import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch import torch.nn.functional as F from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection, ) from diffusers.utils.import_utils import is_invisible_watermark_available from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import ( FromSingleFileMixin, IPAdapterMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, ) from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel from diffusers.models.attention_processor import ( AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor, ) from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, ) from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput if is_invisible_watermark_available(): from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel from diffusers import StableDiffusionXLControlNetPipeline from PIL import Image from torchvision.transforms.functional import to_tensor from einops import rearrange from torch import einsum import math from torchvision.utils import save_image from diffusers.utils import load_image import cv2 logger = logging.get_logger(__name__) # pylint: disable=invalid-name class RegionControlNet_AttnProcessor: def __init__(self, attention_op=None, controller=None, place_in_unet=None): self.attention_op = attention_op self.controller = controller self.place_in_unet = place_in_unet def __call__( self, attn, hidden_states: torch.FloatTensor, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0, **cross_attention_kwargs ) -> torch.Tensor: residual = hidden_states args = () if USE_PEFT_BACKEND else (scale,) if attn.spatial_norm is not None: hidden_states = attn.spatial_norm(hidden_states, temb) input_ndim = hidden_states.ndim if input_ndim == 4: batch_size, channel, height, width = hidden_states.shape hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) batch_size, sequence_length, _ = ( hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape ) attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) if attn.group_norm is not None: hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) query = attn.to_q(hidden_states, *args) is_cross = True if encoder_hidden_states is None: is_cross = False encoder_hidden_states = hidden_states elif attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) key = attn.to_k(encoder_hidden_states, *args) value = attn.to_v(encoder_hidden_states, *args) query = attn.head_to_batch_dim(query) key = attn.head_to_batch_dim(key) value = attn.head_to_batch_dim(value) attention_probs = attn.get_attention_scores(query, key, attention_mask) attention_probs = self.controller(attention_probs, is_cross, self.place_in_unet) hidden_states = torch.bmm(attention_probs, value) hidden_states = attn.batch_to_head_dim(hidden_states) # linear proj hidden_states = attn.to_out[0](hidden_states, *args) # dropout hidden_states = attn.to_out[1](hidden_states) if input_ndim == 4: hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) if attn.residual_connection: hidden_states = hidden_states + residual hidden_states = hidden_states / attn.rescale_output_factor return hidden_states def revise_regionally_controlnet_forward(unet, controller): def change_forward(unet, count, place_in_unet): for name, layer in unet.named_children(): if layer.__class__.__name__ == 'Attention': layer.set_processor(RegionControlNet_AttnProcessor(controller=controller, place_in_unet=place_in_unet)) if 'attn2' in name: count += 1 else: count = change_forward(layer, count, place_in_unet) return count # use this to ensure the order cross_attention_idx = change_forward(unet.down_blocks, 0, "down") cross_attention_idx = change_forward(unet.mid_block, cross_attention_idx, "up") cross_attention_idx = change_forward(unet.up_blocks, cross_attention_idx, "mid") print(f'Number of attention layer registered {cross_attention_idx}') controller.num_att_layers = cross_attention_idx*2 class InstantidMultiConceptPipeline(StableDiffusionXLControlNetPipeline): # leave controlnet out on purpose because it iterates with unet model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae" _optional_components = [ "tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2", "feature_extractor", "image_encoder", ] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: UNet2DConditionModel, controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], scheduler: KarrasDiffusionSchedulers, force_zeros_for_empty_prompt: bool = True, add_watermarker: Optional[bool] = None, feature_extractor: CLIPImageProcessor = None, image_encoder: CLIPVisionModelWithProjection = None, ): if isinstance(controlnet, (list, tuple)): controlnet = MultiControlNetModel(controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, controlnet=controlnet, scheduler=scheduler, feature_extractor=feature_extractor, image_encoder=image_encoder, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True) self.control_image_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False ) add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() if add_watermarker: self.watermark = StableDiffusionXLWatermarker() else: self.watermark = None self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, prompt_2: Optional[Union[str, List[str]]] = None, image: PipelineImageInput = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, guidance_scale: float = 5.0, negative_prompt: Optional[Union[str, List[str]]] = None, negative_prompt_2: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, guess_mode: bool = False, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, original_size: Tuple[int, int] = None, crops_coords_top_left: Tuple[int, int] = (0, 0), target_size: Tuple[int, int] = None, negative_original_size: Optional[Tuple[int, int]] = None, negative_crops_coords_top_left: Tuple[int, int] = (0, 0), negative_target_size: Optional[Tuple[int, int]] = None, clip_skip: