = 1328,
        width: int = 1328,
        # Randomness
        seed: int = None,
        rand_device: str = "cpu",
        # Steps
        num_inference_steps: int = 30,
        exponential_shift_mu: float = None,
        # Blockwise ControlNet
        blockwise_controlnet_inputs: list[ControlNetInput] = None,
        # EliGen
        eligen_entity_prompts: list[str] = None,
        eligen_entity_masks: list[Image.Image] = None,
        eligen_enable_on_negative: bool = False,
        # Qwen-Image-Edit
        edit_image: Image.Image = None,
        edit_image_auto_resize: bool = True,
        edit_rope_interpolation: bool = False,
        # In-context control
        context_image: Image.Image = None,
        # FP8
        enable_fp8_attention: bool = False,
        # Tile
        tiled: bool = False,
        tile_size: int = 128,
        tile_stride: int = 64,
        # Progress bar
        progress_bar_cmd = tqdm,
    ):
        # Scheduler
        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, dynamic_shift_len=(height // 16) * (width // 16), exponential_shift_mu=exponential_shift_mu)
        
        # Parameters
        inputs_posi = {
            "prompt": prompt,
        }
        inputs_nega = {
            "negative_prompt": negative_prompt,
        }
        inputs_shared = {
            "cfg_scale": cfg_scale,
            "input_image": input_image, "denoising_strength": denoising_strength,
            "inpaint_mask": inpaint_mask, "inpaint_blur_size": inpaint_blur_size, "inpaint_blur_sigma": inpaint_blur_sigma,
            "height": height, "width": width,
            "seed": seed, "rand_device": rand_device,
            "enable_fp8_attention": enable_fp8_attention,
            "num_inference_steps": num_inference_steps,
            "blockwise_controlnet_inputs": blockwise_controlnet_inputs,
            "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride,
            "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative,
            "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize, "edit_rope_interpolation": edit_rope_interpolation, 
            "context_image": context_image,
        }
        for unit in self.units:
            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)

        # Denoise
        self.load_models_to_device(self.in_iteration_models)
        models = {name: getattr(self, name) for name in self.in_iteration_models}
        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)

            # Inference
            noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep, progress_id=progress_id)
            if cfg_scale != 1.0:
                noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep, progress_id=progress_id)
                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
            else:
                noise_pred = noise_pred_posi

            # Scheduler
            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
        
        # Decode
        self.load_models_to_device(['vae'])
        image = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        image = self.vae_output_to_image(image)
        self.load_models_to_device([])

        return image



class QwenImageUnit_ShapeChecker(PipelineUnit):
    def __init__(self):
        super().__init__(input_params=("height", "width"))

    def process(self, pipe: QwenImagePipeline, height, width):
        height, width = pipe.check_resize_height_width(height, width)
        return {"height": height, "width": width}



class QwenImageUnit_NoiseInitializer(PipelineUnit):
    def __init__(self):
        super().__init__(input_params=("height", "width", "seed", "rand_device"))

    def process(self, pipe: QwenImagePipeline, height, width, seed, rand_device):
        noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
        return {"noise": noise}



class QwenImageUnit_InputImageEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("input_image", "noise", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("vae",)
        )

    def process(self, pipe: QwenImagePipeline, input_image, noise, tiled, tile_size, tile_stride):
        if input_image is None:
            return {"latents": noise, "input_latents": None}
        pipe.load_models_to_device(['vae'])
        image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
        input_latents = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        if pipe.scheduler.training:
            return {"latents": noise, "input_latents": input_latents}
        else:
            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
            return {"latents": latents, "input_latents": input_latents}



class QwenImageUnit_Inpaint(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("inpaint_mask", "height", "width", "inpaint_blur_size", "inpaint_blur_sigma"),
        )

