fix

pipeline_stable_diffusion_xl_instantid.py

@@ -1,408 +1,1126 @@
-import os
+# 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 torch
-import random
+
 import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from transformers import CLIPTokenizer
 
-import PIL
-from PIL import Image
+from diffusers.image_processor import PipelineImageInput
 
-import diffusers
-from diffusers.utils import load_image
 from diffusers.models import ControlNetModel
 
-import insightface
-from insightface.app import FaceAnalysis
+from diffusers.utils import (
+    deprecate,
+    logging,
+    replace_example_docstring,
+)
+from diffusers.utils.torch_utils import is_compiled_module, is_torch_version
+from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
 
-from style_template import styles
-from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline
+from diffusers import StableDiffusionXLControlNetPipeline
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.utils.import_utils import is_xformers_available
 
-import spaces
-import gradio as gr
+from ip_adapter.resampler import Resampler
 
-# global variable
-MAX_SEED = np.iinfo(np.int32).max
-device = "cuda" if torch.cuda.is_available() else "cpu"
-STYLE_NAMES = list(styles.keys())
-DEFAULT_STYLE_NAME = "Watercolor"
+from ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor
+from ip_adapter.attention_processor import region_control
 
-# download checkpoints
-from huggingface_hub import hf_hub_download
-hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
-hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints")
-hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
-# Load face encoder
-app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
-app.prepare(ctx_id=0, det_size=(640, 640))
 
-# Path to InstantID models
-face_adapter = f'./checkpoints/ip-adapter.bin'
-controlnet_path = f'./checkpoints/ControlNetModel'
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> # !pip install opencv-python transformers accelerate insightface
+        >>> import diffusers
+        >>> from diffusers.utils import load_image
+        >>> from diffusers.models import ControlNetModel
 
-# Load pipeline
-controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+        >>> import cv2
+        >>> import torch
+        >>> import numpy as np
+        >>> from PIL import Image
+        
+        >>> from insightface.app import FaceAnalysis
+        >>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps
 
-base_model_path = 'GHArt/Unstable_Diffusers_YamerMIX_V9_xl_fp16'
+        >>> # download 'antelopev2' under ./models
+        >>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        >>> app.prepare(ctx_id=0, det_size=(640, 640))
+        
+        >>> # download models under ./checkpoints
+        >>> face_adapter = f'./checkpoints/ip-adapter.bin'
+        >>> controlnet_path = f'./checkpoints/ControlNetModel'
+        
+        >>> # load IdentityNet
+        >>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+        
+        >>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
+        ... )
+        >>> pipe.cuda()
+        
+        >>> # load adapter
+        >>> pipe.load_ip_adapter_instantid(face_adapter)
 
-pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
-    base_model_path,
-    controlnet=controlnet,
-    torch_dtype=torch.float16,
-    safety_checker=None,
-    feature_extractor=None,
-)
-pipe.cuda()
-pipe.load_ip_adapter_instantid(face_adapter)
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-def swap_to_gallery(images):
-    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
-
-def upload_example_to_gallery(images, prompt, style, negative_prompt):
-    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
-
-def remove_back_to_files():
-    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
-
-def remove_tips():
-    return gr.update(visible=False)
-
-def get_example():
-    case = [
-        [
-            ['./examples/yann-lecun_resize.jpg'],
-            "a man",
-            "Snow",
-            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
-        ],
-        [
-            ['./examples/musk_resize.jpeg'],
-            "a man",
-            "Mars",
-            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
-        ],
-        [
-            ['./examples/sam_resize.png'],
-            "a man",
-            "Jungle",
-            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, gree",
-        ],
-        [
-            ['./examples/schmidhuber_resize.png'],
-            "a man",
-            "Neon",
-            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
-        ],
-        [
-            ['./examples/kaifu_resize.png'],
-            "a man",
-            "Vibrant Color",
-            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
-        ],
-    ]
-    return case
-
-def convert_from_cv2_to_image(img: np.ndarray) -> Image:
