feat: 初始提交

PhotoMaker/feature-extractor/preprocessor_config.json

+{
+    "crop_size": 224,
+    "do_center_crop": true,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_resize": true,
+    "feature_extractor_type": "CLIPFeatureExtractor",
+    "image_mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "image_std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ],
+    "resample": 3,
+    "size": 224
+  }

PhotoMaker/gradio_demo/app.py

+import torch
+import numpy as np
+import random
+import os
+import sys
+
+from diffusers.utils import load_image
+from diffusers import EulerDiscreteScheduler
+
+from huggingface_hub import hf_hub_download
+import spaces
+import gradio as gr
+
+from photomaker import PhotoMakerStableDiffusionXLPipeline
+
+from style_template import styles
+from aspect_ratio_template import aspect_ratios
+
+# global variable
+base_model_path = 'SG161222/RealVisXL_V4.0'
+try:
+    if torch.cuda.is_available():
+        device = "cuda"
+    elif sys.platform == "darwin" and torch.backends.mps.is_available():
+        device = "mps"
+    else:
+        device = "cpu"
+except:
+    device = "cpu"
+
+MAX_SEED = np.iinfo(np.int32).max
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "Photographic (Default)"
+ASPECT_RATIO_LABELS = list(aspect_ratios)
+DEFAULT_ASPECT_RATIO = ASPECT_RATIO_LABELS[0]
+
+# download PhotoMaker checkpoint to cache
+photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model")
+
+torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+if device == "mps":
+    torch_dtype = torch.float16
+
+pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
+    base_model_path, 
+    torch_dtype=torch_dtype,
+    use_safetensors=True, 
+    variant="fp16",
+    # local_files_only=True,
+).to(device)
+
+pipe.load_photomaker_adapter(
+    os.path.dirname(photomaker_ckpt),
+    subfolder="",
+    weight_name=os.path.basename(photomaker_ckpt),
+    trigger_word="img",
+    pm_version="v1",
+)
+pipe.id_encoder.to(device)
+
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+# pipe.set_adapters(["photomaker"], adapter_weights=[1.0])
+pipe.fuse_lora()
+pipe.to(device)
+
+@spaces.GPU
+def generate_image(upload_images, prompt, negative_prompt, aspect_ratio_name, style_name, num_steps, style_strength_ratio, num_outputs, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)):
+    # check the trigger word
+    image_token_id = pipe.tokenizer.convert_tokens_to_ids(pipe.trigger_word)
+    input_ids = pipe.tokenizer.encode(prompt)
+    if image_token_id not in input_ids:
+        raise gr.Error(f"Cannot find the trigger word '{pipe.trigger_word}' in text prompt! Please refer to step 2️⃣")
+
+    if input_ids.count(image_token_id) > 1:
+        raise gr.Error(f"Cannot use multiple trigger words '{pipe.trigger_word}' in text prompt!")
+
+    # determine output dimensions by the aspect ratio
+    output_w, output_h = aspect_ratios[aspect_ratio_name]
+    print(f"[Debug] Generate image using aspect ratio [{aspect_ratio_name}] => {output_w} x {output_h}")
+
+    # apply the style template
+    prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
+
+    if upload_images is None:
+        raise gr.Error(f"Cannot find any input face image! Please refer to step 1️⃣")
+
+    input_id_images = []
+    for img in upload_images:
+        input_id_images.append(load_image(img))
+    
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    print("Start inference...")
+    print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}")
+    start_merge_step = int(float(style_strength_ratio) / 100 * num_steps)
+    if start_merge_step > 30:
+        start_merge_step = 30
+    print(start_merge_step)
+    images = pipe(
+        prompt=prompt,
+        width=output_w,
+        height=output_h,
+        input_id_images=input_id_images,
+        negative_prompt=negative_prompt,
+        num_images_per_prompt=num_outputs,
+        num_inference_steps=num_steps,
+        start_merge_step=start_merge_step,
+        generator=generator,
+        guidance_scale=guidance_scale,
+    ).images
+    return images, gr.update(visible=True)
+
+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 randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+def apply_style(style_name: str, positive: str, negative: str = ""):
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return p.replace("{prompt}", positive), n + ' ' + negative
+
+def get_image_path_list(folder_name):
+    image_basename_list = os.listdir(folder_name)
+    image_path_list = sorted([os.path.join(folder_name, basename) for basename in image_basename_list])
+    return image_path_list
+
+def get_example():
+    case = [
+        [
+            get_image_path_list('./examples/scarletthead_woman'),
+            "instagram photo, portrait photo of a woman img, colorful, perfect face, natural skin, hard shadows, film grain",
+            "(No style)",
+            "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
+        ],
+        [
+            get_image_path_list('./examples/newton_man'),
+            "sci-fi, closeup portrait photo of a man img wearing the sunglasses in Iron man suit, face, slim body, high quality, film grain",
+            "(No style)",
+            "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
+        ],
+    ]
+    return case
+
+### Description and style
+logo = r"""
+<center><img src='https://photo-maker.github.io/assets/logo.png' alt='PhotoMaker logo' style="width:80px; margin-bottom:10px"></center>
+"""
+title = r"""
+<h1 align="center">PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding</h1>
+"""
+
+description = r"""
+<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/TencentARC/PhotoMaker' target='_blank'><b>PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding</b></a>.<br>
+<br>
+For stylization, you could use our other gradio demo [PhotoMaker-Style](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style).
+<br>
+❗️❗️❗️[<b>Important</b>] Personalization steps:<br>
+1️⃣ Upload images of someone you want to customize. One image is ok, but more is better.  Although we do not perform face detection, the face in the uploaded image should <b>occupy the majority of the image</b>.<br>
+2️⃣ Enter a text prompt, making sure to <b>follow the class word</b> you want to customize with the <b>trigger word</b>: `img`, such as: `man img` or `woman img` or `girl img`.<br>
+3️⃣ Choose your preferred style template.<br>
+4️⃣ Click the <b>Submit</b> button to start customizing.
+"""
+
+article = r"""
+
+If PhotoMaker is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/PhotoMaker' target='_blank'>Github Repo</a>. Thanks! 
+[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/PhotoMaker?style=social)](https://github.com/TencentARC/PhotoMaker)
+---
+📝 **Citation**
+<br>
+If our work is useful for your research, please consider citing:
+
+```bibtex
+@article{li2023photomaker,
+  title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding},
+  author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2024}
+}
+```
+📋 **License**
+<br>
+Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/TencentARC/PhotoMaker/blob/main/LICENSE) for details.
+
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>zhenli1031@gmail.com</b>.
+"""
+
+tips = r"""
+### Usage tips of PhotoMaker
+1. Upload more photos of the person to be customized to **improve ID fidelty**. If the input is Asian face(s), maybe consider adding 'asian' before the class word, e.g., `asian woman img`
+2. When stylizing, does the generated face look too realistic? Try switching to our **other gradio demo** [PhotoMaker-Style](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style). Adjust the **Style strength** to 30-50, the larger the number, the less ID fidelty, but the stylization ability will be better.
+3. For **faster** speed, reduce the number of generated images and sampling steps. However, please note that reducing the sampling steps may compromise the ID fidelity.
+"""
+# We have provided some generate examples and comparisons at: [this website]().
+# 3. Don't make the prompt too long, as we will trim it if it exceeds 77 tokens. 
+# 4. When generating realistic photos, if it's not real enough, try switching to our other gradio application [PhotoMaker-Realistic]().
+
+css = '''
+.gradio-container {width: 85% !important}
+'''
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(logo)
+    gr.Markdown(title)
+    gr.Markdown(description)
+    # gr.DuplicateButton(
+    #     value="Duplicate Space for private use ",
+    #     elem_id="duplicate-button",
+    #     visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    # )
+    with gr.Row():
+        with gr.Column():
+            files = gr.Files(
+                        label="Drag (Select) 1 or more photos of your face",
+                        file_types=["image"]
+                    )
+            uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=200)
+            with gr.Column(visible=False) as clear_button:
+                remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm")
+            prompt = gr.Textbox(label="Prompt",
+                       info="Try something like 'a photo of a man/woman img', 'img' is the trigger word.",
+                       placeholder="A photo of a [man/woman img]...")
+            style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
+            aspect_ratio = gr.Dropdown(label="Output aspect ratio", choices=ASPECT_RATIO_LABELS, value=DEFAULT_ASPECT_RATIO)
+            submit = gr.Button("Submit")
+
+            with gr.Accordion(open=False, label="Advanced Options"):
+                negative_prompt = gr.Textbox(
+                    label="Negative Prompt", 
+                    placeholder="low quality",
+                    value="nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry",
+                )
+                num_steps = gr.Slider( 
+                    label="Number of sample steps",
+                    minimum=20,
+                    maximum=100,
+                    step=1,
+                    value=50,
+                )
+                style_strength_ratio = gr.Slider(
+                    label="Style strength (%)",
+                    minimum=15,
+                    maximum=50,
+                    step=1,
+                    value=20,
+                )
+                num_outputs = gr.Slider(
+                    label="Number of output images",
+                    minimum=1,
+                    maximum=4,
+                    step=1,
+                    value=2,
+                )
+                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=0,
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Column():
+            gallery = gr.Gallery(label="Generated Images")
+            usage_tips = gr.Markdown(label="Usage tips of PhotoMaker", value=tips ,visible=False)
+
+        files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files])
+        remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, 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=[files, prompt, negative_prompt, aspect_ratio, style, num_steps, style_strength_ratio, num_outputs, guidance_scale, seed],
+            outputs=[gallery, usage_tips]
+        )
+
+    gr.Examples(
+        examples=get_example(),
+        inputs=[files, prompt, style, negative_prompt],
+        run_on_click=True,
+        fn=upload_example_to_gallery,
+        outputs=[uploaded_files, clear_button, files],
+    )
+    
+    gr.Markdown(article)
+    
+demo.launch()

