update demo & README.md

.gitignore

@@ -0,0 +1,29 @@
+.DS_Store
+.idea
+.ipynb_checkpoints
+.gradio
+*.swp
+*.pyc
+__pycache__
+*.tar*
+*.zip
+*.pkl
+*.pyc
+*.bak
+*.png
+*.deb
+
+.isort.cfg
+.pre-commit-config.yaml
+
+dataset_stats
+debug*
+locks
+checkpoints
+pretrained_checkpoint
+./models
+models
+results
+wandb
+tmp*
+env*

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,14 +1,18 @@
 ---
-title: InfiniteUFlux
-emoji: 😻
-colorFrom: indigo
-colorTo: purple
+title: InfiniteYou-FLUX
+emoji: 📸
+colorFrom: red
+colorTo: indigo
 sdk: gradio
-sdk_version: 5.18.0
+sdk_version: 5.21.0
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: Infinite You Flux demo
+short_description: Flexible Photo Recrafting While Preserving Your Identity
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Some images in this demo are from public domains or generated by models. These pictures are intended solely to show the capabilities of our research. If you have any concerns, please contact us, and we will promptly remove any inappropriate content.
+
+The code in this demo is licensed under the [Apache License 2.0](./LICENSE), and our model is released under the [Creative Commons Attribution-NonCommercial 4.0 International Public License](https://creativecommons.org/licenses/by-nc/4.0/legalcode) for academic research purposes only. Any manual or automatic downloading of the face models from [InsightFace](https://github.com/deepinsight/insightface), the [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) base model, LoRAs ([Realism](https://civitai.com/models/631986?modelVersionId=706528) and [Anti-blur](https://civitai.com/models/675581/anti-blur-flux-lora)), *etc.*, must follow their original licenses and be used only for academic research purposes.
+
+This research aims to positively impact the field of Generative AI. Users are granted the freedom to create images using this tool, but they must comply with local laws and use it responsibly. The developers do not assume any responsibility for potential misuse by users.

app.py

@@ -1,7 +1,296 @@
+# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. 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 gradio as gr
+import pillow_avif
+import spaces
+import torch
+from huggingface_hub import snapshot_download
+from pillow_heif import register_heif_opener
+
+from pipelines.pipeline_infu_flux import InfUFluxPipeline
+
+
+# Register HEIF support for Pillow
+register_heif_opener()
+
+class ModelVersion:
+    STAGE_1 = "sim_stage1"
+    STAGE_2 = "aes_stage2"
+
+    DEFAULT_VERSION = STAGE_2
+    
+ENABLE_ANTI_BLUR_DEFAULT = False
+ENABLE_REALISM_DEFAULT = False
+
+pipeline = None
+loaded_pipeline_config = {
+    "model_version": "aes_stage2",
+    "enable_realism": False,
+    "enable_anti_blur": False,
+}
+
+
+def download_models():
+    snapshot_download(repo_id='ByteDance/InfiniteYou', local_dir='./models/InfiniteYou', local_dir_use_symlinks=False)
+    try:
+        snapshot_download(repo_id='black-forest-labs/FLUX.1-dev', local_dir='./models/FLUX.1-dev', local_dir_use_symlinks=False)
+    except Exception as e:
+        print(e)
+        print('\nYou are downloading `black-forest-labs/FLUX.1-dev` to `./models/FLUX.1-dev` but failed. '
+              'Please accept the agreement and obtain access at https://huggingface.co/black-forest-labs/FLUX.1-dev. '
+              'Then, use `huggingface-cli login` and your access tokens at https://huggingface.co/settings/tokens to authenticate. '
+              'After that, run the code again.')
+        print('\nYou can also download it manually from HuggingFace and put it in `./models/InfiniteYou`, '
+              'or you can modify `base_model_path` in `app.py` to specify the correct path.')
