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 )