-    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-
-def convert_from_image_to_cv2(img: Image) -> np.ndarray:
-    return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
-
-def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
-    stickwidth = 4
-    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
-    kps = np.array(kps)
-
-    w, h = image_pil.size
-    out_img = np.zeros([h, w, 3])
-
-    for i in range(len(limbSeq)):
-        index = limbSeq[i]
-        color = color_list[index[0]]
-
-        x = kps[index][:, 0]
-        y = kps[index][:, 1]
-        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
-        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
-        polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
-        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
-    out_img = (out_img * 0.6).astype(np.uint8)
-
-    for idx_kp, kp in enumerate(kps):
-        color = color_list[idx_kp]
-        x, y = kp
-        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
-
-    out_img_pil = Image.fromarray(out_img.astype(np.uint8))
-    return out_img_pil
-
-def resize_img(input_image, max_side=1280, min_side=1024, size=None, 
-               pad_to_max_side=False, mode=PIL.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
-
-def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
-    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
-    return p.replace("{prompt}", positive), n + ' ' + negative
-
-@spaces.GPU
-def generate_image(face_image, pose_image, prompt, negative_prompt, style_name, enhance_face_region, num_steps, identitynet_strength_ratio, adapter_strength_ratio, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)):
-
-    if face_image is None:
-        raise gr.Error(f"Cannot find any input face image! Please upload the face image")
-    
-    if prompt is None:
-        prompt = "a person"
-    
-    # apply the style template
-    prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
+        >>> prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality"
+        >>> negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured"
+
+        >>> # load an image
+        >>> image = load_image("your-example.jpg")
+        
+        >>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]
+        >>> face_emb = face_info['embedding']
+        >>> face_kps = draw_kps(face_image, face_info['kps'])
+        
+        >>> pipe.set_ip_adapter_scale(0.8)
+
+        >>> # generate image
+        >>> image = pipe(
+        ...     prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8
+        ... ).images[0]
+        ```
+"""
+
+
+from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline
+class LongPromptWeight(object):
     
-    face_image = load_image(face_image[0])
-    face_image = resize_img(face_image)
-    face_image_cv2 = convert_from_image_to_cv2(face_image)
-    height, width, _ = face_image_cv2.shape
+    """
+    Copied from https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion_xl.py
+    """
     
-    # Extract face features
-    face_info = app.get(face_image_cv2)
+    def __init__(self) -> None:
+        pass
+
+    def parse_prompt_attention(self, text):
+        """
+        Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+        Accepted tokens are:
+        (abc) - increases attention to abc by a multiplier of 1.1
+        (abc:3.12) - increases attention to abc by a multiplier of 3.12
+        [abc] - decreases attention to abc by a multiplier of 1.1
+        \( - literal character '('
+        \[ - literal character '['
+        \) - literal character ')'
+        \] - literal character ']'
+        \\ - literal character '\'
+        anything else - just text
+
+        >>> parse_prompt_attention('normal text')
+        [['normal text', 1.0]]
+        >>> parse_prompt_attention('an (important) word')
+        [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+        >>> parse_prompt_attention('(unbalanced')
+        [['unbalanced', 1.1]]
+        >>> parse_prompt_attention('\(literal\]')
+        [['(literal]', 1.0]]
+        >>> parse_prompt_attention('(unnecessary)(parens)')
+        [['unnecessaryparens', 1.1]]
+        >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+        [['a ', 1.0],
+        ['house', 1.5730000000000004],
+        [' ', 1.1],
+        ['on', 1.0],
+        [' a ', 1.1],
+        ['hill', 0.55],
+        [', sun, ', 1.1],
+        ['sky', 1.4641000000000006],
+        ['.', 1.1]]
+        """
+        import re
+
+        re_attention = re.compile(
+            r"""
+                \\\(|\\\)|\\\[|\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)|
+                \)|]|[^\\()\[\]:]+|:
+            """,
+            re.X,
+        )
+
+        re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
+
+        res = []
+        round_brackets = []
+        square_brackets = []