Optional[int] = None, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], controller=None, concept_models=None, indices_to_alter=None, face_app=None, stage=None, region_masks=None, **kwargs, ): # revise_regionally_controlnet_forward(self.unet, controller) callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) if callback is not None: deprecate( "callback", "1.0.0", "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet # align format for control guidance if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1 control_guidance_start, control_guidance_end = ( mult * [control_guidance_start], mult * [control_guidance_end], ) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, prompt_2, image, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, controlnet_conditioning_scale, control_guidance_start, control_guidance_end, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs # 2. Define call parameters batch_size = 2 device = self._execution_device if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) global_pool_conditions = ( controlnet.config.global_pool_conditions if isinstance(controlnet, ControlNetModel) else controlnet.nets[0].config.global_pool_conditions ) guess_mode = guess_mode or global_pool_conditions # 3.1 Encode input prompt text_encoder_lora_scale = ( self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None ) global_prompt = prompt[0] global_negative_prompt = negative_prompt region_prompts = [pt[0] for pt in prompt[1]] region_negative_prompts = [pt[1] for pt in prompt[1]] ref_images = [pt[2] for pt in prompt[1]] concat_prompts = global_prompt + region_prompts concat_negative_prompts = global_negative_prompt + region_negative_prompts ( concat_prompt_embeds, concat_negative_prompt_embeds, concat_pooled_prompt_embeds, concat_negative_pooled_prompt_embeds, ) = self.encode_prompt( concat_prompts, prompt_2, device, num_images_per_prompt, self.do_classifier_free_guidance, concat_negative_prompts, negative_prompt_2, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=self.clip_skip, ) prompt_embeds = concat_prompt_embeds[:2] negative_prompt_embeds = concat_negative_prompt_embeds[:2] pooled_prompt_embeds = concat_pooled_prompt_embeds[:2] negative_pooled_prompt_embeds = concat_negative_pooled_prompt_embeds[:2] region_prompt_embeds_list = [] region_add_text_embeds_list = [] for region_prompt_embeds, region_negative_prompt_embeds, region_pooled_prompt_embeds, region_negative_pooled_prompt_embeds in zip(concat_prompt_embeds[2:], concat_negative_prompt_embeds[2:], concat_pooled_prompt_embeds[2:], concat_negative_pooled_prompt_embeds[2:]): region_prompt_embeds_list.append( torch.concat([region_negative_prompt_embeds.unsqueeze(0), region_prompt_embeds.unsqueeze(0)], dim=0).to(concept_models._execution_device)) region_add_text_embeds_list.append( torch.concat([region_negative_pooled_prompt_embeds.unsqueeze(0), region_pooled_prompt_embeds.unsqueeze(0)], dim=0).to(concept_models._execution_device)) if stage==2: mask_list = [mask.float().to(dtype=prompt_embeds.dtype, device=device) if mask is not None else None for mask in region_masks] image_embedding_list = get_face_embedding(face_app, ref_images) image_prompt_image_emb_list = [] for image_embeds in image_embedding_list: prompt_image_emb = concept_models._encode_prompt_image_emb(image_embeds, concept_models._execution_device, num_images_per_prompt, concept_models.unet.dtype, True) image_prompt_image_emb_list.append(prompt_image_emb) # 4. Prepare image if isinstance(controlnet, ControlNetModel) and image is not None: image = self.prepare_image( image=image, width=width, height=height, batch_size=1 * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) height, width = image.shape[-2:] elif isinstance(controlnet, MultiControlNetModel) and image is not None: images = [] for image_ in image: image_ = self.prepare_image( image=image_, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) images.append(image_) image = images height, width = image[0].shape[-2:] else: height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps self._num_timesteps = len(timesteps) # 6. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size//2 * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6.1 repeat latent latents = torch.cat([latents, latents.clone()]) # 6.5 Optionally get Guidance Scale Embedding timestep_cond = None if self.unet.config.time_cond_proj_dim is not None: guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) timestep_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7.1 Create tensor stating which controlnets to keep controlnet_keep = [] for i in range(len(timesteps)): keeps = [ 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end) ] controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps) # 7.2 Prepare added time ids & embeddings if isinstance(image, list): original_size = original_size or image[0].shape[-2:] else: original_size = original_size or (height, width) target_size = target_size or (height, width) add_text_embeds = pooled_prompt_embeds if self.