Upload 85 files

ComfyUI-InstantID/.github/FUNDING.yml

@@ -0,0 +1,13 @@
+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
+custom: https://afdian.net/a/ZHOZHO # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

ComfyUI-InstantID/INSTANTID WORKFLOWS/V1.0 InstantID + ArtGallery【Zho】.json

@@ -0,0 +1,615 @@
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ComfyUI-InstantID/INSTANTID WORKFLOWS/V1.0 InstantID_fromhub【Zho】.json

@@ -0,0 +1,400 @@
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ComfyUI-InstantID/INSTANTID WORKFLOWS/V1.0 InstantID_locally【Zho】.json

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ComfyUI-InstantID/INSTANTID WORKFLOWS/V2.0 InstantID_fromhub_pose_ref【Zho】.json

@@ -0,0 +1,451 @@
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ComfyUI-InstantID/INSTANTID WORKFLOWS/V2.0 InstantID_locally_pose_ref【Zho】.json

@@ -0,0 +1,452 @@
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+          "shape": 3
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "u=425589426,3799949985&fm=253&fmt=auto&app=138&f=JPEG.webp",
+        "image"
+      ]
+    },
+    {
+      "id": 10,
+      "type": "IDBaseModelLoader_local",
+      "pos": [
+        350,
+        640
+      ],
+      "size": [
+        310,
+        60
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "controlnet",
+          "type": "MODEL",
+          "link": 11,
+          "slot_index": 0
+        }
+      ],
+      "outputs": [
+        {
+          "name": "pipe",
+          "type": "MODEL",
+          "links": [
+            10
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IDBaseModelLoader_local"
+      },
+      "widgets_values": [
+        "sd_xl_base_1.0.safetensors"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "ID_Prompt_Styler",
+      "pos": [
+        680,
+        640
+      ],
+      "size": [
+        370,
+        190
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "positive_prompt",
+          "type": "STRING",
+          "links": [
+            3
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "negative_prompt",
+          "type": "STRING",
+          "links": [
+            4
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ID_Prompt_Styler"
+      },
+      "widgets_values": [
+        "a woman, retro futurism, retro game",
+        "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly",
+        "Film Noir"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "IDGenerationNode",
+      "pos": [
+        1070,
+        530
+      ],
+      "size": [
+        330,
+        300
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "face_image",
+          "type": "IMAGE",
+          "link": 5,
+          "slot_index": 0
+        },
+        {
+          "name": "pipe",
+          "type": "MODEL",
+          "link": 8
+        },
+        {
+          "name": "insightface",
+          "type": "INSIGHTFACEMODEL",
+          "link": 7
+        },
+        {
+          "name": "pose_image_optional",
+          "type": "IMAGE",
+          "link": 6,
+          "slot_index": 3
+        },
+        {
+          "name": "positive",
+          "type": "STRING",
+          "link": 3,
+          "widget": {
+            "name": "positive"
+          }
+        },
+        {
+          "name": "negative",
+          "type": "STRING",
+          "link": 4,
+          "widget": {
+            "name": "negative"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            9
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IDGenerationNode"
+      },
+      "widgets_values": [
+        "",
+        "",
+        0.8,
+        0.8,
+        50,
+        5,
+        true,
+        210876416756428,
+        "randomize"
+      ]
+    }
+  ],
+  "links": [
+    [
+      3,
+      5,
+      0,
+      6,
+      4,
+      "STRING"
+    ],
+    [
+      4,
+      5,
+      1,
+      6,
+      5,
+      "STRING"
+    ],
+    [
+      5,
+      7,
+      0,
+      6,
+      0,
+      "IMAGE"
+    ],
+    [
+      6,
+      8,
+      0,
+      6,
+      3,
+      "IMAGE"
+    ],
+    [
+      7,
+      1,
+      0,
+      6,
+      2,
+      "INSIGHTFACEMODEL"
+    ],
+    [
+      8,
+      4,
+      0,
+      6,
+      1,
+      "MODEL"
+    ],
+    [
+      9,
+      6,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ],
+    [
+      10,
+      10,
+      0,
+      4,
+      0,
+      "MODEL"
+    ],
+    [
+      11,
+      2,
+      0,
+      10,
+      0,
+      "MODEL"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI-InstantID/INSTANTID WORKFLOWS/V2.0 InstantID_pose_ref + ArtGallery 【Zho】.json

@@ -0,0 +1,665 @@
+{
+  "last_node_id": 13,
+  "last_link_id": 13,
+  "nodes": [
+    {
+      "id": 3,
+      "type": "IDBaseModelLoader_fromhub",
+      "pos": [
+        350,
+        530
+      ],
+      "size": {
+        "0": 310,
+        "1": 60
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "controlnet",
+          "type": "MODEL",
+          "link": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "pipe",
+          "type": "MODEL",
+          "links": [
+            2
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IDBaseModelLoader_fromhub"
+      },
+      "widgets_values": [
+        "wangqixun/YamerMIX_v8"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "Ipadapter_instantidLoader",
+      "pos": [
+        350,
+        640
+      ],
+      "size": {
+        "0": 310,
+        "1": 82
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "pipe",
+          "type": "MODEL",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            8
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Ipadapter_instantidLoader"
+      },
+      "widgets_values": [
+        "/content/ComfyUI/models/checkpoints",
+        "ip-adapter.bin"
+      ]
+    },
+    {
+      "id": 1,
+      "type": "InsightFaceLoader_Zho",
+      "pos": [
+        350,
+        770
+      ],
+      "size": [
+        310,
+        60
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "INSIGHTFACEMODEL",
+          "type": "INSIGHTFACEMODEL",
+          "links": [
+            7
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "InsightFaceLoader_Zho"
+      },
+      "widgets_values": [
+        "CUDA"
+      ]
+    },
+    {
+      "id": 2,
+      "type": "IDControlNetLoader",
+      "pos": [
+        350,
+        420
+      ],
+      "size": {
+        "0": 310,
+        "1": 60
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "controlnet",
+          "type": "MODEL",
+          "links": [
+            1
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IDControlNetLoader"
+      },
+      "widgets_values": [
+        "/content/ComfyUI/models/controlnet"
+      ]
+    },
+    {
+      "id": 13,
+      "type": "ConcatText_Zho",
+      "pos": [
+        1340,
+        530
+      ],
+      "size": [
+        300,
+        96
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text_1",
+          "type": "STRING",
+          "link": 12,
+          "widget": {
+            "name": "text_1"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": [
+            13
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ConcatText_Zho"
+      },
+      "widgets_values": [
+        "",
+        "a woman, retro futurism, retro game"
+      ]
+    },
+    {
+      "id": 11,
+      "type": "MovementsImage_Zho",
+      "pos": [
+        1010,
+        420
+      ],
+      "size": [
+        310,
+        410
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "name",
+          "type": "STRING",
+          "links": [
+            11
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MovementsImage_Zho"
+      },
+      "widgets_values": [
+        "Abstract Expressionism.png",
+        1.2
+      ]
+    },
+    {
+      "id": 12,
+      "type": "ConcatText_Zho",
+      "pos": [
+        1340,
+        420
+      ],
+      "size": [
+        300,
+        66.00003337860107
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text_1",
+          "type": "STRING",
+          "link": 10,
+          "widget": {
+            "name": "text_1"
+          }
+        },
+        {
+          "name": "text_2",
+          "type": "STRING",
+          "link": 11,
+          "widget": {
+            "name": "text_2"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": [
+            12
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ConcatText_Zho"
+      },
+      "widgets_values": [
+        "",
+        ""
+      ]
+    },
+    {
+      "id": 7,
+      "type": "LoadImage",
+      "pos": [
+        350,
+        880
+      ],
+      "size": {
+        "0": 310,
+        "1": 430
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            5
+          ],
+          "shape": 3
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "download.jpg",
+        "image"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "ID_Prompt_Styler",
+      "pos": [
+        1340,
+        670
+      ],
+      "size": [
+        300,
+        160
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "prompt",
+          "type": "STRING",
+          "link": 13,
+          "widget": {
+            "name": "prompt"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive_prompt",
+          "type": "STRING",
+          "links": [
+            3
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "negative_prompt",
+          "type": "STRING",
+          "links": [
+            4
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ID_Prompt_Styler"
+      },
+      "widgets_values": [
+        "",
+        "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly",
+        "Neon"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "LoadImage",
+      "pos": [
+        350,
+        1360
+      ],
+      "size": [
+        310,
+        270
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            6
+          ],
+          "shape": 3
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "u=2585260240,4217738218&fm=253&fmt=auto&app=120&f=JPEG.webp",
+        "image"
+      ]
+    },
+    {
+      "id": 9,
+      "type": "PreviewImage",
+      "pos": [
+        680,
+        880
+      ],
+      "size": [
+        1310,
+        750
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 6,
+      "type": "IDGenerationNode",
+      "pos": [
+        1660,
+        420
+      ],
+      "size": [
+        330,
+        410
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "face_image",
+          "type": "IMAGE",
+          "link": 5,
+          "slot_index": 0
+        },
+        {
+          "name": "pipe",
+          "type": "MODEL",
+          "link": 8
+        },
+        {
+          "name": "insightface",
+          "type": "INSIGHTFACEMODEL",
+          "link": 7
+        },
+        {
+          "name": "pose_image_optional",
+          "type": "IMAGE",
+          "link": 6,
+          "slot_index": 3
+        },
+        {
+          "name": "positive",
+          "type": "STRING",
+          "link": 3,
+          "widget": {
+            "name": "positive"
+          }
+        },
+        {
+          "name": "negative",
+          "type": "STRING",
+          "link": 4,
+          "widget": {
+            "name": "negative"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            9
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "IDGenerationNode"
+      },
+      "widgets_values": [
+        "",
+        "",
+        0.8,
+        0.8,
+        50,
+        5,
+        true,
+        1041987604602403,
+        "fixed"
+      ]
+    },
+    {
+      "id": 10,
+      "type": "ArtistsImage_Zho",
+      "pos": [
+        680,
+        420
+      ],
+      "size": [
+        310,
+        410
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "name",
+          "type": "STRING",
+          "links": [
+            10
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ArtistsImage_Zho"
+      },
+      "widgets_values": [
+        "Atey Ghailan .png",
+        1.2
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      2,
+      3,
+      0,
+      4,
+      0,
+      "MODEL"
+    ],
+    [
+      3,
+      5,
+      0,
+      6,
+      4,
+      "STRING"
+    ],
+    [
+      4,
+      5,
+      1,
+      6,
+      5,
+      "STRING"
+    ],
+    [
+      5,
+      7,
+      0,
+      6,
+      0,
+      "IMAGE"
+    ],
+    [
+      6,
+      8,
+      0,
+      6,
+      3,
+      "IMAGE"
+    ],
+    [
+      7,
+      1,
+      0,
+      6,
+      2,
+      "INSIGHTFACEMODEL"
+    ],
+    [
+      8,
+      4,
+      0,
+      6,
+      1,
+      "MODEL"
+    ],
+    [
+      9,
+      6,
+      0,
+      9,
+      0,
+      "IMAGE"
+    ],
+    [
+      10,
+      10,
+      0,
+      12,
+      0,
+      "STRING"
+    ],
+    [
+      11,
+      11,
+      0,
+      12,
+      1,
+      "STRING"
+    ],
+    [
+      12,
+      12,
+      0,
+      13,
+      0,
+      "STRING"
+    ],
+    [
+      13,
+      13,
+      0,
+      5,
+      0,
+      "STRING"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