+
+        round_bracket_multiplier = 1.1
+        square_bracket_multiplier = 1 / 1.1
+
+        def multiply_range(start_position, multiplier):
+            for p in range(start_position, len(res)):
+                res[p][1] *= multiplier
+
+        for m in re_attention.finditer(text):
+            text = m.group(0)
+            weight = m.group(1)
+
+            if text.startswith("\\"):
+                res.append([text[1:], 1.0])
+            elif text == "(":
+                round_brackets.append(len(res))
+            elif text == "[":
+                square_brackets.append(len(res))
+            elif weight is not None and len(round_brackets) > 0:
+                multiply_range(round_brackets.pop(), float(weight))
+            elif text == ")" and len(round_brackets) > 0:
+                multiply_range(round_brackets.pop(), round_bracket_multiplier)
+            elif text == "]" and len(square_brackets) > 0:
+                multiply_range(square_brackets.pop(), square_bracket_multiplier)
+            else:
+                parts = re.split(re_break, text)
+                for i, part in enumerate(parts):
+                    if i > 0:
+                        res.append(["BREAK", -1])
+                    res.append([part, 1.0])
+
+        for pos in round_brackets:
+            multiply_range(pos, round_bracket_multiplier)
+
+        for pos in square_brackets:
+            multiply_range(pos, square_bracket_multiplier)
+
+        if len(res) == 0:
+            res = [["", 1.0]]
+
+        # merge runs of identical weights
+        i = 0
+        while i + 1 < len(res):
+            if res[i][1] == res[i + 1][1]:
+                res[i][0] += res[i + 1][0]
+                res.pop(i + 1)
+            else:
+                i += 1
+
+        return res
+
+    def get_prompts_tokens_with_weights(self, clip_tokenizer: CLIPTokenizer, prompt: str):
+        """
+        Get prompt token ids and weights, this function works for both prompt and negative prompt
+
+        Args:
+            pipe (CLIPTokenizer)
+                A CLIPTokenizer
+            prompt (str)
+                A prompt string with weights
+
+        Returns:
+            text_tokens (list)
+                A list contains token ids
+            text_weight (list)
+                A list contains the correspodent weight of token ids
+
+        Example:
+            import torch
+            from transformers import CLIPTokenizer
+
+            clip_tokenizer = CLIPTokenizer.from_pretrained(
+                "stablediffusionapi/deliberate-v2"
+                , subfolder = "tokenizer"
+                , dtype = torch.float16
+            )
+
+            token_id_list, token_weight_list = get_prompts_tokens_with_weights(
+                clip_tokenizer = clip_tokenizer
+                ,prompt = "a (red:1.5) cat"*70
+            )
+        """
+        texts_and_weights = self.parse_prompt_attention(prompt)
+        text_tokens, text_weights = [], []
+        for word, weight in texts_and_weights:
+            # tokenize and discard the starting and the ending token
+            token = clip_tokenizer(word, truncation=False).input_ids[1:-1]  # so that tokenize whatever length prompt
+            # the returned token is a 1d list: [320, 1125, 539, 320]
+
+            # merge the new tokens to the all tokens holder: text_tokens
+            text_tokens = [*text_tokens, *token]
+
+            # each token chunk will come with one weight, like ['red cat', 2.0]
+            # need to expand weight for each token.
+            chunk_weights = [weight] * len(token)
+
+            # append the weight back to the weight holder: text_weights
+            text_weights = [*text_weights, *chunk_weights]
+        return text_tokens, text_weights
+
+    def group_tokens_and_weights(self, token_ids: list, weights: list, pad_last_block=False):
+        """
+        Produce tokens and weights in groups and pad the missing tokens
+
+        Args:
+            token_ids (list)
+                The token ids from tokenizer
+            weights (list)
+                The weights list from function get_prompts_tokens_with_weights
+            pad_last_block (bool)
+                Control if fill the last token list to 75 tokens with eos
+        Returns:
+            new_token_ids (2d list)
+            new_weights (2d list)
+
+        Example:
+            token_groups,weight_groups = group_tokens_and_weights(
+                token_ids = token_id_list
+                , weights = token_weight_list
+            )
+        """
+        bos, eos = 49406, 49407
+
+        # this will be a 2d list
+        new_token_ids = []
+        new_weights = []
+        while len(token_ids) >= 75:
+            # get the first 75 tokens
+            head_75_tokens = [token_ids.pop(0) for _ in range(75)]
+            head_75_weights = [weights.pop(0) for _ in range(75)]
+
+            # extract token ids and weights