+        exit()
+
+
+def prepare_pipeline(model_version, enable_realism, enable_anti_blur):
+    global pipeline
+
+    if (
+        pipeline 
+        and loaded_pipeline_config["enable_realism"] == enable_realism 
+        and loaded_pipeline_config["enable_anti_blur"] == enable_anti_blur
+        and model_version == loaded_pipeline_config["model_version"]
+    ):
+        return
+    
+    loaded_pipeline_config["enable_realism"] = enable_realism
+    loaded_pipeline_config["enable_anti_blur"] = enable_anti_blur
+    loaded_pipeline_config["model_version"] = model_version
+
+    if pipeline is None or pipeline.model_version != model_version:
+        del pipeline
+
+        model_path = f'./models/InfiniteYou/infu_flux_v1.0/{model_version}'
+        print(f'loading model from {model_path}')
+
+        pipeline = InfUFluxPipeline(
+            base_model_path='./models/FLUX.1-dev',
+            infu_model_path=model_path,
+            insightface_root_path='./models/InfiniteYou/supports/insightface',
+            image_proj_num_tokens=8,
+            infu_flux_version='v1.0',
+            model_version=model_version,
+        )
+
+    pipeline.pipe.delete_adapters(['realism', 'anti_blur'])
+    loras = []
+    if enable_realism:
+        loras.append(['./models/InfiniteYou/supports/optional_loras/flux_realism_lora.safetensors', 'realism', 1.0])
+    if enable_anti_blur:
+        loras.append(['./models/InfiniteYou/supports/optional_loras/flux_anti_blur_lora.safetensors', 'anti_blur', 1.0])
+    pipeline.load_loras(loras)
+
+
+@spaces.GPU
+def generate_image(
+    input_image, 
+    control_image, 
+    prompt, 
+    seed, 
+    width,
+    height,
+    guidance_scale, 
+    num_steps, 
+    infusenet_conditioning_scale, 
+    infusenet_guidance_start,
+    infusenet_guidance_end,
+    enable_realism,
+    enable_anti_blur,
+    model_version
+):
+    global pipeline
+
+    prepare_pipeline(model_version=model_version, enable_realism=enable_realism, enable_anti_blur=enable_anti_blur)
+
+    if seed == 0:
+        seed = torch.seed() & 0xFFFFFFFF
+
+    try:
+        image = pipeline(
+            id_image=input_image,
+            prompt=prompt,
+            control_image=control_image,
+            seed=seed,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            num_steps=num_steps,
+            infusenet_conditioning_scale=infusenet_conditioning_scale,
+            infusenet_guidance_start=infusenet_guidance_start,
+            infusenet_guidance_end=infusenet_guidance_end,
+        )
+    except Exception as e:
+        print(e)
+        gr.Error(f"An error occurred: {e}")
+        return gr.update()
+
+    return gr.update(value = image, label=f"Generated image, seed = {seed}")
+
+
+def generate_examples(id_image, control_image, prompt_text, seed, enable_realism, enable_anti_blur, model_version):
+    return generate_image(id_image, control_image, prompt_text, seed, 864, 1152, 3.5, 30, 1.0, 0.0, 1.0, enable_realism, enable_anti_blur, model_version)
+
+
+sample_list = [
+    ['./assets/examples/yann-lecun_resize.jpg', None, 'A sophisticated gentleman exuding confidence. He is dressed in a 1990s brown plaid jacket with a high collar, paired with a dark grey turtleneck. His trousers are tailored and charcoal in color, complemented by a sleek leather belt. The background showcases an elegant library with bookshelves, a marble fireplace, and warm lighting, creating a refined and cozy atmosphere. His relaxed posture and casual hand-in-pocket stance add to his composed and stylish demeanor', 666, False, False, 'aes_stage2'],
+    ['./assets/examples/yann-lecun_resize.jpg', './assets/examples/man_pose.jpg', 'A man, portrait, cinematic', 42, True, False, 'aes_stage2'],
+    ['./assets/examples/yann-lecun_resize.jpg', './assets/examples/yann-lecun_resize.jpg', 'A man, portrait, cinematic', 12345, False, False, 'sim_stage1'],
+    ['./assets/examples/yangmi.jpg', None, 'A woman, portrait, cinematic', 1621695706, False, False, 'sim_stage1'],
+    ['./assets/examples/yangmi.jpg', None, 'A young woman holding a sign with the text "InfiniteYou", "Infinite" in black and "You" in red, pure background', 3724009366, False, False, 'aes_stage2'],
+    ['./assets/examples/yangmi.jpg', None, 'A photo of an elegant Javanese bride in traditional attire, with long hair styled into intricate a braid made of many fresh flowers, wearing a delicate headdress made from sequins and beads. She\'s holding flowers, light smiling at the camera, against a backdrop adorned with orchid blooms. The scene captures her grace as she stands amidst soft pastel colors, adding to its dreamy atmosphere', 42, True, False, 'aes_stage2'],
+    ['./assets/examples/yangmi.jpg', None, 'A photo of an elegant Javanese bride in traditional attire, with long hair styled into intricate a braid made of many fresh flowers, wearing a delicate headdress made from sequins and beads. She\'s holding flowers, light smiling at the camera, against a backdrop adorned with orchid blooms. The scene captures her grace as she stands amidst soft pastel colors, adding to its dreamy atmosphere', 42, False, False, 'sim_stage1'],
+]
+
+with gr.Blocks() as demo:
+    session_state = gr.State({})
+    default_model_version = "v1.0"
+
+    gr.Markdown("""
+    <div style="text-align: center; max-width: 900px; margin: 0 auto;">
+        <h1 style="font-size: 1.5rem; font-weight: 700; display: block;">InfiniteYou-FLUX</h1>
+        <h2 style="font-size: 1.2rem; font-weight: 300; margin-bottom: 1rem; display: block;">Official Gradio Demo for <a href="https://arxiv.org/abs/2503.xxxxx">InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity</a></h2>
+        <a href="https://bytedance.github.io/InfiniteYou">[Project Page]</a>&ensp;
+        <a href="https://arxiv.org/abs/2503.xxxxx">[Paper]</a>&ensp;
+        <a href="https://github.com/bytedance/InfiniteYou">[Code]</a>&ensp;
+        <a href="https://huggingface.co/ByteDance/InfiniteYou">[Model]</a>
+    </div>
+
+    ### 💡 How to Use This Demo:
+    1. **Upload an identity (ID) image containing a human face.** For images with multiple faces, only the largest face will be detected. The face should ideally be clear and large enough, without significant occlusions or blur.
