diff --git a/StoryMaker/infer.py b/StoryMaker/infer.py new file mode 100644 index 0000000..c57a9dc --- /dev/null +++ b/StoryMaker/infer.py @@ -0,0 +1,141 @@ +import cv2, os +import torch +import numpy as np +from PIL import Image +# from pillow_heif import register_heif_opener +# register_heif_opener() +# import pillow_heif +# pillow_heif.register_avif_opener() +from diffusers.utils import load_image +from diffusers import EulerAncestralDiscreteScheduler, UniPCMultistepScheduler + +from insightface.app import FaceAnalysis +from pipeline_sdxl_storymaker import StableDiffusionXLStoryMakerPipeline + +def resize_img(input_image, max_side=1280, min_side=960, size=None, + pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64): + + w, h = input_image.size + if size is not None: + w_resize_new, h_resize_new = size + else: + ratio = min_side / min(h, w) + w, h = round(ratio*w), round(ratio*h) + ratio = max_side / max(h, w) + input_image = input_image.resize([round(ratio*w), round(ratio*h)], mode) + w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number + h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number + input_image = input_image.resize([w_resize_new, h_resize_new], mode) + + if pad_to_max_side: + res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255 + offset_x = (max_side - w_resize_new) // 2 + offset_y = (max_side - h_resize_new) // 2 + res[offset_y:offset_y+h_resize_new, offset_x:offset_x+w_resize_new] = np.array(input_image) + input_image = Image.fromarray(res) + return input_image + + +# Load face encoder +app = FaceAnalysis(name='buffalo_l', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) +app.prepare(ctx_id=0, det_size=(640, 640)) + +# Path to models +face_adapter = f'checkpoints/mask.bin' +image_encoder_path = 'laion/CLIP-ViT-H-14-laion2B-s32B-b79K' # from https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K +base_model_path = 'huaquan/YamerMIX_v11' # from https://huggingface.co/huaquan/YamerMIX_v11 + +pipe = StableDiffusionXLStoryMakerPipeline.from_pretrained( + base_model_path, + torch_dtype=torch.float16, +) +pipe.cuda() +pipe.load_storymaker_adapter(image_encoder_path, face_adapter, scale=0.8, lora_scale=0.8) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +def demo(): + prompt = "a person is taking a selfie, the person is wearing a red hat, and a volcano is in the distance" + n_prompt = "bad quality, NSFW, low quality, ugly, disfigured, deformed" + + image = Image.open("examples/ldh.png").convert('RGB') + mask_image = Image.open("examples/ldh_mask.png").convert('RGB') + + # image = resize_img(image) + face_info = app.get(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)) + face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face + + generator = torch.Generator(device='cuda').manual_seed(666) + for i in range(4): + output = pipe( + image=image, mask_image=mask_image, face_info=face_info, + prompt=prompt, + negative_prompt=n_prompt, + ip_adapter_scale=0.8, lora_scale=0.8, + num_inference_steps=25, + guidance_scale=7.5, + height=1280, width=960, + generator=generator, + ).images[0] + output.save(f'examples/results/ldh666_{i}.jpg') + +def demo_two(): + prompt = "A man and a woman are taking a selfie, and a volcano is in the distance" + n_prompt = "bad quality, NSFW, low quality, ugly, disfigured, deformed" + + image = Image.open("examples/ldh.png").convert('RGB') + mask_image = Image.open("examples/ldh_mask.png").convert('RGB') + image_2 = Image.open("examples/tsy.png").convert('RGB') + mask_image_2 = Image.open("examples/tsy_mask.png").convert('RGB') + + face_info = app.get(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)) + face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face + face_info_2 = app.get(cv2.cvtColor(np.array(image_2), cv2.COLOR_RGB2BGR)) + face_info_2 = sorted(face_info_2, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face + + generator = torch.Generator(device='cuda').manual_seed(666) + for i in range(4): + output = pipe( + image=image, mask_image=mask_image,face_info=face_info, # first person + image_2=image_2, mask_image_2=mask_image_2,face_info_2=face_info_2, # second person + prompt=prompt, + negative_prompt=n_prompt, + ip_adapter_scale=0.8, lora_scale=0.8, + num_inference_steps=25, + guidance_scale=7.5, + height=1280, width=960, + generator=generator, + ).images[0] + output.save(f'examples/results/ldh_tsy666_{i}.jpg') + +def demo_swapcloth(): + prompt = "a person is taking a selfie, and a volcano is in the distance" + n_prompt = "bad quality, NSFW, low quality, ugly, disfigured, deformed" + + image = Image.open("examples/ldh.png").convert('RGB') + mask_image = Image.open("examples/ldh_mask.png").convert('RGB') + cloth = Image.open("examples/cloth2.png").convert('RGB') + + face_info = app.get(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)) + face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face + + generator = torch.Generator(device='cuda').manual_seed(666) + for i in range(4): + output = pipe( + image=image, mask_image=mask_image, face_info=face_info, cloth=cloth, + prompt=prompt, + negative_prompt=n_prompt, + ip_adapter_scale=0.8, lora_scale=0.8, + num_inference_steps=25, + guidance_scale=7.5, + height=1280, width=960, + generator=generator, + ).images[0] + output.save(f'examples/results/ldh_cloth_{i}.jpg') + + +# if __name__ == "__main__": +# # single portrait generation +# demo() +# +# # two portrait generation +# # demo_two() diff --git a/StoryMaker/pipeline_sdxl_storymaker.py b/StoryMaker/pipeline_sdxl_storymaker.py new file mode 100644 index 0000000..f945ae1 --- /dev/null +++ b/StoryMaker/pipeline_sdxl_storymaker.py @@ -0,0 +1,693 @@ +# Copyright 2024 The InstantX Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import cv2 +import math + +import numpy as np +import PIL.Image +from PIL import Image +import torch, traceback, pdb +import torch.nn.functional as F + +from diffusers.image_processor import PipelineImageInput + +from diffusers.models import ControlNetModel + +from diffusers.utils import ( + deprecate, + logging, + replace_example_docstring, +) +from diffusers.utils.torch_utils import is_compiled_module, is_torch_version +try: + from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput +except: + try: + from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import tableDiffusionXLPipelineOutput + except: + raise "load diffuser error check your diffuser version!" + +from diffusers import StableDiffusionXLPipeline +from diffusers.utils.import_utils import is_xformers_available + +from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection +from insightface.utils import face_align + +from .ip_adapter.resampler import Resampler +from .ip_adapter.utils import is_torch2_available +from .ip_adapter.ip_adapter_faceid import faceid_plus + +from .ip_adapter.attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor +from .ip_adapter.attention_processor_faceid import LoRAIPAttnProcessor2_0 as LoRAIPAttnProcessor, LoRAAttnProcessor2_0 as LoRAAttnProcessor + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> # !pip install opencv-python transformers accelerate insightface + >>> import diffusers + >>> from diffusers.utils import load_image + >>> import cv2 + >>> import torch + >>> import numpy as np + >>> from PIL import Image + + >>> from insightface.app import FaceAnalysis + >>> from pipeline_sdxl_storymaker import StableDiffusionXLStoryMakerPipeline + + >>> # download 'buffalo_l' under ./models + >>> app = FaceAnalysis(name='buffalo_l', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + >>> app.prepare(ctx_id=0, det_size=(640, 640)) + + >>> # download models under ./checkpoints + >>> storymaker_adapter = f'./checkpoints/ip-adapter.bin' + + >>> pipe = StableDiffusionXLStoryMakerPipeline.from_pretrained( + ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 + ... ) + >>> pipe.cuda() + + >>> # load adapter + >>> pipe.load_storymaker_adapter(storymaker_adapter) + + >>> prompt = "a person is taking a selfie, the person is wearing a red hat, and a volcano is in the distance" + >>> negative_prompt = "bad quality, NSFW, low quality, ugly, disfigured, deformed" + + >>> # load an image + >>> image = load_image("your-example.jpg") + >>> # load the mask image of portrait + >>> mask_image = load_image("your-mask.jpg") + + >>> face_info = app.get(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR))[-1] + + >>> # generate image + >>> image = pipe( + ... prompt, image=image, mask_image=mask_image,face_info=face_info, controlnet_conditioning_scale=0.8 + ... ).images[0] + ``` +""" + +def bounding_rectangle(ori_img, mask): + """ + Calculate the bounding rectangle of multiple rectangles. + Args: + rectangles (list of tuples): List of rectangles, where each rectangle is represented as (x, y, w, h) + Returns: + tuple: The bounding rectangle (x, y, w, h) + """ + contours, _ = cv2.findContours(mask[:,:,0], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + rectangles = [cv2.boundingRect(contour) for contour in contours] + + min_x = float('inf') + min_y = float('inf') + max_x = float('-inf') + max_y = float('-inf') + for x, y, w, h in rectangles: + min_x = min(min_x, x) + min_y = min(min_y, y) + max_x = max(max_x, x + w) + max_y = max(max_y, y + h) + try: + crop = ori_img[min_y:max_y, min_x:max_x] + mask = mask[min_y:max_y, min_x:max_x] + except: + traceback.print_exc() + return crop, mask + + + +class StableDiffusionXLStoryMakerPipeline(StableDiffusionXLPipeline): + + def cuda(self, dtype=torch.float16, use_xformers=False): + self.to('cuda', dtype) + if hasattr(self, 'image_proj_model'): + self.image_proj_model.to(self.unet.device).to(self.unet.dtype) + + def