workflows

FaceEnhancementProd.json

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+      },
+      "widgets_values": [
+        "chatgpt_elon.png",
+        "image"
+      ]
+    },
+    {
+      "id": 24,
+      "type": "LoadImage",
+      "pos": [
+        -352.1936340332031,
+        770.800537109375
+      ],
+      "size": [
+        404.0055236816406,
+        404.2244567871094
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            176
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "elon (1).jpg",
+        "image"
+      ]
+    },
+    {
+      "id": 31,
+      "type": "ApplyPulidFlux",
+      "pos": [
+        221.46853637695312,
+        1532.384033203125
+      ],
+      "size": [
+        315,
+        346
+      ],
+      "flags": {},
+      "order": 23,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 171
+        },
+        {
+          "name": "pulid_flux",
+          "type": "PULIDFLUX",
+          "link": 77
+        },
+        {
+          "name": "eva_clip",
+          "type": "EVA_CLIP",
+          "link": 78
+        },
+        {
+          "name": "face_analysis",
+          "type": "FACEANALYSIS",
+          "link": 79
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 175
+        },
+        {
+          "name": "attn_mask",
+          "type": "MASK",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "prior_image",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            204,
+            215
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ApplyPulidFlux"
+      },
+      "widgets_values": [
+        0.75,
+        0.1,
+        1,
+        "mean",
+        1,
+        0,
+        1000,
+        true
+      ]
+    },
+    {
+      "id": 131,
+      "type": "BasicScheduler",
+      "pos": [
+        1313.8270263671875,
+        1318.2603759765625
+      ],
+      "size": [
+        315,
+        106
+      ],
+      "flags": {},
+      "order": 25,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 215
+        }
+      ],
+      "outputs": [
+        {
+          "name": "SIGMAS",
+          "type": "SIGMAS",
+          "links": [
+            214
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "BasicScheduler"
+      },
+      "widgets_values": [
+        "beta",
+        28,
+        0.75
+      ]
+    },
+    {
+      "id": 39,
+      "type": "RandomNoise",
+      "pos": [
+        1311.832763671875,
+        1088.896728515625
+      ],
+      "size": [
+        315,
+        82
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "NOISE",
+          "type": "NOISE",
+          "links": [
+            68
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "RandomNoise"
+      },
+      "widgets_values": [
+        1,
+        "fixed"
+      ]
+    },
+    {
+      "id": 1,
+      "type": "SamplerCustomAdvanced",
+      "pos": [
+        1767.5123291015625,
+        874.9805908203125
+      ],
+      "size": [
+        355.20001220703125,
+        106
+      ],
+      "flags": {},
+      "order": 26,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "noise",
+          "type": "NOISE",
+          "link": 68
+        },
+        {
+          "name": "guider",
+          "type": "GUIDER",
+          "link": 202
+        },
+        {
+          "name": "sampler",
+          "type": "SAMPLER",
+          "link": 70
+        },
+        {
+          "name": "sigmas",
+          "type": "SIGMAS",
+          "link": 214
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 72
+        }
+      ],
+      "outputs": [
+        {
+          "name": "output",
+          "type": "LATENT",
+          "links": [
+            190
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "denoised_output",
+          "type": "LATENT",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SamplerCustomAdvanced"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 114,
+      "type": "VAEDecode",
+      "pos": [
+        1771.2335205078125,
+        1040.572021484375
+      ],
+      "size": [
+        210,
+        46
+      ],
+      "flags": {},
+      "order": 27,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 190
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 191
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            207
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      },
+      "widgets_values": []
+    }
+  ],
+  "links": [
+    [
+      68,
+      39,
+      0,
+      1,
+      0,
+      "NOISE"
+    ],
+    [
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+    ],
+    [
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+    [
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+    ],
+    [
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+      2,
+      "EVA_CLIP"
+    ],
+    [
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+      3,
+      "FACEANALYSIS"
+    ],
+    [
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+    [
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+    [
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+      2,