+    2. **Enter the text prompt to describe the generated image and select the model version.** Please refer to **important usage tips** under the Generated Image field.
+    3. *[Optional] Upload a control image containing a human face.* Only five facial keypoints will be extracted to control the generation. If not provided, we use a black control image, indicating no control.
+    4. *[Optional] Adjust advanced hyperparameters or apply optional LoRAs to meet personal needs.* Please refer to **important usage tips** under the Generated Image field.
+    5. **Click the "Generate" button to generate an image.** Enjoy!
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            with gr.Row():
+                ui_id_image = gr.Image(label="Identity Image", type="pil", scale=3, height=370, min_width=100)
+
+                with gr.Column(scale=2, min_width=100):
+                    ui_control_image = gr.Image(label="Control Image [Optional]", type="pil", height=370, min_width=100)
+            
+            ui_prompt_text = gr.Textbox(label="Prompt", value="Portrait, 4K, high quality, cinematic")
+            ui_model_version = gr.Dropdown(
+                label="Model Version",
+                choices=[ModelVersion.STAGE_1, ModelVersion.STAGE_2],
+                value=ModelVersion.DEFAULT_VERSION,
+            )
+
+            ui_btn_generate = gr.Button("Generate")
+            with gr.Accordion("Advanced", open=False):
+                with gr.Row():
+                    ui_num_steps = gr.Number(label="num steps", value=30)
+                    ui_seed = gr.Number(label="seed (0 for random)", value=0)
+                with gr.Row():
+                    ui_width = gr.Number(label="width", value=864)
+                    ui_height = gr.Number(label="height", value=1152)
+                ui_guidance_scale = gr.Number(label="guidance scale", value=3.5, step=0.5)
+                ui_infusenet_conditioning_scale = gr.Slider(minimum=0.0, maximum=1.0, value=1.0, step=0.05, label="infusenet conditioning scale")
+                with gr.Row():
+                    ui_infusenet_guidance_start = gr.Slider(minimum=0.0, maximum=1.0, value=0.0, step=0.05, label="infusenet guidance start")
+                    ui_infusenet_guidance_end = gr.Slider(minimum=0.0, maximum=1.0, value=1.0, step=0.05, label="infusenet guidance end")
+
+            with gr.Accordion("LoRAs [Optional]", open=True):
+                with gr.Row():
+                    ui_enable_realism = gr.Checkbox(label="Enable realism LoRA", value=ENABLE_REALISM_DEFAULT)
+                    ui_enable_anti_blur = gr.Checkbox(label="Enable anti-blur LoRA", value=ENABLE_ANTI_BLUR_DEFAULT)
+
+        with gr.Column(scale=2):
+            image_output = gr.Image(label="Generated Image", interactive=False, height=550, format='png')
+            gr.Markdown(
+                """
+                ### ❗️ Important Usage Tips:
+                - **Model Version**: `aes_stage2` is used by default for better text-image alignment and aesthetics. For higher ID similarity, please try `sim_stage1`.
+                - **Useful Hyperparameters**: Usually, there is NO need to adjust too much. If necessary, try a slightly larger `--infusenet_guidance_start` (*e.g.*, `0.1`) only (especially helpful for `sim_stage1`). If still not satisfactory, then try a slightly smaller `--infusenet_conditioning_scale` (*e.g.*, `0.9`).
+                - **Optional LoRAs**: `realism` and `anti-blur`. To enable them, please check the corresponding boxes. They are optional and were NOT used in our paper.