load_storymaker_adapter(self, image_encoder_path, model_ckpt, image_emb_dim=512, num_tokens=20, scale=0.8, lora_scale=0.8): + self.clip_image_processor = CLIPImageProcessor() + self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(image_encoder_path).to(self.device, dtype=self.dtype) + self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens) + self.set_ip_adapter(model_ckpt, num_tokens) + self.set_ip_adapter_scale(scale, lora_scale) + print(f'successful load adapter.') + + def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16): + + image_proj_model = faceid_plus( + cross_attention_dim=self.unet.config.cross_attention_dim, + id_embeddings_dim=512, + clip_embeddings_dim=1280, + ) + image_proj_model.eval() + + self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype) + state_dict = torch.load(model_ckpt, map_location="cpu") + if 'image_proj_model' in state_dict: + state_dict = state_dict["image_proj_model"] + self.image_proj_model.load_state_dict(state_dict) + + def set_ip_adapter(self, model_ckpt, num_tokens, lora_rank=128): + + unet = self.unet + attn_procs = {} + for name in unet.attn_processors.keys(): + cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim + if name.startswith("mid_block"): + hidden_size = unet.config.block_out_channels[-1] + elif name.startswith("up_blocks"): + block_id = int(name[len("up_blocks.")]) + hidden_size = list(reversed(unet.config.block_out_channels))[block_id] + elif name.startswith("down_blocks"): + block_id = int(name[len("down_blocks.")]) + hidden_size = unet.config.block_out_channels[block_id] + if cross_attention_dim is None: + attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank).to(unet.device, dtype=unet.dtype) + else: + attn_procs[name] = LoRAIPAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank).to(unet.device, dtype=unet.dtype) + unet.set_attn_processor(attn_procs) + + state_dict = torch.load(model_ckpt, map_location="cpu") + ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values()) + if 'ip_adapter' in state_dict: + state_dict = state_dict['ip_adapter'] + ip_layers.load_state_dict(state_dict) + + def set_ip_adapter_scale(self, scale, lora_scale=0.8): + unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet + for attn_processor in unet.attn_processors.values(): + if isinstance(attn_processor, LoRAIPAttnProcessor) or isinstance(attn_processor, LoRAAttnProcessor): + attn_processor.scale = scale + attn_processor.lora_scale = lora_scale + + def crop_image(self, ori_img, ori_mask, face_info): + ori_img = np.array(ori_img) + ori_mask = np.array(ori_mask) + crop, mask = bounding_rectangle(ori_img, ori_mask) + mask = cv2.GaussianBlur(mask, (5, 5), 0)/255. + crop = (255*np.ones_like(mask)*(1-mask)+mask*crop).astype(np.uint8) + # cv2.imwrite('examples/results/0crop.jpg', crop[:,:,::-1]) + # cv2.imwrite('examples/results/0mask.jpg', (mask*255).astype(np.uint8)) + + face_kps = face_info['kps'] + # face_image = face_align.norm_crop(crop, landmark=face_kps.numpy(), image_size=224) # 224 + face_image = face_align.norm_crop(ori_img, landmark=face_kps, image_size=224) # 224 + clip_face = self.clip_image_processor(images=face_image, return_tensors="pt").pixel_values + + ref_img = Image.fromarray(crop) + ref_img = ref_img.resize((224, 224)) + clip_img = self.clip_image_processor(images=ref_img, return_tensors="pt").pixel_values + return clip_img, clip_face, torch.from_numpy(face_info.normed_embedding).unsqueeze(0) + + def _encode_prompt_image_emb(self, image, image_2, mask_image, mask_image_2, face_info, face_info_2, cloth, cloth_2, \ + device, num_images_per_prompt, dtype, do_classifier_free_guidance): + crop_list = []; face_list = []; id_list = [] + if image is not None: + clip_img, clip_face, face_emb = self.crop_image(image, mask_image, face_info) + crop_list.append(clip_img) + face_list.append(clip_face) + id_list.append(face_emb) + if image_2 is not None: + clip_img, clip_face, face_emb = self.crop_image(image_2, mask_image_2, face_info_2) + crop_list.append(clip_img) + face_list.append(clip_face) + id_list.append(face_emb) + if cloth is not None: + crop_list = [] + clip_img = self.clip_image_processor(images=cloth.resize((224, 224)), return_tensors="pt").pixel_values + crop_list.append(clip_img) + if cloth_2 is not None: + clip_img = self.clip_image_processor(images=cloth_2.resize((224, 224)), return_tensors="pt").pixel_values + crop_list.append(clip_img) + assert len(crop_list)>0, f"input error, images is None" + clip_image = torch.cat(crop_list, dim=0).to(device, dtype=dtype) + clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2] + clip_face = torch.cat(face_list, dim=0).to(device, dtype=dtype) + clip_face_embeds = self.image_encoder(clip_face, output_hidden_states=True).hidden_states[-2] + id_embeds = torch.cat(id_list, dim=0).to(device, dtype=dtype) + # print(f'clip_image_embeds: {clip_image_embeds.shape}, clip_face_embeds:{clip_face_embeds.shape}, id_embeds:{id_embeds.shape}') + #print(do_classifier_free_guidance) + if do_classifier_free_guidance: + prompt_image_emb = self.image_proj_model(id_embeds, clip_image_embeds, clip_face_embeds) + B, C, D = prompt_image_emb.shape + prompt_image_emb = prompt_image_emb.view(1, B*C, D) + neg_emb = self.image_proj_model(torch.zeros_like(id_embeds), torch.zeros_like(clip_image_embeds), torch.zeros_like(clip_face_embeds)) + neg_emb = neg_emb.view(1, B*C, D) + prompt_image_emb = torch.cat([neg_emb, prompt_image_emb], dim=0) #torch.Size([2, 40, 2048]) + #print(prompt_image_emb.shape) + else: + prompt_image_emb = torch.cat([prompt_image_emb], dim=0) + B, C, D = prompt_image_emb.shape + prompt_image_emb = prompt_image_emb.view(1, B*C, D) + + #print(f'prompt_image_emb: {prompt_image_emb.shape}')#torch.Size([2, 40, 2048]) + bs_embed, seq_len, _ = prompt_image_emb.shape + + prompt_image_emb = prompt_image_emb.repeat(1, num_images_per_prompt, 1) + prompt_image_emb = prompt_image_emb.view(bs_embed * num_images_per_prompt, seq_len, -1) + + return prompt_image_emb.to(device=device, dtype=dtype) + + @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, + image: PipelineImageInput = None, + mask_image: Union[torch.Tensor, PIL.Image.Image] = None, + image_2: PipelineImageInput = None, + mask_image_2: Union[torch.Tensor, PIL.Image.Image] = 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, + 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"], + + # IP adapter + ip_adapter_scale=None, + lora_scale=None, + face_info = None, + face_info_2 = None, + cloth = None, + cloth_2 = None, + + **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.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: + `List[List[torch.FloatTensor]]`, `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.FloatTensor`, 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. + 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.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 is 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 (prompt weighting). If not + provided, text embeddings are generated from the `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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. + 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`, *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 pipeine 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`", + ) + + # 0. set ip_adapter_scale + if ip_adapter_scale is not None and lora_scale is not None: + self.set_ip_adapter_scale(ip_adapter_scale, lora_scale) + + # 1. Check inputs. Raise error if not correct + # self.check_inputs( + # prompt=prompt, + # prompt_2=prompt_2, + # height=height, width=width, + # callback_steps=callback_steps, + # negative_prompt=negative_prompt, + # negative_prompt_2=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, + # callback_on_step_end_tensor_inputs=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 + 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] + #print(batch_size) + device = self.unet.device + # pdb.set_trace() + # 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 image prompt + prompt_image_emb = self._encode_prompt_image_emb(image, image_2, mask_image, mask_image_2, face_info, face_info_2, cloth,cloth_2, + device, num_images_per_prompt, + self.unet.dtype, self.do_classifier_free_guidance) + + # 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 * 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) + + # 7.2 Prepare added time ids & embeddings + 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, + ) + + 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 + #print(self.do_classifier_free_guidance) + if self.do_classifier_free_guidance: + #rint(negative_prompt_embeds.shape,prompt_embeds.shape) + 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) + # print(prompt_embeds.shape, prompt_image_emb.shape) torch.Size([4, 77, 2048])? torch.Size([2, 40, 2048]) + prompt_image_emb=torch.cat((prompt_image_emb,) * batch_size, dim=0) + #print(prompt_embeds.shape, prompt_image_emb.shape) + encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1) + + # 8. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + is_unet_compiled = is_compiled_module(self.unet) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + + # 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} + + # predict the noise residual + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=encoder_hidden_states, + timestep_cond=timestep_cond, + cross_attention_kwargs=self.cross_attention_kwargs, + 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) + + # 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 + + 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: + 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) \ No newline at end of file