init

.gitattributes

@@ -0,0 +1,37 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/examples/man.jpg filter=lfs diff=lfs merge=lfs -text
+assets/examples/woman.jpg filter=lfs diff=lfs merge=lfs -text

.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 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   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.

README.md

@@ -0,0 +1,18 @@
+---
+title: InfiniteYou-FLUX
+emoji: 📸
+colorFrom: red
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.23.1
+app_file: app.py
+pinned: true
+license: apache-2.0
+short_description: Flexible Photo Recrafting While Preserving Your Identity
+---
+
+The images used in this demo are sourced from consented subjects or generated by the 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. 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. Any usage of this method must be responsible and comply with local laws. The developers do not assume any responsibility for any potential misuse. We added the "AI Generated" watermark for enhanced safety.

app.py

@@ -0,0 +1,152 @@
+# Copyright (c) 2025 All rights reserved.
+
+import os
+import torch
+import gradio as gr
+import huggingface_hub
+from huggingface_hub import snapshot_download
+from PIL import Image, ImageDraw, ImageFont
+
+# Import the base pipeline from diffusers
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from transformers import CLIPTextModel, CLIPTokenizer
+
+# Define default parameters
+DEFAULT_SEED = 42
+DEFAULT_STEPS = 30
+DEFAULT_GUIDANCE_SCALE = 7.5
+RED_BG_COLOR = "#ffcccc"  # Light red background
+
+# Initialize the model
+def download_model():
+    # Download the model (using a simple SD model as example)
+    snapshot_download(repo_id='runwayml/stable-diffusion-v1-5', local_dir='./models/stable-diffusion', local_dir_use_symlinks=False)
+
+def init_pipeline():
+    # Initialize a simple text-to-image pipeline
+    pipeline = StableDiffusionControlNetPipeline.from_pretrained(
+        "./models/stable-diffusion",
+        torch_dtype=torch.float16,
+        safety_checker=None
+    )
+    pipeline = pipeline.to("cuda")
+    return pipeline
+
+# Generate image function
+def generate_image(prompt, seed, num_steps, guidance_scale):
+    try:
+        # Make sure we have a valid seed
+        if seed == 0:
+            seed = torch.seed() & 0xFFFFFFFF
+        
+        # Set up generator for reproducibility
+        generator = torch.Generator("cuda").manual_seed(seed)
+        
+        # Generate the image
+        image = pipeline(
+            prompt=prompt,
+            num_inference_steps=num_steps,
+            guidance_scale=guidance_scale,
+            generator=generator
+        ).images[0]
+        
+        # Add watermark
+        image = add_safety_watermark(image)
+        
+    except Exception as e:
+        print(f"Error generating image: {e}")
+        return gr.update()
+    
+    return gr.update(value=image, label=f"Generated Image, seed = {seed}")
+
+# Add watermark to image
+def add_safety_watermark(image, text='AI Generated'):
+    width, height = image.size
+    draw = ImageDraw.Draw(image)
+    
+    # Set font size based on image height
+    font_size = int(height * 0.028)
+    font = ImageFont.load_default()
+    
+    # Calculate text position
+    text_width = len(text) * font_size * 0.6  # Approximate width
+    x = width - text_width - 10
+    y = height - font_size - 20
+    
+    # Add shadow and text
+    draw.text((x+2, y+2), text, fill="black")
+    draw.text((x, y), text, fill="white")
+    
+    return image
+
+# Create example function
+def generate_example(prompt, seed):
+    return generate_image(prompt, seed, DEFAULT_STEPS, DEFAULT_GUIDANCE_SCALE)
+
+# Sample examples
+sample_list = [
+    ['A majestic mountain landscape with snow peaks and pine trees', 123],
+    ['A futuristic city with flying cars and tall skyscrapers', 456],
+    ['A serene beach scene with clear blue waters', 789],
+]
+
+# Create the Gradio interface
+with gr.Blocks(css=f".gradio-container {{ background-color: {RED_BG_COLOR} !important; }}") as demo:
+    gr.HTML("""
+    <div style="text-align: center; max-width: 800px; margin: 0 auto;">
+        <h1 style="font-size: 2rem; font-weight: 700;">Simple Text to Image Generator</h1>
+        <h2 style="font-size: 1.2rem; font-weight: 300; margin-bottom: 1rem;">Convert your text descriptions into images</h2>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            # Input components
+            ui_prompt_text = gr.Textbox(label="Text Prompt", value="A beautiful landscape with mountains and trees")
+            ui_seed = gr.Number(label="Seed (0 for random)", value=DEFAULT_SEED)
+            ui_steps = gr.Slider(minimum=10, maximum=50, value=DEFAULT_STEPS, step=1, label="Number of Steps")
+            ui_guidance_scale = gr.Slider(minimum=1.0, maximum=15.0, value=DEFAULT_GUIDANCE_SCALE, step=0.5, label="Guidance Scale")
+            
+            ui_btn_generate = gr.Button("Generate Image")
+        
+        with gr.Column(scale=3):
+            # Output components
+            image_output = gr.Image(label="Generated Image", interactive=False, height=512)
+    
+    gr.Examples(
+        sample_list,
+        inputs=[ui_prompt_text, ui_seed],
+        outputs=[image_output],
+        fn=generate_example,
+        cache_examples=True
+    )
+    
+    ui_btn_generate.click(
+        generate_image,
+        inputs=[ui_prompt_text, ui_seed, ui_steps, ui_guidance_scale],
+        outputs=[image_output]
+    )
+    
+    gr.Markdown(
+        """
+        ### How to Use:
+        1. Enter a detailed text description of the image you want to create
+        2. Adjust the parameters if needed (or leave as default)
+        3. Click "Generate Image" and wait for the result
+        
+        ### Tips:
+        - Detailed prompts work better than short ones
+        - Try different seeds for different variations
+        - Higher guidance scale values make the image follow the prompt more closely
+        """
+    )
+
+# Initialize and launch
+print("Downloading models...")
+download_model()
+
+print("Initializing pipeline...")
+pipeline = init_pipeline()
+
+print("Launching Gradio interface...")
+demo.launch()