    def process(self, pipe: QwenImagePipeline, inpaint_mask, height, width, inpaint_blur_size, inpaint_blur_sigma):
        if inpaint_mask is None:
            return {}
        inpaint_mask = pipe.preprocess_image(inpaint_mask.convert("RGB").resize((width // 8, height // 8)), min_value=0, max_value=1)
        inpaint_mask = inpaint_mask.mean(dim=1, keepdim=True)
        if inpaint_blur_size is not None and inpaint_blur_sigma is not None:
            from torchvision.transforms import GaussianBlur
            blur = GaussianBlur(kernel_size=inpaint_blur_size * 2 + 1, sigma=inpaint_blur_sigma)
            inpaint_mask = blur(inpaint_mask)
        return {"inpaint_mask": inpaint_mask}


class QwenImageUnit_PromptEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            seperate_cfg=True,
            input_params_posi={"prompt": "prompt"},
            input_params_nega={"prompt": "negative_prompt"},
            input_params=("edit_image",),
            onload_model_names=("text_encoder",)
        )
        
    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 calculate_dimensions(self, target_area, ratio):
        import math
        width = math.sqrt(target_area * ratio)
        height = width / ratio
        width = round(width / 32) * 32
        height = round(height / 32) * 32
        return width, height
    
    def resize_image(self, image, target_area=384*384):
        width, height = self.calculate_dimensions(target_area, image.size[0] / image.size[1])
        return image.resize((width, height))
    
    def encode_prompt(self, pipe: QwenImagePipeline, prompt):
        template = "<|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"
        drop_idx = 34
        txt = [template.format(e) for e in prompt]
        model_inputs = pipe.tokenizer(txt, max_length=4096+drop_idx, padding=True, truncation=True, return_tensors="pt").to(pipe.device)
        if model_inputs.input_ids.shape[1] >= 1024:
            print(f"Warning!!! QwenImage model was trained on prompts up to 512 tokens. Current prompt requires {model_inputs['input_ids'].shape[1] - drop_idx} tokens, which may lead to unpredictable behavior.")
        hidden_states = pipe.text_encoder(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask, output_hidden_states=True,)[-1]
        split_hidden_states = self.extract_masked_hidden(hidden_states, model_inputs.attention_mask)
        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
        return split_hidden_states
        
    def encode_prompt_edit(self, pipe: QwenImagePipeline, prompt, edit_image):
        template =  "<|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"
        drop_idx = 64
        txt = [template.format(e) for e in prompt]
        model_inputs = pipe.processor(text=txt, images=edit_image, padding=True, return_tensors="pt").to(pipe.device)
        hidden_states = pipe.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,)[-1]
        split_hidden_states = self.extract_masked_hidden(hidden_states, model_inputs.attention_mask)
        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
        return split_hidden_states
    
    def encode_prompt_edit_multi(self, pipe: QwenImagePipeline, prompt, edit_image):
        template =  "<|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"
        drop_idx = 64
        img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>"
        base_img_prompt = "".join([img_prompt_template.format(i + 1) for i in range(len(edit_image))])
        txt = [template.format(base_img_prompt + e) for e in prompt]
        edit_image = [self.resize_image(image) for image in edit_image]
        model_inputs = pipe.processor(text=txt, images=edit_image, padding=True, return_tensors="pt").to(pipe.device)
        hidden_states = pipe.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,)[-1]
        split_hidden_states = self.extract_masked_hidden(hidden_states, model_inputs.attention_mask)
        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
        return split_hidden_states

    def process(self, pipe: QwenImagePipeline, prompt, edit_image=None) -> dict:
        if pipe.text_encoder is not None:
            prompt = [prompt]
            if edit_image is None:
                split_hidden_states = self.encode_prompt(pipe, prompt)
            elif isinstance(edit_image, Image.Image):
                split_hidden_states = self.encode_prompt_edit(pipe, prompt, edit_image)
            else:
                split_hidden_states = self.encode_prompt_edit_multi(pipe, prompt, edit_image)
            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=pipe.torch_dtype, device=pipe.device)
            return {"prompt_emb": prompt_embeds, "prompt_emb_mask": encoder_attention_mask}
        else:
            return {}


class QwenImageUnit_EntityControl(PipelineUnit):
    def __init__(self):
        super().__init__(
            take_over=True,
            onload_model_names=("text_encoder",)
        )

