ADD

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()

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)