+      "CONTROL_NET"
+    ],
+    [
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+    [
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+    [
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+    [
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+    [
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+    [
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+      "LATENT"
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+    [
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+      1,
+      "VAE"
+    ],
+    [
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+      "ANALYSIS_MODELS"
+    ],
+    [
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+      1,
+      "IMAGE"
+    ],
+    [
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+    [
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+    [
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+    [
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+    [
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+      1,
+      "IMAGE"
+    ],
+    [
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+      2,
+      "IMAGE"
+    ],
+    [
+      211,
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+      0,
+      "IMAGE"
+    ],
+    [
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+      0,
+      "IMAGE"
+    ],
+    [
+      214,
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+      0,
+      1,
+      3,
+      "SIGMAS"
+    ],
+    [
+      215,
+      31,
+      0,
+      131,
+      0,
+      "MODEL"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.4594972986357322,
+      "offset": [
+        946.7575830609429,
+        -566.8107983375609
+      ]
+    },
+    "node_versions": {
+      "comfy-core": "0.3.26",
+      "ComfyUI-PuLID-Flux-Enhanced": "04e1b52320f1f14383afe18959349703623c5b88",
+      "rgthree-comfy": "1.0.0",
+      "comfyui_faceanalysis": "4919e4e931db0edb219ba5086b3c10b8af750631"
+    },
+    "ue_links": []
+  },
+  "version": 0.4
+}

FaceEnhancementProdApi.py

@@ -0,0 +1,299 @@
+import os
+import random
+import sys
+from typing import Sequence, Mapping, Any, Union
+import torch
+
+
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    """Returns the value at the given index of a sequence or mapping.
+
+    If the object is a sequence (like list or string), returns the value at the given index.
+    If the object is a mapping (like a dictionary), returns the value at the index-th key.
+
+    Some return a dictionary, in these cases, we look for the "results" key
+
+    Args:
+        obj (Union[Sequence, Mapping]): The object to retrieve the value from.
+        index (int): The index of the value to retrieve.
+
+    Returns:
+        Any: The value at the given index.
+
+    Raises:
+        IndexError: If the index is out of bounds for the object and the object is not a mapping.
+    """
+    try:
+        return obj[index]
+    except KeyError:
+        return obj["result"][index]
+
+
+def find_path(name: str, path: str = None) -> str:
+    """
+    Recursively looks at parent folders starting from the given path until it finds the given name.
+    Returns the path as a Path object if found, or None otherwise.
+    """
+    # If no path is given, use the current working directory
+    if path is None:
+        path = os.getcwd()
+
+    # Check if the current directory contains the name
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+
+    # Get the parent directory
+    parent_directory = os.path.dirname(path)
+
+    # If the parent directory is the same as the current directory, we've reached the root and stop the search
+    if parent_directory == path:
+        return None
+
+    # Recursively call the function with the parent directory
+    return find_path(name, parent_directory)
+
+
+def add_comfyui_directory_to_sys_path() -> None:
+    """
+    Add 'ComfyUI' to the sys.path
+    """
+    comfyui_path = find_path("ComfyUI")
+    if comfyui_path is not None and os.path.isdir(comfyui_path):
+        sys.path.append(comfyui_path)
+        print(f"'{comfyui_path}' added to sys.path")
+
+
+def add_extra_model_paths() -> None:
+    """
+    Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path.
+    """
+    try:
+        from main import load_extra_path_config
+    except ImportError:
+        print(
+            "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead."
+        )
+        from utils.extra_config import load_extra_path_config
+
+    extra_model_paths = find_path("extra_model_paths.yaml")
+
+    if extra_model_paths is not None:
+        load_extra_path_config(extra_model_paths)
+    else:
+        print("Could not find the extra_model_paths config file.")
+
+
+add_comfyui_directory_to_sys_path()
+add_extra_model_paths()
+
+
+def import_custom_nodes() -> None:
+    """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS
+
+    This function sets up a new asyncio event loop, initializes the PromptServer,
+    creates a PromptQueue, and initializes the custom nodes.