+            temp_77_token_ids = [bos] + head_75_tokens + [eos]
+            temp_77_weights = [1.0] + head_75_weights + [1.0]
+
+            # add 77 token and weights chunk to the holder list
+            new_token_ids.append(temp_77_token_ids)
+            new_weights.append(temp_77_weights)
+
+        # padding the left
+        if len(token_ids) >= 0:
+            padding_len = 75 - len(token_ids) if pad_last_block else 0
+
+            temp_77_token_ids = [bos] + token_ids + [eos] * padding_len + [eos]
+            new_token_ids.append(temp_77_token_ids)
+
+            temp_77_weights = [1.0] + weights + [1.0] * padding_len + [1.0]
+            new_weights.append(temp_77_weights)
+
+        return new_token_ids, new_weights
+
+    def get_weighted_text_embeddings_sdxl(
+        self,
+        pipe: StableDiffusionXLPipeline,
+        prompt: str = "",
+        prompt_2: str = None,
+        neg_prompt: str = "",
+        neg_prompt_2: str = None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        pooled_prompt_embeds=None,
+        negative_pooled_prompt_embeds=None,
+        extra_emb=None,
+        extra_emb_alpha=0.6,
+    ):
+        """
+        This function can process long prompt with weights, no length limitation
+        for Stable Diffusion XL
+
+        Args:
+            pipe (StableDiffusionPipeline)
+            prompt (str)
+            prompt_2 (str)
+            neg_prompt (str)
+            neg_prompt_2 (str)
+        Returns:
+            prompt_embeds (torch.Tensor)
+            neg_prompt_embeds (torch.Tensor)
+        """
+        # 
+        if prompt_embeds is not None and \
+            negative_prompt_embeds is not None and \
+            pooled_prompt_embeds is not None and \
+            negative_pooled_prompt_embeds is not None:
+            return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+        if prompt_2:
+            prompt = f"{prompt} {prompt_2}"
+
+        if neg_prompt_2:
+            neg_prompt = f"{neg_prompt} {neg_prompt_2}"
+
+        eos = pipe.tokenizer.eos_token_id
+
+        # tokenizer 1
+        prompt_tokens, prompt_weights = self.get_prompts_tokens_with_weights(pipe.tokenizer, prompt)
+        neg_prompt_tokens, neg_prompt_weights = self.get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt)
+
+        # tokenizer 2
+        # prompt_tokens_2, prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer_2, prompt)
+        # neg_prompt_tokens_2, neg_prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer_2, neg_prompt)
+        # tokenizer 2 遇到 !! !!!! 等多感叹号和tokenizer 1的效果不一致
+        prompt_tokens_2, prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer, prompt)
+        neg_prompt_tokens_2, neg_prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt)
+
+        # padding the shorter one for prompt set 1
+        prompt_token_len = len(prompt_tokens)
+        neg_prompt_token_len = len(neg_prompt_tokens)
+
+        if prompt_token_len > neg_prompt_token_len:
+            # padding the neg_prompt with eos token
+            neg_prompt_tokens = neg_prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
+            neg_prompt_weights = neg_prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
+        else:
+            # padding the prompt
+            prompt_tokens = prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
+            prompt_weights = prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
+
+        # padding the shorter one for token set 2
+        prompt_token_len_2 = len(prompt_tokens_2)
+        neg_prompt_token_len_2 = len(neg_prompt_tokens_2)
+
+        if prompt_token_len_2 > neg_prompt_token_len_2:
+            # padding the neg_prompt with eos token
+            neg_prompt_tokens_2 = neg_prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+            neg_prompt_weights_2 = neg_prompt_weights_2 + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+        else:
+            # padding the prompt
+            prompt_tokens_2 = prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+            prompt_weights_2 = prompt_weights + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+
+        embeds = []
+        neg_embeds = []
+
+        prompt_token_groups, prompt_weight_groups = self.group_tokens_and_weights(prompt_tokens.copy(), prompt_weights.copy())
+
+        neg_prompt_token_groups, neg_prompt_weight_groups = self.group_tokens_and_weights(
+            neg_prompt_tokens.copy(), neg_prompt_weights.copy()
+        )
+
+        prompt_token_groups_2, prompt_weight_groups_2 = self.group_tokens_and_weights(
+            prompt_tokens_2.copy(), prompt_weights_2.copy()
+        )
+
+        neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = self.group_tokens_and_weights(
+            neg_prompt_tokens_2.copy(), neg_prompt_weights_2.copy()
+        )
+
+        # get prompt embeddings one by one is not working.