PhotoMaker/gradio_demo/app_v2.py

+import torch
+import torchvision.transforms.functional as TF
+import numpy as np
+import random
+import os
+import sys
+
+from diffusers.utils import load_image
+from diffusers import EulerDiscreteScheduler, T2IAdapter
+
+from huggingface_hub import hf_hub_download
+import spaces
+import gradio as gr
+
+from photomaker import PhotoMakerStableDiffusionXLAdapterPipeline
+from photomaker import FaceAnalysis2, analyze_faces
+
+from style_template import styles
+from aspect_ratio_template import aspect_ratios
+
+# global variable
+base_model_path = 'SG161222/RealVisXL_V4.0'
+face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
+face_detector.prepare(ctx_id=0, det_size=(640, 640))
+
+try:
+    if torch.cuda.is_available():
+        device = "cuda"
+    elif sys.platform == "darwin" and torch.backends.mps.is_available():
+        device = "mps"
+    else:
+        device = "cpu"
+except:
+    device = "cpu"
+
+MAX_SEED = np.iinfo(np.int32).max
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "Photographic (Default)"
+ASPECT_RATIO_LABELS = list(aspect_ratios)
+DEFAULT_ASPECT_RATIO = ASPECT_RATIO_LABELS[0]
+
+enable_doodle_arg = False
+photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker-V2", filename="photomaker-v2.bin", repo_type="model")
+
+torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+if device == "mps":
+    torch_dtype = torch.float16
+    
+# load adapter
+adapter = T2IAdapter.from_pretrained(
+    "TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch_dtype, variant="fp16"
+).to(device)
+
+pipe = PhotoMakerStableDiffusionXLAdapterPipeline.from_pretrained(
+    base_model_path, 
+    adapter=adapter, 
+    torch_dtype=torch_dtype,
+    use_safetensors=True, 
+    variant="fp16",
+).to(device)
+
+pipe.load_photomaker_adapter(
+    os.path.dirname(photomaker_ckpt),
+    subfolder="",
+    weight_name=os.path.basename(photomaker_ckpt),
+    trigger_word="img",
+    pm_version="v2",
+)
+pipe.id_encoder.to(device)
+
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+# pipe.set_adapters(["photomaker"], adapter_weights=[1.0])
+pipe.fuse_lora()
+pipe.to(device)
+
+
+@spaces.GPU
+def generate_image(
+    upload_images, 
+    prompt, 
+    negative_prompt, 
+    aspect_ratio_name, 
+    style_name, 
+    num_steps, 
+    style_strength_ratio, 
+    num_outputs, 
+    guidance_scale, 
+    seed, 
+    use_doodle,
+    sketch_image,
+    adapter_conditioning_scale,
+    adapter_conditioning_factor,
+    progress=gr.Progress(track_tqdm=True)
+):
+    if use_doodle:
+        sketch_image = sketch_image["composite"]
+        r, g, b, a = sketch_image.split()
+        sketch_image = a.convert("RGB")
+        sketch_image = TF.to_tensor(sketch_image) > 0.5 # Inversion 
+        sketch_image = TF.to_pil_image(sketch_image.to(torch.float32))
+        adapter_conditioning_scale = adapter_conditioning_scale
+        adapter_conditioning_factor = adapter_conditioning_factor
+    else:
+        adapter_conditioning_scale = 0.
+        adapter_conditioning_factor = 0.
+        sketch_image = None
+
+    # check the trigger word
+    image_token_id = pipe.tokenizer.convert_tokens_to_ids(pipe.trigger_word)
+    input_ids = pipe.tokenizer.encode(prompt)
+    if image_token_id not in input_ids:
+        raise gr.Error(f"Cannot find the trigger word '{pipe.trigger_word}' in text prompt! Please refer to step 2️⃣")
+
+    if input_ids.count(image_token_id) > 1:
+        raise gr.Error(f"Cannot use multiple trigger words '{pipe.trigger_word}' in text prompt!")
+
+    # determine output dimensions by the aspect ratio
+    output_w, output_h = aspect_ratios[aspect_ratio_name]
+    print(f"[Debug] Generate image using aspect ratio [{aspect_ratio_name}] => {output_w} x {output_h}")
+
+    # apply the style template
+    prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
+
+    if upload_images is None:
+        raise gr.Error(f"Cannot find any input face image! Please refer to step 1️⃣")
+
+    input_id_images = []
+    for img in upload_images:
+        input_id_images.append(load_image(img))
+    
+    id_embed_list = []
+
+    for img in input_id_images:
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        faces = analyze_faces(face_detector, img)
+        if len(faces) > 0:
+            id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
+
+    if len(id_embed_list) == 0:
+        raise gr.Error(f"No face detected, please update the input face image(s)")
+    
+    id_embeds = torch.stack(id_embed_list)
+
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    print("Start inference...")
+    print(f"[Debug] Seed: {seed}")
+    print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}")
+    start_merge_step = int(float(style_strength_ratio) / 100 * num_steps)
+    if start_merge_step > 30:
+        start_merge_step = 30
+    print(start_merge_step)
+    images = pipe(
+        prompt=prompt,
+        width=output_w,
+        height=output_h,
+        input_id_images=input_id_images,
+        negative_prompt=negative_prompt,
+        num_images_per_prompt=num_outputs,
+        num_inference_steps=num_steps,
+        start_merge_step=start_merge_step,
+        generator=generator,
+        guidance_scale=guidance_scale,
+        id_embeds=id_embeds,
+        image=sketch_image,
+        adapter_conditioning_scale=adapter_conditioning_scale,
+        adapter_conditioning_factor=adapter_conditioning_factor,
+    ).images
+    return images, gr.update(visible=True)
+
+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 change_doodle_space(use_doodle):
+    if use_doodle:
+        return gr.update(visible=True)
+    else:
+        return gr.update(visible=False)
+
+def remove_tips():
+    return gr.update(visible=False)
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+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
+
+def get_image_path_list(folder_name):
+    image_basename_list = os.listdir(folder_name)
+    image_path_list = sorted([os.path.join(folder_name, basename) for basename in image_basename_list])
+    return image_path_list
+
+def get_example():
+    case = [
+        [
+            get_image_path_list('./examples/scarletthead_woman'),
+            "instagram photo, portrait photo of a woman img, colorful, perfect face, natural skin, hard shadows, film grain",
+            "(No style)",
+            "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
+        ],
+        [
+            get_image_path_list('./examples/newton_man'),
+            "sci-fi, closeup portrait photo of a man img wearing the sunglasses in Iron man suit, face, slim body, high quality, film grain",
+            "(No style)",
+            "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
+        ],
+    ]
+    return case
+
+### Description and style
+logo = r"""
+<center><img src='https://photo-maker.github.io/assets/logo.png' alt='PhotoMaker logo' style="width:80px; margin-bottom:10px"></center>
+"""
+title = r"""
+<h1 align="center">PhotoMaker V2: Improved ID Fidelity and Better Controllability than PhotoMaker V1</h1>
+"""
+
+description = r"""
+<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/TencentARC/PhotoMaker' target='_blank'><b>PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding</b></a>.<br>
+The details of PhotoMaker V2 can be found in 
+<br>
+<br>
+For previous version of PhotoMaker, you could use our original gradio demos [PhotoMaker](https://huggingface.co/spaces/TencentARC/PhotoMaker) and [PhotoMaker-Style](https://huggingface.co/spaces/TencentARC/PhotoMaker-Style).
+<br>
+❗️❗️❗️[<b>Important</b>] Personalization steps:<br>
+1️⃣ Upload images of someone you want to customize. One image is ok, but more is better.  Although we do not perform face detection, the face in the uploaded image should <b>occupy the majority of the image</b>.<br>
+2️⃣ Enter a text prompt, making sure to <b>follow the class word</b> you want to customize with the <b>trigger word</b>: `img`, such as: `man img` or `woman img` or `girl img`.<br>
+3️⃣ Choose your preferred style template.<br>
+4️⃣ <b>(Optional: but new feature)</b> Select the ‘Enable Drawing Doodle...’ option and draw on the canvas<br>
+5️⃣ Click the <b>Submit</b> button to start customizing.
+"""
+
+article = r"""
+
+If PhotoMaker V2 is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/PhotoMaker' target='_blank'>Github Repo</a>. Thanks! 
+[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/PhotoMaker?style=social)](https://github.com/TencentARC/PhotoMaker)
+---
+📝 **Citation**
+<br>
+If our work is useful for your research, please consider citing:
+
+```bibtex
+@article{li2023photomaker,
+  title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding},
+  author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2024}
+}
+```
+📋 **License**
+<br>
+Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/TencentARC/PhotoMaker/blob/main/LICENSE) for details.
+
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>zhenli1031@gmail.com</b>.
+"""
+
+tips = r"""
+### Usage tips of PhotoMaker
+1. Upload **more photos**of the person to be customized to **improve ID fidelty**.
+2. If you find that the image quality is poor when using doodle for control, you can reduce the conditioning scale and factor of the adapter.
+Besides, you could enlarge the ratio for more consistency of your doodle. <br>
+If you have any issues, leave the issue in the discussion page of the space. For a more stable (queue-free) experience, you can duplicate the space.
+"""
+# We have provided some generate examples and comparisons at: [this website]().
+
+css = '''
+.gradio-container {width: 85% !important}
+'''
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(logo)
+    gr.Markdown(title)
+    gr.Markdown(description)
+    # gr.DuplicateButton(
+    #     value="Duplicate Space for private use ",
+    #     elem_id="duplicate-button",
+    #     visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    # )
+    with gr.Row():
+        with gr.Column():
+            files = gr.Files(
+                        label="Drag (Select) 1 or more photos of your face",
+                        file_types=["image"]
+                    )
+            uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=200)
+            with gr.Column(visible=False) as clear_button:
+                remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm")
+            prompt = gr.Textbox(label="Prompt",
+                       info="Try something like 'a photo of a man/woman img', 'img' is the trigger word.",
+                       placeholder="A photo of a [man/woman img]...")
+            style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
+            aspect_ratio = gr.Dropdown(label="Output aspect ratio", choices=ASPECT_RATIO_LABELS, value=DEFAULT_ASPECT_RATIO)
+            submit = gr.Button("Submit")
+
+            enable_doodle = gr.Checkbox(
+                label="Enable Drawing Doodle for Control", value=enable_doodle_arg,
+                info="After enabling this option, PhotoMaker will generate content based on your doodle on the canvas, driven by the T2I-Adapter (Quality may be decreased)",
+            )
+            with gr.Accordion("T2I-Adapter-Doodle (Optional)", visible=False) as doodle_space:
+                with gr.Row():
+                    sketch_image = gr.Sketchpad(
+                        label="Canvas",
+                        type="pil",
+                        crop_size=[1024,1024],
+                        layers=False,
+                        canvas_size=(350, 350),
+                        brush=gr.Brush(default_size=5, colors=["#000000"], color_mode="fixed")
+                    )
+                    with gr.Group():
+                        adapter_conditioning_scale = gr.Slider(
+                            label="Adapter conditioning scale",
+                            minimum=0.5,
+                            maximum=1,
+                            step=0.1,
+                            value=0.7,
+                        )
+                        adapter_conditioning_factor = gr.Slider(
+                            label="Adapter conditioning factor",
+                            info="Fraction of timesteps for which adapter should be applied",
+                            minimum=0.5,
+                            maximum=1,
+                            step=0.1,
+                            value=0.8,
+                        )
+            with gr.Accordion(open=False, label="Advanced Options"):
+                negative_prompt = gr.Textbox(
+                    label="Negative Prompt", 
+                    placeholder="low quality",
+                    value="nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry",
+                )
+                num_steps = gr.Slider( 
+                    label="Number of sample steps",
+                    minimum=20,
+                    maximum=100,
+                    step=1,
+                    value=50,
+                )
+                style_strength_ratio = gr.Slider(
+                    label="Style strength (%)",
+                    minimum=15,
+                    maximum=50,
+                    step=1,
+                    value=20,
+                )
+                num_outputs = gr.Slider(
+                    label="Number of output images",
+                    minimum=1,
+                    maximum=4,
+                    step=1,
+                    value=2,
+                )
+                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=0,
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Column():
+            gallery = gr.Gallery(label="Generated Images")
+            usage_tips = gr.Markdown(label="Usage tips of PhotoMaker", value=tips ,visible=False)
+
+        files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files])
+        remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files])
+        enable_doodle.select(fn=change_doodle_space, inputs=enable_doodle, outputs=doodle_space)
+
+        input_list = [
+            files, 
+            prompt, 
+            negative_prompt, 
+            aspect_ratio, 
+            style, 
+            num_steps, 
+            style_strength_ratio, 
+            num_outputs, 
+            guidance_scale, 
+            seed,
+            enable_doodle,
+            sketch_image,
+            adapter_conditioning_scale,
+            adapter_conditioning_factor
+        ]
+
+        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=input_list,
+            outputs=[gallery, usage_tips]
+        )
+
+    gr.Examples(
+        examples=get_example(),
+        inputs=[files, prompt, style, negative_prompt],
+        run_on_click=True,
+        fn=upload_example_to_gallery,
+        outputs=[uploaded_files, clear_button, files],
+    )
+    
+    gr.Markdown(article)
+    
+demo.launch()