ComfyUI-InstantID/InstantIDNode.py

@@ -0,0 +1,352 @@
+import diffusers
+from diffusers.utils import load_image
+from diffusers.models import ControlNetModel
+from .style_template import styles
+
+import os
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+import folder_paths
+
+from huggingface_hub import hf_hub_download
+from insightface.app import FaceAnalysis
+from .pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps
+
+
+current_directory = os.path.dirname(os.path.abspath(__file__))
+device = "cuda" if torch.cuda.is_available() else "cpu"
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "Neon"
+
+
+def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
+        p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+        return p.replace("{prompt}", positive), n + ' ' + negative
+
+
+def resize_img(input_image, max_side=1280, min_side=1024, size=None, 
+               pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64):
+
+    image_np = (255. * input_image.cpu().numpy().squeeze()).clip(0, 255).astype(np.uint8)
+    input_image = Image.fromarray(image_np)
+
+    w, h = input_image.size
+    if size is not None:
+        w_resize_new, h_resize_new = size
+    else:
+        ratio = min_side / min(h, w)
+        w, h = round(ratio*w), round(ratio*h)
+        ratio = max_side / max(h, w)
+        input_image = input_image.resize([round(ratio*w), round(ratio*h)], mode)
+        w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
+        h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
+    input_image = input_image.resize([w_resize_new, h_resize_new], mode)
+
+    if pad_to_max_side:
+        res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
+        offset_x = (max_side - w_resize_new) // 2
+        offset_y = (max_side - h_resize_new) // 2
+        res[offset_y:offset_y+h_resize_new, offset_x:offset_x+w_resize_new] = np.array(input_image)
+        input_image = Image.fromarray(res)
+    return input_image
+
+
+class InsightFaceLoader_Node_Zho:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "provider": (["CUDA", "CPU"], ),
+            },
+        }
+
+    RETURN_TYPES = ("INSIGHTFACEMODEL",)
+    FUNCTION = "load_insight_face_antelopev2"
+    CATEGORY = "📷InstantID"
+
+    def load_insight_face_antelopev2(self, provider):
+            
+        model = FaceAnalysis(name="antelopev2", root=current_directory, providers=[provider + 'ExecutionProvider',])
+        model.prepare(ctx_id=0, det_size=(640, 640))
+
+        return (model,)
+
+
+class IDControlNetLoaderNode_Zho:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "controlnet_path": ("STRING", {"default": "enter your path"}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("controlnet",)
+    FUNCTION = "load_idcontrolnet"
+    CATEGORY = "📷InstantID"
+    
+    def load_idcontrolnet(self, controlnet_path):
+
+        controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+
+        return [controlnet]
+
+
+class IDBaseModelLoader_fromhub_Node_Zho:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "base_model_path": ("STRING", {"default": "wangqixun/YamerMIX_v8"}),
+                "controlnet": ("MODEL",)
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("pipe",)
+    FUNCTION = "load_model"
+    CATEGORY = "📷InstantID"
+  
+    def load_model(self, base_model_path, controlnet):
+        # Code to load the base model
+        pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+            base_model_path,
+            controlnet=controlnet,
+            torch_dtype=torch.float16,
+            local_dir="./checkpoints"
+        ).to(device)
+        return [pipe]
+
+
+class IDBaseModelLoader_local_Node_Zho:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "controlnet": ("MODEL",)
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("pipe",)
+    FUNCTION = "load_model"
+    CATEGORY = "📷InstantID"
+  
+    def load_model(self, ckpt_name, controlnet):
+        # Code to load the base model
+        if not ckpt_name:
+            raise ValueError("Please provide the ckpt_name parameter with the name of the checkpoint file.")
+
+        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+            
+        if not os.path.exists(ckpt_path):
+            raise FileNotFoundError(f"Checkpoint file {ckpt_path} not found.")
+                
+        pipe = StableDiffusionXLInstantIDPipeline.from_single_file(
+            pretrained_model_link_or_path=ckpt_path,
+            controlnet=controlnet,
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+            variant="fp16"
+        ).to(device)
+        return [pipe]
+
+
+class Ipadapter_instantidLoader_Node_Zho:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "Ipadapter_instantid_path": ("STRING", {"default": "enter your path"}),
+                "filename": ("STRING", {"default": "ip-adapter.bin"}),
+                "pipe": ("MODEL",),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_ip_adapter_instantid"
+    CATEGORY = "📷InstantID"
+
+    def load_ip_adapter_instantid(self, pipe, Ipadapter_instantid_path, filename):
+        # 使用hf_hub_download方法获取PhotoMaker文件的路径
+        face_adapter = os.path.join(Ipadapter_instantid_path, filename)
+
+        # load adapter
+        pipe.load_ip_adapter_instantid(face_adapter)
+
+        return [pipe]
+
+
+class ID_Prompt_Style_Zho:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {"default": "a woman, retro futurism, retro game", "multiline": True}),
+                "negative_prompt": ("STRING", {"default": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly", "multiline": True}),
+                "style_name": (STYLE_NAMES, {"default": DEFAULT_STYLE_NAME})
+            }
+        }
+
+    RETURN_TYPES = ('STRING','STRING',)
+    RETURN_NAMES = ('positive_prompt','negative_prompt',)
+    FUNCTION = "id_prompt_style"
+    CATEGORY = "📷InstantID"
+
+    def id_prompt_style(self, style_name, prompt, negative_prompt):
+        prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
+        
+        return prompt, negative_prompt
+
+
+class IDGenerationNode_Zho:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "face_image": ("IMAGE",),
+                "pipe": ("MODEL",),
+                "insightface": ("INSIGHTFACEMODEL",),
+                "positive": ("STRING", {"multiline": True, "forceInput": True}),
+                "negative": ("STRING", {"multiline": True, "forceInput": True}),
+                "ip_adapter_scale": ("FLOAT", {"default": 0.8, "min": 0, "max": 1.0, "display": "slider"}),
+                "controlnet_conditioning_scale": ("FLOAT", {"default": 0.8, "min": 0, "max": 1.0, "display": "slider"}),
+                "steps": ("INT", {"default": 50, "min": 1, "max": 100, "step": 1, "display": "slider"}),
+                "guidance_scale": ("FLOAT", {"default": 5, "min": 0, "max": 10, "display": "slider"}),
+                "enhance_face_region": ("BOOLEAN", {"default": True}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "pose_image_optional": ("IMAGE",), 
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "id_generate_image"
+    CATEGORY = "📷InstantID"
+                       
+    def id_generate_image(self, insightface, positive, negative, face_image, pipe, ip_adapter_scale, controlnet_conditioning_scale, steps, guidance_scale, seed, enhance_face_region, pose_image_optional=None):
+
+        face_image = resize_img(face_image)
+        
+        # prepare face emb
+        face_info = insightface.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
+        if not face_info:
+            return "No face detected"
+
+        face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[-1]
+        face_emb = face_info['embedding']
+        face_kps = draw_kps(face_image, face_info['kps'])
+        width, height = face_kps.size
+
+        if pose_image_optional is not None:
+            pose_image = resize_img(pose_image_optional)
+            face_info = insightface.get(cv2.cvtColor(np.array(pose_image), cv2.COLOR_RGB2BGR))
+            if len(face_info) == 0:
+                raise gr.Error(f"Cannot find any face in the reference image! Please upload another person image")
+        
+            face_info = face_info[-1]
+            face_kps = draw_kps(pose_image, face_info['kps'])
+        
+            width, height = face_kps.size
+
+        if enhance_face_region:
+            control_mask = np.zeros([height, width, 3])
+            x1, y1, x2, y2 = face_info['bbox']
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            control_mask[y1:y2, x1:x2] = 255
+            control_mask = Image.fromarray(control_mask.astype(np.uint8))
+        else:
+            control_mask = None
+            
+        generator = torch.Generator(device=device).manual_seed(seed)
+
+        pipe.set_ip_adapter_scale(ip_adapter_scale)
+
+        output = pipe(
+            prompt=positive,
+            negative_prompt=negative,
+            image_embeds=face_emb,
+            image=face_kps,
+            control_mask=control_mask,
+            controlnet_conditioning_scale=controlnet_conditioning_scale,
+            num_inference_steps=steps,
+            generator=generator,
+            guidance_scale=guidance_scale,
+            width=width,
+            height=height,
+            return_dict=False
+            )
+
+        # 检查输出类型并相应处理
+        if isinstance(output, tuple):
+            # 当返回的是元组时，第一个元素是图像列表
+            images_list = output[0]
+        else:
+            # 如果返回的是 StableDiffusionXLPipelineOutput，需要从中提取图像
+            images_list = output.images
+
+        # 转换图像为 torch.Tensor，并调整维度顺序为 NHWC
+        images_tensors = []
+        for img in images_list:
+            # 将 PIL.Image 转换为 numpy.ndarray
+            img_array = np.array(img)
+            # 转换 numpy.ndarray 为 torch.Tensor
+            img_tensor = torch.from_numpy(img_array).float() / 255.
+            # 转换图像格式为 CHW (如果需要)
+            if img_tensor.ndim == 3 and img_tensor.shape[-1] == 3:
+                img_tensor = img_tensor.permute(2, 0, 1)
+            # 添加批次维度并转换为 NHWC
+            img_tensor = img_tensor.unsqueeze(0).permute(0, 2, 3, 1)
+            images_tensors.append(img_tensor)
+
+        if len(images_tensors) > 1:
+            output_image = torch.cat(images_tensors, dim=0)
+        else:
+            output_image = images_tensors[0]
+
+        return (output_image,)
+
+
+
+NODE_CLASS_MAPPINGS = {
+    "InsightFaceLoader_Zho": InsightFaceLoader_Node_Zho,
+    "IDControlNetLoader": IDControlNetLoaderNode_Zho,
+    "IDBaseModelLoader_fromhub": IDBaseModelLoader_fromhub_Node_Zho,
+    "IDBaseModelLoader_local": IDBaseModelLoader_local_Node_Zho,
+    "Ipadapter_instantidLoader": Ipadapter_instantidLoader_Node_Zho,
+    "ID_Prompt_Styler": ID_Prompt_Style_Zho,
+    "IDGenerationNode": IDGenerationNode_Zho
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "InsightFaceLoader_Zho": "📷InsightFace Loader",
+    "IDControlNetLoader": "📷ID ControlNet Loader",
+    "IDBaseModelLoader_fromhub": "📷ID Base Model Loader from hub 🤗",
+    "IDBaseModelLoader_local": "📷ID Base Model Loader locally",
+    "Ipadapter_instantidLoader": "📷Ipadapter_instantid Loader",
+    "ID_Prompt_Styler": "📷ID Prompt_Styler",
+    "IDGenerationNode": "📷InstantID Generation"
+}