+    """
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    import server
+
+    # Creating a new event loop and setting it as the default loop
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    # Creating an instance of PromptServer with the loop
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+
+    # Initializing custom nodes
+    init_extra_nodes()
+
+
+from nodes import (
+    VAELoader,
+    ControlNetLoader,
+    ControlNetApplyAdvanced,
+    LoadImage,
+    NODE_CLASS_MAPPINGS,
+    UNETLoader,
+    CLIPTextEncode,
+    VAEEncode,
+    SaveImage,
+    VAEDecode,
+    DualCLIPLoader,
+)
+
+
+def main():
+    import_custom_nodes()
+    with torch.inference_mode():
+        dualcliploader = DualCLIPLoader()
+        dualcliploader_94 = dualcliploader.load_clip(
+            clip_name1="t5xxl_fp16.safetensors",
+            clip_name2="clip_l.safetensors",
+            type="flux",
+            device="default",
+        )
+
+        cliptextencode = CLIPTextEncode()
+        cliptextencode_23 = cliptextencode.encode(
+            text="", clip=get_value_at_index(dualcliploader_94, 0)
+        )
+
+        loadimage = LoadImage()
+        loadimage_24 = loadimage.load_image(image="elon (1).jpg")
+
+        loadimage_40 = loadimage.load_image(image="chatgpt_elon.png")
+
+        vaeloader = VAELoader()
+        vaeloader_95 = vaeloader.load_vae(vae_name="FLUX1/ae.safetensors")
+
+        vaeencode = VAEEncode()
+        vaeencode_35 = vaeencode.encode(
+            pixels=get_value_at_index(loadimage_40, 0),
+            vae=get_value_at_index(vaeloader_95, 0),
+        )
+
+        randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
+        randomnoise_39 = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))
+
+        cliptextencode_42 = cliptextencode.encode(
+            text="", clip=get_value_at_index(dualcliploader_94, 0)
+        )
+
+        pulidfluxmodelloader = NODE_CLASS_MAPPINGS["PulidFluxModelLoader"]()
+        pulidfluxmodelloader_44 = pulidfluxmodelloader.load_model(
+            pulid_file="pulid_flux_v0.9.1.safetensors"
+        )
+
+        pulidfluxevacliploader = NODE_CLASS_MAPPINGS["PulidFluxEvaClipLoader"]()
+        pulidfluxevacliploader_45 = pulidfluxevacliploader.load_eva_clip()
+
+        pulidfluxinsightfaceloader = NODE_CLASS_MAPPINGS["PulidFluxInsightFaceLoader"]()
+        pulidfluxinsightfaceloader_46 = pulidfluxinsightfaceloader.load_insightface(
+            provider="CUDA"
+        )
+
+        controlnetloader = ControlNetLoader()
+        controlnetloader_49 = controlnetloader.load_controlnet(
+            control_net_name="Flux_Dev_ControlNet_Union_Pro_ShakkerLabs.safetensors"
+        )
+
+        ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
+        ksamplerselect_50 = ksamplerselect.get_sampler(sampler_name="euler")
+
+        unetloader = UNETLoader()
+        unetloader_93 = unetloader.load_unet(
+            unet_name="flux1-dev.safetensors", weight_dtype="default"
+        )
+
+        faceanalysismodels = NODE_CLASS_MAPPINGS["FaceAnalysisModels"]()
+        faceanalysismodels_118 = faceanalysismodels.load_models(
+            library="insightface", provider="CUDA"
+        )
+
+        applypulidflux = NODE_CLASS_MAPPINGS["ApplyPulidFlux"]()
+        setunioncontrolnettype = NODE_CLASS_MAPPINGS["SetUnionControlNetType"]()
+        controlnetapplyadvanced = ControlNetApplyAdvanced()
+        basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]()
+        basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()
+        samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
+        vaedecode = VAEDecode()
+        faceembeddistance = NODE_CLASS_MAPPINGS["FaceEmbedDistance"]()
+        display_any_rgthree = NODE_CLASS_MAPPINGS["Display Any (rgthree)"]()
+        image_comparer_rgthree = NODE_CLASS_MAPPINGS["Image Comparer (rgthree)"]()