PhotoMaker/gradio_demo/aspect_ratio_template.py

+# Note: Since output width & height need to be divisible by 8, the w & h -values do
+#       not exactly match the stated aspect ratios... but they are "close enough":)
+
+aspect_ratio_list = [
+    {
+        "name": "Instagram (1:1)",
+        "w": 1024,
+        "h": 1024,
+    },
+    {
+        "name": "35mm film / Landscape (3:2)",
+        "w": 1024,
+        "h": 680,
+    },
+    {
+        "name": "35mm film / Portrait (2:3)",
+        "w": 680,
+        "h": 1024,
+    },
+    {
+        "name": "CRT Monitor / Landscape (4:3)",
+        "w": 1024,
+        "h": 768,
+    },
+    {
+        "name": "CRT Monitor / Portrait (3:4)",
+        "w": 768,
+        "h": 1024,
+    },
+    {
+        "name": "Widescreen TV / Landscape (16:9)",
+        "w": 1024,
+        "h": 576,
+    },
+    {
+        "name": "Widescreen TV / Portrait (9:16)",
+        "w": 576,
+        "h": 1024,
+    },
+    {
+        "name": "Widescreen Monitor / Landscape (16:10)",
+        "w": 1024,
+        "h": 640,
+    },
+    {
+        "name": "Widescreen Monitor / Portrait (10:16)",
+        "w": 640,
+        "h": 1024,
+    },
+    {
+        "name": "Cinemascope (2.39:1)",
+        "w": 1024,
+        "h": 424,
+    },
+    {
+        "name": "Widescreen Movie (1.85:1)",
+        "w": 1024,
+        "h": 552,
+    },
+    {
+        "name": "Academy Movie (1.37:1)",
+        "w": 1024,
+        "h": 744,
+    },
+    {
+        "name": "Sheet-print (A-series) / Landscape (297:210)",
+        "w": 1024,
+        "h": 720,
+    },
+    {
+        "name": "Sheet-print (A-series) / Portrait (210:297)",
+        "w": 720,
+        "h": 1024,
+    },
+]
+
+aspect_ratios = {k["name"]: (k["w"], k["h"]) for k in aspect_ratio_list}

PhotoMaker/gradio_demo/style_template.py

+style_list = [
+    {
+        "name": "(No style)",
+        "prompt": "{prompt}",
+        "negative_prompt": "",
+    },
+    {
+        "name": "Cinematic",
+        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
+        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+    },
+    {
+        "name": "Disney Charactor",
+        "prompt": "A Pixar animation character of {prompt} . pixar-style, studio anime, Disney, high-quality",
+        "negative_prompt": "lowres, bad anatomy, bad hands, text, bad eyes, bad arms, bad legs, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, blurry, grayscale, noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo",
+    },
+    {
+        "name": "Digital Art",
+        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
+        "negative_prompt": "photo, photorealistic, realism, ugly",
+    },
+    {
+        "name": "Photographic (Default)",
+        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
+        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
+    },
+    {
+        "name": "Fantasy art",
+        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
+        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
+    },
+    {
+        "name": "Neonpunk",
+        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
+        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
+    },
+    {
+        "name": "Enhance",
+        "prompt": "breathtaking {prompt} . award-winning, professional, highly detailed",
+        "negative_prompt": "ugly, deformed, noisy, blurry, distorted, grainy",
+    },
+    {
+        "name": "Comic book",
+        "prompt": "comic {prompt} . graphic illustration, comic art, graphic novel art, vibrant, highly detailed",
+        "negative_prompt": "photograph, deformed, glitch, noisy, realistic, stock photo",
+    },
+    {
+        "name": "Lowpoly",
+        "prompt": "low-poly style {prompt} . low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition",
+        "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo",
+    },
+    {
+        "name": "Line art",
+        "prompt": "line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics",
+        "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic",
+    }
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
\ No newline at end of file

PhotoMaker/inference_scripts/inference_pmv2.py

+# !pip install opencv-python transformers accelerate
+import os
+import sys
+
+import numpy as np
+import torch
+from diffusers.utils import load_image
+from diffusers import EulerDiscreteScheduler
+from huggingface_hub import hf_hub_download
+from photomaker import PhotoMakerStableDiffusionXLPipeline
+from photomaker import FaceAnalysis2, analyze_faces
+
+face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
+face_detector.prepare(ctx_id=0, det_size=(640, 640))
+
+try:
+    if torch.cuda.is_available():
+        device = "cuda"
+    elif sys.platform == "darwin" and torch.backends.mps.is_available():
+        device = "mps"
+    else:
+        device = "cpu"
+except:
+    device = "cpu"
+
+torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+if device == "mps":
+    torch_dtype = torch.float16
+
+output_dir = "./outputs"
+os.makedirs(output_dir, exist_ok=True)
+
+photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker-V2", filename="photomaker-v2.bin", repo_type="model")
+
+prompt = "instagram photo, portrait photo of a woman img, colorful, perfect face, natural skin, hard shadows, film grain, best quality"
+negative_prompt = "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth"
+
+# initialize the models and pipeline
+### Load base model
+pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
+    "SG161222/RealVisXL_V4.0", torch_dtype=torch_dtype
+).to("cuda")
+
+### Load PhotoMaker checkpoint
+pipe.load_photomaker_adapter(
+    os.path.dirname(photomaker_path),
+    subfolder="",
+    weight_name=os.path.basename(photomaker_path),
+    trigger_word="img"  # define the trigger word
+)     
+### Also can cooperate with other LoRA modules
+# pipe.load_lora_weights(os.path.dirname(lora_path), weight_name=lora_model_name, adapter_name="lcm-lora")
+# pipe.set_adapters(["photomaker", "lcm-lora"], adapter_weights=[1.0, 0.5])
+
+pipe.fuse_lora()
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+
+pipe.enable_model_cpu_offload()
+
+### define the input ID images
+input_folder_name = './examples/scarletthead_woman'
+image_basename_list = os.listdir(input_folder_name)
+image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
+
+input_id_images = []
+for image_path in image_path_list:
+    input_id_images.append(load_image(image_path))
+
+id_embed_list = []
+
+for img in input_id_images:
+    img = np.array(img)
+    img = img[:, :, ::-1]
+    faces = analyze_faces(face_detector, img)
+    if len(faces) > 0:
+        id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
+
+if len(id_embed_list) == 0:
+    raise ValueError(f"No face detected in input image pool")
+
+id_embeds = torch.stack(id_embed_list)
+
+# generate image
+images = pipe(
+    prompt, 
+    negative_prompt=negative_prompt, 
+    input_id_images=input_id_images,
+    id_embeds=id_embeds,
+    num_images_per_prompt=2,
+    start_merge_step=10,
+).images
+
+for idx, img in enumerate(images): 
+    img.save(os.path.join(output_dir, f"output_pmv2_{idx}.jpg"))

PhotoMaker/inference_scripts/inference_pmv2_contronet.py

+# !pip install opencv-python transformers accelerate
+import os
+import sys
+
+import numpy as np
+import torch
+from diffusers.utils import load_image
+from diffusers import EulerDiscreteScheduler, ControlNetModel
+
+from huggingface_hub import hf_hub_download
+from controlnet_aux import OpenposeDetector
+from photomaker import PhotoMakerStableDiffusionXLControlNetPipeline
+from photomaker import FaceAnalysis2, analyze_faces
+
+face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
+face_detector.prepare(ctx_id=0, det_size=(640, 640))
+
+try:
+    if torch.cuda.is_available():
+        device = "cuda"
+    elif sys.platform == "darwin" and torch.backends.mps.is_available():
+        device = "mps"
+    else:
+        device = "cpu"
+except:
+    device = "cpu"
+
+torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+if device == "mps":
+    torch_dtype = torch.float16
+
+output_dir = "./outputs"
+os.makedirs(output_dir, exist_ok=True)
+
+photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker-V2", filename="photomaker-v2.bin", repo_type="model")
+
+openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+controlnet_pose_model = "thibaud/controlnet-openpose-sdxl-1.0"
+controlnet_pose = ControlNetModel.from_pretrained(
+    controlnet_pose_model, torch_dtype=torch_dtype,
+).to("cuda")
+
+prompt = "instagram photo, a photo of a woman img, colorful, perfect face, natural skin, hard shadows, film grain, best quality"
+negative_prompt = "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth"
+
+# download an image
+pose_image = load_image(
+    "./examples/pos_ref.png"
+)
+pose_image = openpose(pose_image, detect_resolution=512, image_resolution=1024)
+
+# initialize the models and pipeline
+controlnet_conditioning_scale = 1.0  # recommended for good generalization
+
+### Load base model
+pipe = PhotoMakerStableDiffusionXLControlNetPipeline.from_pretrained(
+    "SG161222/RealVisXL_V4.0", 
+    controlnet=controlnet_pose,
+    torch_dtype=torch_dtype,
+).to("cuda")
+
+### Load PhotoMaker checkpoint
+pipe.load_photomaker_adapter(
+    os.path.dirname(photomaker_ckpt),
+    subfolder="",
+    weight_name=os.path.basename(photomaker_ckpt),
+    trigger_word="img"  # define the trigger word
+)     
+### Also can cooperate with other LoRA modules
+# pipe.load_lora_weights(os.path.dirname(lora_path), weight_name=lora_model_name, adapter_name="lcm-lora")
+# pipe.set_adapters(["photomaker", "lcm-lora"], adapter_weights=[1.0, 0.5])
+
+pipe.fuse_lora()
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+
+pipe.enable_model_cpu_offload()
+
+### define the input ID images
+input_folder_name = './examples/scarletthead_woman'
+image_basename_list = os.listdir(input_folder_name)
+image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
+
+input_id_images = []
+for image_path in image_path_list:
+    input_id_images.append(load_image(image_path))
+
+### extract insightface embedding
+input_id_images = []
+for image_path in image_path_list:
+    input_id_images.append(load_image(image_path))
+
+id_embed_list = []
+
+for img in input_id_images:
+    img = np.array(img)
+    img = img[:, :, ::-1]
+    faces = analyze_faces(face_detector, img)
+    if len(faces) > 0:
+        id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
+
+if len(id_embed_list) == 0:
+    raise ValueError(f"No face detected in input image pool")
+
+id_embeds = torch.stack(id_embed_list)
+
+# generate image
+images = pipe(
+    prompt, 
+    negative_prompt=negative_prompt, 
+    input_id_images=input_id_images,
+    id_embeds=id_embeds,
+    controlnet_conditioning_scale=controlnet_conditioning_scale, 
+    image=pose_image,
+    num_images_per_prompt=2,
+    start_merge_step=10,
+).images
+
+for idx, img in enumerate(images): 
+    img.save(os.path.join(output_dir, f"output_pmv2_cn_{idx}.jpg"))