    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_prompt_emb(self, pipe: QwenImagePipeline, prompt) -> dict:
        if pipe.text_encoder is not None:
            prompt = [prompt]
            template = "<|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"
            drop_idx = 34
            txt = [template.format(e) for e in prompt]
            txt_tokens = pipe.tokenizer(txt, max_length=1024+drop_idx, padding=True, truncation=True, return_tensors="pt").to(pipe.device)
            hidden_states = pipe.text_encoder(input_ids=txt_tokens.input_ids, attention_mask=txt_tokens.attention_mask, output_hidden_states=True,)[-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=pipe.torch_dtype, device=pipe.device)
            return {"prompt_emb": prompt_embeds, "prompt_emb_mask": encoder_attention_mask}
        else:
            return {}

    def preprocess_masks(self, pipe, masks, height, width, dim):
        out_masks = []
        for mask in masks:
            mask = pipe.preprocess_image(mask.resize((width, height), resample=Image.NEAREST)).mean(dim=1, keepdim=True) > 0
            mask = mask.repeat(1, dim, 1, 1).to(device=pipe.device, dtype=pipe.torch_dtype)
            out_masks.append(mask)
        return out_masks

    def prepare_entity_inputs(self, pipe, entity_prompts, entity_masks, width, height):
        entity_masks = self.preprocess_masks(pipe, entity_masks, height//8, width//8, 1)
        entity_masks = torch.cat(entity_masks, dim=0).unsqueeze(0) # b, n_mask, c, h, w
        prompt_embs, prompt_emb_masks = [], []
        for entity_prompt in entity_prompts:
            prompt_emb_dict = self.get_prompt_emb(pipe, entity_prompt)
            prompt_embs.append(prompt_emb_dict['prompt_emb'])
            prompt_emb_masks.append(prompt_emb_dict['prompt_emb_mask'])
        return prompt_embs, prompt_emb_masks, entity_masks

    def prepare_eligen(self, pipe, prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, enable_eligen_on_negative, cfg_scale):
        entity_prompt_emb_posi, entity_prompt_emb_posi_mask, entity_masks_posi = self.prepare_entity_inputs(pipe, eligen_entity_prompts, eligen_entity_masks, width, height)
        if enable_eligen_on_negative and cfg_scale != 1.0:
            entity_prompt_emb_nega = [prompt_emb_nega['prompt_emb']] * len(entity_prompt_emb_posi)
            entity_prompt_emb_nega_mask = [prompt_emb_nega['prompt_emb_mask']] * len(entity_prompt_emb_posi)
            entity_masks_nega = entity_masks_posi
        else:
            entity_prompt_emb_nega, entity_prompt_emb_nega_mask, entity_masks_nega = None, None, None
        eligen_kwargs_posi = {"entity_prompt_emb": entity_prompt_emb_posi, "entity_masks": entity_masks_posi, "entity_prompt_emb_mask": entity_prompt_emb_posi_mask}
        eligen_kwargs_nega = {"entity_prompt_emb": entity_prompt_emb_nega, "entity_masks": entity_masks_nega, "entity_prompt_emb_mask": entity_prompt_emb_nega_mask}
        return eligen_kwargs_posi, eligen_kwargs_nega

    def process(self, pipe: QwenImagePipeline, inputs_shared, inputs_posi, inputs_nega):
        eligen_entity_prompts, eligen_entity_masks = inputs_shared.get("eligen_entity_prompts", None), inputs_shared.get("eligen_entity_masks", None)
        if eligen_entity_prompts is None or eligen_entity_masks is None or len(eligen_entity_prompts) == 0 or len(eligen_entity_masks) == 0:
            return inputs_shared, inputs_posi, inputs_nega
        pipe.load_models_to_device(self.onload_model_names)
        eligen_enable_on_negative = inputs_shared.get("eligen_enable_on_negative", False)
        eligen_kwargs_posi, eligen_kwargs_nega = self.prepare_eligen(pipe, inputs_nega,
            eligen_entity_prompts, eligen_entity_masks, inputs_shared["width"], inputs_shared["height"],
            eligen_enable_on_negative, inputs_shared["cfg_scale"])
        inputs_posi.update(eligen_kwargs_posi)
        if inputs_shared.get("cfg_scale", 1.0) != 1.0:
            inputs_nega.update(eligen_kwargs_nega)
        return inputs_shared, inputs_posi, inputs_nega



