First commit

Author: niknah

.github/workflows/publish_action.yml

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+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} ## Add your own personal access token to your Github Repository secrets and reference it here.

InfiniteYouSampler.py

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+import os
+import comfy
+import torch
+import torchvision
+import sys
+import folder_paths
+import numpy
+import pygit2
+from PIL import Image, ImageOps
+
+this_path = os.path.dirname(__file__)
+repo_dir = os.path.join(this_path,"InfiniteYou")
+requirements_txt = os.path.join(repo_dir,"requirements.txt")
+
+if not os.path.exists(requirements_txt):
+    pygit2.clone_repository("https://github.com/bytedance/InfiniteYou", repo_dir, depth=1)
+    if not os.path.exists(requirements_txt):
+        print("*** Could not get InfiniteYou repository.  Please install git.")
+
+sys.path.insert(0, this_path)
+from InfiniteYou.pipelines.pipeline_infu_flux import InfUFluxPipeline
+sys.path.remove(this_path)
+
+class InfiniteYouSampler:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "id_image": ("IMAGE",),
+                "base_model": (['auto'] + folder_paths.get_filename_list("diffusion_models"),),
+                "seed": ("INT", {
+                }),
+                "prompt": ("STRING", {
+                    "multiline": True,
+                    "default": "a robot",
+                    "lazy": True
+                }),
+                "steps": ("INT", {
+                    "default": 30
+                }),
+            },
+            "optional": {
+                "guidance": ("FLOAT", {
+                    "default": 3.5,
+                    "min": 0.0,
+                    "step": 0.1,
+                    "round": 0.01,
+                    "display": "number",
+                    "lazy": True
+                }),
+                "infusenet_conditioning_scale": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "step": 0.1,
+                    "round": 0.01,
+                    "display": "number",
+                    "lazy": True
+                }),
+                "infusenet_guidance_start": ("FLOAT", {
+                    "default": 0.0,
+                    "min": 0.0,
+                    "step": 0.1,
+                    "round": 0.01,
+                    "display": "number",
+                    "lazy": True
+                }),
+                "infusenet_guidance_end": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "step": 0.1,
+                    "round": 0.01,
+                    "display": "number",
+                    "lazy": True
+                }),
+                "model_version": (['aes_stage2', 'sim_stage1'], {
+                    "default": 'aes_stage2'
+                }),
+                "control_image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "sampler"
+    CATEGORY = "sampling"
+
+    def sampler(
+        self,
+        id_image,
+        base_model,  # TODO
+        seed,
+        prompt,
+        steps=30,
+        guidance=3.5,
+        infusenet_conditioning_scale=1.0,
+        infusenet_guidance_start=0.0,
+        infusenet_guidance_end=1.0,
+        model_version='aes_stage2',
+        control_image=None,
+    ):
+        torch.cuda.set_device(comfy.model_management.get_torch_device())
+
+        infu_flux_version = 'v1.0'
+        model_dir = 'ByteDance/InfiniteYou'
+        if base_model == 'auto':
+            base_model_path = 'black-forest-labs/FLUX.1-dev'
+        else:
+            base_model_path = folder_paths.get_full_path_or_raise("diffusion_models", base_model)
+
+        infu_model_path = os.path.join(
+            model_dir, f'infu_flux_{infu_flux_version}',
+            model_version
+        )
+        insightface_root_path = os.path.join(
+            model_dir, 'supports', 'insightface'
+            )
+        pipe = InfUFluxPipeline(
+            base_model_path=base_model_path,
+            infu_model_path=infu_model_path,
+            insightface_root_path=insightface_root_path,
+            infu_flux_version=infu_flux_version,
+            model_version=model_version,
+        )
+
+        control_image_pil = None
+        if control_image is not None:
+            control_i = 255. * control_image[0].cpu().numpy()
+            control_image_pil = Image.fromarray(numpy.clip(i, 0, 255).astype(numpy.uint8))
+        i = 255. * id_image[0].cpu().numpy()
+        img = Image.fromarray(numpy.clip(i, 0, 255).astype(numpy.uint8))
+        image = pipe(
+            id_image=img,
+            prompt=prompt,
+            control_image=control_image_pil,
+            seed=seed,
+            guidance_scale=guidance,
+            num_steps=steps,
+            infusenet_conditioning_scale=infusenet_conditioning_scale,
+            infusenet_guidance_start=infusenet_guidance_start,
+            infusenet_guidance_end=infusenet_guidance_end,
+        )
+
+        image = image.convert("RGB")
+        image = numpy.array(image).astype(numpy.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        images = []
+        images.append(image)
+
+        if len(images) > 1:
+            images = torch.cat(images, dim=0)
+        else:
+            images = images[0]
+
+        return (images,)

LICENSE

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+
+Put anyone's face on anything with Byte Dance's [InfiniteYou](https://github.com/bytedance/InfiniteYou).
+
+## Hardware
+
+Uses 40GB of VRAM.  Takes about a minute on an RTX A6000, 48GB.
+
+Note: I only had temporary access to the hardware to run this.  Might not be able to help much.
+
+
+## Installation
+
+To get FLUX, you need to run `huggingface-cli login`.
+And put in your huggingface access token from [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+
+At the moment it needs `diffusers==0.31` `numpy<2`.  If something goes wrong make sure these versions are installed `pip show numpy diffusers`
+
+
+## runpod
+
+Link the ~/.cache folder to permanent storage or else it'll get wiped every time and it'll redownload the models every time.
+

README.md

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+
+from .InfiniteYouSampler import InfiniteYouSampler
+
+
+NODE_CLASS_MAPPINGS = {
+    "InfiniteYouSampler": InfiniteYouSampler
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "InfiniteYouSampler": "InfiniteYou Sampler"
+}

__init__.py

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+# pyproject.toml
+[project]
+name = "ComfyUI-InifiniteYou" # Unique identifier for your node. Immutable after creation.
+description = "ComfyUI custom_node for ByteDance's InfiniteYou"
+version = "1.0.0" # Custom Node version. Must be semantically versioned.
+license = {text = "MIT License"}
+
+[project.urls]
+Repository = "https://github.com/niknah/ComfyUI-InfiniteYou"
+
+[tool.comfy]
+PublisherId = "niknah"
+DisplayName = "ComfyUI-InfiniteYou" # Display name for the Custom Node. Can be changed later.

pyproject.toml

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+accelerate
+diffusers==0.31
+facexlib
+huggingface-hub
+insightface
+numpy
+numpy==1.26.4
+onnxruntime
+opencv-python
+pillow
+pillow-avif-plugin
+pillow-heif
+sentencepiece
+torch
+torchvision
+transformers
+peft
+pygit2