ComfyUI-InstantID/README.md

@@ -0,0 +1,213 @@
+
+![ISID_](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/01393483-3145-4691-9daa-7ce9035c9bd0)
+
+
+# ComfyUI InstantID
+
+Unofficial implementation of [InstantID](https://github.com/InstantID/InstantID) for ComfyUI
+
+![Dingtalk_20240123182131](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/7a99b32c-b4a2-4c46-acb0-f796fc46f9ee)
+
++ pose_ref
+
+![Dingtalk_20240124232946](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/caa60456-f2d8-4315-864b-659a9e7cea89)
+
+
+## 项目介绍 | Info
+
+- 来自对[InstantID](https://github.com/InstantID/InstantID)的非官方实现
+  
+- 版本：V2.0 支持姿势参考图
+
+<!---
+  同时支持本地、huggingface hub模型，支持通用styler（也与 PhotoMaker Styler 通用）
+--->
+
+## 视频演示
+
+V2.0
+
+
+https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/083c9e5e-06a0-4623-b5ac-05f7e85a74f2
+
+
+V1.0
+
+https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/7295c0d7-1d1b-4044-aea3-8efa67047362
+
+
+
+## 节点说明 | Features
+
+- 基础模型加载 | base model loader
+    - 📷ID Base Model Loader from hub 🤗：支持从 huggingface hub 自动下载模型，输入模型名称（如：wangqixun/YamerMIX_v8）即可
+    - 📷ID Base Model Loader locally：支持加载本地模型（需 SDXL 系列模型）
+
+- InsightFace 模型加载 | 📷InsightFace Loader
+    - ：支持 CUDA 和 CPU
+
+- ID ControlNet 模型加载 | 📷ID ControlNet Loader
+    - controlnet_path：ID ControlNet 模型地址
+
+- Ipadapter_instantid 模型加载 | 📷Ipadapter_instantid Loader
+    - Ipadapter_instantid_path：模型路径
+    - filename：模型名称
+
+ - 提示词 + 风格 | 📷ID Prompt_Styler
+    - 与各种提示词（文本）输入（如肖像大师等）、styler、 Photomaker Prompt_Styler 兼容
+    - prompt、negative：正负提示词
+    - style_name：支持官方提供的8种风格
+        - (No style)
+        - Watercolor
+        - Film Noir
+        - Neon
+        - Jungle
+        - Mars
+        - Vibrant Color
+        - Snow
+        - Line art
+
+- InstantID 生成 | 📷InstantID Generation 🆕
+    - face_image：接入脸部参考图像
+    - pipe：接入模型
+    - insightface：接入 insightface 模型 🆕
+    - pose_image_optional（非必要）：接入姿势参考图像（注意：仅对面部周围姿势起效，与通常的 openpose 不同）
+    - positivet、negative：正负提示词
+    - ip_adapter_scale：IPA 强度
+    - controlnet_conditioning_scale：ID Controlnet 强度
+    - step：步数，官方默认30步
+    - guidance_scale：提示词相关度，一般默认为5
+    - enhance_face_region：脸部增强选项 🆕
+    - seed：种子
+
+
+## 风格 | Styles
+
+![ISID_STYLE](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/142bda7a-798b-46b3-aa69-1b88701c8311)
+
+
+
+## 安装 | Install
+
+
+- 推荐使用管理器 ComfyUI Manager 安装（On the Way）
+
+
+- 手动安装：
+    1. `cd custom_nodes`
+    2. `git clone https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID.git`
+    3. `cd custom_nodes/ComfyUI-InstantID`
+    4. `pip install -r requirements.txt`
+    5. 重启 ComfyUI
+
+
+## 使用方法 | How to Use
+
+- 下载 [InstantID/ControlNetModel](https://huggingface.co/InstantX/InstantID/tree/main/ControlNetModel) 中的 config.json 和 diffusion_pytorch_model.safetensors ，将模型地址填入 📷ID ControlNet Loader 节点中（例如：ComfyUI/custom_nodes/ComfyUI-InstantID/checkpoints/controlnet）
+
+- 下载 [InstantID/ip-adapter](https://huggingface.co/InstantX/InstantID/tree/main) 中的 ip-adapter.bin ，将其地址填入 📷Ipadapter_instantid Loader 节点中（例如：ComfyUI/custom_nodes/ComfyUI-InstantID/checkpoints）
+
+- 下载 [DIAMONIK7777/antelopev2](https://huggingface.co/DIAMONIK7777/antelopev2/tree/main) 中的所有模型，将其放入 ComfyUI//custom_nodes/ComfyUI-InstantID/models/antelopev2 中
+
+- 兼容性： CUDA11 支持默认安装的 onnxruntime-gpu（1.16.0），如果是 CUDA12 则需手动安装 onnxruntime-gpu==1.17.0 [地址](https://dev.azure.com/onnxruntime/onnxruntime/_artifacts/feed/onnxruntime-cuda-12/PyPI/onnxruntime-gpu/overview/1.17.0)
+
+  
+## 工作流 | Workflows
+
+V2.0
+
+- [V2.0 InstantID_pose_ref + ArtGallery](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/blob/main/INSTANTID%20WORKFLOWS/V2.0%20InstantID_pose_ref%20%2B%20ArtGallery%20%E3%80%90Zho%E3%80%91.json)
+
+  ![Dingtalk_20240124232833](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/99be9592-775d-4c33-bafc-5bd5c95a7222)
+
+
+- [V2.0 自动下载 huggingface hub](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/blob/main/INSTANTID%20WORKFLOWS/V2.0%20InstantID_fromhub_pose_ref%E3%80%90Zho%E3%80%91.json)
+
+  ![Dingtalk_20240124230145](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/95c4a1dd-864d-4a46-8c45-a48866aef29f)
+
+
+- [V2.0 InstantID_locally_pose_ref](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/blob/main/INSTANTID%20WORKFLOWS/V2.0%20InstantID_locally_pose_ref%E3%80%90Zho%E3%80%91.json)
+
+  ![Dingtalk_20240124230609](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/d4c22389-f853-44bd-9ea2-568b2ac7ed06)
+
+
+V1.0 工作流仅适用于V1.0 版本
+
+- [V1.0  InstantID + ArtGallery](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/blob/main/INSTANTID%20WORKFLOWS/V1.0%20InstantID%20%2B%20ArtGallery%E3%80%90Zho%E3%80%91.json)
+
+
+  ![Dingtalk_20240123182440](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/c6ee25bf-a528-4d78-9b35-f5b0d0303601)
+
+
+- [V1.0 本地模型 locally](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/blob/main/INSTANTID%20WORKFLOWS/V1.0%20InstantID_locally%E3%80%90Zho%E3%80%91.json)
+
+  ![Dingtalk_20240123175624](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/459bfede-59e8-4d8d-941c-a950c4827c49)
+
+
+- [V1.0 自动下载 huggingface hub](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/blob/main/INSTANTID%20WORKFLOWS/V1.0%20InstantID_fromhub%E3%80%90Zho%E3%80%91.json)
+
+  ![Dingtalk_20240123174950](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/50133961-1752-4ec8-ac0b-068d998b8534)
+
+
+
+
+## 更新日志
+
+- 20240124
+
+  更新为 V2.0 ：新增姿势参考图、优化代码
+
+  修复 insightfaceloader 冲突问题
+
+  修复 onnxruntime-gpu 版本兼容性的问题
+
+- 20240123
+
+  V1.0 上线：同时支持本地、huggingface hub托管模型，支持8种风格
+
+- 20240122
+
+  创建项目
+
+
+## 速度实测 | Speed
+
+- V1.0 
+
+    - A100 50步 14s
+
+    ![image](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID/assets/140084057/dc535e67-3f56-4faf-be81-621b84bb6ee2)
+
+
+
+## Stars 
+
+[![Star History Chart](https://api.star-history.com/svg?repos=ZHO-ZHO-ZHO/ComfyUI-InstantID&type=Date)](https://star-history.com/#ZHO-ZHO-ZHO/ComfyUI-InstantID&Date)
+
+
+## 关于我 | About me
+
+📬 **联系我**：
+- 邮箱：zhozho3965@gmail.com
+- QQ 群：839821928
+
+🔗 **社交媒体**：
+- 个人页：[-Zho-](https://jike.city/zho)
+- Bilibili：[我的B站主页](https://space.bilibili.com/484366804)
+- X（Twitter）：[我的Twitter](https://twitter.com/ZHOZHO672070)
+- 小红书：[我的小红书主页](https://www.xiaohongshu.com/user/profile/63f11530000000001001e0c8?xhsshare=CopyLink&appuid=63f11530000000001001e0c8&apptime=1690528872)
+
+💡 **支持我**：
+- B站：[B站充电](https://space.bilibili.com/484366804)
+- 爱发电：[为我充电](https://afdian.net/a/ZHOZHO)
+
+
+## Credits
+
+[InstantID](https://github.com/InstantID/InstantID)
+
+📷InsightFace Loader 代码修改自 [ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus)，感谢 [@cubiq](https://github.com/cubiq)！
+
+感谢 [@hidecloud](https://twitter.com/hidecloud) 对 onnxruntime 版本兼容性的测试与反馈！
+
+感谢 [esheep](https://www.esheep.com/) 技术人员对节点冲突问题的反馈！