+                - **Gender Prompt**: If the generated gender is not preferred, add specific words in the text prompt, such as 'a man', 'a woman', *etc*. We encourage using inclusive and respectful language.
+                """
+            )
+
+    gr.Examples(
+        sample_list,
+        inputs=[ui_id_image, ui_control_image, ui_prompt_text, ui_seed, ui_enable_realism, ui_enable_anti_blur, ui_model_version],
+        outputs=[image_output],
+        fn=generate_examples,
+        cache_examples=True,
+    )
+
+    ui_btn_generate.click(
+        generate_image, 
+        inputs=[
+            ui_id_image, 
+            ui_control_image, 
+            ui_prompt_text, 
+            ui_seed, 
+            ui_width,
+            ui_height,
+            ui_guidance_scale, 
+            ui_num_steps, 
+            ui_infusenet_conditioning_scale, 
+            ui_infusenet_guidance_start, 
+            ui_infusenet_guidance_end,
+            ui_enable_realism,
+            ui_enable_anti_blur,
+            ui_model_version
+        ], 
+        outputs=[image_output], 
+        concurrency_id="gpu"
+    )
+
+    with gr.Accordion("Local Gradio Demo for Developers", open=False):
+        gr.Markdown(
+            'Please refer to our GitHub repository to [run the InfiniteYou-FLUX gradio demo locally](https://github.com/bytedance/InfiniteYou#local-gradio-demo).'
+        )
+    
+    gr.Markdown(
+        """
+        ---
+        ### 📜 Disclaimer and Licenses 
+        Some images in this demo are from public domains or generated by models. These pictures are intended solely to show the capabilities of our research. If you have any concerns, please contact us, and we will promptly remove any inappropriate content.
+        
+        The use of the released code, model, and demo must strictly adhere to the respective licenses. Our code is released under the Apache 2.0 License, and our model is released under the Creative Commons Attribution-NonCommercial 4.0 International Public License for academic research purposes only. Any manual or automatic downloading of the face models from [InsightFace](https://github.com/deepinsight/insightface), the [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) base model, LoRAs, etc., must follow their original licenses and be used only for academic research purposes.
+
+        This research aims to positively impact the Generative AI field. Users are granted freedom to create images using this tool, but they must comply with local laws and use it responsibly. The developers do not assume any responsibility for potential misuse.
+        
+        ### 📖 Citation
+
+        If you find InfiniteYou useful for your research or applications, please cite our paper:
+
+        ```bibtex
+        @article{jiang2025infiniteyou,
+          title={{InfiniteYou}: Flexible Photo Recrafting While Preserving Your Identity},
+          author={Jiang, Liming and Yan, Qing and Jia, Yumin and Liu, Zichuan and Kang, Hao and Lu, Xin},
+          journal={arXiv preprint},
+          volume={arXiv:2503.xxxxx},
+          year={2025}
+        }
+        ```
+
+        We also appreciate it if you could give a star ⭐ to our [Github repository](https://github.com/bytedance/InfiniteYou). Thanks a lot!
+        """
+    )
+
+download_models()
 
-def greet(name):
-    return "Hello " + name + "!!"
+prepare_pipeline(model_version=ModelVersion.DEFAULT_VERSION, enable_realism=ENABLE_REALISM_DEFAULT, enable_anti_blur=ENABLE_ANTI_BLUR_DEFAULT)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+demo.queue()
+demo.launch(server_name='0.0.0.0')  # IPv4
+# demo.launch(server_name='[::]')  # IPv6

pipelines/pipeline_flux_infusenet.py

@@ -0,0 +1,611 @@
+# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates.
+# Copyright (c) 2024 Black Forest Labs, The HuggingFace Team and The InstantX Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from diffusers import FluxControlNetPipeline
+from diffusers.models.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+from diffusers.utils import replace_example_docstring, is_torch_xla_available, logging
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# 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,
+):
+    r"""
+    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 FluxInfuseNetPipeline(FluxControlNetPipeline):
+    @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 = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        controlnet_guidance_scale: float = 1.0,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
+        control_image: PipelineImageInput = None,
+        control_mode: Optional[Union[int, List[int]]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+
+        # ID-specific parameters
+        controlnet_prompt_embeds: Optional[torch.FloatTensor] = None,
+
+        # True CFG parameters
+        true_guidance_scale: float = 1.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            controlnet_guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `controlnet_guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the ControlNet starts applying.
+            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the ControlNet stops applying.
+            control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
+                    `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
+                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
+                specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be accepted
+                as an image. The dimensions of the output image defaults to `image`'s dimensions. If height and/or
+                width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,
+                images must be passed as a list such that each element of the list can be correctly batched for input
+                to a single ControlNet.