+        for i in range(len(prompt_token_groups)):
+            # get positive prompt embeddings with weights
+            token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
+            weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
+
+            token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
+
+            # use first text encoder
+            prompt_embeds_1 = pipe.text_encoder(token_tensor.to(pipe.device), output_hidden_states=True)
+            prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]
+
+            # use second text encoder
+            prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(pipe.device), output_hidden_states=True)
+            prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]
+            pooled_prompt_embeds = prompt_embeds_2[0]
+
+            prompt_embeds_list = [prompt_embeds_1_hidden_states, prompt_embeds_2_hidden_states]
+            token_embedding = torch.concat(prompt_embeds_list, dim=-1).squeeze(0)
+
+            for j in range(len(weight_tensor)):
+                if weight_tensor[j] != 1.0:
+                    token_embedding[j] = (
+                        token_embedding[-1] + (token_embedding[j] - token_embedding[-1]) * weight_tensor[j]
+                    )
+
+            token_embedding = token_embedding.unsqueeze(0)
+            embeds.append(token_embedding)
+
+            # get negative prompt embeddings with weights
+            neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
+            neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
+            neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
+
+            # use first text encoder
+            neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(pipe.device), output_hidden_states=True)
+            neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]
+
+            # use second text encoder
+            neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(pipe.device), output_hidden_states=True)
+            neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]
+            negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]
+
+            neg_prompt_embeds_list = [neg_prompt_embeds_1_hidden_states, neg_prompt_embeds_2_hidden_states]
+            neg_token_embedding = torch.concat(neg_prompt_embeds_list, dim=-1).squeeze(0)
+
+            for z in range(len(neg_weight_tensor)):
+                if neg_weight_tensor[z] != 1.0:
+                    neg_token_embedding[z] = (
+                        neg_token_embedding[-1] + (neg_token_embedding[z] - neg_token_embedding[-1]) * neg_weight_tensor[z]
+                    )
+
+            neg_token_embedding = neg_token_embedding.unsqueeze(0)
+            neg_embeds.append(neg_token_embedding)
+
+        prompt_embeds = torch.cat(embeds, dim=1)
+        negative_prompt_embeds = torch.cat(neg_embeds, dim=1)
+
+        if extra_emb is not None:
+            extra_emb = extra_emb.to(prompt_embeds.device, dtype=prompt_embeds.dtype) * extra_emb_alpha
+            prompt_embeds = torch.cat([prompt_embeds, extra_emb], 1)
+            negative_prompt_embeds = torch.cat([negative_prompt_embeds, torch.zeros_like(extra_emb)], 1)
+            print(f'fix prompt_embeds, extra_emb_alpha={extra_emb_alpha}')
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+    def get_prompt_embeds(self, *args, **kwargs):
+        prompt_embeds, negative_prompt_embeds, _, _ = self.get_weighted_text_embeddings_sdxl(*args, **kwargs)
+        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+        return prompt_embeds
+
     
-    if len(face_info) == 0:
-        raise gr.Error(f"Cannot find any face in the image! Please upload another person image")
+class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
     
-    face_info = face_info[-1]
-    face_emb = face_info['embedding']
-    face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info['kps'])
+    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)
+        
+        if use_xformers:
+            if is_xformers_available():
+                import xformers
+                from packaging import version
+
+                xformers_version = version.parse(xformers.__version__)
+                if xformers_version == version.parse("0.0.16"):
+                    logger.warn(
+                        "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                    )
+                self.enable_xformers_memory_efficient_attention()
+            else:
+                raise ValueError("xformers is not available. Make sure it is installed correctly")
     
-    if pose_image is not None:
-        pose_image = load_image(pose_image[0])
-        pose_image = resize_img(pose_image)
-        pose_image_cv2 = convert_from_image_to_cv2(pose_image)
+    def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5):     
+        self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens)
+        self.set_ip_adapter(model_ckpt, num_tokens, scale)
         
-        face_info = app.get(pose_image_cv2)
+    def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16):
         
-        if len(face_info) == 0:
-            raise gr.Error(f"Cannot find any face in the reference image! Please upload another person image")
+        image_proj_model = Resampler(
+            dim=1280,
+            depth=4,
+            dim_head=64,
+            heads=20,
+            num_queries=num_tokens,
+            embedding_dim=image_emb_dim,
+            output_dim=self.unet.config.cross_attention_dim,
+            ff_mult=4,
+        )
+
+        image_proj_model.eval()
         
-        face_info = face_info[-1]
-        face_kps = draw_kps(pose_image, face_info['kps'])
+        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' in state_dict:
+            state_dict = state_dict["image_proj"]
+        self.image_proj_model.load_state_dict(state_dict)
         
-        width, height = face_kps.size
-    
-    if enhance_face_region:
-        control_mask = np.zeros([height, width, 3])
-        x1, y1, x2, y2 = face_info['bbox']
-        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-        control_mask[y1:y2, x1:x2] = 255
-        control_mask = Image.fromarray(control_mask.astype(np.uint8))
-    else:
-        control_mask = None
+        self.image_proj_model_in_features = image_emb_dim
     
-    generator = torch.Generator(device=device).manual_seed(seed)
-    
-    print("Start inference...")