PhotoMaker/inference_scripts/inference_pmv2_ip_adapter.py

+# !pip install opencv-python transformers accelerate
+import os
+import sys
+
+import numpy as np
+import torch
+from diffusers.utils import load_image
+from diffusers import EulerDiscreteScheduler
+from huggingface_hub import hf_hub_download
+from photomaker import PhotoMakerStableDiffusionXLPipeline
+from photomaker import FaceAnalysis2, analyze_faces
+
+face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
+face_detector.prepare(ctx_id=0, det_size=(640, 640))
+
+try:
+    if torch.cuda.is_available():
+        device = "cuda"
+    elif sys.platform == "darwin" and torch.backends.mps.is_available():
+        device = "mps"
+    else:
+        device = "cpu"
+except:
+    device = "cpu"
+
+torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+if device == "mps":
+    torch_dtype = torch.float16
+
+output_dir = "./outputs"
+os.makedirs(output_dir, exist_ok=True)
+
+photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker-V2", filename="photomaker-v2.bin", repo_type="model")
+
+prompt = "portrait photo of a woman img, colorful, perfect face, best quality"
+negative_prompt = "(asymmetry, worst quality, low quality, illustration), open mouth"
+
+# # initialize the models and pipeline
+### Load base model
+pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
+    "SG161222/RealVisXL_V4.0", torch_dtype=torch_dtype,
+).to("cuda")
+
+pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+pipe.set_ip_adapter_scale(0.7)
+
+print("Loading images...")
+style_images = [load_image(f"./examples/statue.png")]
+
+### Load PhotoMaker checkpoint
+pipe.load_photomaker_adapter(
+    os.path.dirname(photomaker_ckpt),
+    subfolder="",
+    weight_name=os.path.basename(photomaker_ckpt),
+    trigger_word="img"  # define the trigger word
+)     
+### Also can cooperate with other LoRA modules
+# pipe.load_lora_weights(os.path.dirname(lora_path), weight_name=lora_model_name, adapter_name="lcm-lora")
+# pipe.set_adapters(["photomaker", "lcm-lora"], adapter_weights=[1.0, 0.5])
+
+pipe.fuse_lora()
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+
+pipe.enable_model_cpu_offload()
+
+### define the input ID images
+input_folder_name = './examples/scarletthead_woman'
+image_basename_list = os.listdir(input_folder_name)
+image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
+
+input_id_images = []
+for image_path in image_path_list:
+    input_id_images.append(load_image(image_path))
+
+id_embed_list = []
+
+for img in input_id_images:
+    img = np.array(img)
+    img = img[:, :, ::-1]
+    faces = analyze_faces(face_detector, img)
+    if len(faces) > 0:
+        id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
+
+if len(id_embed_list) == 0:
+    raise ValueError(f"No face detected in input image pool")
+
+id_embeds = torch.stack(id_embed_list)
+
+# generate image
+images = pipe(
+    prompt, 
+    negative_prompt=negative_prompt, 
+    input_id_images=input_id_images,
+    id_embeds=id_embeds,
+    ip_adapter_image=[style_images],
+    num_images_per_prompt=2,
+    start_merge_step=10,
+).images
+
+for idx, img in enumerate(images): 
+    img.save(os.path.join(output_dir, f"output_pmv2_ipa_{idx}.jpg"))

PhotoMaker/inference_scripts/inference_pmv2_t2i_adapter.py

+# !pip install opencv-python transformers accelerate
+import os
+import sys
+
+import numpy as np
+import torch
+from diffusers.utils import load_image
+from diffusers import EulerDiscreteScheduler, T2IAdapter
+
+from huggingface_hub import hf_hub_download
+from controlnet_aux import OpenposeDetector
+from photomaker import PhotoMakerStableDiffusionXLAdapterPipeline
+from photomaker import FaceAnalysis2, analyze_faces
+
+face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
+face_detector.prepare(ctx_id=0, det_size=(640, 640))
+
+try:
+    if torch.cuda.is_available():
+        device = "cuda"
+    elif sys.platform == "darwin" and torch.backends.mps.is_available():
+        device = "mps"
+    else:
+        device = "cpu"
+except:
+    device = "cpu"
+
+torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+if device == "mps":
+    torch_dtype = torch.float16
+
+output_dir = "./outputs"
+os.makedirs(output_dir, exist_ok=True)
+
+photomaker_ckpt = hf_hub_download(repo_id="TencentARC/PhotoMaker-V2", filename="photomaker-v2.bin", repo_type="model")
+
+openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+# load adapter
+adapter = T2IAdapter.from_pretrained(
+  "TencentARC/t2i-adapter-openpose-sdxl-1.0", torch_dtype=torch_dtype,
+).to("cuda")
+
+prompt = "instagram photo, a photo of a woman img, colorful, perfect face, natural skin, hard shadows, film grain, best quality"
+negative_prompt = "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth"
+
+# download an image
+pose_image = load_image(
+    "./examples/pos_ref.png"
+)
+pose_image = openpose(pose_image, detect_resolution=512, image_resolution=1024)
+
+# initialize the models and pipeline
+adapter_conditioning_scale = 0.8  # recommended for good generalization
+adapter_conditioning_factor = 0.8
+
+### Load base model
+pipe = PhotoMakerStableDiffusionXLAdapterPipeline.from_pretrained(
+    "SG161222/RealVisXL_V4.0", 
+    adapter=adapter,
+    torch_dtype=torch_dtype,
+).to("cuda")
+
+### Load PhotoMaker checkpoint
+pipe.load_photomaker_adapter(
+    os.path.dirname(photomaker_ckpt),
+    subfolder="",
+    weight_name=os.path.basename(photomaker_ckpt),
+    trigger_word="img"  # define the trigger word
+)     
+### Also can cooperate with other LoRA modules
+# pipe.load_lora_weights(os.path.dirname(lora_path), weight_name=lora_model_name, adapter_name="lcm-lora")
+# pipe.set_adapters(["photomaker", "lcm-lora"], adapter_weights=[1.0, 0.5])
+
+pipe.fuse_lora()
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+
+pipe.enable_model_cpu_offload()
+
+### define the input ID images
+input_folder_name = './examples/scarletthead_woman'
+image_basename_list = os.listdir(input_folder_name)
+image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
+
+input_id_images = []
+for image_path in image_path_list:
+    input_id_images.append(load_image(image_path))
+
+id_embed_list = []
+
+for img in input_id_images:
+    img = np.array(img)
+    img = img[:, :, ::-1]
+    faces = analyze_faces(face_detector, img)
+    if len(faces) > 0:
+        id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
+
+if len(id_embed_list) == 0:
+    raise ValueError(f"No face detected in input image pool")
+
+id_embeds = torch.stack(id_embed_list)
+
+# generate image
+images = pipe(
+    prompt, 
+    negative_prompt=negative_prompt, 
+    input_id_images=input_id_images,
+    id_embeds=id_embeds,
+    adapter_conditioning_scale=adapter_conditioning_scale,
+    image=pose_image,
+    num_images_per_prompt=2,
+    start_merge_step=10,
+).images
+
+for idx, img in enumerate(images): 
+    img.save(os.path.join(output_dir, f"output_pmv2_t2ia_{idx}.jpg"))

PhotoMaker/photomaker/__init__.py

+from .model import PhotoMakerIDEncoder
+from .model_v2 import PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken
+from .resampler import FacePerceiverResampler
+from .pipeline import PhotoMakerStableDiffusionXLPipeline
+from .pipeline import PhotoMakerStableDiffusionXLPipeline
+from .pipeline_controlnet import PhotoMakerStableDiffusionXLControlNetPipeline
+from .pipeline_t2i_adapter import PhotoMakerStableDiffusionXLAdapterPipeline
+
+# InsightFace Package
+from .insightface_package import FaceAnalysis2, analyze_faces
+
+__all__ = [
+    "FaceAnalysis2",
+    "analyze_faces",
+    "FacePerceiverResampler",
+    "PhotoMakerIDEncoder",
+    "PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken",
+    "PhotoMakerStableDiffusionXLPipeline",
+    "PhotoMakerStableDiffusionXLControlNetPipeline",
+    "PhotoMakerStableDiffusionXLAdapterPipeline",
+]
\ No newline at end of file

PhotoMaker/photomaker/insightface_package.py

+import numpy as np
+# pip install insightface==0.7.3
+from insightface.app import FaceAnalysis
+from insightface.data import get_image as ins_get_image
+
+### 
+# https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/165#issue-2055829543
+###
+class FaceAnalysis2(FaceAnalysis):
+    # NOTE: allows setting det_size for each detection call.
+    # the model allows it but the wrapping code from insightface
+    # doesn't show it, and people end up loading duplicate models
+    # for different sizes where there is absolutely no need to
+    def get(self, img, max_num=0, det_size=(640, 640)):
+        if det_size is not None:
+            self.det_model.input_size = det_size
+
+        return super().get(img, max_num)
+
+def analyze_faces(face_analysis: FaceAnalysis, img_data: np.ndarray, det_size=(640, 640)):
+    # NOTE: try detect faces, if no faces detected, lower det_size until it does
+    detection_sizes = [None] + [(size, size) for size in range(640, 256, -64)] + [(256, 256)]
+
+    for size in detection_sizes:
+        faces = face_analysis.get(img_data, det_size=size)
+        if len(faces) > 0:
+            return faces
+
+    return []

PhotoMaker/photomaker/model.py

+# Merge image encoder and fuse module to create an ID Encoder
+# send multiple ID images, we can directly obtain the updated text encoder containing a stacked ID embedding
+
+import torch
+import torch.nn as nn
+from transformers.models.clip.modeling_clip import CLIPVisionModelWithProjection
+from transformers.models.clip.configuration_clip import CLIPVisionConfig
+from transformers import PretrainedConfig
+
+VISION_CONFIG_DICT = {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768
+}
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True):
+        super().__init__()
+        if use_residual:
+            assert in_dim == out_dim
+        self.layernorm = nn.LayerNorm(in_dim)
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+        self.use_residual = use_residual
+        self.act_fn = nn.GELU()
+
+    def forward(self, x):
+        residual = x
+        x = self.layernorm(x)
+        x = self.fc1(x)
+        x = self.act_fn(x)
+        x = self.fc2(x)
+        if self.use_residual:
+            x = x + residual
+        return x
+
+
+class FuseModule(nn.Module):
+    def __init__(self, embed_dim):
+        super().__init__()
+        self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False)
+        self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True)
+        self.layer_norm = nn.LayerNorm(embed_dim)
+
+    def fuse_fn(self, prompt_embeds, id_embeds):
+        stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)
+        stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds
+        stacked_id_embeds = self.mlp2(stacked_id_embeds)
+        stacked_id_embeds = self.layer_norm(stacked_id_embeds)
+        return stacked_id_embeds
+
+    def forward(
+        self,
+        prompt_embeds,
+        id_embeds,
+        class_tokens_mask,
+    ) -> torch.Tensor:
+        # id_embeds shape: [b, max_num_inputs, 1, 2048]
+        id_embeds = id_embeds.to(prompt_embeds.dtype)
+        num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
+        batch_size, max_num_inputs = id_embeds.shape[:2]
+        # seq_length: 77
+        seq_length = prompt_embeds.shape[1]
+        # flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
+        flat_id_embeds = id_embeds.view(
+            -1, id_embeds.shape[-2], id_embeds.shape[-1]
+        )
+        # valid_id_mask [b*max_num_inputs]
+        valid_id_mask = (
+            torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
+            < num_inputs[:, None]
+        )
+        valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]
+
+        prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
+        class_tokens_mask = class_tokens_mask.view(-1)
+        valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
+        # slice out the image token embeddings
+        image_token_embeds = prompt_embeds[class_tokens_mask]
+        stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
+        assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
+        prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
+        updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
+        return updated_prompt_embeds
+
+class PhotoMakerIDEncoder(CLIPVisionModelWithProjection):
+    def __init__(self):
+        super().__init__(CLIPVisionConfig(**VISION_CONFIG_DICT))
+        self.visual_projection_2 = nn.Linear(1024, 1280, bias=False)
+        self.fuse_module = FuseModule(2048)
+
+    def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask):
+        b, num_inputs, c, h, w = id_pixel_values.shape
+        id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)
+
+        shared_id_embeds = self.vision_model(id_pixel_values)[1]
+        id_embeds = self.visual_projection(shared_id_embeds)
+        id_embeds_2 = self.visual_projection_2(shared_id_embeds)
+
+        id_embeds = id_embeds.view(b, num_inputs, 1, -1)
+        id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)    
+
+        id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
+        updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)
+
+        return updated_prompt_embeds
+
+
+if __name__ == "__main__":
+    PhotoMakerIDEncoder()
\ No newline at end of file

