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models/pipeline.py

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+import numpy as np
+from typing import Any, Callable, Dict, List, Optional, Union
+import einops
+
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.utils import is_torch_xla_available, logging
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps, FluxPipeline
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from models.multiLayer_adapter import MultiLayerAdapter
+
+from PIL import Image
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm # type: ignore
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+class CustomFluxPipeline(FluxPipeline):
+
+    @staticmethod
+    def _prepare_latent_image_ids(height, width, list_layer_box, device, dtype):
+
+        latent_image_ids_list = []
+        for layer_idx in range(len(list_layer_box)):
+            if list_layer_box[layer_idx] == None:
+                continue
+            else:
+                latent_image_ids = torch.zeros(height // 2, width // 2, 3) # [h/2, w/2, 3]
+                latent_image_ids[..., 0] = layer_idx # use the first dimension for layer representation
+                latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+                latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+                x1, y1, x2, y2 = list_layer_box[layer_idx]
+                x1, y1, x2, y2 = x1 // 16, y1 // 16, x2 // 16, y2 // 16
+                latent_image_ids = latent_image_ids[y1:y2, x1:x2, :]
+
+                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
+                )
+
+                latent_image_ids_list.append(latent_image_ids)
+
+        full_latent_image_ids = torch.cat(latent_image_ids_list, dim=0)
+
+        return full_latent_image_ids.to(device=device, dtype=dtype)
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_layers,
+        num_channels_latents,
+        height,
+        width,
+        list_layer_box,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = 2 * (int(height) // self.vae_scale_factor)     # Here, the vae_scale_factor is 16, but the actual latent size is height // 8, so we need to multiply by 2.
+        width = 2 * (int(width) // self.vae_scale_factor)
+
+        shape = (batch_size, num_layers, num_channels_latents, height, width)  # (1, 15, 16, 64, 64)
+
+        if latents is not None:
+            latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype)
+            return latents.to(device=device, dtype=dtype), latent_image_ids
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # [bs, f, c_latent, h, w]
+
+        latent_image_ids = self._prepare_latent_image_ids(height, width, list_layer_box, device, dtype)
+
+        return latents, latent_image_ids
+    
+    def prepare_image(
+            self,
+            image,
+            width,
+            height,
+            batch_size,
+            num_images_per_prompt,
+            device,
+            dtype,
+            do_classifier_free_guidance=False,
+        ):
+        # Prepare image
+        if isinstance(image, torch.Tensor):
+            pass
+        else:
+            image = self.image_processor.preprocess(image, height=height, width=width)
+
+        image_batch_size = image.shape[0]
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+        image = image.repeat_interleave(repeat_by, dim=0)
+        image = image.to(device=device, dtype=dtype)      # (1, C, H, W)
+
+        # create blank mask
+        mask = Image.new("RGB", (width, height), (0, 0, 0))     # Currently, the mask is not being used in practice.
+
+        # Prepare mask
+        if isinstance(mask, torch.Tensor):
+            pass
+        else:
+            self.mask_processor = VaeImageProcessor(
+                vae_scale_factor=self.vae_scale_factor,
+                do_resize=True,
+                do_convert_grayscale=True,
+                do_normalize=False,
+                do_binarize=True,
+            )
+            mask = self.mask_processor.preprocess(mask, height=height, width=width)
+        mask = mask.repeat_interleave(repeat_by, dim=0)
+        mask = mask.to(device=device, dtype=dtype)      # (1, 1, H, W)
+
+        # Get masked image
+        masked_image = image.clone()
+        masked_image[(mask > 0.5).repeat(1, 3, 1, 1)] = -1      # (1, 3, H, W)
+
+        # Encode to latents
+        image_latents = self.vae.encode(masked_image.to(self.vae.dtype)).latent_dist.sample()
+        image_latents = (
+            image_latents - self.vae.config.shift_factor
+        ) * self.vae.config.scaling_factor
+        image_latents = image_latents.to(dtype)     # (1, 16, H/8, W/8)
+
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // self.vae_scale_factor * 2, width // self.vae_scale_factor * 2)
+        )
+        mask = 1 - mask     # (1, 1, H/8, W/8)
+
+        adapter_image = torch.cat([image_latents, mask], dim=1)
+
+        # Pack cond latents
+        packed_adapter_image = self._pack_latents(
+            adapter_image,
+            batch_size * num_images_per_prompt,
+            adapter_image.shape[1],
+            adapter_image.shape[2],
+            adapter_image.shape[3],
+        )
+        
+        if do_classifier_free_guidance:
+            packed_adapter_image = torch.cat([packed_adapter_image] * 2)
+
+        return packed_adapter_image, height, width
+    
+    def set_multiLayerAdapter(self, multiLayerAdapter):
+        self.multiLayerAdapter = multiLayerAdapter
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        validation_box: List[tuple] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[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,
+        num_layers: int = 5,
+        sdxl_vae: nn.Module = None,
+        transparent_decoder: nn.Module = None,
+    ):
+        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.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead
+            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.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                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.