+            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
+                the corresponding scale as a list.
+            control_mode (`int` or `List[int]`,, *optional*, defaults to None):
+                The control mode when applying ControlNet-Union.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            pooled_prompt_embeds (`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.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+            controlnet_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated embeddings for the InfuseNet. Can be used to easily tweak inputs, *e.g.* image embeddings.
+                If not provided, embeddings will be generated from `prompt` or `prompt_embeds` input arguments.
+            true_guidance_scale (`float`, *optional*, defaults to 1.0):
+                True CFG scale as defined in [Classifier-Free Diffusion Guidance]((https://arxiv.org/abs/2207.12598).
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The negative prompt or negative prompts to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds`. instead.
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The negative prompt or negative prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined,
+                `negative_prompt` is will be used instead.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative text embeddings will be generated from `negative_prompt` input
+                argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative pooled text embeddings will be generated from
+                `negative_prompt` input argument.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        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(self.controlnet.nets) if isinstance(self.controlnet, FluxMultiControlNetModel) 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,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._controlnet_guidance_scale = controlnet_guidance_scale
+        self._true_guidance_scale = true_guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        dtype = self.transformer.dtype
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+        if negative_prompt is not None or (negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None):
+            (
+                negative_prompt_embeds,
+                negative_pooled_prompt_embeds,
+                negative_text_ids,
+            ) = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_2=negative_prompt_2,
+                prompt_embeds=negative_prompt_embeds,
+                pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                lora_scale=lora_scale,
+            )
+        
+        if controlnet_prompt_embeds is None:
+            controlnet_prompt_embeds = prompt_embeds
+        (
+            controlnet_prompt_embeds,
+            pooled_prompt_embeds,
+            controlnet_text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=controlnet_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 3. Prepare control image
+        num_channels_latents = self.transformer.config.in_channels // 4
+        if isinstance(self.controlnet, FluxControlNetModel):
+            control_image = self.prepare_image(
+                image=control_image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=self.vae.dtype,
+            )
+            height, width = control_image.shape[-2:]
+
+            # xlab controlnet has a input_hint_block and instantx controlnet does not
+            controlnet_blocks_repeat = False if self.controlnet.input_hint_block is None else True
+            if self.controlnet.input_hint_block is None:
+                # vae encode
+                control_image = self.vae.encode(control_image).latent_dist.sample()
+                control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+                # pack
+                height_control_image, width_control_image = control_image.shape[2:]
+                control_image = self._pack_latents(
+                    control_image,
+                    batch_size * num_images_per_prompt,
+                    num_channels_latents,
+                    height_control_image,
+                    width_control_image,
+                )
+
+            # Here we ensure that `control_mode` has the same length as the control_image.
+            if control_mode is not None:
+                if not isinstance(control_mode, int):
+                    raise ValueError(" For `FluxControlNet`, `control_mode` should be an `int` or `None`")
+                control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
+                control_mode = control_mode.view(-1, 1).expand(control_image.shape[0], 1)
+
+        elif isinstance(self.controlnet, FluxMultiControlNetModel):
+            control_images = []
+            # xlab controlnet has a input_hint_block and instantx controlnet does not
+            controlnet_blocks_repeat = False if self.controlnet.nets[0].input_hint_block is None else True
+            for i, control_image_ in enumerate(control_image):
+                control_image_ = self.prepare_image(
+                    image=control_image_,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_images_per_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    device=device,
+                    dtype=self.vae.dtype,
+                )
+                height, width = control_image_.shape[-2:]
+
+                if self.controlnet.nets[0].input_hint_block is None:
+                    # vae encode
+                    control_image_ = self.vae.encode(control_image_).latent_dist.sample()
+                    control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+                    # pack
+                    height_control_image, width_control_image = control_image_.shape[2:]
+                    control_image_ = self._pack_latents(
+                        control_image_,
+                        batch_size * num_images_per_prompt,
+                        num_channels_latents,
+                        height_control_image,
+                        width_control_image,
+                    )
+                control_images.append(control_image_)
+
+            control_image = control_images
+
+            # Here we ensure that `control_mode` has the same length as the control_image.