PhotoMaker/photomaker/model_v2.py

+# Merge image encoder and fuse module to create an ID Encoder
+# send multiple ID images, we can directly obtain the updated text encoder containing a stacked ID embedding
+
+import torch
+import torch.nn as nn
+from transformers.models.clip.modeling_clip import CLIPVisionModelWithProjection
+from transformers.models.clip.configuration_clip import CLIPVisionConfig
+
+from .resampler import FacePerceiverResampler
+
+VISION_CONFIG_DICT = {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768
+}
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True):
+        super().__init__()
+        if use_residual:
+            assert in_dim == out_dim
+        self.layernorm = nn.LayerNorm(in_dim)
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+        self.use_residual = use_residual
+        self.act_fn = nn.GELU()
+
+    def forward(self, x):
+        residual = x
+        x = self.layernorm(x)
+        x = self.fc1(x)
+        x = self.act_fn(x)
+        x = self.fc2(x)
+        if self.use_residual:
+            x = x + residual
+        return x
+
+
+class QFormerPerceiver(nn.Module):
+    def __init__(self, id_embeddings_dim, cross_attention_dim, num_tokens, embedding_dim=1024, use_residual=True, ratio=4):
+        super().__init__()
+
+        self.num_tokens = num_tokens
+        self.cross_attention_dim = cross_attention_dim
+        self.use_residual = use_residual
+        print(cross_attention_dim*num_tokens)
+        self.token_proj = nn.Sequential(
+            nn.Linear(id_embeddings_dim, id_embeddings_dim*ratio),
+            nn.GELU(),
+            nn.Linear(id_embeddings_dim*ratio, cross_attention_dim*num_tokens),
+        )
+        self.token_norm = nn.LayerNorm(cross_attention_dim)
+        self.perceiver_resampler = FacePerceiverResampler(
+            dim=cross_attention_dim,
+            depth=4,
+            dim_head=128,
+            heads=cross_attention_dim // 128,
+            embedding_dim=embedding_dim,
+            output_dim=cross_attention_dim,
+            ff_mult=4,
+        )
+
+    def forward(self, x, last_hidden_state):
+        x = self.token_proj(x)
+        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
+        x = self.token_norm(x) # cls token
+        out = self.perceiver_resampler(x, last_hidden_state) # retrieve from patch tokens
+        if self.use_residual: # TODO: if use_residual is not true
+            out = x + 1.0 * out 
+        return out
+
+
+class FuseModule(nn.Module):
+    def __init__(self, embed_dim):
+        super().__init__()
+        self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False)
+        self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True)
+        self.layer_norm = nn.LayerNorm(embed_dim)
+
+    def fuse_fn(self, prompt_embeds, id_embeds):
+        stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)
+        stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds
+        stacked_id_embeds = self.mlp2(stacked_id_embeds)
+        stacked_id_embeds = self.layer_norm(stacked_id_embeds)
+        return stacked_id_embeds
+
+    def forward(
+        self,
+        prompt_embeds,
+        id_embeds,
+        class_tokens_mask,
+    ) -> torch.Tensor:
+        # id_embeds shape: [b, max_num_inputs, 1, 2048]
+        id_embeds = id_embeds.to(prompt_embeds.dtype)
+        num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
+        batch_size, max_num_inputs = id_embeds.shape[:2]
+        # seq_length: 77
+        seq_length = prompt_embeds.shape[1]
+        # flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
+        flat_id_embeds = id_embeds.view(
+            -1, id_embeds.shape[-2], id_embeds.shape[-1]
+        )
+        # valid_id_mask [b*max_num_inputs]
+        valid_id_mask = (
+            torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
+            < num_inputs[:, None]
+        )
+        valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]
+
+        prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
+        class_tokens_mask = class_tokens_mask.view(-1)
+        valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
+        # slice out the image token embeddings
+        image_token_embeds = prompt_embeds[class_tokens_mask]
+        stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
+        assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
+        prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
+        updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
+        return updated_prompt_embeds
+
+
+class PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken(CLIPVisionModelWithProjection):
+    def __init__(self, id_embeddings_dim=512):
+        super().__init__(CLIPVisionConfig(**VISION_CONFIG_DICT))
+        self.fuse_module = FuseModule(2048)
+        self.visual_projection_2 = nn.Linear(1024, 1280, bias=False)
+
+        cross_attention_dim = 2048
+        # projection
+        self.num_tokens = 2
+        self.cross_attention_dim = cross_attention_dim
+        self.qformer_perceiver = QFormerPerceiver(
+                                    id_embeddings_dim, 
+                                    cross_attention_dim, 
+                                    self.num_tokens,
+                                )
+
+    def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds):
+        b, num_inputs, c, h, w = id_pixel_values.shape
+        id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)
+
+        last_hidden_state = self.vision_model(id_pixel_values)[0]
+        id_embeds = id_embeds.view(b * num_inputs, -1)
+
+        id_embeds = self.qformer_perceiver(id_embeds, last_hidden_state)
+        id_embeds = id_embeds.view(b, num_inputs, self.num_tokens, -1)
+        updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)
+
+        return updated_prompt_embeds
+
+if __name__ == "__main__":
+    PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken()