+        saveimage = SaveImage()
+
+        for q in range(10):
+            applypulidflux_31 = applypulidflux.apply_pulid_flux(
+                weight=0.75,
+                start_at=0.1,
+                end_at=1,
+                fusion="mean",
+                fusion_weight_max=1,
+                fusion_weight_min=0,
+                train_step=1000,
+                use_gray=True,
+                model=get_value_at_index(unetloader_93, 0),
+                pulid_flux=get_value_at_index(pulidfluxmodelloader_44, 0),
+                eva_clip=get_value_at_index(pulidfluxevacliploader_45, 0),
+                face_analysis=get_value_at_index(pulidfluxinsightfaceloader_46, 0),
+                image=get_value_at_index(loadimage_24, 0),
+                unique_id=17601617597548643515,
+            )
+
+            setunioncontrolnettype_41 = setunioncontrolnettype.set_controlnet_type(
+                type="tile", control_net=get_value_at_index(controlnetloader_49, 0)
+            )
+
+            controlnetapplyadvanced_37 = controlnetapplyadvanced.apply_controlnet(
+                strength=1,
+                start_percent=0.1,
+                end_percent=0.8,
+                positive=get_value_at_index(cliptextencode_42, 0),
+                negative=get_value_at_index(cliptextencode_23, 0),
+                control_net=get_value_at_index(setunioncontrolnettype_41, 0),
+                image=get_value_at_index(loadimage_40, 0),
+                vae=get_value_at_index(vaeloader_95, 0),
+            )
+
+            basicguider_122 = basicguider.get_guider(
+                model=get_value_at_index(applypulidflux_31, 0),
+                conditioning=get_value_at_index(controlnetapplyadvanced_37, 0),
+            )
+
+            basicscheduler_131 = basicscheduler.get_sigmas(
+                scheduler="beta",
+                steps=28,
+                denoise=0.75,
+                model=get_value_at_index(applypulidflux_31, 0),
+            )
+
+            samplercustomadvanced_1 = samplercustomadvanced.sample(
+                noise=get_value_at_index(randomnoise_39, 0),
+                guider=get_value_at_index(basicguider_122, 0),
+                sampler=get_value_at_index(ksamplerselect_50, 0),
+                sigmas=get_value_at_index(basicscheduler_131, 0),
+                latent_image=get_value_at_index(vaeencode_35, 0),
+            )
+
+            vaedecode_114 = vaedecode.decode(
+                samples=get_value_at_index(samplercustomadvanced_1, 0),
+                vae=get_value_at_index(vaeloader_95, 0),
+            )
+
+            faceembeddistance_117 = faceembeddistance.analize(
+                similarity_metric="cosine",
+                filter_thresh=100,
+                filter_best=0,
+                generate_image_overlay=True,
+                analysis_models=get_value_at_index(faceanalysismodels_118, 0),
+                reference=get_value_at_index(loadimage_24, 0),
+                image=get_value_at_index(vaedecode_114, 0),
+            )
+
+            display_any_rgthree_121 = display_any_rgthree.main(
+                source=get_value_at_index(faceembeddistance_117, 1)
+            )
+
+            image_comparer_rgthree_123 = image_comparer_rgthree.compare_images(
+                image_a=get_value_at_index(loadimage_40, 0),
+                image_b=get_value_at_index(vaedecode_114, 0),
+            )
+
+            saveimage_128 = saveimage.save_images(
+                filename_prefix="FaceEnhanced",
+                images=get_value_at_index(vaedecode_114, 0),
+            )
+
+            saveimage_129 = saveimage.save_images(
+                filename_prefix="FaceEmbedDist",
+                images=get_value_at_index(faceembeddistance_117, 0),
+            )
+
+
+if __name__ == "__main__":
+    main()