+            if isinstance(control_mode, list) and len(control_mode) != len(control_image):
+                raise ValueError(
+                    "For Multi-ControlNet, `control_mode` must be a list of the same "
+                    + " length as the number of controlnets (control images) specified"
+                )
+            if not isinstance(control_mode, list):
+                control_mode = [control_mode] * len(control_image)
+            # set control mode
+            control_modes = []
+            for cmode in control_mode:
+                if cmode is None:
+                    cmode = -1
+                control_mode = torch.tensor(cmode).expand(control_images[0].shape[0]).to(device, dtype=torch.long)
+                control_modes.append(control_mode)
+            control_mode = control_modes
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. 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(self.controlnet, FluxControlNetModel) else keeps)
+
+        # 7. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                if isinstance(self.controlnet, FluxMultiControlNetModel):
+                    use_guidance = self.controlnet.nets[0].config.guidance_embeds
+                else:
+                    use_guidance = self.controlnet.config.guidance_embeds
+
+                guidance = torch.tensor([controlnet_guidance_scale], device=device) if use_guidance else None
+                guidance = guidance.expand(latents.shape[0]) if guidance is not None else None
+
+                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]
+
+                # controlnet
+                controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
+                    hidden_states=latents,
+                    controlnet_cond=control_image,
+                    controlnet_mode=control_mode,
+                    conditioning_scale=cond_scale,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=controlnet_prompt_embeds,
+                    txt_ids=controlnet_text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )
+
+                guidance = (
+                    torch.tensor([guidance_scale], device=device) if self.transformer.config.guidance_embeds else None
+                )
+                guidance = guidance.expand(latents.shape[0]) if guidance is not None else None
+
+                noise_pred = self.transformer(
+                    hidden_states=latents,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    controlnet_block_samples=controlnet_block_samples,
+                    controlnet_single_block_samples=controlnet_single_block_samples,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                    controlnet_blocks_repeat=controlnet_blocks_repeat,
+                )[0]
+
+                # perform true CFG
+                if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None and negative_text_ids is not None:
+                    noise_pred_uncond = self.transformer(
+                        hidden_states=latents,
+                        timestep=timestep / 1000,
+                        guidance=guidance,
+                        pooled_projections=negative_pooled_prompt_embeds,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        controlnet_block_samples=None,
+                        controlnet_single_block_samples=None,
+                        txt_ids=negative_text_ids,
+                        img_ids=latent_image_ids,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                        controlnet_blocks_repeat=controlnet_blocks_repeat,
+                    )[0]
+
+                    noise_pred = noise_pred_uncond + true_guidance_scale * (noise_pred - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, 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)
+
+                # 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 XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+            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 FluxPipelineOutput(images=image)

pipelines/pipeline_infu_flux.py

@@ -0,0 +1,299 @@
+# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. 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 math
+import os
+import random
+from typing import Optional
+
+import cv2
+import numpy as np
+import torch
+from diffusers.models import FluxControlNetModel
+from facexlib.recognition import init_recognition_model
+from huggingface_hub import snapshot_download
+from insightface.app import FaceAnalysis
+from insightface.utils import face_align
+from PIL import Image
+
+from .pipeline_flux_infusenet import FluxInfuseNetPipeline
+from .resampler import Resampler
+
+
+def seed_everything(seed, deterministic=False):
+    """Set random seed.
+
+    Args:
+        seed (int): Seed to be used.
+        deterministic (bool): Whether to set the deterministic option for
+            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
+            to True and `torch.backends.cudnn.benchmark` to False.
+            Default: False.
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    if deterministic:
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+
+
+# modified from https://github.com/instantX-research/InstantID/blob/main/pipeline_stable_diffusion_xl_instantid.py
+def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
+    stickwidth = 4
+    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
+    kps = np.array(kps)
+
+    w, h = image_pil.size
+    out_img = np.zeros([h, w, 3])
+
+    for i in range(len(limbSeq)):
+        index = limbSeq[i]
+        color = color_list[index[0]]
+
+        x = kps[index][:, 0]
+        y = kps[index][:, 1]
+        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
+        polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
+    out_img = (out_img * 0.6).astype(np.uint8)
+
+    for idx_kp, kp in enumerate(kps):
+        color = color_list[idx_kp]
+        x, y = kp
+        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
+
+    out_img_pil = Image.fromarray(out_img.astype(np.uint8))
+    return out_img_pil
+
+
+def extract_arcface_bgr_embedding(in_image, landmark, arcface_model=None, in_settings=None):
+    kps = landmark
+    arc_face_image = face_align.norm_crop(in_image, landmark=np.array(kps), image_size=112)
+    arc_face_image = torch.from_numpy(arc_face_image).unsqueeze(0).permute(0,3,1,2) / 255.