class QwenImageUnit_BlockwiseControlNet(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("blockwise_controlnet_inputs", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("vae",)
        )

    def apply_controlnet_mask_on_latents(self, pipe, latents, mask):
        mask = (pipe.preprocess_image(mask) + 1) / 2
        mask = mask.mean(dim=1, keepdim=True)
        mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:])
        latents = torch.concat([latents, mask], dim=1)
        return latents

    def apply_controlnet_mask_on_image(self, pipe, image, mask):
        mask = mask.resize(image.size)
        mask = pipe.preprocess_image(mask).mean(dim=[0, 1]).cpu()
        image = np.array(image)
        image[mask > 0] = 0
        image = Image.fromarray(image)
        return image

    def process(self, pipe: QwenImagePipeline, blockwise_controlnet_inputs: list[ControlNetInput], tiled, tile_size, tile_stride):
        if blockwise_controlnet_inputs is None:
            return {}
        pipe.load_models_to_device(self.onload_model_names)
        conditionings = []
        for controlnet_input in blockwise_controlnet_inputs:
            image = controlnet_input.image
            if controlnet_input.inpaint_mask is not None:
                image = self.apply_controlnet_mask_on_image(pipe, image, controlnet_input.inpaint_mask)

            image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype)
            image = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)

            if controlnet_input.inpaint_mask is not None:
                image = self.apply_controlnet_mask_on_latents(pipe, image, controlnet_input.inpaint_mask)
            conditionings.append(image)
            
        return {"blockwise_controlnet_conditioning": conditionings}


class QwenImageUnit_EditImageEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("edit_image", "tiled", "tile_size", "tile_stride", "edit_image_auto_resize"),
            onload_model_names=("vae",)
        )


    def calculate_dimensions(self, target_area, ratio):
        import math
        width = math.sqrt(target_area * ratio)
        height = width / ratio
        width = round(width / 32) * 32
        height = round(height / 32) * 32
        return width, height


    def edit_image_auto_resize(self, edit_image):
        calculated_width, calculated_height = self.calculate_dimensions(1024 * 1024, edit_image.size[0] / edit_image.size[1])
        return edit_image.resize((calculated_width, calculated_height))


    def process(self, pipe: QwenImagePipeline, edit_image, tiled, tile_size, tile_stride, edit_image_auto_resize=False):
        if edit_image is None:
            return {}
        pipe.load_models_to_device(['vae'])
        if isinstance(edit_image, Image.Image):
            resized_edit_image = self.edit_image_auto_resize(edit_image) if edit_image_auto_resize else edit_image
            edit_image = pipe.preprocess_image(resized_edit_image).to(device=pipe.device, dtype=pipe.torch_dtype)
            edit_latents = pipe.vae.encode(edit_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        else:
            resized_edit_image, edit_latents = [], []
            for image in edit_image:
                if edit_image_auto_resize:
                    image = self.edit_image_auto_resize(image)
                resized_edit_image.append(image)
                image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype)
                latents = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
                edit_latents.append(latents)
        return {"edit_latents": edit_latents, "edit_image": resized_edit_image}


class QwenImageUnit_ContextImageEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("context_image", "height", "width", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("vae",)
        )

    def process(self, pipe: QwenImagePipeline, context_image, height, width, tiled, tile_size, tile_stride):
        if context_image is None:
            return {}
        pipe.load_models_to_device(['vae'])
        context_image = pipe.preprocess_image(context_image.resize((width, height))).to(device=pipe.device, dtype=pipe.torch_dtype)
        context_latents = pipe.vae.encode(context_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
        return {"context_latents": context_latents}