PhotoMaker/photomaker/pipeline.py

+#####
+# Modified from https://github.com/huggingface/diffusers/blob/v0.29.1/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
+# PhotoMaker v2 @ TencentARC and MCG-NKU 
+# Author: Zhen Li
+#####
+
+# Copyright 2024 The HuggingFace 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.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import PIL
+
+import torch
+from transformers import CLIPImageProcessor
+
+from safetensors import safe_open
+from huggingface_hub.utils import validate_hf_hub_args
+from diffusers import StableDiffusionXLPipeline
+from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
+from diffusers.loaders import (
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.callbacks import (
+    MultiPipelineCallbacks,
+    PipelineCallback,
+)
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.utils import (
+    _get_model_file,
+    USE_PEFT_BACKEND,
+    deprecate,
+    is_torch_xla_available,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+from . import (
+    PhotoMakerIDEncoder, # PhotoMaker v1
+    PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken, # PhotoMaker v2
+)
+
+PipelineImageInput = Union[
+    PIL.Image.Image,
+    torch.FloatTensor,
+    List[PIL.Image.Image],
+    List[torch.FloatTensor],
+]
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+    
+
+class PhotoMakerStableDiffusionXLPipeline(StableDiffusionXLPipeline):
+    @validate_hf_hub_args
+    def load_photomaker_adapter(
+        self,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        weight_name: str,
+        subfolder: str = '',
+        trigger_word: str = 'img',
+        pm_version: str = 'v2',
+        **kwargs,
+    ):
+        """
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                Can be either:
+
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+                      with [`ModelMixin.save_pretrained`].
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+
+            weight_name (`str`):
+                The weight name NOT the path to the weight.
+
+            subfolder (`str`, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+
+            trigger_word (`str`, *optional*, defaults to `"img"`):
+                The trigger word is used to identify the position of class word in the text prompt, 
+                and it is recommended not to set it as a common word. 
+                This trigger word must be placed after the class word when used, otherwise, it will affect the performance of the personalized generation.           
+        """
+
+        # Load the main state dict first.
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+
+        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+            model_file = _get_model_file(
+                pretrained_model_name_or_path_or_dict,
+                weights_name=weight_name,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                subfolder=subfolder,
+                user_agent=user_agent,
+            )
+            if weight_name.endswith(".safetensors"):
+                state_dict = {"id_encoder": {}, "lora_weights": {}}
+                with safe_open(model_file, framework="pt", device="cpu") as f:
+                    for key in f.keys():
+                        if key.startswith("id_encoder."):
+                            state_dict["id_encoder"][key.replace("id_encoder.", "")] = f.get_tensor(key)
+                        elif key.startswith("lora_weights."):
+                            state_dict["lora_weights"][key.replace("lora_weights.", "")] = f.get_tensor(key)
+            else:
+                state_dict = torch.load(model_file, map_location="cpu")
+        else:
+            state_dict = pretrained_model_name_or_path_or_dict
+
+        keys = list(state_dict.keys())
+        if keys != ["id_encoder", "lora_weights"]:
+            raise ValueError("Required keys are (`id_encoder` and `lora_weights`) missing from the state dict.")
+
+        self.num_tokens =2
+        self.pm_version = pm_version
+        self.trigger_word = trigger_word
+        # load finetuned CLIP image encoder and fuse module here if it has not been registered to the pipeline yet
+        print(f"Loading PhotoMaker {pm_version} components [1] id_encoder from [{pretrained_model_name_or_path_or_dict}]...")
+        self.id_image_processor = CLIPImageProcessor()
+        if pm_version == "v1": # PhotoMaker v1 
+            id_encoder = PhotoMakerIDEncoder()
+        elif pm_version == "v2": # PhotoMaker v2
+            id_encoder = PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken()
+        else:
+            raise NotImplementedError(f"The PhotoMaker version [{pm_version}] does not support")
+
+        id_encoder.load_state_dict(state_dict["id_encoder"], strict=True)
+        id_encoder = id_encoder.to(self.device, dtype=self.unet.dtype)    
+        self.id_encoder = id_encoder
+
+        # load lora into models
+        print(f"Loading PhotoMaker {pm_version} components [2] lora_weights from [{pretrained_model_name_or_path_or_dict}]")
+        self.load_lora_weights(state_dict["lora_weights"], adapter_name="photomaker")
+
+        # Add trigger word token
+        if self.tokenizer is not None: 
+            self.tokenizer.add_tokens([self.trigger_word], special_tokens=True)
+        
+        self.tokenizer_2.add_tokens([self.trigger_word], special_tokens=True)
+        
+
+    def encode_prompt_with_trigger_word(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        negative_prompt_2: Optional[str] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+        ### Added args
+        num_id_images: int = 1,
+        class_tokens_mask: Optional[torch.LongTensor] = None,
+    ):
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder, lora_scale)
+
+            if self.text_encoder_2 is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Find the token id of the trigger word
+        image_token_id = self.tokenizer_2.convert_tokens_to_ids(self.trigger_word)
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids 
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    print(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                clean_index = 0
+                clean_input_ids = []
+                class_token_index = []
+                # Find out the corresponding class word token based on the newly added trigger word token
+                for i, token_id in enumerate(text_input_ids.tolist()[0]):
+                    if token_id == image_token_id:
+                        class_token_index.append(clean_index - 1)
+                    else:
+                        clean_input_ids.append(token_id)
+                        clean_index += 1
+
+                if len(class_token_index) != 1:
+                    raise ValueError(
+                        f"PhotoMaker currently does not support multiple trigger words in a single prompt.\
+                            Trigger word: {self.trigger_word}, Prompt: {prompt}."
+                    )
+                class_token_index = class_token_index[0]
+
+                # Expand the class word token and corresponding mask
+                class_token = clean_input_ids[class_token_index]
+                clean_input_ids = clean_input_ids[:class_token_index] + [class_token] * num_id_images * self.num_tokens + \
+                    clean_input_ids[class_token_index+1:]                
+                    
+                # Truncation or padding
+                max_len = tokenizer.model_max_length
+                if len(clean_input_ids) > max_len:
+                    clean_input_ids = clean_input_ids[:max_len]
+                else:
+                    clean_input_ids = clean_input_ids + [tokenizer.pad_token_id] * (
+                        max_len - len(clean_input_ids)
+                    )
+
+                class_tokens_mask = [True if class_token_index <= i < class_token_index+(num_id_images * self.num_tokens) else False \
+                     for i in range(len(clean_input_ids))]
+                
+                clean_input_ids = torch.tensor(clean_input_ids, dtype=torch.long).unsqueeze(0)
+                class_tokens_mask = torch.tensor(class_tokens_mask, dtype=torch.bool).unsqueeze(0)
+
+                prompt_embeds = text_encoder(clean_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+                
+                prompt_embeds_list.append(prompt_embeds)
+
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        class_tokens_mask = class_tokens_mask.to(device=device) # TODO: ignoring two-prompt case
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+
+            uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if self.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+            
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, class_tokens_mask
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        # Added parameters (for PhotoMaker)
+        input_id_images: PipelineImageInput = None,
+        start_merge_step: int = 10, # TODO: change to `style_strength_ratio` in the future
+        class_tokens_mask: Optional[torch.LongTensor] = None,
+        id_embeds: Optional[torch.FloatTensor] = None,
+        prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Only the parameters introduced by PhotoMaker are discussed here. 
+        For explanations of the previous parameters in StableDiffusionXLPipeline, please refer to https://github.com/huggingface/diffusers/blob/v0.25.0/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
+
+        Args:
+            input_id_images (`PipelineImageInput`, *optional*): 
+                Input ID Image to work with PhotoMaker.
+            class_tokens_mask (`torch.LongTensor`, *optional*):
+                Pre-generated class token. When the `prompt_embeds` parameter is provided in advance, it is necessary to prepare the `class_tokens_mask` beforehand for marking out the position of class word.
+            prompt_embeds_text_only (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds_text_only (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated 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 use `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 use `callback_on_step_end`",
+            )
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 0. Default height and width to unet
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._interrupt = False
+
+        #        
+        if prompt_embeds is not None and class_tokens_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `class_tokens_mask` also have to be passed. Make sure to generate `class_tokens_mask` from the same tokenizer that was used to generate `prompt_embeds`."
+            )
+        # check the input id images
+        if input_id_images is None:
+            raise ValueError(
+                "Provide `input_id_images`. Cannot leave `input_id_images` undefined for PhotoMaker pipeline."
+            )
+        if not isinstance(input_id_images, list):
+            input_id_images = [input_id_images]
+
+        # 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
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+        
+        num_id_images = len(input_id_images)
+        (
+            prompt_embeds, 
+            _,
+            pooled_prompt_embeds,
+            _,
+            class_tokens_mask,
+        ) = self.encode_prompt_with_trigger_word(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_id_images=num_id_images,
+            class_tokens_mask=class_tokens_mask,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            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,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # 4. Encode input prompt without the trigger word for delayed conditioning
+        # encode, remove trigger word token, then decode
+        tokens_text_only = self.tokenizer.encode(prompt, add_special_tokens=False)
+        trigger_word_token = self.tokenizer.convert_tokens_to_ids(self.trigger_word)
+        tokens_text_only.remove(trigger_word_token)
+        prompt_text_only = self.tokenizer.decode(tokens_text_only, add_special_tokens=False)
+        (
+            prompt_embeds_text_only,
+            negative_prompt_embeds,
+            pooled_prompt_embeds_text_only, # TODO: replace the pooled_prompt_embeds with text only prompt
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt_text_only,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds_text_only,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds_text_only,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # 5. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+
+        # 6. Prepare the input ID images
+        dtype = next(self.id_encoder.parameters()).dtype
+        if not isinstance(input_id_images[0], torch.Tensor):
+            id_pixel_values = self.id_image_processor(input_id_images, return_tensors="pt").pixel_values
+
+        id_pixel_values = id_pixel_values.unsqueeze(0).to(device=device, dtype=dtype) # TODO: multiple prompts
+
+        # 7. Get the update text embedding with the stacked ID embedding
+        if id_embeds is not None:
+            id_embeds = id_embeds.unsqueeze(0).to(device=device, dtype=dtype)
+            prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds)
+        else:
+            prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask)
+        
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # 8. 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,
+        )
+
+        # 9. 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)
+
+        # 10. Prepare added time ids & embeddings
+        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:
+            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)
+
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 11. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        # 11.1 Apply denoising_end
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        # 12. 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)
+
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # 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)
+
+                if i <= start_merge_step:
+                    current_prompt_embeds = torch.cat(
+                        [negative_prompt_embeds, prompt_embeds_text_only], dim=0
+                    ) if self.do_classifier_free_guidance else prompt_embeds_text_only
+                    add_text_embeds = torch.cat(
+                        [negative_pooled_prompt_embeds, pooled_prompt_embeds_text_only], dim=0
+                        ) if self.do_classifier_free_guidance else pooled_prompt_embeds_text_only
+                else:
+                    current_prompt_embeds = torch.cat(
+                        [negative_prompt_embeds, prompt_embeds], dim=0
+                    ) if self.do_classifier_free_guidance else prompt_embeds
+                    add_text_embeds = torch.cat(
+                        [negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0
+                        ) if self.do_classifier_free_guidance else pooled_prompt_embeds
+                    
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=current_prompt_embeds,
+                    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 + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)             
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        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)
+            elif latents.dtype != self.vae.dtype:
+                if torch.backends.mps.is_available():
+                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                    self.vae = self.vae.to(latents.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
+            return StableDiffusionXLPipelineOutput(images=image)
+
+        # apply watermark if available
+        # if self.watermark is not None:
+        #     image = self.watermark.apply_watermark(image)
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
\ No newline at end of file