+    arc_face_image = 2 * arc_face_image - 1
+    arc_face_image = arc_face_image.cuda().contiguous()
+    if arcface_model is None:
+        arcface_model = init_recognition_model('arcface', device='cuda')
+    face_emb = arcface_model(arc_face_image)[0] # [512], normalized
+    return face_emb
+
+
+def resize_and_pad_image(source_img, target_img_size):
+    # Get original and target sizes
+    source_img_size = source_img.size
+    target_width, target_height = target_img_size
+    
+    # Determine the new size based on the shorter side of target_img
+    if target_width <= target_height:
+        new_width = target_width
+        new_height = int(target_width * (source_img_size[1] / source_img_size[0]))
+    else:
+        new_height = target_height
+        new_width = int(target_height * (source_img_size[0] / source_img_size[1]))
+    
+    # Resize the source image using LANCZOS interpolation for high quality
+    resized_source_img = source_img.resize((new_width, new_height), Image.LANCZOS)
+    
+    # Compute padding to center resized image
+    pad_left = (target_width - new_width) // 2
+    pad_top = (target_height - new_height) // 2
+    
+    # Create a new image with white background
+    padded_img = Image.new("RGB", target_img_size, (255, 255, 255))
+    padded_img.paste(resized_source_img, (pad_left, pad_top))
+    
+    return padded_img
+
+
+class InfUFluxPipeline:
+    def __init__(
+            self, 
+            base_model_path, 
+            infu_model_path, 
+            insightface_root_path = './',
+            image_proj_num_tokens=8,
+            infu_flux_version='v1.0',
+            model_version='aes_stage2',
+        ):
+
+        self.infu_flux_version = infu_flux_version
+        self.model_version = model_version
+        
+        # Load pipeline
+        try:
+            infusenet_path = os.path.join(infu_model_path, 'InfuseNetModel')
+            self.infusenet = FluxControlNetModel.from_pretrained(infusenet_path, torch_dtype=torch.bfloat16)
+        except:
+            print("No InfiniteYou model found. Downloading from HuggingFace `ByteDance/InfiniteYou` to `./models/InfiniteYou` ...")
+            snapshot_download(repo_id='ByteDance/InfiniteYou', local_dir='./models/InfiniteYou', local_dir_use_symlinks=False)
+            infu_model_path = os.path.join('./models/InfiniteYou', f'infu_flux_{infu_flux_version}', model_version)
+            infusenet_path = os.path.join(infu_model_path, 'InfuseNetModel')
+            self.infusenet = FluxControlNetModel.from_pretrained(infusenet_path, torch_dtype=torch.bfloat16)
+            insightface_root_path = './models/InfiniteYou/supports/insightface'
+        try:
+            pipe = FluxInfuseNetPipeline.from_pretrained(
+                base_model_path,
+                controlnet=self.infusenet,
+                torch_dtype=torch.bfloat16,
+            )
+        except:
+            try:
+                pipe = FluxInfuseNetPipeline.from_single_file(
+                    base_model_path,
+                    controlnet=self.infusenet,
+                    torch_dtype=torch.bfloat16,
+                )
+            except Exception as e:
+                print(e)
+                print('\nIf you are using `black-forest-labs/FLUX.1-dev` and have not downloaded it into a local directory, '
+                      'please accept the agreement and obtain access at https://huggingface.co/black-forest-labs/FLUX.1-dev. '
+                      'Then, use `huggingface-cli login` and your access tokens at https://huggingface.co/settings/tokens to authenticate. '
+                      'After that, run the code again. If you have downloaded it, please use `base_model_path` to specify the correct path.')
+                print('\nIf you are using other models, please download them to a local directory and use `base_model_path` to specify the correct path.')