+            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 ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`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.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `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.flux.FluxPipelineOutput`] 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` defaults to 512): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_layers,
+            num_channels_latents,
+            height,
+            width,
+            validation_box,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = latent_image_ids.shape[0] # ???
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        # 6. 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]).to(latents.dtype)
+
+                noise_pred = self.transformer(
+                    hidden_states=latents,
+                    list_layer_box=validation_box,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        # create a grey latent
+        bs, n_frames, channel_latent, height, width = latents.shape
+
+        pixel_grey = torch.zeros(size=(bs*n_frames, 3, height*8, width*8), device=latents.device, dtype=latents.dtype)
+        latent_grey = self.vae.encode(pixel_grey).latent_dist.sample()
+        latent_grey = (latent_grey - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+        latent_grey = latent_grey.view(bs, n_frames, channel_latent, height, width)  # [bs, f, c_latent, h, w]
+        
+        # fill in the latents
+        for layer_idx in range(latent_grey.shape[1]):
+            x1, y1, x2, y2 = validation_box[layer_idx]
+            x1, y1, x2, y2 = x1 // 8, y1 // 8, x2 // 8, y2 // 8
+            latent_grey[:, layer_idx, :, y1:y2, x1:x2] = latents[:, layer_idx, :, y1:y2, x1:x2]
+        latents = latent_grey
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            latents = latents.reshape(bs * n_frames, channel_latent, height, width)
+            image = self.vae.decode(latents, return_dict=False)[0]
+            if sdxl_vae is not None:
+                sdxl_vae = sdxl_vae.to(dtype=image.dtype, device=image.device)
+                sdxl_latents = sdxl_vae.encode(image).latent_dist.sample()
+                transparent_decoder = transparent_decoder.to(dtype=image.dtype, device=image.device)
+                result_list, vis_list = transparent_decoder(sdxl_vae, sdxl_latents)
+            else:
+                result_list, vis_list = None, None
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, result_list, vis_list)
+
+        return FluxPipelineOutput(images=image), result_list, vis_list
+
+
+class CustomFluxPipelineCfgLayer(CustomFluxPipeline):
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        validation_box: List[tuple] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        true_gs: float = 3.5,
+        adapter_image: PipelineImageInput = None,
+        adapter_mask: PipelineImageInput = None,
+        adapter_conditioning_scale: Union[float, List[float]] = 1.0,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[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,
+        num_layers: int = 5,
+        sdxl_vae: nn.Module = None,
+        transparent_decoder: nn.Module = None,
+    ):
+        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.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead
+            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.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                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.