-    print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}")
+    def set_ip_adapter(self, model_ckpt, num_tokens, scale):
+        
+        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] = AttnProcessor().to(unet.device, dtype=unet.dtype)
+            else:
+                attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, 
+                                                   cross_attention_dim=cross_attention_dim, 
+                                                   scale=scale,
+                                                   num_tokens=num_tokens).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)
     
-    pipe.set_ip_adapter_scale(adapter_strength_ratio)
-    images = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image_embeds=face_emb,
-        image=face_kps,
-        control_mask=control_mask,
-        controlnet_conditioning_scale=float(identitynet_strength_ratio),
-        num_inference_steps=num_steps,
-        guidance_scale=guidance_scale,
-        height=height,
-        width=width,
-        generator=generator
-    ).images
-
-    return images, gr.update(visible=True)
-
-### Description
-title = r"""
-<h1 align="center">InstantID: Zero-shot Identity-Preserving Generation in Seconds</h1>
-"""
+    def set_ip_adapter_scale(self, scale):
+        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, IPAttnProcessor):
+                attn_processor.scale = scale
 
-description = r"""
-<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/InstantID/InstantID' target='_blank'><b>InstantID: Zero-shot Identity-Preserving Generation in Seconds</b></a>.<br>
+    def _encode_prompt_image_emb(self, prompt_image_emb, device, dtype, do_classifier_free_guidance):
+        
+        if isinstance(prompt_image_emb, torch.Tensor):
+            prompt_image_emb = prompt_image_emb.clone().detach()
+        else:
+            prompt_image_emb = torch.tensor(prompt_image_emb)
+            
+        prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype)
+        prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])
+        
+        if do_classifier_free_guidance:
+            prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)
+        else:
+            prompt_image_emb = torch.cat([prompt_image_emb], dim=0)
+        
+        prompt_image_emb = self.image_proj_model(prompt_image_emb)
+        return prompt_image_emb
 
-How to use:<br>
-1. Upload a person image. For multiple person images, we will only detect the biggest face. Make sure face is not too small and not significantly blocked or blurred.
-2. (Optionally) upload another person image as reference pose. If not uploaded, we will use the first person image to extract landmarks. If you use a cropped face at step1, it is recommeneded to upload it to extract a new pose.
-3. Enter a text prompt as done in normal text-to-image models.
-4. Click the <b>Submit</b> button to start customizing.
-5. Share your customizd photo with your friends, enjoy😊!
-"""
+    @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,
+        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,
+        image_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"],
+        control_mask = None,
+        **kwargs,
+    ):
+        r"""
+        The call function to the pipeline for generation.
 
-article = r"""
----
-📝 **Citation**
-<br>
-If our work is helpful for your research or applications, please cite us via:
-```bibtex
-@article{wang2024instantid,
-  title={InstantID: Zero-shot Identity-Preserving Generation in Seconds},
-  author={Wang, Qixun and Bai, Xu and Wang, Haofan and Qin, Zekui and Chen, Anthony},
-  journal={arXiv preprint arXiv:2401.07519},
-  year={2024}
-}
-```
-📧 **Contact**
-<br>
-If you have any questions, please feel free to open an issue or directly reach us out at <b>haofanwang.ai@gmail.com</b>.
