from diffusers import StableDiffusionXLControlNetPipeline from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl import * from .pulid_encoder import PuLIDEncoder from csd_clip import create_model_and_transforms as create_csd_clip_model_and_transforms from csd_clip import CSD_CLIP from ip_adapter_diffusers.ip_adapter import * from transformers import CLIPVisionModelWithProjection class ArtisticPortraitXLPipeline(StableDiffusionXLControlNetPipeline): 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, style_adapter_path=None, id_adapter_path=None, style_image_encoder_path="models/h94/IP-Adapter/sdxl_models/image_encoder", device=None, ): super().__init__( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, controlnet=controlnet, scheduler=scheduler, force_zeros_for_empty_prompt=force_zeros_for_empty_prompt, add_watermarker=add_watermarker, feature_extractor=feature_extractor, image_encoder=image_encoder, ) self.id_image_encoder = PuLIDEncoder(device=device) if "art" in style_adapter_path: self.style_image_encoder = create_csd_clip_model_and_transforms()[0] else: self.style_image_encoder = CLIPVisionModelWithProjection.from_pretrained( style_image_encoder_path ) self.style_image_processor = CLIPImageProcessor() load_multi_ip_adapter( self.unet, paths=[style_adapter_path, id_adapter_path], ) self.style_image_projection_layer = ( self.unet.encoder_hid_proj.image_projection_layers[0] ) self.id_image_projection_layer = ( self.unet.encoder_hid_proj.image_projection_layers[1] ) def load_style_adapter_to_controlnet(self, style_adapter_path): load_ip_adapter(self.controlnet, style_adapter_path) def get_id_hidden_states(self, image): if not isinstance(image, list): image = [image] image = [ ( single_image if isinstance(single_image, np.ndarray) else np.array(single_image) ) for single_image in image ] id_cond, id_vit_hidden, id_uncond, id_vit_hidden_uncond = ( self.id_image_encoder.get_id_embedding(image) ) id_vit_hidden = [x.to(dtype=self.unet.dtype) for x in id_vit_hidden] id_vit_hidden_uncond = [ x.to(dtype=self.unet.dtype) for x in id_vit_hidden_uncond ] uncond_id_embedding = self.id_image_projection_layer( id_uncond.to(self.unet.device, self.unet.dtype), id_vit_hidden_uncond, ) id_embedding = self.id_image_projection_layer( id_cond.to(self.unet.device, self.unet.dtype), id_vit_hidden ) id_hidden_states = torch.concat([uncond_id_embedding, id_embedding], dim=0) torch.cuda.empty_cache() return id_hidden_states def get_style_hidden_states(self, image): if isinstance(self.style_image_encoder, CSD_CLIP): self.style_image_encoder = self.style_image_encoder.to( self._execution_device, dtype=torch.float32 ) style_pixel_values = self.style_image_processor.preprocess( image, return_tensors="pt" ).pixel_values _, __, style_image_embeds = self.style_image_encoder( style_pixel_values.to(self._execution_device, torch.float32) ) style_image_embeds = torch.stack( [ torch.zeros_like(style_image_embeds).to(self._execution_device), style_image_embeds, ] ).to(self._execution_device, torch.float16) style_ip_adapter_hidden_states = self.style_image_projection_layer( style_image_embeds ) elif isinstance(self.style_image_encoder, CLIPVisionModelWithProjection): self.style_image_encoder = self.style_image_encoder.to( self._execution_device, dtype=torch.float16 ) style_pixel_values = self.style_image_processor.preprocess( image, return_tensors="pt" ).pixel_values style_image_embeds = self.style_image_encoder( style_pixel_values.to(self._execution_device, torch.float16) ).image_embeds style_image_embeds = torch.stack( [ torch.zeros_like(style_image_embeds).to(self._execution_device), style_image_embeds, ] ).to(self._execution_device, torch.float16) style_ip_adapter_hidden_states = self.style_image_projection_layer( style_image_embeds ) torch.cuda.empty_cache() self.style_image_encoder = self.style_image_encoder.to("cpu") return style_ip_adapter_hidden_states def set_style_adapter_scale(self, style_adapter_scale): for name, processor in self.unet.attn_processors.items(): if ( isinstance(processor, torch.nn.Module) and "up_blocks.0.attentions.1" in name ): processor.scale = [style_adapter_scale, 0.0] def set_id_adapter_scale(self, id_adapter_scale): for name, processor in self.unet.attn_processors.items(): if ( isinstance(processor, torch.nn.Module) and "up_blocks.0.attentions.1" not in name ): processor.scale = [0.0, id_adapter_scale] @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, prompt_2: Optional[Union[str, List[str]]] = None, control_image: PipelineImageInput = None, style_image: PipelineImageInput = None, id_image: PipelineImageInput = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, timesteps: List[int] = None, sigmas: List[float] = None, denoising_end: Optional[float] = None, guidance_scale: float = 5.0, id_adapter_scale=1.0, style_adapter_scale=1.0, 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.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, pooled_prompt_embeds: Optional[torch.Tensor] = None, negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[List[torch.Tensor]] = 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, style_guidance_start=0.0, style_guidance_end=1.0, id_guidance_start=0.0, id_guidance_end=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[ Union[ Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks, ] ] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], **kwargs, ): r""" The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. 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 used in both text-encoders. image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): The ControlNet input condition to provide guidance to the `unet` for generation. If the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`, images must be passed as a list such that each element of the list can be correctly batched for input to a single ControlNet. height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated image. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated image. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. 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. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. denoising_end (`float`, *optional*): When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) guidance_scale (`float`, *optional*, defaults to 5.0): A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). negative_prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.Tensor`, *optional*): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random `generator`. prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. pooled_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, pooled text embeddings are generated from `prompt` input argument. negative_pooled_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input argument. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set the corresponding scale as a list. guess_mode (`bool`, *optional*, defaults to `False`): The ControlNet encoder tries to recognize the content of the input image even if you remove all prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended. control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): The percentage of total steps at which the ControlNet starts applying. control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): The percentage of total steps at which the ControlNet stops applying. original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): For most cases, `target_size` should be set to the desired height and width of the generated image. If not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): To negatively condition the generation process based on a specific image resolution. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): To negatively condition the generation process based on a target image resolution. It should be as same as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of each denoising step during the inference. 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. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned containing the output images. """ 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`", ) if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs 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, # control_image, # callback_steps, # negative_prompt, # negative_prompt_2, # prompt_embeds, # negative_prompt_embeds, # pooled_prompt_embeds, # ip_adapter_image, # ip_adapter_image_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 self._denoising_end = denoising_end 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 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 ) ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = self.encode_prompt( prompt, prompt_2, device, num_images_per_prompt, self.do_classifier_free_guidance, negative_prompt, 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, ) # 3.2 Encode ip_adapter_image style_hidden_states = self.get_style_hidden_states(style_image) id_hidden_states = self.get_id_hidden_states(id_image) set_multi_ip_hidden_states( self.unet, [ style_hidden_states, id_hidden_states, ], ) set_ip_hidden_states(self.controlnet, style_hidden_states) self.set_id_adapter_scale(id_adapter_scale) self.set_style_adapter_scale(style_adapter_scale) # 4. Prepare image if isinstance(controlnet, ControlNetModel) and control_image is not None: control_image = self.prepare_image( image=control_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, ) height, width = control_image.shape[-2:] elif isinstance(controlnet, MultiControlNetModel) and control_image is not None: images = [] for image_ in control_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_) control_image = images height, width = control_image[0].shape[-2:] # 5. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas ) self._num_timesteps = len(timesteps) # 6. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 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 control_image is None: original_size = original_size original_size = original_size or (height, width) target_size = target_size or (height, width) else: if isinstance(control_image, list): original_size = original_size or control_image[0].shape[-2:] else: original_size = original_size or control_image.shape[-2:] 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, ) 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 # 8.1 Apply denoising_end if ( self.denoising_end is not None and isinstance(self.denoising_end, float) and self.denoising_end > 0 and self.denoising_end < 1 ): discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps - (self.denoising_end * self.scheduler.config.num_train_timesteps) ) ) num_inference_steps = len( list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)) ) timesteps = timesteps[:num_inference_steps] 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") with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue # 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] if control_image is not None and controlnet_conditioning_scale != 0.0: down_block_res_samples, mid_block_res_sample = self.controlnet( control_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=control_image, conditioning_scale=cond_scale, guess_mode=guess_mode, added_cond_kwargs=controlnet_added_cond_kwargs, return_dict=False, ) else: down_block_res_samples = None mid_block_res_sample = None if ( guess_mode and self.do_classifier_free_guidance and control_image is not None ): # Inferred 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] ) # if ( # i / num_inference_steps >= style_guidance_start # and i / num_inference_steps <= style_guidance_end # ): # self.set_style_adapter_scale(style_adapter_scale) # else: # self.set_style_adapter_scale(0.0) # if ( # i / num_inference_steps >= id_guidance_start # and i / num_inference_steps <= id_guidance_end # ): # self.set_id_adapter_scale(id_adapter_scale) # else: # self.set_id_adapter_scale(0.0) # 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, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # perform guidance 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 ) add_text_embeds = callback_outputs.pop( "add_text_embeds", add_text_embeds ) negative_pooled_prompt_embeds = callback_outputs.pop( "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds ) add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) negative_add_time_ids = callback_outputs.pop( "negative_add_time_ids", negative_add_time_ids ) # 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) 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 ) # unscale/denormalize the latents # denormalize with the mean and std if available and not None has_latents_mean = ( hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None ) has_latents_std = ( hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None ) if has_latents_mean and has_latents_std: latents_mean = ( torch.tensor(self.vae.config.latents_mean) .view(1, 4, 1, 1) .to(latents.device, latents.dtype) ) latents_std = ( torch.tensor(self.vae.config.latents_std) .view(1, 4, 1, 1) .to(latents.device, latents.dtype) ) latents = ( latents * latents_std / self.vae.config.scaling_factor + latents_mean ) else: latents = latents / self.vae.config.scaling_factor control_image = self.vae.decode(latents, return_dict=False)[0] # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) else: control_image = latents if not output_type == "latent": # apply watermark if available if self.watermark is not None: control_image = self.watermark.apply_watermark(control_image) control_image = self.image_processor.postprocess( control_image, output_type=output_type ) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (control_image,) return StableDiffusionXLPipelineOutput(images=control_image)