def model_fn_qwen_image(
    dit: QwenImageDiT = None,
    blockwise_controlnet: QwenImageBlockwiseMultiControlNet = None,
    latents=None,
    timestep=None,
    prompt_emb=None,
    prompt_emb_mask=None,
    height=None,
    width=None,
    blockwise_controlnet_conditioning=None,
    blockwise_controlnet_inputs=None,
    progress_id=0,
    num_inference_steps=1,
    entity_prompt_emb=None,
    entity_prompt_emb_mask=None,
    entity_masks=None,
    edit_latents=None,
    context_latents=None,
    enable_fp8_attention=False,
    use_gradient_checkpointing=False,
    use_gradient_checkpointing_offload=False,
    edit_rope_interpolation=False,
    **kwargs
):
    img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)]
    txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist()
    timestep = timestep / 1000
    
    image = rearrange(latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2)
    image_seq_len = image.shape[1]

    if context_latents is not None:
        img_shapes += [(context_latents.shape[0], context_latents.shape[2]//2, context_latents.shape[3]//2)]
        context_image = rearrange(context_latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=context_latents.shape[2]//2, W=context_latents.shape[3]//2, P=2, Q=2)
        image = torch.cat([image, context_image], dim=1)
    if edit_latents is not None:
        edit_latents_list = edit_latents if isinstance(edit_latents, list) else [edit_latents]
        img_shapes += [(e.shape[0], e.shape[2]//2, e.shape[3]//2) for e in edit_latents_list]
        edit_image = [rearrange(e, "B C (H P) (W Q) -> B (H W) (C P Q)", H=e.shape[2]//2, W=e.shape[3]//2, P=2, Q=2) for e in edit_latents_list]
        image = torch.cat([image] + edit_image, dim=1)

    image = dit.img_in(image)
    conditioning = dit.time_text_embed(timestep, image.dtype)

    if entity_prompt_emb is not None:
        text, image_rotary_emb, attention_mask = dit.process_entity_masks(
            latents, prompt_emb, prompt_emb_mask, entity_prompt_emb, entity_prompt_emb_mask,
            entity_masks, height, width, image, img_shapes,
        )
    else:
        text = dit.txt_in(dit.txt_norm(prompt_emb))
        if edit_rope_interpolation:
            image_rotary_emb = dit.pos_embed.forward_sampling(img_shapes, txt_seq_lens, device=latents.device)
        else:
            image_rotary_emb = dit.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
        attention_mask = None
        
    if blockwise_controlnet_conditioning is not None:
        blockwise_controlnet_conditioning = blockwise_controlnet.preprocess(
            blockwise_controlnet_inputs, blockwise_controlnet_conditioning)

    for block_id, block in enumerate(dit.transformer_blocks):
        text, image = gradient_checkpoint_forward(
            block,
            use_gradient_checkpointing,
            use_gradient_checkpointing_offload,
            image=image,
            text=text,
            temb=conditioning,
            image_rotary_emb=image_rotary_emb,
            attention_mask=attention_mask,
            enable_fp8_attention=enable_fp8_attention,
        )
        if blockwise_controlnet_conditioning is not None:
            image_slice = image[:, :image_seq_len].clone()
            controlnet_output = blockwise_controlnet.blockwise_forward(
                image=image_slice, conditionings=blockwise_controlnet_conditioning,
                controlnet_inputs=blockwise_controlnet_inputs, block_id=block_id,
                progress_id=progress_id, num_inference_steps=num_inference_steps,
            )
            image[:, :image_seq_len] = image_slice + controlnet_output
    
    image = dit.norm_out(image, conditioning)
    image = dit.proj_out(image)
    image = image[:, :image_seq_len]
    
    latents = rearrange(image, "B (H W) (C P Q) -> B C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2)
    return latents