+                exit()
+        pipe.to('cuda', torch.bfloat16)
+        self.pipe = pipe
+
+        # Load image proj model
+        num_tokens = image_proj_num_tokens
+        image_emb_dim = 512
+        image_proj_model = Resampler(
+            dim=1280,
+            depth=4,
+            dim_head=64,
+            heads=20,
+            num_queries=num_tokens,
+            embedding_dim=image_emb_dim,
+            output_dim=4096,
+            ff_mult=4,
+        )
+        image_proj_model_path = os.path.join(infu_model_path, 'image_proj_model.bin')
+        ipm_state_dict = torch.load(image_proj_model_path, map_location="cpu")
+        image_proj_model.load_state_dict(ipm_state_dict['image_proj'])
+        del ipm_state_dict
+        image_proj_model.to('cuda', torch.bfloat16)
+        image_proj_model.eval()
+
+        self.image_proj_model = image_proj_model
+
+        # Load face encoder
+        self.app_640 = FaceAnalysis(name='antelopev2', 
+                                root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        self.app_640.prepare(ctx_id=0, det_size=(640, 640))
+
+        self.app_320 = FaceAnalysis(name='antelopev2', 
+                                root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        self.app_320.prepare(ctx_id=0, det_size=(320, 320))
+
+        self.app_160 = FaceAnalysis(name='antelopev2', 
+                                root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        self.app_160.prepare(ctx_id=0, det_size=(160, 160))
+
+        self.arcface_model = init_recognition_model('arcface', device='cuda')
+
+    def load_loras(self, loras):
+        names, scales = [],[]
+        for lora_path, lora_name, lora_scale in loras:
+            if lora_path != "":
+                print(f"loading lora {lora_path}")
+                self.pipe.load_lora_weights(lora_path, adapter_name = lora_name)
+                names.append(lora_name)
+                scales.append(lora_scale)
+
+        if len(names) > 0:
+            self.pipe.set_adapters(names, adapter_weights=scales)
+
+    def _detect_face(self, id_image_cv2):
+        face_info = self.app_640.get(id_image_cv2)
+        if len(face_info) > 0:
+            return face_info
+        
+        face_info = self.app_320.get(id_image_cv2)
+        if len(face_info) > 0:
+            return face_info
+
+        face_info = self.app_160.get(id_image_cv2)
+        return face_info
+
+    def __call__(
+        self,
+        id_image: Image.Image,  # PIL.Image.Image (RGB)
+        prompt: str,
+        control_image: Optional[Image.Image] = None,  # PIL.Image.Image (RGB) or None
+        width = 864,
+        height = 1152,
+        seed = 42,
+        guidance_scale = 3.5,
+        num_steps = 30,
+        infusenet_conditioning_scale = 1.0,
+        infusenet_guidance_start = 0.0,
+        infusenet_guidance_end = 1.0,
+    ):        
+        # Extract ID embeddings
+        print('Preparing ID embeddings')
+        id_image_cv2 = cv2.cvtColor(np.array(id_image), cv2.COLOR_RGB2BGR)
+        face_info = self._detect_face(id_image_cv2)
+        if len(face_info) == 0:
+            raise ValueError('No face detected in the input ID image')
+        
+        face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face
+        landmark = face_info['kps']
+        id_embed = extract_arcface_bgr_embedding(id_image_cv2, landmark, self.arcface_model)
+        id_embed = id_embed.clone().unsqueeze(0).float().cuda()
+        id_embed = id_embed.reshape([1, -1, 512])
+        id_embed = id_embed.to(device='cuda', dtype=torch.bfloat16)
+        with torch.no_grad():
+            id_embed = self.image_proj_model(id_embed)
+            bs_embed, seq_len, _ = id_embed.shape
+            id_embed = id_embed.repeat(1, 1, 1)
+            id_embed = id_embed.view(bs_embed * 1, seq_len, -1)
+            id_embed = id_embed.to(device='cuda', dtype=torch.bfloat16)
+        
+        # Load control image
+        print('Preparing the control image')
+        if control_image is not None:
+            control_image = control_image.convert("RGB")
+            control_image = resize_and_pad_image(control_image, (width, height))
+            face_info = self._detect_face(cv2.cvtColor(np.array(control_image), cv2.COLOR_RGB2BGR))
+            if len(face_info) == 0:
+                raise ValueError('No face detected in the control image')
+            face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1] # only use the maximum face
+            control_image = draw_kps(control_image, face_info['kps'])
+        else:
+            out_img = np.zeros([height, width, 3])
+            control_image = Image.fromarray(out_img.astype(np.uint8))
+
+        # Perform inference
+        print('Generating image')
+        seed_everything(seed)
+        image = self.pipe(
+            prompt=prompt,
+            controlnet_prompt_embeds=id_embed,
+            control_image=control_image,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_steps,
+            controlnet_guidance_scale=1.0,
+            controlnet_conditioning_scale=infusenet_conditioning_scale,
+            control_guidance_start=infusenet_guidance_start,
+            control_guidance_end=infusenet_guidance_end,
+            height=height,
+            width=width,
+        ).images[0]
+
+        return image

pipelines/resampler.py

@@ -0,0 +1,121 @@
+# Modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+
+import math
+
+import torch
+import torch.nn as nn
+
+
+# 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,
+    ):
+        super().__init__()
+        
+        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.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):
+        
+        latents = self.latents.repeat(x.size(0), 1, 1)
+        
+        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)

requirements.txt

@@ -0,0 +1,19 @@
+accelerate==1.0.1
+diffusers==0.31.0
+facexlib==0.3.0
+gradio==5.21.0
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.28.1
+insightface==0.7.3
+numpy==1.26.4
+onnxruntime==1.19.2
+opencv-python==4.11.0.86
+pillow==10.4.0
+pillow-avif-plugin==1.5.0
+pillow-heif==0.21.0
+sentencepiece==0.2.0
+torch==2.2.1
+torchvision==0.17.1
+transformers==4.48.0
+peft==0.14.0