PhotoMaker/photomaker/pipeline_controlnet.py

+#####
+# Modified from https://github.com/huggingface/diffusers/blob/v0.29.1/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
+# PhotoMaker v2 @ TencentARC and MCG-NKU 
+# Author: Zhen Li
+#####
+
+# Copyright 2024 The HuggingFace 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.
+
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+
+from safetensors import safe_open
+from huggingface_hub.utils import validate_hf_hub_args
+
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+    FromSingleFileMixin,
+    IPAdapterMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.utils import _get_model_file
+
+from . import (
+    PhotoMakerIDEncoder, # PhotoMaker v1
+    PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken, # PhotoMaker v2
+)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class PhotoMakerStableDiffusionXLControlNetPipeline(StableDiffusionXLControlNetPipeline):
+    @validate_hf_hub_args
+    def load_photomaker_adapter(
+        self,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        weight_name: str,
+        subfolder: str = '',
+        trigger_word: str = 'img',
+        pm_version: str = 'v2',
+        **kwargs,
+    ):
+        """
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                Can be either:
+
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+                      with [`ModelMixin.save_pretrained`].
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+
+            weight_name (`str`):
+                The weight name NOT the path to the weight.
+
+            subfolder (`str`, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+
+            trigger_word (`str`, *optional*, defaults to `"img"`):
+                The trigger word is used to identify the position of class word in the text prompt, 
+                and it is recommended not to set it as a common word. 
+                This trigger word must be placed after the class word when used, otherwise, it will affect the performance of the personalized generation.           
+        """
+
+        # Load the main state dict first.
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+
+        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+            model_file = _get_model_file(
+                pretrained_model_name_or_path_or_dict,
+                weights_name=weight_name,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                subfolder=subfolder,
+                user_agent=user_agent,
+            )
+            if weight_name.endswith(".safetensors"):
+                state_dict = {"id_encoder": {}, "lora_weights": {}}
+                with safe_open(model_file, framework="pt", device="cpu") as f:
+                    for key in f.keys():
+                        if key.startswith("id_encoder."):
+                            state_dict["id_encoder"][key.replace("id_encoder.", "")] = f.get_tensor(key)
+                        elif key.startswith("lora_weights."):
+                            state_dict["lora_weights"][key.replace("lora_weights.", "")] = f.get_tensor(key)
+            else:
+                state_dict = torch.load(model_file, map_location="cpu")
+        else:
+            state_dict = pretrained_model_name_or_path_or_dict
+
+        keys = list(state_dict.keys())
+        if keys != ["id_encoder", "lora_weights"]:
+            raise ValueError("Required keys are (`id_encoder` and `lora_weights`) missing from the state dict.")
+
+        self.num_tokens = 2
+        self.trigger_word = trigger_word
+        # load finetuned CLIP image encoder and fuse module here if it has not been registered to the pipeline yet
+        print(f"Loading PhotoMaker {pm_version} components [1] id_encoder from [{pretrained_model_name_or_path_or_dict}]...")
+        self.id_image_processor = CLIPImageProcessor()
+        if pm_version == "v1": # PhotoMaker v1 
+            id_encoder = PhotoMakerIDEncoder()
+        elif pm_version == "v2": # PhotoMaker v2
+            id_encoder = PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken()
+        else:
+            raise NotImplementedError(f"The PhotoMaker version [{pm_version}] does not support")
+
+        id_encoder.load_state_dict(state_dict["id_encoder"], strict=True)
+        id_encoder = id_encoder.to(self.device, dtype=self.unet.dtype)    
+        self.id_encoder = id_encoder
+
+        # load lora into models
+        print(f"Loading PhotoMaker {pm_version} components [2] lora_weights from [{pretrained_model_name_or_path_or_dict}]")
+        self.load_lora_weights(state_dict["lora_weights"], adapter_name="photomaker")
+
+        # Add trigger word token
+        if self.tokenizer is not None: 
+            self.tokenizer.add_tokens([self.trigger_word], special_tokens=True)
+        
+        self.tokenizer_2.add_tokens([self.trigger_word], special_tokens=True)
+
+
+    def encode_prompt_with_trigger_word(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        negative_prompt_2: Optional[str] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+        ### Added args
+        num_id_images: int = 1,
+        class_tokens_mask: Optional[torch.LongTensor] = None,
+    ):
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder, lora_scale)
+
+            if self.text_encoder_2 is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Find the token id of the trigger word
+        image_token_id = self.tokenizer_2.convert_tokens_to_ids(self.trigger_word)
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids 
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    print(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                clean_index = 0
+                clean_input_ids = []
+                class_token_index = []
+                # Find out the corresponding class word token based on the newly added trigger word token
+                for i, token_id in enumerate(text_input_ids.tolist()[0]):
+                    if token_id == image_token_id:
+                        class_token_index.append(clean_index - 1)
+                    else:
+                        clean_input_ids.append(token_id)
+                        clean_index += 1
+
+                if len(class_token_index) != 1:
+                    raise ValueError(
+                        f"PhotoMaker currently does not support multiple trigger words in a single prompt.\
+                            Trigger word: {self.trigger_word}, Prompt: {prompt}."
+                    )
+                class_token_index = class_token_index[0]
+
+                # Expand the class word token and corresponding mask
+                class_token = clean_input_ids[class_token_index]
+                clean_input_ids = clean_input_ids[:class_token_index] + [class_token] * num_id_images * self.num_tokens + \
+                    clean_input_ids[class_token_index+1:]                
+                    
+                # Truncation or padding
+                max_len = tokenizer.model_max_length
+                if len(clean_input_ids) > max_len:
+                    clean_input_ids = clean_input_ids[:max_len]
+                else:
+                    clean_input_ids = clean_input_ids + [tokenizer.pad_token_id] * (
+                        max_len - len(clean_input_ids)
+                    )
+
+                class_tokens_mask = [True if class_token_index <= i < class_token_index+(num_id_images * self.num_tokens) else False \
+                     for i in range(len(clean_input_ids))]
+                
+                clean_input_ids = torch.tensor(clean_input_ids, dtype=torch.long).unsqueeze(0)
+                class_tokens_mask = torch.tensor(class_tokens_mask, dtype=torch.bool).unsqueeze(0)
+
+                prompt_embeds = text_encoder(clean_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+                
+                prompt_embeds_list.append(prompt_embeds)
+
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        class_tokens_mask = class_tokens_mask.to(device=device) # TODO: ignoring two-prompt case
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+
+            uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if self.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+            
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, class_tokens_mask
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        denoising_end: Optional[float] = None,
+        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.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        guess_mode: bool = False,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
+        original_size: Tuple[int, int] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        # Added parameters (for PhotoMaker)
+        input_id_images: PipelineImageInput = None,
+        start_merge_step: int = 10, # TODO: change to `style_strength_ratio` in the future
+        class_tokens_mask: Optional[torch.LongTensor] = None,
+        id_embeds: Optional[torch.FloatTensor] = None,
+        prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Only the parameters introduced by PhotoMaker are discussed here. 
+        For explanations of the previous parameters in StableDiffusionXLControlNetPipeline, please refer to https://github.com/huggingface/diffusers/blob/v0.25.0/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
+
+        Args:
+            input_id_images (`PipelineImageInput`, *optional*): 
+                Input ID Image to work with PhotoMaker.
+            class_tokens_mask (`torch.LongTensor`, *optional*):
+                Pre-generated class token. When the `prompt_embeds` parameter is provided in advance, it is necessary to prepare the `class_tokens_mask` beforehand for marking out the position of class word.
+            prompt_embeds_text_only (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds_text_only (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+            
+        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
+
+        # align format for control guidance
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            negative_pooled_prompt_embeds,
+            controlnet_conditioning_scale,
+            control_guidance_start,
+            control_guidance_end,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+
+        #        
+        if prompt_embeds is not None and class_tokens_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `class_tokens_mask` also have to be passed. Make sure to generate `class_tokens_mask` from the same tokenizer that was used to generate `prompt_embeds`."
+            )
+        # check the input id images
+        if input_id_images is None:
+            raise ValueError(
+                "Provide `input_id_images`. Cannot leave `input_id_images` undefined for PhotoMaker pipeline."
+            )
+        if not isinstance(input_id_images, list):
+            input_id_images = [input_id_images]
+
+        # 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. Encode input prompt
+        text_encoder_lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+        
+        num_id_images = len(input_id_images)
+        
+        (
+            prompt_embeds, 
+            _,
+            pooled_prompt_embeds,
+            _,
+            class_tokens_mask,
+        ) = self.encode_prompt_with_trigger_word(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_id_images=num_id_images,
+            class_tokens_mask=class_tokens_mask,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            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,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=self.clip_skip,
+        )
+        # print(negative_pooled_prompt_embeds.shape, pooled_prompt_embeds_text_only.shape)
+
+        # 4. Encode input prompt without the trigger word for delayed conditioning
+        # encode, remove trigger word token, then decode
+        tokens_text_only = self.tokenizer.encode(prompt, add_special_tokens=False)
+        trigger_word_token = self.tokenizer.convert_tokens_to_ids(self.trigger_word)
+        tokens_text_only.remove(trigger_word_token)
+        prompt_text_only = self.tokenizer.decode(tokens_text_only, add_special_tokens=False)
+        (
+            prompt_embeds_text_only,
+            negative_prompt_embeds,
+            pooled_prompt_embeds_text_only, # TODO: replace the pooled_prompt_embeds with text only prompt
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt_text_only,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds_text_only,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds_text_only,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=self.clip_skip,
+        )
+        
+        # 4.2 Encode ip_adapter_image
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+            
+        # 5. Prepare the input ID images
+        dtype = next(self.id_encoder.parameters()).dtype
+        if not isinstance(input_id_images[0], torch.Tensor):
+            id_pixel_values = self.id_image_processor(input_id_images, return_tensors="pt").pixel_values
+
+        id_pixel_values = id_pixel_values.unsqueeze(0).to(device=device, dtype=dtype) # TODO: multiple prompts
+
+        # 6. Get the update text embedding with the stacked ID embedding
+        if id_embeds is not None:
+            id_embeds = id_embeds.unsqueeze(0).to(device=device, dtype=dtype)
+            prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds)
+        else:
+            prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # 7. Prepare ControlNet 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,
+                )
+
+                images.append(image_)
+
+            image = images
+            height, width = image[0].shape[-2:]
+        else:
+            assert False
+
+        # 7. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+        self._num_timesteps = len(timesteps)
+
+        # 8. 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,
+        )
+
+        # 8.5 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)
+
+        # 9. 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)
+
+        # 9.1 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)
+
+        # 9.2 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:
+            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)
+
+        # 11. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        is_unet_compiled = is_compiled_module(self.unet)
+        is_controlnet_compiled = is_compiled_module(self.controlnet)
+        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # 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)
+
+                if i <= start_merge_step:
+                    current_prompt_embeds = torch.cat(
+                        [negative_prompt_embeds, prompt_embeds_text_only], dim=0
+                    ) if self.do_classifier_free_guidance else prompt_embeds_text_only
+                    add_text_embeds = torch.cat(
+                        [negative_pooled_prompt_embeds, pooled_prompt_embeds_text_only], dim=0
+                        ) if self.do_classifier_free_guidance else pooled_prompt_embeds_text_only
+                else:
+                    current_prompt_embeds = torch.cat(
+                        [negative_prompt_embeds, prompt_embeds], dim=0
+                    ) if self.do_classifier_free_guidance else prompt_embeds
+                    add_text_embeds = torch.cat(
+                        [negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0
+                        ) if self.do_classifier_free_guidance else pooled_prompt_embeds
+
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+                # controlnet(s) inference
+                controlnet_prompt_embeds_text_only = torch.cat(
+                        [negative_prompt_embeds, prompt_embeds_text_only], dim=0
+                    )
+                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 = controlnet_prompt_embeds_text_only.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 = controlnet_prompt_embeds_text_only
+                    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=controlnet_prompt_embeds,
+                    controlnet_cond=image,
+                    conditioning_scale=cond_scale,
+                    guess_mode=guess_mode,
+                    added_cond_kwargs=controlnet_added_cond_kwargs,
+                    return_dict=False,
+                )
+
+                if guess_mode and self.do_classifier_free_guidance:
+                    # Infered ControlNet only for the conditional batch.
+                    # To apply the output of ControlNet to both the unconditional and conditional batches,
+                    # add 0 to the unconditional batch to keep it unchanged.
+                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=current_prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)            
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
\ No newline at end of file