+            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 ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`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.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `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.flux.FluxPipelineOutput`] 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` defaults to 512): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+        (
+            neg_prompt_embeds,
+            neg_pooled_prompt_embeds,
+            neg_text_ids,
+        ) = self.encode_prompt(
+            prompt="",
+            prompt_2=None,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 3. Prepare image
+        num_channels_latents = self.transformer.config.in_channels // 4
+        if isinstance(self.multiLayerAdapter, MultiLayerAdapter):
+            adapter_image, _, _ = self.prepare_image(
+                image=adapter_image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=self.transformer.dtype,
+            )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_layers,
+            num_channels_latents,
+            height,
+            width,
+            validation_box,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = latent_image_ids.shape[0]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        # 6. 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]).to(latents.dtype)
+
+                (
+                    adapter_block_samples,
+                    adapter_single_block_samples,
+                ) = self.multiLayerAdapter(
+                    hidden_states=latents,
+                    list_layer_box=validation_box,
+                    adapter_cond=adapter_image,
+                    conditioning_scale=adapter_conditioning_scale,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )
+
+                noise_pred = self.transformer(
+                    hidden_states=latents,
+                    list_layer_box=validation_box,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    adapter_block_samples=[
+                        sample.to(dtype=self.transformer.dtype)
+                        for sample in adapter_block_samples
+                    ],
+                    adapter_single_block_samples=[
+                        sample.to(dtype=self.transformer.dtype)
+                        for sample in adapter_single_block_samples
+                    ] if adapter_single_block_samples is not None else adapter_single_block_samples,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                neg_noise_pred = self.transformer(
+                    hidden_states=latents,
+                    list_layer_box=validation_box,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=neg_pooled_prompt_embeds,
+                    encoder_hidden_states=neg_prompt_embeds,
+                    adapter_block_samples=[
+                        sample.to(dtype=self.transformer.dtype)
+                        for sample in adapter_block_samples
+                    ],
+                    adapter_single_block_samples=[
+                        sample.to(dtype=self.transformer.dtype)
+                        for sample in adapter_single_block_samples
+                    ] if adapter_single_block_samples is not None else adapter_single_block_samples,
+                    txt_ids=neg_text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                noise_pred = neg_noise_pred + true_gs * (noise_pred - neg_noise_pred)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        # create a grey latent
+        bs, n_frames, channel_latent, height, width = latents.shape
+
+        def encode_in_chunks(vae, images, chunk=8):
+            parts = []
+            for i in range(0, images.shape[0], chunk):
+                chunk_img = images[i : i + chunk]
+                part_latent = vae.encode(chunk_img).latent_dist.sample()
+                parts.append(part_latent)
+                torch.cuda.empty_cache()
+            return torch.cat(parts, dim=0)
+
+        pixel_grey = torch.zeros(size=(bs*n_frames, 3, height*8, width*8), device=latents.device, dtype=latents.dtype)
+        # latent_grey = self.vae.encode(pixel_grey).latent_dist.sample()
+        latent_grey = encode_in_chunks(self.vae, pixel_grey, chunk=16)
+        latent_grey = (latent_grey - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+        latent_grey = latent_grey.view(bs, n_frames, channel_latent, height, width)  # [bs, f, c_latent, h, w]
+        
+        # fill in the latents
+        for layer_idx in range(latent_grey.shape[1]):
+            if validation_box[layer_idx] == None:
+                continue
+            x1, y1, x2, y2 = validation_box[layer_idx]
+            x1, y1, x2, y2 = x1 // 8, y1 // 8, x2 // 8, y2 // 8
+            latent_grey[:, layer_idx, :, y1:y2, x1:x2] = latents[:, layer_idx, :, y1:y2, x1:x2]
+        latents = latent_grey
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            bs, num_layers, c, h, w = latents.shape
+            latents = latents.reshape(bs * n_frames, channel_latent, height, width)
+            latents_segs = torch.split(latents, 8, dim=0)
+            image_segs = [self.vae.decode(latents_seg, return_dict=False)[0] for latents_seg in latents_segs]
+            image = torch.cat(image_segs, dim=0)
+            if sdxl_vae is not None:
+                sdxl_vae = sdxl_vae.to(dtype=image.dtype, device=image.device)
+
+                # Prepare input parameters
+                _, c1, h1, w1 = image.shape  # Get channels and spatial dimensions from image
+                x = image.view(bs, num_layers, c1, h1, w1).permute(0, 2, 1, 3, 4).to(image.device)  # Reshape to (bs, c, num_layers, h, w)
+                box = [validation_box] * bs  # Create box info for each sample
+                use_layers = [list(range(len(b))) for b in box]  # Use all layers
+                z_2d = latents.view(bs, num_layers, -1, h, w)  # Reshape to (bs, num_layers, c, h, w)
+                z_2d = einops.rearrange(z_2d, "b t c h w -> b c t h w").to(image.device)  # Reshape to (bs, c, num_layers, h, w)
+
+                # Call transparent VAE decoder
+                x_hat = sdxl_vae(x, box, use_layers, z_2d).to(x.dtype).clamp(-1, 1)
+            else:
+                result_list, vis_list = None, None
+            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 (
+            x_hat,           # Final decoded result including foreground and transparency
+            image,           # Final generated RGB image
+            latents          # Latent variables
+        )