ComfyUI-InstantID/__init__.py

@@ -0,0 +1,3 @@
+from .InstantIDNode import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

ComfyUI-InstantID/checkpoints/put_models_here.txt

@@ -0,0 +1 @@
+

ComfyUI-InstantID/ip_adapter/attention_processor.py

@@ -0,0 +1,308 @@
+# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    import xformers
+    import xformers.ops
+    xformers_available = True
+except Exception as e:
+    xformers_available = False
+
+
+
+class RegionControler(object):
+    def __init__(self) -> None:
+        self.prompt_image_conditioning = []
+region_control = RegionControler()
+
+
+class AttnProcessor(nn.Module):
+    r"""
+    Default processor for performing attention-related computations.
+    """
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+    ):
+        super().__init__()
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+    
+    
+class IPAttnProcessor(nn.Module):
+    r"""
+    Attention processor for IP-Adapater.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = encoder_hidden_states[:, :end_pos, :], encoder_hidden_states[:, end_pos:, :]
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        if xformers_available:
+            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
+        else:
+            attention_probs = attn.get_attention_scores(query, key, attention_mask)
+            hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+        
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+        
+        ip_key = attn.head_to_batch_dim(ip_key)
+        ip_value = attn.head_to_batch_dim(ip_value)
+        
+        if xformers_available:
+            ip_hidden_states = self._memory_efficient_attention_xformers(query, ip_key, ip_value, None)
+        else:
+            ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
+            ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
+        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
+        
+        # region control
+        if len(region_control.prompt_image_conditioning) == 1:
+            region_mask = region_control.prompt_image_conditioning[0].get('region_mask', None)
+            if region_mask is not None:
+                h, w = region_mask.shape[:2]
+                ratio = (h * w / query.shape[1]) ** 0.5
+                mask = F.interpolate(region_mask[None, None], scale_factor=1/ratio, mode='nearest').reshape([1, -1, 1])
+            else:
+                mask = torch.ones_like(ip_hidden_states)
+            ip_hidden_states = ip_hidden_states * mask            
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+    def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):
+        # TODO attention_mask
+        query = query.contiguous()
+        key = key.contiguous()
+        value = value.contiguous()
+        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
+        # hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
+        return hidden_states
+
+
+class AttnProcessor2_0(torch.nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states

ComfyUI-InstantID/ip_adapter/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)

ComfyUI-InstantID/ip_adapter/utils.py

@@ -0,0 +1,5 @@
+import torch.nn.functional as F
+
+
+def is_torch2_available():
+    return hasattr(F, "scaled_dot_product_attention")

ComfyUI-InstantID/models/antelopev2/1k3d68.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42c8b6575bc827380f1b27e4c80b326962531da96186fd1597fa35693fdc9515
+size 137

ComfyUI-InstantID/models/antelopev2/2d106det.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f75c331a32849d158fda14b6f8dbe84cf31ccb5db41e031bab768680b96f873f
+size 135

ComfyUI-InstantID/models/antelopev2/genderage.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f53e9ee9d98434da0782d1b205576c3a370dbc2331cd70188f8426a3aef121b9
+size 135

ComfyUI-InstantID/models/antelopev2/glintr100.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af374ec7db9ea896ee633dee1e1721e9f7957e19c43ce3fc9869950f82290c1e
+size 137

ComfyUI-InstantID/models/antelopev2/scrfd_10g_bnkps.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e683c9e3c78b5b43aa3b5aa9566d06ab1befeae995ebc8259a7eb30f7b2421b
+size 136