pipelines/pipeline_flux_infusenet.py

@@ -0,0 +1,622 @@
+# 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
+        device = 'cuda'
+        dtype = self.transformer.dtype
+
+        # CPU offload controlnet, move back T5 to GPU
+        self.controlnet.cpu()
+        torch.cuda.empty_cache()
+        self.text_encoder_2.to(device)
+
+        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,
+        )
+
+        # CPU offload T5, move back controlnet to GPU
+        self.text_encoder_2.cpu()
+        torch.cuda.empty_cache()
+        self.controlnet.to(device)
+
+        # 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 +1,357 @@
+# 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 copy
+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(os.path.dirname(infu_model_path), 'aes_stage2', 'InfuseNetModel')
+            self.infusenet_aes = FluxControlNetModel.from_pretrained(infusenet_path, torch_dtype=torch.bfloat16)
+            infusenet_path = os.path.join(os.path.dirname(infu_model_path), 'sim_stage1', 'InfuseNetModel')
+            self.infusenet_sim = 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}', 'aes_stage2')
+            infusenet_path = os.path.join(infu_model_path, 'InfuseNetModel')
+            self.infusenet_aes = FluxControlNetModel.from_pretrained(infusenet_path, torch_dtype=torch.bfloat16)
+            infu_model_path = os.path.join('./models/InfiniteYou', f'infu_flux_{infu_flux_version}', 'sim_stage1')
+            infusenet_path = os.path.join(infu_model_path, 'InfuseNetModel')
+            self.infusenet_sim = FluxControlNetModel.from_pretrained(infusenet_path, torch_dtype=torch.bfloat16)
+            insightface_root_path = './models/InfiniteYou/supports/insightface'
+        self.infusenet_sim.cpu()
+        torch.cuda.empty_cache()
+        try:
+            pipe = FluxInfuseNetPipeline.from_pretrained(
+                base_model_path,
+                controlnet=self.infusenet_aes,
+                torch_dtype=torch.bfloat16,
+            )
+        except:
+            try:
+                pipe = FluxInfuseNetPipeline.from_single_file(
+                    base_model_path,
+                    controlnet=self.infusenet_aes,
+                    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)
+        # CPU offload controlnet and T5 in advance
+        pipe.controlnet.cpu()
+        pipe.text_encoder_2.cpu()
+        torch.cuda.empty_cache()
+        pipe.text_encoder.to('cuda')
+        pipe.transformer.to('cuda')
+        pipe.vae.to('cuda')
+        # pipe.enable_model_cpu_offload()
+        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(os.path.dirname(infu_model_path), 'aes_stage2', '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
+        self.image_proj_model_aes = copy.deepcopy(image_proj_model)
+        self.image_proj_model_aes.to('cuda', torch.bfloat16)
+        self.image_proj_model_aes.eval()
+        
+        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(os.path.dirname(infu_model_path), 'sim_stage1', '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
+        self.image_proj_model_sim = image_proj_model
+        self.image_proj_model_sim.to('cpu', dtype=torch.bfloat16)
+        self.image_proj_model_sim.eval()
+
+        self.image_proj_model = self.image_proj_model_aes
+
+        # 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')
+
+        # Load LoRAs in advance
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+        self.loras_state_dict = {}
+        self.loras_state_dict['realism'] = self.pipe._fetch_state_dict(os.path.join(os.path.dirname(insightface_root_path), 'optional_loras', 'flux_realism_lora.safetensors'),
+            weight_name=None, use_safetensors=True, local_files_only=None, cache_dir=None, force_download=False, proxies=None, token=None, revision=None, subfolder=None, user_agent=user_agent, allow_pickle=True)
+        self.loras_state_dict['anti_blur'] = self.pipe._fetch_state_dict(os.path.join(os.path.dirname(insightface_root_path), 'optional_loras', 'flux_anti_blur_lora.safetensors'),
+            weight_name=None, use_safetensors=True, local_files_only=None, cache_dir=None, force_download=False, proxies=None, token=None, revision=None, subfolder=None, user_agent=user_agent, allow_pickle=True)
+
+    def load_loras_state_dict(self, loras):
+        names, scales = [],[]
+        for lora_name, lora_scale in loras:
+            print(f"loading lora state dict of {lora_name}")
+            self.pipe.load_lora_weights(self.loras_state_dict[lora_name], 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 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()
+        id_embed = id_embed.reshape([1, -1, 512])
+        id_embed = id_embed.to(device='cuda', dtype=torch.bfloat16)
+        self.image_proj_model.to('cuda', 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 +1,20 @@
+accelerate==1.0.1
+diffusers==0.31.0
+facexlib==0.3.0
+gradio==5.23.1
+gradio_client==1.8.0
+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
+peft==0.14.0
+pillow==10.4.0
+pillow-avif-plugin==1.5.0
+pillow-heif==0.21.0
+sentencepiece==0.2.0
+torch==2.2.2
+torchvision==0.17.2
+transformers==4.48.0