PhotoMaker/photomaker/pipeline_t2i_adapter.py

+#####
+# Modified from https://github.com/huggingface/diffusers/blob/v0.29.1/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py
+# PhotoMaker v2 @ TencentARC and MCG-NKU 
+# Author: Zhen Li
+#####
+
+# Copyright 2024 TencentARC and The HuggingFace 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.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+    FromSingleFileMixin,
+    IPAdapterMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, ImageProjection, MultiAdapter, T2IAdapter, UNet2DConditionModel
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    USE_PEFT_BACKEND,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.pipelines import StableDiffusionXLAdapterPipeline
+from diffusers.utils import _get_model_file
+from safetensors import safe_open
+from huggingface_hub.utils import validate_hf_hub_args
+
+from . import (
+    PhotoMakerIDEncoder, # PhotoMaker v1
+    PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken, # PhotoMaker v2
+)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+def _preprocess_adapter_image(image, height, width):
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [image]
+
+    if isinstance(image[0], PIL.Image.Image):
+        image = [np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])) for i in image]
+        image = [
+            i[None, ..., None] if i.ndim == 2 else i[None, ...] for i in image
+        ]  # expand [h, w] or [h, w, c] to [b, h, w, c]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        if image[0].ndim == 3:
+            image = torch.stack(image, dim=0)
+        elif image[0].ndim == 4:
+            image = torch.cat(image, dim=0)
+        else:
+            raise ValueError(
+                f"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}"
+            )
+    return image
+
+
+class PhotoMakerStableDiffusionXLAdapterPipeline(StableDiffusionXLAdapterPipeline):
+    @validate_hf_hub_args
+    def load_photomaker_adapter(
+        self,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        weight_name: str,
+        subfolder: str = '',
+        trigger_word: str = 'img',
+        pm_version: str = 'v2',
+        **kwargs,
+    ):
+        """
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                Can be either:
+
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+                      with [`ModelMixin.save_pretrained`].
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+
+            weight_name (`str`):
+                The weight name NOT the path to the weight.
+
+            subfolder (`str`, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+
+            trigger_word (`str`, *optional*, defaults to `"img"`):
+                The trigger word is used to identify the position of class word in the text prompt, 
+                and it is recommended not to set it as a common word. 
+                This trigger word must be placed after the class word when used, otherwise, it will affect the performance of the personalized generation.           
+        """
+
+        # Load the main state dict first.
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+
+        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+            model_file = _get_model_file(
+                pretrained_model_name_or_path_or_dict,
+                weights_name=weight_name,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                subfolder=subfolder,
+                user_agent=user_agent,
+            )
+            if weight_name.endswith(".safetensors"):
+                state_dict = {"id_encoder": {}, "lora_weights": {}}
+                with safe_open(model_file, framework="pt", device="cpu") as f:
+                    for key in f.keys():
+                        if key.startswith("id_encoder."):
+                            state_dict["id_encoder"][key.replace("id_encoder.", "")] = f.get_tensor(key)
+                        elif key.startswith("lora_weights."):
+                            state_dict["lora_weights"][key.replace("lora_weights.", "")] = f.get_tensor(key)
+            else:
+                state_dict = torch.load(model_file, map_location="cpu")
+        else:
+            state_dict = pretrained_model_name_or_path_or_dict
+
+        keys = list(state_dict.keys())
+        if keys != ["id_encoder", "lora_weights"]:
+            raise ValueError("Required keys are (`id_encoder` and `lora_weights`) missing from the state dict.")
+
+        self.num_tokens =2
+        self.trigger_word = trigger_word
+        # load finetuned CLIP image encoder and fuse module here if it has not been registered to the pipeline yet
+        print(f"Loading PhotoMaker {pm_version} components [1] id_encoder from [{pretrained_model_name_or_path_or_dict}]...")
+        self.id_image_processor = CLIPImageProcessor()
+        if pm_version == "v1": # PhotoMaker v1 
+            id_encoder = PhotoMakerIDEncoder()
+        elif pm_version == "v2": # PhotoMaker v2
+            id_encoder = PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken()
+        else:
+            raise NotImplementedError(f"The PhotoMaker version [{pm_version}] does not support")
+
+        id_encoder.load_state_dict(state_dict["id_encoder"], strict=True)
+        id_encoder = id_encoder.to(self.device, dtype=self.unet.dtype)    
+        self.id_encoder = id_encoder
+
+        # load lora into models
+        print(f"Loading PhotoMaker {pm_version} components [2] lora_weights from [{pretrained_model_name_or_path_or_dict}]")
+        self.load_lora_weights(state_dict["lora_weights"], adapter_name="photomaker")
+
+        # Add trigger word token
+        if self.tokenizer is not None: 
+            self.tokenizer.add_tokens([self.trigger_word], special_tokens=True)
+        
+        self.tokenizer_2.add_tokens([self.trigger_word], special_tokens=True)
+        
+
+    def encode_prompt_with_trigger_word(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        negative_prompt_2: Optional[str] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+        ### Added args
+        num_id_images: int = 1,
+        class_tokens_mask: Optional[torch.LongTensor] = None,
+    ):
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder, lora_scale)
+
+            if self.text_encoder_2 is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Find the token id of the trigger word
+        image_token_id = self.tokenizer_2.convert_tokens_to_ids(self.trigger_word)
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids 
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    print(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                clean_index = 0
+                clean_input_ids = []
+                class_token_index = []
+                # Find out the corresponding class word token based on the newly added trigger word token
+                for i, token_id in enumerate(text_input_ids.tolist()[0]):
+                    if token_id == image_token_id:
+                        class_token_index.append(clean_index - 1)
+                    else:
+                        clean_input_ids.append(token_id)
+                        clean_index += 1
+
+                if len(class_token_index) != 1:
+                    raise ValueError(
+                        f"PhotoMaker currently does not support multiple trigger words in a single prompt.\
+                            Trigger word: {self.trigger_word}, Prompt: {prompt}."
+                    )
+                class_token_index = class_token_index[0]
+
+                # Expand the class word token and corresponding mask
+                class_token = clean_input_ids[class_token_index]
+                clean_input_ids = clean_input_ids[:class_token_index] + [class_token] * num_id_images * self.num_tokens + \
+                    clean_input_ids[class_token_index+1:]                
+                    
+                # Truncation or padding
+                max_len = tokenizer.model_max_length
+                if len(clean_input_ids) > max_len:
+                    clean_input_ids = clean_input_ids[:max_len]
+                else:
+                    clean_input_ids = clean_input_ids + [tokenizer.pad_token_id] * (
+                        max_len - len(clean_input_ids)
+                    )
+
+                class_tokens_mask = [True if class_token_index <= i < class_token_index+(num_id_images * self.num_tokens) else False \
+                     for i in range(len(clean_input_ids))]
+                
+                clean_input_ids = torch.tensor(clean_input_ids, dtype=torch.long).unsqueeze(0)
+                class_tokens_mask = torch.tensor(class_tokens_mask, dtype=torch.bool).unsqueeze(0)
+
+                prompt_embeds = text_encoder(clean_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+                prompt_embeds_list.append(prompt_embeds)
+
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        class_tokens_mask = class_tokens_mask.to(device=device) # TODO: ignoring two-prompt case
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+
+            uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if self.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, class_tokens_mask
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        denoising_end: Optional[float] = None,
+        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.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Optional[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,
+        adapter_conditioning_scale: Union[float, List[float]] = 1.0,
+        adapter_conditioning_factor: float = 1.0,
+        clip_skip: Optional[int] = None,
+        # Added parameters (for PhotoMaker)
+        input_id_images: PipelineImageInput = None,
+        start_merge_step: int = 10, # TODO: change to `style_strength_ratio` in the future
+        class_tokens_mask: Optional[torch.LongTensor] = None,
+        id_embeds: Optional[torch.FloatTensor] = None,
+        prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds_text_only: Optional[torch.FloatTensor] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Only the parameters introduced by PhotoMaker are discussed here. 
+        For explanations of the previous parameters in StableDiffusionXLControlNetPipeline, please refer to https://github.com/huggingface/diffusers/blob/v0.25.0/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
+
+        Args:
+            input_id_images (`PipelineImageInput`, *optional*): 
+                Input ID Image to work with PhotoMaker.
+            class_tokens_mask (`torch.LongTensor`, *optional*):
+                Pre-generated class token. When the `prompt_embeds` parameter is provided in advance, it is necessary to prepare the `class_tokens_mask` beforehand for marking out the position of class word.
+            prompt_embeds_text_only (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds_text_only (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+        height, width = self._default_height_width(height, width, image)
+        device = self._execution_device
+        
+        use_adapter = True if image is not None else False
+        print(f"Use adapter: {use_adapter} ｜ output size: {(height, width)}")
+        if use_adapter:
+            if isinstance(self.adapter, MultiAdapter):
+                adapter_input = []
+
+                for one_image in image:
+                    one_image = _preprocess_adapter_image(one_image, height, width)
+                    one_image = one_image.to(device=device, dtype=self.adapter.dtype)
+                    adapter_input.append(one_image)
+            else:
+                adapter_input = _preprocess_adapter_image(image, height, width)
+                adapter_input = adapter_input.to(device=device, dtype=self.adapter.dtype)
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+
+        #        
+        if prompt_embeds is not None and class_tokens_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `class_tokens_mask` also have to be passed. Make sure to generate `class_tokens_mask` from the same tokenizer that was used to generate `prompt_embeds`."
+            )
+        # check the input id images
+        if input_id_images is None:
+            raise ValueError(
+                "Provide `input_id_images`. Cannot leave `input_id_images` undefined for PhotoMaker pipeline."
+            )
+        if not isinstance(input_id_images, list):
+            input_id_images = [input_id_images]
+
+        # 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
+
+        # 3. Encode input prompt
+        lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        
+        num_id_images = len(input_id_images)
+        
+        (
+            prompt_embeds, 
+            _,
+            pooled_prompt_embeds,
+            _,
+            class_tokens_mask,
+        ) = self.encode_prompt_with_trigger_word(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_id_images=num_id_images,
+            class_tokens_mask=class_tokens_mask,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            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,
+            lora_scale=lora_scale,
+            clip_skip=self._clip_skip,
+        )
+
+        # 4. Encode input prompt without the trigger word for delayed conditioning
+        # encode, remove trigger word token, then decode
+        tokens_text_only = self.tokenizer.encode(prompt, add_special_tokens=False)
+        trigger_word_token = self.tokenizer.convert_tokens_to_ids(self.trigger_word)
+        tokens_text_only.remove(trigger_word_token)
+        prompt_text_only = self.tokenizer.decode(tokens_text_only, add_special_tokens=False)
+        (
+            prompt_embeds_text_only,
+            negative_prompt_embeds,
+            pooled_prompt_embeds_text_only, # TODO: replace the pooled_prompt_embeds with text only prompt
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt_text_only,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds_text_only,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds_text_only,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=lora_scale,
+            clip_skip=self._clip_skip,
+        )
+
+        # 5. Prepare the input ID images
+        dtype = next(self.id_encoder.parameters()).dtype
+        if not isinstance(input_id_images[0], torch.Tensor):
+            id_pixel_values = self.id_image_processor(input_id_images, return_tensors="pt").pixel_values
+
+        id_pixel_values = id_pixel_values.unsqueeze(0).to(device=device, dtype=dtype) # TODO: multiple prompts
+
+        # 6. Get the update text embedding with the stacked ID embedding
+        if id_embeds is not None:
+            id_embeds = id_embeds.unsqueeze(0).to(device=device, dtype=dtype)
+            prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds)
+        else:
+            prompt_embeds = self.id_encoder(id_pixel_values, prompt_embeds, class_tokens_mask)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # 6.1 Get the ip adapter embedding
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 7. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+
+        # 8. 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,
+        )
+        # 9. 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)
+
+        # 8.5 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)
+
+        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        if use_adapter:
+            if isinstance(self.adapter, MultiAdapter):
+                adapter_state = self.adapter(adapter_input, adapter_conditioning_scale)
+                for k, v in enumerate(adapter_state):
+                    adapter_state[k] = v
+            else:
+                adapter_state = self.adapter(adapter_input)
+                for k, v in enumerate(adapter_state):
+                    adapter_state[k] = v * adapter_conditioning_scale
+            if num_images_per_prompt > 1:
+                for k, v in enumerate(adapter_state):
+                    adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)
+            if self.do_classifier_free_guidance:
+                for k, v in enumerate(adapter_state):
+                    adapter_state[k] = torch.cat([v] * 2, dim=0)
+
+        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:
+            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)
+
+        # 11. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        # Apply denoising_end
+        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        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)
+
+                if i <= start_merge_step:
+                    current_prompt_embeds = torch.cat(
+                        [negative_prompt_embeds, prompt_embeds_text_only], dim=0
+                    ) if self.do_classifier_free_guidance else prompt_embeds_text_only
+                    add_text_embeds = torch.cat(
+                        [negative_pooled_prompt_embeds, pooled_prompt_embeds_text_only], dim=0
+                        ) if self.do_classifier_free_guidance else pooled_prompt_embeds_text_only
+                else:
+                    current_prompt_embeds = torch.cat(
+                        [negative_prompt_embeds, prompt_embeds], dim=0
+                    ) if self.do_classifier_free_guidance else prompt_embeds
+                    add_text_embeds = torch.cat(
+                        [negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0
+                        ) if self.do_classifier_free_guidance else pooled_prompt_embeds
+
+                if i < int(num_inference_steps * adapter_conditioning_factor) and (use_adapter):
+                    down_intrablock_additional_residuals = [state.clone() for state in adapter_state]
+                else:
+                    down_intrablock_additional_residuals = None
+
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=current_prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    down_intrablock_additional_residuals=down_intrablock_additional_residuals,
+                    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)
+
+                if self.do_classifier_free_guidance and guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                # 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]
+
+                # 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
+            return StableDiffusionXLPipelineOutput(images=image)
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
\ No newline at end of file

PhotoMaker/photomaker/resampler.py

+#### Borrowed from https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/resampler.py 
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+
+import math
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+
+class FacePerceiverResampler(torch.nn.Module):
+    def __init__(
+        self,
+        *,
+        dim=768,
+        depth=4,
+        dim_head=64,
+        heads=16,
+        embedding_dim=1280,
+        output_dim=768,
+        ff_mult=4,
+    ):
+        super().__init__()
+        
+        self.proj_in = torch.nn.Linear(embedding_dim, dim)
+        self.proj_out = torch.nn.Linear(dim, output_dim)
+        self.norm_out = torch.nn.LayerNorm(output_dim)
+        self.layers = torch.nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                torch.nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, latents, x):
+        x = self.proj_in(x)
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1024,
+        depth=8,
+        dim_head=64,
+        heads=16,
+        num_queries=8,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+        max_seq_len: int = 257,  # CLIP tokens + CLS token
+        apply_pos_emb: bool = False,
+        num_latents_mean_pooled: int = 0,  # number of latents derived from mean pooled representation of the sequence
+    ):
+        super().__init__()
+        self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
+
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.to_latents_from_mean_pooled_seq = (
+            nn.Sequential(
+                nn.LayerNorm(dim),
+                nn.Linear(dim, dim * num_latents_mean_pooled),
+                Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
+            )
+            if num_latents_mean_pooled > 0
+            else None
+        )
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        if self.pos_emb is not None:
+            n, device = x.shape[1], x.device
+            pos_emb = self.pos_emb(torch.arange(n, device=device))
+            x = x + pos_emb
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        x = self.proj_in(x)
+
+        if self.to_latents_from_mean_pooled_seq:
+            meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
+            meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
+            latents = torch.cat((meanpooled_latents, latents), dim=-2)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+
+def masked_mean(t, *, dim, mask=None):
+    if mask is None:
+        return t.mean(dim=dim)
+
+    denom = mask.sum(dim=dim, keepdim=True)
+    mask = rearrange(mask, "b n -> b n 1")
+    masked_t = t.masked_fill(~mask, 0.0)
+
+    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)