ComfyUI-InstantID/pipeline_stable_diffusion_xl_instantid.py

@@ -0,0 +1,753 @@
+# Copyright 2024 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.
+
+
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import cv2
+import math
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+
+from diffusers.image_processor import PipelineImageInput
+
+from diffusers.models import ControlNetModel
+
+from diffusers.utils import (
+    deprecate,
+    logging,
+    replace_example_docstring,
+)
+from diffusers.utils.torch_utils import is_compiled_module, is_torch_version
+from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
+
+from diffusers import StableDiffusionXLControlNetPipeline
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.utils.import_utils import is_xformers_available
+
+from .ip_adapter.resampler import Resampler
+from .ip_adapter.utils import is_torch2_available
+
+from .ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> # !pip install opencv-python transformers accelerate insightface
+        >>> import diffusers
+        >>> from diffusers.utils import load_image
+        >>> from diffusers.models import ControlNetModel
+
+        >>> import cv2
+        >>> import torch
+        >>> import numpy as np
+        >>> from PIL import Image
+        
+        >>> from insightface.app import FaceAnalysis
+        >>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps
+
+        >>> # download 'antelopev2' under ./models
+        >>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        >>> app.prepare(ctx_id=0, det_size=(640, 640))
+        
+        >>> # download models under ./checkpoints
+        >>> face_adapter = f'./checkpoints/ip-adapter.bin'
+        >>> controlnet_path = f'./checkpoints/ControlNetModel'
+        
+        >>> # load IdentityNet
+        >>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+        
+        >>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
+        ... )
+        >>> pipe.cuda()
+        
+        >>> # load adapter
+        >>> pipe.load_ip_adapter_instantid(face_adapter)
+
+        >>> prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality"
+        >>> negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured"
+
+        >>> # load an image
+        >>> image = load_image("your-example.jpg")
+        
+        >>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]
+        >>> face_emb = face_info['embedding']
+        >>> face_kps = draw_kps(face_image, face_info['kps'])
+        
+        >>> pipe.set_ip_adapter_scale(0.8)
+
+        >>> # generate image
+        >>> image = pipe(
+        ...     prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8
+        ... ).images[0]
+        ```
+"""
+
+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 = PIL.Image.fromarray(out_img.astype(np.uint8))
+    return out_img_pil
+    
+class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
+    
+    def cuda(self, dtype=torch.float16, use_xformers=False):
+        self.to('cuda', dtype)
+        
+        if hasattr(self, 'image_proj_model'):
+            self.image_proj_model.to(self.unet.device).to(self.unet.dtype)
+        
+        if use_xformers:
+            if is_xformers_available():
+                import xformers
+                from packaging import version
+
+                xformers_version = version.parse(xformers.__version__)
+                if xformers_version == version.parse("0.0.16"):
+                    logger.warn(
+                        "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                    )
+                self.enable_xformers_memory_efficient_attention()
+            else:
+                raise ValueError("xformers is not available. Make sure it is installed correctly")
+    
+    def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5):     
+        self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens)
+        self.set_ip_adapter(model_ckpt, num_tokens, scale)
+        
+    def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16):
+        
+        image_proj_model = Resampler(
+            dim=1280,
+            depth=4,
+            dim_head=64,
+            heads=20,
+            num_queries=num_tokens,
+            embedding_dim=image_emb_dim,
+            output_dim=self.unet.config.cross_attention_dim,
+            ff_mult=4,
+        )
+
+        image_proj_model.eval()
+        
+        self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype)
+        state_dict = torch.load(model_ckpt, map_location="cpu")
+        if 'image_proj' in state_dict:
+            state_dict = state_dict["image_proj"]
+        self.image_proj_model.load_state_dict(state_dict)
+        
+        self.image_proj_model_in_features = image_emb_dim
+    
+    def set_ip_adapter(self, model_ckpt, num_tokens, scale):
+        
+        unet = self.unet
+        attn_procs = {}
+        for name in unet.attn_processors.keys():
+            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+            if name.startswith("mid_block"):
+                hidden_size = unet.config.block_out_channels[-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = unet.config.block_out_channels[block_id]
+            if cross_attention_dim is None:
+                attn_procs[name] = AttnProcessor().to(unet.device, dtype=unet.dtype)
+            else:
+                attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, 
+                                                   cross_attention_dim=cross_attention_dim, 
+                                                   scale=scale,
+                                                   num_tokens=num_tokens).to(unet.device, dtype=unet.dtype)
+        unet.set_attn_processor(attn_procs)
+        
+        state_dict = torch.load(model_ckpt, map_location="cpu")
+        ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())
+        if 'ip_adapter' in state_dict:
+            state_dict = state_dict['ip_adapter']
+        ip_layers.load_state_dict(state_dict)
+    
+    def set_ip_adapter_scale(self, scale):
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        for attn_processor in unet.attn_processors.values():
+            if isinstance(attn_processor, IPAttnProcessor):
+                attn_processor.scale = scale
+
+    def _encode_prompt_image_emb(self, prompt_image_emb, device, dtype, do_classifier_free_guidance):
+        
+        if isinstance(prompt_image_emb, torch.Tensor):
+            prompt_image_emb = prompt_image_emb.clone().detach()
+        else:
+            prompt_image_emb = torch.tensor(prompt_image_emb)
+            
+        prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype)
+        prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])
+        
+        if do_classifier_free_guidance:
+            prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)
+        else:
+            prompt_image_emb = torch.cat([prompt_image_emb], dim=0)
+        
+        prompt_image_emb = self.image_proj_model(prompt_image_emb)
+        return prompt_image_emb
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        image_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        guess_mode: bool = False,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
+        original_size: Tuple[int, int] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        **kwargs,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            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
+                used in both text-encoders.
+            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
+                    `List[List[torch.FloatTensor]]`, `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.FloatTensor`, 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.
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated image. Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated image. Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            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.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`
+                and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](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 is 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 (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, pooled text embeddings are generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
+                weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
+                argument.
+            image_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated image embeddings.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            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.
+            guess_mode (`bool`, *optional*, defaults to `False`):
+                The ControlNet encoder tries to recognize the content of the input image even if you remove all
+                prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
+            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.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            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 pipeine class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned containing the output images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+
+        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
+
+        # align format for control guidance
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            controlnet_conditioning_scale,
+            control_guidance_start,
+            control_guidance_end,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
+            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
+
+        global_pool_conditions = (
+            controlnet.config.global_pool_conditions
+            if isinstance(controlnet, ControlNetModel)
+            else controlnet.nets[0].config.global_pool_conditions
+        )
+        guess_mode = guess_mode or global_pool_conditions
+
+        # 3.1 Encode input prompt
+        text_encoder_lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt,
+            prompt_2,
+            device,
+            num_images_per_prompt,
+            self.do_classifier_free_guidance,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=self.clip_skip,
+        )
+        
+        # 3.2 Encode image prompt
+        prompt_image_emb = self._encode_prompt_image_emb(image_embeds, 
+                                                         device,
+                                                         self.unet.dtype,
+                                                         self.do_classifier_free_guidance)
+        
+        # 4. Prepare image
+        if isinstance(controlnet, ControlNetModel):
+            image = self.prepare_image(
+                image=image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=controlnet.dtype,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                guess_mode=guess_mode,
+            )
+            height, width = image.shape[-2:]
+        elif isinstance(controlnet, MultiControlNetModel):
+            images = []
+
+            for image_ in image:
+                image_ = self.prepare_image(
+                    image=image_,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_images_per_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    device=device,
+                    dtype=controlnet.dtype,
+                    do_classifier_free_guidance=self.do_classifier_free_guidance,