-"""
+        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.
+            image_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated image embeddings.
+            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.
 
-tips = r"""
-### Usage tips of InstantID
-1. If you're unsatisfied with the similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter).
-2. If the generated image is over-saturated, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale.
-3. If text control is not as expected, decrease ip_adapter_scale.
-4. Find a good base model always makes a difference.
-"""
+        Examples:
 
-css = '''
-.gradio-container {width: 85% !important}
-'''
-with gr.Blocks(css=css) as demo:
+        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.
+        """
+        lpw = LongPromptWeight()
 
-    # description
-    gr.Markdown(title)
-    gr.Markdown(description)
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
 
-    with gr.Row():
-        with gr.Column():
-            
-            # upload face image
-            face_files = gr.Files(
-                        label="Upload a photo of your face",
-                        file_types=["image"]
-                    )
-            uploaded_faces = gr.Gallery(label="Your images", visible=False, columns=1, rows=1, height=512)
-            with gr.Column(visible=False) as clear_button_face:
-                remove_and_reupload_faces = gr.ClearButton(value="Remove and upload new ones", components=face_files, size="sm")
-            
-            # optional: upload a reference pose image
-            pose_files = gr.Files(
-                        label="Upload a reference pose image (optional)",
-                        file_types=["image"]
-                    )
-            uploaded_poses = gr.Gallery(label="Your images", visible=False, columns=1, rows=1, height=512)
-            with gr.Column(visible=False) as clear_button_pose:
-                remove_and_reupload_poses = gr.ClearButton(value="Remove and upload new ones", components=pose_files, size="sm")
-            
-            # prompt
-            prompt = gr.Textbox(label="Prompt",
-                       info="Give simple prompt is enough to achieve good face fedility",
-                       placeholder="A photo of a person",
-                       value="")
-            
-            submit = gr.Button("Submit", variant="primary")
-            
-            style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
-            
-            # strength
-            identitynet_strength_ratio = gr.Slider(
-                label="IdentityNet strength (for fedility)",
-                minimum=0,
-                maximum=1.5,
-                step=0.05,
-                value=0.80,
+        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`",
             )
-            adapter_strength_ratio = gr.Slider(
-                label="Image adapter strength (for detail)",
-                minimum=0,
-                maximum=1.5,
-                step=0.05,
-                value=0.80,
+        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`",
             )
-            
-            with gr.Accordion(open=False, label="Advanced Options"):
-                negative_prompt = gr.Textbox(
-                    label="Negative Prompt", 
-                    placeholder="low quality",
-                    value="(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
-                )
-                num_steps = gr.Slider( 
-                    label="Number of sample steps",
-                    minimum=20,
-                    maximum=100,
-                    step=1,
-                    value=30,
-                )
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.1,
-                    maximum=10.0,
-                    step=0.1,
-                    value=5,
-                )
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=42,
+
+        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
+
+        # align format for control guidance
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            controlnet_conditioning_scale,
+            control_guidance_start,
+            control_guidance_end,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+
+        # 2. Define call parameters
+        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
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = lpw.get_weighted_text_embeddings_sdxl(
+            pipe=self, 
+            prompt=prompt, 
+            neg_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+        )
+        
+        # 3.2 Encode image prompt
+        prompt_image_emb = self._encode_prompt_image_emb(image_embeds, 
+                                                         device,
+                                                         self.unet.dtype,
+                                                         self.do_classifier_free_guidance)
+        
+        # 4. Prepare image
+        if isinstance(controlnet, ControlNetModel):
+            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,
+            )
+            height, width = image.shape[-2:]
+        elif isinstance(controlnet, MultiControlNetModel):
+            images = []
+
+            for image_ in image:
+                image_ = self.prepare_image(
+                    image=image_,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_images_per_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    device=device,
+                    dtype=controlnet.dtype,