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim add_time_ids = self._get_add_time_ids( original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) add_time_ids_list = [] region_add_time_ids = concept_models._get_add_time_ids(original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim) for _ in range(len(prompt[1])): add_time_ids_list.append(torch.concat([region_add_time_ids, region_add_time_ids], dim=0).to(concept_models._execution_device)) if negative_original_size is not None and negative_target_size is not None: negative_add_time_ids = self._get_add_time_ids( negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) else: negative_add_time_ids = add_time_ids if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) prompt_embeds = prompt_embeds.to(device) add_text_embeds = add_text_embeds.to(device) add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order is_unet_compiled = is_compiled_module(self.unet) is_controlnet_compiled = is_compiled_module(self.controlnet) is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1") # hyper-parameters scale_range = np.linspace(1, 0.5, len(self.scheduler.timesteps)) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # Relevant thread: # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428 if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1: torch._inductor.cudagraph_mark_step_begin() # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} # controlnet(s) inference if guess_mode and self.do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. control_model_input = latents control_model_input = self.scheduler.scale_model_input(control_model_input, t) controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] controlnet_added_cond_kwargs = { "text_embeds": add_text_embeds.chunk(2)[1], "time_ids": add_time_ids.chunk(2)[1], } else: control_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds controlnet_added_cond_kwargs = added_cond_kwargs if isinstance(controlnet_keep[i], list): cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] else: controlnet_cond_scale = controlnet_conditioning_scale if isinstance(controlnet_cond_scale, list): controlnet_cond_scale = controlnet_cond_scale[0] cond_scale = controlnet_cond_scale * controlnet_keep[i] # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] if i > 15 and stage == 2: region_mask = self.get_region_mask(mask_list, noise_pred.shape[2], noise_pred.shape[3]) edit_noise = torch.concat([noise_pred[1:2], noise_pred[3:4]], dim=0) new_noise_pred = torch.zeros_like(edit_noise) new_noise_pred[:, :, region_mask == 0] = edit_noise[:, :, region_mask == 0] replace_ratio = 1.0 new_noise_pred[:, :, region_mask != 0] = (1 - replace_ratio) * edit_noise[:, :, region_mask != 0] for region_prompt_embeds, region_add_text_embeds, region_add_time_ids, concept_mask, region_prompt, region_prompt_image_emb in zip(region_prompt_embeds_list, region_add_text_embeds_list, add_time_ids_list, mask_list, region_prompts, image_prompt_image_emb_list): if concept_mask is not None: concept_mask = F.interpolate(concept_mask.unsqueeze(0).unsqueeze(0), size=(noise_pred.shape[2], noise_pred.shape[3]), mode='nearest').squeeze().to(dtype=noise_pred.dtype, device=concept_models._execution_device) region_latent_model_input = latent_model_input[3:4].clone().to(concept_models._execution_device) region_latent_model_input = torch.cat([region_latent_model_input] * 2) region_added_cond_kwargs = {"text_embeds": region_add_text_embeds, "time_ids": region_add_time_ids} if image is not None: down_block_res_samples, mid_block_res_sample = self.controlnet( region_latent_model_input, t, encoder_hidden_states=region_prompt_image_emb, controlnet_cond=image, conditioning_scale=cond_scale, guess_mode=guess_mode, added_cond_kwargs=region_added_cond_kwargs, return_dict=False, ) if guess_mode and self.do_classifier_free_guidance: # Infered ControlNet only for the conditional batch. # To apply the output of ControlNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] mid_block_res_sample = torch.cat( [torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) else: down_block_res_samples = None mid_block_res_sample = None region_encoder_hidden_states = torch.cat([region_prompt_embeds, region_prompt_image_emb], dim=1) region_noise_pred = concept_models.unet( region_latent_model_input, t, encoder_hidden_states=region_encoder_hidden_states, cross_attention_kwargs=None, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, added_cond_kwargs=region_added_cond_kwargs, return_dict=False, )[0] new_noise_pred = new_noise_pred.to(concept_models._execution_device) new_noise_pred[:, :, concept_mask==1] += replace_ratio * (region_noise_pred[:, :, concept_mask==1] / (concept_mask.reshape(1, 1, *concept_mask.shape)[:, :, concept_mask==1].to(region_noise_pred.device))) new_noise_pred = new_noise_pred.to(noise_pred.device) noise_pred[1, :, :, :] = new_noise_pred[0] noise_pred[3, :, :, :] = new_noise_pred[1] if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) # manually for max memory savings if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: self.upcast_vae() latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) if not output_type == "latent": # make sure the VAE is in float32 mode, as it overflows in float16 needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast if needs_upcasting: self.upcast_vae() latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) else: image = latents if not output_type == "latent": # apply watermark if available if self.watermark is not None: image = self.watermark.apply_watermark(image) 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 StableDiffusionXLPipelineOutput(images=image) def check_image(self, image, prompt, prompt_embeds): pass def get_region_mask(self, mask_list, feat_height, feat_width): exclusive_mask = torch.zeros((feat_height, feat_width)) for mask in mask_list: if mask is not None: mask = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(feat_height, feat_width), mode='nearest').squeeze().to(dtype=exclusive_mask.dtype, device=exclusive_mask.device) exclusive_mask = ((mask == 1) | (exclusive_mask == 1)).to(dtype=mask.dtype) return exclusive_mask def get_face_embedding(face_app, ref_images): emb_list = [] for img_path in ref_images: face_image = load_image(img_path) # prepare face emb face_info = face_app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR)) face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * x['bbox'][3] - x['bbox'][1])[0] # only use the maximum face face_emb = face_info['embedding'] emb_list.append(face_emb) # face_kps = draw_kps(face_image, face_info['kps']) return emb_list