+                    guess_mode=guess_mode,
+                )
+
+                images.append(image_)
+
+            image = images
+            height, width = image[0].shape[-2:]
+        else:
+            assert False
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+        self._num_timesteps = len(timesteps)
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6.5 Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7.1 Create tensor stating which controlnets to keep
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end)
+            ]
+            controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
+
+        # 7.2 Prepare added time ids & embeddings
+        if isinstance(image, list):
+            original_size = original_size or image[0].shape[-2:]
+        else:
+            original_size = original_size or image.shape[-2:]
+        target_size = target_size or (height, width)
+
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+        encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        is_unet_compiled = is_compiled_module(self.unet)
+        is_controlnet_compiled = is_compiled_module(self.controlnet)
+        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
+                
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # Relevant thread:
+                # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
+                if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:
+                    torch._inductor.cudagraph_mark_step_begin()
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+                # controlnet(s) inference
+                if guess_mode and self.do_classifier_free_guidance:
+                    # Infer ControlNet only for the conditional batch.
+                    control_model_input = latents
+                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
+                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
+                    controlnet_added_cond_kwargs = {
+                        "text_embeds": add_text_embeds.chunk(2)[1],
+                        "time_ids": add_time_ids.chunk(2)[1],
+                    }
+                else:
+                    control_model_input = latent_model_input
+                    controlnet_prompt_embeds = prompt_embeds
+                    controlnet_added_cond_kwargs = added_cond_kwargs
+                
+                if isinstance(controlnet_keep[i], list):
+                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
+                else:
+                    controlnet_cond_scale = controlnet_conditioning_scale
+                    if isinstance(controlnet_cond_scale, list):
+                        controlnet_cond_scale = controlnet_cond_scale[0]
+                    cond_scale = controlnet_cond_scale * controlnet_keep[i]
+
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    control_model_input,
+                    t,
+                    encoder_hidden_states=prompt_image_emb,
+                    controlnet_cond=image,
+                    conditioning_scale=cond_scale,
+                    guess_mode=guess_mode,
+                    added_cond_kwargs=controlnet_added_cond_kwargs,
+                    return_dict=False,
+                )
+
+                if guess_mode and self.do_classifier_free_guidance:
+                    # Infered ControlNet only for the conditional batch.
+                    # To apply the output of ControlNet to both the unconditional and conditional batches,
+                    # add 0 to the unconditional batch to keep it unchanged.
+                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=encoder_hidden_states,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+        
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)            
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)

ComfyUI-InstantID/requirements.txt

@@ -0,0 +1,6 @@
+opencv-python 
+transformers 
+accelerate 
+insightface
+diffusers
+onnxruntime-gpu

ComfyUI-InstantID/style_template.py

@@ -0,0 +1,49 @@
+style_list = [
+    {
+        "name": "(No style)",
+        "prompt": "{prompt}",
+        "negative_prompt": "",
+    },
+    {
+        "name": "Watercolor",
+        "prompt": "watercolor painting, {prompt}. vibrant, beautiful, painterly, detailed, textural, artistic",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy",
+    },
+    {
+        "name": "Film Noir",
+        "prompt": "film noir style, ink sketch|vector, {prompt} highly detailed, sharp focus, ultra sharpness, monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Neon",
+        "prompt": "masterpiece painting, buildings in the backdrop, kaleidoscope, lilac orange blue cream fuchsia bright vivid gradient colors, the scene is cinematic, {prompt}, emotional realism, double exposure, watercolor ink pencil, graded wash, color layering, magic realism, figurative painting, intricate motifs, organic tracery, polished",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Jungle",
+        "prompt": 'waist-up "{prompt} in a Jungle" by Syd Mead, tangerine cold color palette, muted colors, detailed, 8k,photo r3al,dripping paint,3d toon style,3d style,Movie Still',
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Mars",
+        "prompt": "{prompt}, Post-apocalyptic. Mars Colony, Scavengers roam the wastelands searching for valuable resources, rovers, bright morning sunlight shining, (detailed) (intricate) (8k) (HDR) (cinematic lighting) (sharp focus)",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Vibrant Color",
+        "prompt": "vibrant colorful, ink sketch|vector|2d colors, at nightfall, sharp focus, {prompt}, highly detailed, sharp focus, the clouds,colorful,ultra sharpness",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Snow",
+        "prompt": "cinema 4d render, {prompt}, high contrast, vibrant and saturated, sico style, surrounded by magical glow,floating ice shards, snow crystals, cold, windy background, frozen natural landscape in background  cinematic atmosphere,highly detailed, sharp focus, intricate design, 3d, unreal engine, octane render, CG best quality, highres, photorealistic, dramatic lighting, artstation, concept art, cinematic, epic Steven Spielberg movie still, sharp focus, smoke, sparks, art by pascal blanche and greg rutkowski and repin, trending on artstation, hyperrealism painting, matte painting, 4k resolution",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Line art",
+        "prompt": "line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics",
+        "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic",
+    },
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}

ComfyUI-KJNodes/.github/FUNDING.yml

@@ -0,0 +1,2 @@
+github: [kijai]
+custom: ["https://www.paypal.me/kijaidesign"]

ComfyUI-KJNodes/.github/workflows/publish.yml

@@ -0,0 +1,21 @@
+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:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

ComfyUI-KJNodes/.gitignore

@@ -0,0 +1,11 @@
+__pycache__
+/venv
+*.code-workspace
+.history
+.vscode
+*.ckpt
+*.pth
+types
+models
+jsconfig.json
+custom_dimensions.json

ComfyUI-KJNodes/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
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+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
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+
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ComfyUI-KJNodes/README.md

@@ -0,0 +1,65 @@
+# KJNodes for ComfyUI
+
+Various quality of life and masking related -nodes and scripts made by combining functionality of existing nodes for ComfyUI.
+
+I know I'm bad at documentation, especially this project that has grown from random practice nodes to... too many lines in one file.
+I have however started to add descriptions to the nodes themselves, there's a small ? you can click for info what the node does.
+This is still work in progress, like everything else.
+
+# Installation
+1. Clone this repo into `custom_nodes` folder.
+2. Install dependencies: `pip install -r requirements.txt`
+   or if you use the portable install, run this in ComfyUI_windows_portable -folder:
+
+  `python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-KJNodes\requirements.txt`
+   
+
+## Javascript
+
+### browserstatus.js
+Sets the favicon to green circle when not processing anything, sets it to red when processing and shows progress percentage and the lenghth of your queue. 
+Default off, needs to be enabled from options, overrides Custom-Scripts favicon when enabled.
+
+## Nodes:
+
+### Set/Get
+
+Javascript nodes to set and get constants to reduce unnecessary lines. Takes in and returns anything, purely visual nodes.
+On the right click menu of these nodes there's now an options to visualize the paths, as well as option to jump to the corresponding node on the other end.
+
+**Known limitations**:
+  - Will not work with any node that dynamically sets it's outpute, such as reroute or other Set/Get node
+  - Will not work when directly connected to a bypassed node
+  - Other possible conflicts with javascript based nodes.
+
+### ColorToMask
+
+RBG color value to mask, works with batches and AnimateDiff.
+
+### ConditioningMultiCombine
+
+Combine any number of conditions, saves space.
+
+### ConditioningSetMaskAndCombine
+
+Mask and combine two sets of conditions, saves space.
+
+### GrowMaskWithBlur
+
+Grows or shrinks (with negative values) mask, option to invert input, returns mask and inverted mask. Additionally Blurs the mask, this is a slow operation especially with big batches.
+
+### RoundMask
+
+![image](https://github.com/kijai/ComfyUI-KJNodes/assets/40791699/52c85202-f74e-4b96-9dac-c8bda5ddcc40)
+
+### WidgetToString
+Outputs the value of a widget on any node as a string
+![example of use](docs/images/2024-04-03_20_49_29-ComfyUI.png)
+
+Enable node id display from Manager menu, to get the ID of the node you want to read a widget from:
+![enable node id display](docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png)
+
+Use the node id of the target node, and add the name of the widget to read from
+![use node id and widget name](docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png)
+
+Recreating or reloading the target node will change its id, and the WidgetToString node will no longer be able to find it until you update the node id value with the new id.

ComfyUI-KJNodes/__init__.py

@@ -0,0 +1,195 @@
+from .nodes.nodes import *
+from .nodes.curve_nodes import *
+from .nodes.batchcrop_nodes import *
+from .nodes.audioscheduler_nodes import *
+from .nodes.image_nodes import *
+from .nodes.intrinsic_lora_nodes import *
+from .nodes.mask_nodes import *
+NODE_CONFIG = {
+    #constants
+    "BOOLConstant": {"class": BOOLConstant, "name": "BOOL Constant"},
+    "INTConstant": {"class": INTConstant, "name": "INT Constant"},
+    "FloatConstant": {"class": FloatConstant, "name": "Float Constant"},
+    "StringConstant": {"class": StringConstant, "name": "String Constant"},
+    "StringConstantMultiline": {"class": StringConstantMultiline, "name": "String Constant Multiline"},
+    #conditioning
+    "ConditioningMultiCombine": {"class": ConditioningMultiCombine, "name": "Conditioning Multi Combine"},
+    "ConditioningSetMaskAndCombine": {"class": ConditioningSetMaskAndCombine, "name": "ConditioningSetMaskAndCombine"},
+    "ConditioningSetMaskAndCombine3": {"class": ConditioningSetMaskAndCombine3, "name": "ConditioningSetMaskAndCombine3"},