+                    do_classifier_free_guidance=self.do_classifier_free_guidance,
+                    guess_mode=guess_mode,
                 )
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                enhance_face_region = gr.Checkbox(label="Enhance non-face region", value=True)
-
-        with gr.Column():
-            gallery = gr.Gallery(label="Generated Images")
-            usage_tips = gr.Markdown(label="Usage tips of InstantID", value=tips ,visible=False)
-
-        face_files.upload(fn=swap_to_gallery, inputs=face_files, outputs=[uploaded_faces, clear_button_face, face_files])
-        pose_files.upload(fn=swap_to_gallery, inputs=pose_files, outputs=[uploaded_poses, clear_button_pose, pose_files])
-
-        remove_and_reupload_faces.click(fn=remove_back_to_files, outputs=[uploaded_faces, clear_button_face, face_files])
-        remove_and_reupload_poses.click(fn=remove_back_to_files, outputs=[uploaded_poses, clear_button_pose, pose_files])
-
-        submit.click(
-            fn=remove_tips,
-            outputs=usage_tips,            
-        ).then(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=generate_image,
-            inputs=[face_files, pose_files, prompt, negative_prompt, style, enhance_face_region, num_steps, identitynet_strength_ratio, adapter_strength_ratio, guidance_scale, seed],
-            outputs=[gallery, usage_tips]
+
+                images.append(image_)
+
+            image = images
+            height, width = image[0].shape[-2:]
+        else:
+            assert False
+
+        # 4.1 Region control
+        if control_mask is not None:
+            mask_weight_image = control_mask
+            mask_weight_image = np.array(mask_weight_image)
+            mask_weight_image_tensor = torch.from_numpy(mask_weight_image).to(device=device, dtype=prompt_embeds.dtype)
+            mask_weight_image_tensor = mask_weight_image_tensor[:, :, 0] / 255.
+            mask_weight_image_tensor = mask_weight_image_tensor[None, None]
+            h, w = mask_weight_image_tensor.shape[-2:]
+            control_mask_wight_image_list = []
+            for scale in [8, 8, 8, 16, 16, 16, 32, 32, 32]:
+                scale_mask_weight_image_tensor = F.interpolate(
+                    mask_weight_image_tensor,(h // scale, w // scale), mode='bilinear')
+                control_mask_wight_image_list.append(scale_mask_weight_image_tensor)
+            region_mask = torch.from_numpy(np.array(control_mask)[:, :, 0]).to(self.unet.device, dtype=self.unet.dtype) / 255.
+            region_control.prompt_image_conditioning = [dict(region_mask=region_mask)]
+        else:
+            control_mask_wight_image_list = None
+            region_control.prompt_image_conditioning = [dict(region_mask=None)]
+
+        # 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,
         )
-    
-    gr.Examples(
-        examples=get_example(),
-        inputs=[face_files, prompt, style, negative_prompt],
-        run_on_click=True,
-        fn=upload_example_to_gallery,
-        outputs=[uploaded_faces, clear_button_face, face_files],
-    )
-    
-    gr.Markdown(article)
 
-demo.launch()
+        # 6.5 Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7.1 Create tensor stating which controlnets to keep
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end)
+            ]
+            controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
+
+        # 7.2 Prepare added time ids & embeddings
+        if isinstance(image, list):
+            original_size = original_size or image[0].shape[-2:]
+        else:
+            original_size = original_size or 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)
+        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)
+        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):
+                # 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]
+
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    control_model_input,
+                    t,
+                    encoder_hidden_states=prompt_image_emb,
+                    controlnet_cond=image,
+                    conditioning_scale=cond_scale,
+                    guess_mode=guess_mode,
+                    added_cond_kwargs=controlnet_added_cond_kwargs,
+                    return_dict=False,
+                )
+
+                # controlnet mask
+                if control_mask_wight_image_list is not None:
+                    down_block_res_samples = [
+                        down_block_res_sample * mask_weight
+                        for down_block_res_sample, mask_weight in zip(down_block_res_samples, control_mask_wight_image_list)
+                    ]
+                    mid_block_res_sample *= control_mask_wight_image_list[-1]
+
+                if guess_mode and self.do_classifier_free_guidance:
+                    # Infered ControlNet only for the conditional batch.
+                    # To apply the output of ControlNet to both the unconditional and conditional batches,
+                    # add 0 to the unconditional batch to keep it unchanged.
+                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+
+                # 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,
+                    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)
+
+                # 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)
+            
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)            
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)