+    "ConditioningSetMaskAndCombine4": {"class": ConditioningSetMaskAndCombine4, "name": "ConditioningSetMaskAndCombine4"},
+    "ConditioningSetMaskAndCombine5": {"class": ConditioningSetMaskAndCombine5, "name": "ConditioningSetMaskAndCombine5"},
+    "CondPassThrough": {"class": CondPassThrough},
+    #masking
+    "DownloadAndLoadCLIPSeg": {"class": DownloadAndLoadCLIPSeg, "name": "(Down)load CLIPSeg"},
+    "BatchCLIPSeg": {"class": BatchCLIPSeg, "name": "Batch CLIPSeg"},
+    "ColorToMask": {"class": ColorToMask, "name": "Color To Mask"},
+    "CreateGradientMask": {"class": CreateGradientMask, "name": "Create Gradient Mask"},
+    "CreateTextMask": {"class": CreateTextMask, "name": "Create Text Mask"},
+    "CreateAudioMask": {"class": CreateAudioMask, "name": "Create Audio Mask"},
+    "CreateFadeMask": {"class": CreateFadeMask, "name": "Create Fade Mask"},
+    "CreateFadeMaskAdvanced": {"class": CreateFadeMaskAdvanced, "name": "Create Fade Mask Advanced"},
+    "CreateFluidMask": {"class": CreateFluidMask, "name": "Create Fluid Mask"},
+    "CreateShapeMask": {"class": CreateShapeMask, "name": "Create Shape Mask"},
+    "CreateVoronoiMask": {"class": CreateVoronoiMask, "name": "Create Voronoi Mask"},
+    "CreateMagicMask": {"class": CreateMagicMask, "name": "Create Magic Mask"},
+    "GetMaskSizeAndCount": {"class": GetMaskSizeAndCount, "name": "Get Mask Size & Count"},
+    "GrowMaskWithBlur": {"class": GrowMaskWithBlur, "name": "Grow Mask With Blur"},
+    "MaskBatchMulti": {"class": MaskBatchMulti, "name": "Mask Batch Multi"},
+    "OffsetMask": {"class": OffsetMask, "name": "Offset Mask"},
+    "RemapMaskRange": {"class": RemapMaskRange, "name": "Remap Mask Range"},
+    "ResizeMask": {"class": ResizeMask, "name": "Resize Mask"},
+    "RoundMask": {"class": RoundMask, "name": "Round Mask"},
+    #images
+    "AddLabel": {"class": AddLabel, "name": "Add Label"},
+    "ColorMatch": {"class": ColorMatch, "name": "Color Match"},
+    "CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"},
+    "CrossFadeImagesMulti": {"class": CrossFadeImagesMulti, "name": "Cross Fade Images Multi"},
+    "GetImagesFromBatchIndexed": {"class": GetImagesFromBatchIndexed, "name": "Get Images From Batch Indexed"},
+    "GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image or Mask Range From Batch"},
+    "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"},
+    "FastPreview": {"class": FastPreview, "name": "Fast Preview"},
+    "ImageAndMaskPreview": {"class": ImageAndMaskPreview},
+    "ImageAddMulti": {"class": ImageAddMulti, "name": "Image Add Multi"},
+    "ImageBatchMulti": {"class": ImageBatchMulti, "name": "Image Batch Multi"},
+    "ImageBatchRepeatInterleaving": {"class": ImageBatchRepeatInterleaving},
+    "ImageBatchTestPattern": {"class": ImageBatchTestPattern, "name": "Image Batch Test Pattern"},
+    "ImageConcanate": {"class": ImageConcanate, "name": "Image Concatenate"},
+    "ImageConcatFromBatch": {"class": ImageConcatFromBatch, "name": "Image Concatenate From Batch"},
+    "ImageConcatMulti": {"class": ImageConcatMulti, "name": "Image Concatenate Multi"},
+    "ImageCropByMaskAndResize": {"class": ImageCropByMaskAndResize, "name": "Image Crop By Mask And Resize"},
+    "ImageUncropByMask": {"class": ImageUncropByMask, "name": "Image Uncrop By Mask"},
+    "ImageGrabPIL": {"class": ImageGrabPIL, "name": "Image Grab PIL"},
+    "ImageGridComposite2x2": {"class": ImageGridComposite2x2, "name": "Image Grid Composite 2x2"},
+    "ImageGridComposite3x3": {"class": ImageGridComposite3x3, "name": "Image Grid Composite 3x3"},
+    "ImageGridtoBatch": {"class": ImageGridtoBatch, "name": "Image Grid To Batch"},
+    "ImageNormalize_Neg1_To_1": {"class": ImageNormalize_Neg1_To_1, "name": "Image Normalize -1 to 1"},
+    "ImagePass": {"class": ImagePass},
+    "ImagePadForOutpaintMasked": {"class": ImagePadForOutpaintMasked, "name": "Image Pad For Outpaint Masked"},
+    "ImagePadForOutpaintTargetSize": {"class": ImagePadForOutpaintTargetSize, "name": "Image Pad For Outpaint Target Size"},
+    "ImageResizeKJ": {"class": ImageResizeKJ, "name": "Resize Image"},
+    "ImageUpscaleWithModelBatched": {"class": ImageUpscaleWithModelBatched, "name": "Image Upscale With Model Batched"},
+    "InsertImagesToBatchIndexed": {"class": InsertImagesToBatchIndexed, "name": "Insert Images To Batch Indexed"},
+    "LoadAndResizeImage": {"class": LoadAndResizeImage, "name": "Load & Resize Image"},
+    "LoadImagesFromFolderKJ": {"class": LoadImagesFromFolderKJ, "name": "Load Images From Folder (KJ)"},
+    "MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"},
+    "PreviewAnimation": {"class": PreviewAnimation, "name": "Preview Animation"},
+    "RemapImageRange": {"class": RemapImageRange, "name": "Remap Image Range"},
+    "ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"},
+    "ReplaceImagesInBatch": {"class": ReplaceImagesInBatch, "name": "Replace Images In Batch"},
+    "SaveImageWithAlpha": {"class": SaveImageWithAlpha, "name": "Save Image With Alpha"},
+    "SaveImageKJ": {"class": SaveImageKJ, "name": "Save Image KJ"},
+    "ShuffleImageBatch": {"class": ShuffleImageBatch, "name": "Shuffle Image Batch"},
+    "SplitImageChannels": {"class": SplitImageChannels, "name": "Split Image Channels"},
+    "TransitionImagesMulti": {"class": TransitionImagesMulti, "name": "Transition Images Multi"},
+    "TransitionImagesInBatch": {"class": TransitionImagesInBatch, "name": "Transition Images In Batch"},
+    #batch cropping
+    "BatchCropFromMask": {"class": BatchCropFromMask, "name": "Batch Crop From Mask"},
+    "BatchCropFromMaskAdvanced": {"class": BatchCropFromMaskAdvanced, "name": "Batch Crop From Mask Advanced"},
+    "FilterZeroMasksAndCorrespondingImages": {"class": FilterZeroMasksAndCorrespondingImages},
+    "InsertImageBatchByIndexes": {"class": InsertImageBatchByIndexes, "name": "Insert Image Batch By Indexes"},
+    "BatchUncrop": {"class": BatchUncrop, "name": "Batch Uncrop"},
+    "BatchUncropAdvanced": {"class": BatchUncropAdvanced, "name": "Batch Uncrop Advanced"},
+    "SplitBboxes": {"class": SplitBboxes, "name": "Split Bboxes"},
+    "BboxToInt": {"class": BboxToInt, "name": "Bbox To Int"},
+    "BboxVisualize": {"class": BboxVisualize, "name": "Bbox Visualize"},
+    #noise
+    "GenerateNoise": {"class": GenerateNoise, "name": "Generate Noise"},
+    "FlipSigmasAdjusted": {"class": FlipSigmasAdjusted, "name": "Flip Sigmas Adjusted"},
+    "InjectNoiseToLatent": {"class": InjectNoiseToLatent, "name": "Inject Noise To Latent"},
+    "CustomSigmas": {"class": CustomSigmas, "name": "Custom Sigmas"},
+    #utility
+    "WidgetToString": {"class": WidgetToString, "name": "Widget To String"},
+    "DummyOut": {"class": DummyOut, "name": "Dummy Out"},
+    "GetLatentsFromBatchIndexed": {"class": GetLatentsFromBatchIndexed, "name": "Get Latents From Batch Indexed"},
+    "ScaleBatchPromptSchedule": {"class": ScaleBatchPromptSchedule, "name": "Scale Batch Prompt Schedule"},
+    "CameraPoseVisualizer": {"class": CameraPoseVisualizer, "name": "Camera Pose Visualizer"},
+    "AppendStringsToList": {"class": AppendStringsToList, "name": "Append Strings To List"},
+    "JoinStrings": {"class": JoinStrings, "name": "Join Strings"},
+    "JoinStringMulti": {"class": JoinStringMulti, "name": "Join String Multi"},
+    "SomethingToString": {"class": SomethingToString, "name": "Something To String"},
+    "Sleep": {"class": Sleep, "name": "Sleep"},
+    "VRAM_Debug": {"class": VRAM_Debug, "name": "VRAM Debug"},
+    "SomethingToString": {"class": SomethingToString, "name": "Something To String"},
+    "EmptyLatentImagePresets": {"class": EmptyLatentImagePresets, "name": "Empty Latent Image Presets"},
+    "EmptyLatentImageCustomPresets": {"class": EmptyLatentImageCustomPresets, "name": "Empty Latent Image Custom Presets"},
+    "ModelPassThrough": {"class": ModelPassThrough, "name": "ModelPass"},
+    "ModelSaveKJ": {"class": ModelSaveKJ, "name": "Model Save KJ"},
+    "SetShakkerLabsUnionControlNetType": {"class": SetShakkerLabsUnionControlNetType, "name": "Set Shakker Labs Union ControlNet Type"},
+    #audioscheduler stuff
+    "NormalizedAmplitudeToMask": {"class": NormalizedAmplitudeToMask},
+    "NormalizedAmplitudeToFloatList": {"class": NormalizedAmplitudeToFloatList},
+    "OffsetMaskByNormalizedAmplitude": {"class": OffsetMaskByNormalizedAmplitude},
+    "ImageTransformByNormalizedAmplitude": {"class": ImageTransformByNormalizedAmplitude},
+    #curve nodes
+    "SplineEditor": {"class": SplineEditor, "name": "Spline Editor"},
+    "CreateShapeImageOnPath": {"class": CreateShapeImageOnPath, "name": "Create Shape Image On Path"},
+    "CreateShapeMaskOnPath": {"class": CreateShapeMaskOnPath, "name": "Create Shape Mask On Path"},
+    "CreateTextOnPath": {"class": CreateTextOnPath, "name": "Create Text On Path"},
+    "CreateGradientFromCoords": {"class": CreateGradientFromCoords, "name": "Create Gradient From Coords"},
+    "GradientToFloat": {"class": GradientToFloat, "name": "Gradient To Float"},
+    "WeightScheduleExtend": {"class": WeightScheduleExtend, "name": "Weight Schedule Extend"},
+    "MaskOrImageToWeight": {"class": MaskOrImageToWeight, "name": "Mask Or Image To Weight"},
+    "WeightScheduleConvert": {"class": WeightScheduleConvert, "name": "Weight Schedule Convert"},
+    "FloatToMask": {"class": FloatToMask, "name": "Float To Mask"},
+    "FloatToSigmas": {"class": FloatToSigmas, "name": "Float To Sigmas"},
+    "SigmasToFloat": {"class": SigmasToFloat, "name": "Sigmas To Float"},
+    "PlotCoordinates": {"class": PlotCoordinates, "name": "Plot Coordinates"},
+    "InterpolateCoords": {"class": InterpolateCoords, "name": "Interpolate Coords"},
+    "PointsEditor": {"class": PointsEditor, "name": "Points Editor"},
+    #experimental
+    "StabilityAPI_SD3": {"class": StabilityAPI_SD3, "name": "Stability API SD3"},
+    "SoundReactive": {"class": SoundReactive, "name": "Sound Reactive"},
+    "StableZero123_BatchSchedule": {"class": StableZero123_BatchSchedule, "name": "Stable Zero123 Batch Schedule"},
+    "SV3D_BatchSchedule": {"class": SV3D_BatchSchedule, "name": "SV3D Batch Schedule"},
+    "LoadResAdapterNormalization": {"class": LoadResAdapterNormalization},
+    "Superprompt": {"class": Superprompt, "name": "Superprompt"},
+    "GLIGENTextBoxApplyBatchCoords": {"class": GLIGENTextBoxApplyBatchCoords},
+    "Intrinsic_lora_sampling": {"class": Intrinsic_lora_sampling, "name": "Intrinsic Lora Sampling"},
+    "CheckpointPerturbWeights": {"class": CheckpointPerturbWeights, "name": "CheckpointPerturbWeights"},
+    "Screencap_mss": {"class": Screencap_mss, "name": "Screencap mss"},
+    "WebcamCaptureCV2": {"class": WebcamCaptureCV2, "name": "Webcam Capture CV2"},
+    "DifferentialDiffusionAdvanced": {"class": DifferentialDiffusionAdvanced, "name": "Differential Diffusion Advanced"},
+    "FluxBlockLoraLoader": {"class": FluxBlockLoraLoader, "name": "Flux Block Lora Loader"},
+    "FluxBlockLoraSelect": {"class": FluxBlockLoraSelect, "name": "Flux Block Lora Select"},
+    "CustomControlNetWeightsFluxFromList": {"class": CustomControlNetWeightsFluxFromList, "name": "Custom ControlNet Weights Flux From List"},
+    "CheckpointLoaderKJ": {"class": CheckpointLoaderKJ, "name": "CheckpointLoaderKJ"},
+    "TorchCompileModelFluxAdvanced": {"class": TorchCompileModelFluxAdvanced, "name": "TorchCompileModelFluxAdvanced"},
+    "TorchCompileVAE": {"class": TorchCompileVAE, "name": "TorchCompileVAE"},
+    "TorchCompileControlNet": {"class": TorchCompileControlNet, "name": "TorchCompileControlNet"},
+    "PatchModelPatcherOrder": {"class": PatchModelPatcherOrder, "name": "Patch Model Patcher Order"},
+
+    #instance diffusion
+    "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},
+    "AppendInstanceDiffusionTracking": {"class": AppendInstanceDiffusionTracking},
+    "DrawInstanceDiffusionTracking": {"class": DrawInstanceDiffusionTracking},
+}
+
+def generate_node_mappings(node_config):
+    node_class_mappings = {}
+    node_display_name_mappings = {}
+
+    for node_name, node_info in node_config.items():
+        node_class_mappings[node_name] = node_info["class"]
+        node_display_name_mappings[node_name] = node_info.get("name", node_info["class"].__name__)
+
+    return node_class_mappings, node_display_name_mappings
+
+NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS = generate_node_mappings(NODE_CONFIG)
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
+
+WEB_DIRECTORY = "./web"
+
+from aiohttp import web
+from server import PromptServer
+from pathlib import Path
+
+if hasattr(PromptServer, "instance"):
+    try:
+        # NOTE: we add an extra static path to avoid comfy mechanism
+        # that loads every script in web.
+        PromptServer.instance.app.add_routes(
+            [web.static("/kjweb_async", (Path(__file__).parent.absolute() / "kjweb_async").as_posix())]
+        )
+    except:
+        pass

ComfyUI-KJNodes/config.json

@@ -0,0 +1,3 @@
+{
+    "sai_api_key": "your_api_key_here"
+}

ComfyUI-KJNodes/custom_dimensions.json

@@ -0,0 +1,22 @@
+[
+    {
+        "label": "SD",
+        "value": "512x512"
+    },
+    {
+        "label": "HD",
+        "value": "768x768"
+    },
+    {
+        "label": "Full HD",
+        "value": "1024x1024"
+    },
+    {
+        "label": "4k",
+        "value": "2048x2048"
+    },
+    {
+        "label": "SVD",
+        "value": "1024x576"
+    }
+]

ComfyUI-KJNodes/intrinsic_loras/intrinsic_lora_sd15_albedo.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d897f04ff2bb452e29a8f2a3c5c3cd5c55e95f314242cd645fbbe24a5ac59961
+size 6416109

ComfyUI-KJNodes/intrinsic_loras/intrinsic_lora_sd15_depth.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f199d6bf3180fe7271073c3769dcb764b40f35f41b30fcb183ae5bf4b6a9997f
+size 6416109

ComfyUI-KJNodes/intrinsic_loras/intrinsic_lora_sd15_normal.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:02934db0a0b92a9cdda402e42548560beda7d31b268e561dbc6815551e876268
+size 6416109

ComfyUI-KJNodes/intrinsic_loras/intrinsic_lora_sd15_shading.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:635e998063a10211633edd3e4b1676201822cd67f790ec71dba5f32d8b625c8b
+size 6416109

ComfyUI-KJNodes/intrinsic_loras/intrinsic_loras.txt

@@ -0,0 +1,4 @@
+source for the loras:
+https://github.com/duxiaodan/intrinsic-lora
+
+Renamed and conveted to .safetensors

ComfyUI-KJNodes/kjweb_async/marked.min.js

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+ * Copyright (c) 2011-2024, Christopher Jeffrey. (MIT Licensed)
+ * https://github.com/markedjs/marked
+ */
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a="*"===s[0][0]?this.rules.inline.emStrongRDelimAst:this.rules.inline.emStrongRDelimUnd;for(a.lastIndex=0,t=t.slice(-1*e.length+n);null!=(s=a.exec(t));){if(r=s[1]||s[2]||s[3]||s[4]||s[5]||s[6],!r)continue;if(i=[...r].length,s[3]||s[4]){l+=i;continue}if((s[5]||s[6])&&n%3&&!((n+i)%3)){o+=i;continue}if(l-=i,l>0)continue;i=Math.min(i,i+l+o);const t=[...s[0]][0].length,a=e.slice(0,n+s.index+t+i);if(Math.min(n,i)%2){const e=a.slice(1,-1);return{type:"em",raw:a,text:e,tokens:this.lexer.inlineTokens(e)}}const c=a.slice(2,-2);return{type:"strong",raw:a,text:c,tokens:this.lexer.inlineTokens(c)}}}}codespan(e){const t=this.rules.inline.code.exec(e);if(t){let e=t[2].replace(/\n/g," ");const n=/[^ ]/.test(e),s=/^ /.test(e)&&/ $/.test(e);return n&&s&&(e=e.substring(1,e.length-1)),e=c(e,!0),{type:"codespan",raw:t[0],text:e}}}br(e){const t=this.rules.inline.br.exec(e);if(t)return{type:"br",raw:t[0]}}del(e){const t=this.rules.inline.del.exec(e);if(t)return{type:"del",raw:t[0],text:t[2],tokens:this.lexer.inlineTokens(t[2])}}autolink(e){const t=this.rules.inline.autolink.exec(e);if(t){let e,n;return"@"===t[2]?(e=c(t[1]),n="mailto:"+e):(e=c(t[1]),n=e),{type:"link",raw:t[0],text:e,href:n,tokens:[{type:"text",raw:e,text:e}]}}}url(e){let t;if(t=this.rules.inline.url.exec(e)){let e,n;if("@"===t[2])e=c(t[0]),n="mailto:"+e;else{let s;do{s=t[0],t[0]=this.rules.inline._backpedal.exec(t[0])?.[0]??""}while(s!==t[0]);e=c(t[0]),n="www."===t[1]?"http://"+t[0]:t[0]}return{type:"link",raw:t[0],text:e,href:n,tokens:[{type:"text",raw:e,text:e}]}}}inlineText(e){const t=this.rules.inline.text.exec(e);if(t){let e;return e=this.lexer.state.inRawBlock?t[0]:c(t[0]),{type:"text",raw:t[0],text:e}}}}const m=/^ {0,3}((?:-[\t ]*){3,}|(?:_[ \t]*){3,}|(?:\*[ \t]*){3,})(?:\n+|$)/,y=/(?:[*+-]|\d{1,9}[.)])/,$=k(/^(?!bull |blockCode|fences|blockquote|heading|html)((?:.|\n(?!\s*?\n|bull 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\t][^\n]+?)?(?:\n|$)/).replace(/bull/g,y).getRegex(),A="address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|search|section|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul",S=/<!--(?:-?>|[\s\S]*?(?:-->|$))/,I=k("^ {0,3}(?:<(script|pre|style|textarea)[\\s>][\\s\\S]*?(?:</\\1>[^\\n]*\\n+|$)|comment[^\\n]*(\\n+|$)|<\\?[\\s\\S]*?(?:\\?>\\n*|$)|<![A-Z][\\s\\S]*?(?:>\\n*|$)|<!\\[CDATA\\[[\\s\\S]*?(?:\\]\\]>\\n*|$)|</?(tag)(?: +|\\n|/?>)[\\s\\S]*?(?:(?:\\n *)+\\n|$)|<(?!script|pre|style|textarea)([a-z][\\w-]*)(?:attribute)*? */?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|</(?!script|pre|style|textarea)[a-z][\\w-]*\\s*>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))","i").replace("comment",S).replace("tag",A).replace("attribute",/ +[a-zA-Z:_][\w.:-]*(?: 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*(?:\\n{2,}|\\s*$))").replace("comment",S).replace(/tag/g,"(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b").getRegex(),def:/^ *\[([^\]]+)\]: *<?([^\s>]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/,heading:/^(#{1,6})(.*)(?:\n+|$)/,fences:f,lheading:/^(.+?)\n {0,3}(=+|-+) *(?:\n+|$)/,paragraph:k(z).replace("hr",m).replace("heading"," *#{1,6} *[^\n]").replace("lheading",$).replace("|table","").replace("blockquote"," {0,3}>").replace("|fences","").replace("|list","").replace("|html","").replace("|tag","").getRegex()},Q=/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,v=/^( {2,}|\\)\n(?!\s*$)/,B="\\p{P}\\p{S}",C=k(/^((?![*_])[\spunctuation])/,"u").replace(/punctuation/g,B).getRegex(),M=k(/^(?:\*+(?:((?!\*)[punct])|[^\s*]))|^_+(?:((?!_)[punct])|([^\s_]))/,"u").replace(/punct/g,B).getRegex(),O=k("^[^_*]*?__[^_*]*?\\*[^_*]*?(?=__)|[^*]+(?=[^*])|(?!\\*)[punct](\\*+)(?=[\\s]|$)|[^punct\\s](\\*+)(?!\\*)(?=[punct\\s]|$)|(?!\\*)[punct\\s](\\*+)(?=[^punct\\s])|[\\s](\\*+)(?!\\*)(?=[punct])|(?!\\*)[punct](\\*+)(?!\\*)(?=[punct])|[^punct\\s](\\*+)(?=[^punct\\s])","gu").replace(/punct/g,B).getRegex(),D=k("^[^_*]*?\\*\\*[^_*]*?_[^_*]*?(?=\\*\\*)|[^_]+(?=[^_])|(?!_)[punct](_+)(?=[\\s]|$)|[^punct\\s](_+)(?!_)(?=[punct\\s]|$)|(?!_)[punct\\s](_+)(?=[^punct\\s])|[\\s](_+)(?!_)(?=[punct])|(?!_)[punct](_+)(?!_)(?=[punct])","gu").replace(/punct/g,B).getRegex(),j=k(/\\([punct])/,"gu").replace(/punct/g,B).getRegex(),H=k(/^<(scheme:[^\s\x00-\x1f<>]*|email)>/).replace("scheme",/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/).replace("email",/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/).getRegex(),U=k(S).replace("(?:--\x3e|$)","--\x3e").getRegex(),X=k("^comment|^</[a-zA-Z][\\w:-]*\\s*>|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^<![a-zA-Z]+\\s[\\s\\S]*?>|^<!\\[CDATA\\[[\\s\\S]*?\\]\\]>").replace("comment",U).replace("attribute",/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/).getRegex(),F=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/,N=k(/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/).replace("label",F).replace("href",/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/).replace("title",/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/).getRegex(),G=k(/^!?\[(label)\]\[(ref)\]/).replace("label",F).replace("ref",T).getRegex(),J=k(/^!?\[(ref)\](?:\[\])?/).replace("ref",T).getRegex(),K={_backpedal:f,anyPunctuation:j,autolink:H,blockSkip:/\[[^[\]]*?\]\([^\(\)]*?\)|`[^`]*?`|<[^<>]*?>/g,br:v,code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,del:f,emStrongLDelim:M,emStrongRDelimAst:O,emStrongRDelimUnd:D,escape:Q,link:N,nolink:J,punctuation:C,reflink:G,reflinkSearch:k("reflink|nolink(?!\\()","g").replace("reflink",G).replace("nolink",J).getRegex(),tag:X,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\<!\[`*_]|\b_|$)|[^ ](?= {2,}\n)))/,url:f},V={...K,link:k(/^!?\[(label)\]\((.*?)\)/).replace("label",F).getRegex(),reflink:k(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",F).getRegex()},W={...K,escape:k(Q).replace("])","~|])").getRegex(),url:k(/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,"i").replace("email",/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/).getRegex(),_backpedal:/(?:[^?!.,:;*_'"~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_'"~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\<!\[`*~_]|\b_|https?:\/\/|ftp:\/\/|www\.|$)|[^ ](?= {2,}\n)|[^a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-](?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)))/},Y={...W,br:k(v).replace("{2,}","*").getRegex(),text:k(W.text).replace("\\b_","\\b_| {2,}\\n").replace(/\{2,\}/g,"*").getRegex()},ee={normal:q,gfm:L,pedantic:P},te={normal:K,gfm:W,breaks:Y,pedantic:V};class ne{tokens;options;state;tokenizer;inlineQueue;constructor(t){this.tokens=[],this.tokens.links=Object.create(null),this.options=t||e.defaults,this.options.tokenizer=this.options.tokenizer||new w,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const n={block:ee.normal,inline:te.normal};this.options.pedantic?(n.block=ee.pedantic,n.inline=te.pedantic):this.options.gfm&&(n.block=ee.gfm,this.options.breaks?n.inline=te.breaks:n.inline=te.gfm),this.tokenizer.rules=n}static get rules(){return{block:ee,inline:te}}static lex(e,t){return new ne(t).lex(e)}static lexInline(e,t){return new ne(t).inlineTokens(e)}lex(e){e=e.replace(/\r\n|\r/g,"\n"),this.blockTokens(e,this.tokens);for(let e=0;e<this.inlineQueue.length;e++){const t=this.inlineQueue[e];this.inlineTokens(t.src,t.tokens)}return this.inlineQueue=[],this.tokens}blockTokens(e,t=[]){let n,s,r,i;for(e=this.options.pedantic?e.replace(/\t/g,"    ").replace(/^ +$/gm,""):e.replace(/^( *)(\t+)/gm,((e,t,n)=>t+"    ".repeat(n.length)));e;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some((s=>!!(n=s.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.space(e))e=e.substring(n.raw.length),1===n.raw.length&&t.length>0?t[t.length-1].raw+="\n":t.push(n);else if(n=this.tokenizer.code(e))e=e.substring(n.raw.length),s=t[t.length-1],!s||"paragraph"!==s.type&&"text"!==s.type?t.push(n):(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue[this.inlineQueue.length-1].src=s.text);else if(n=this.tokenizer.fences(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.heading(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.hr(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.blockquote(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.list(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.html(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.def(e))e=e.substring(n.raw.length),s=t[t.length-1],!s||"paragraph"!==s.type&&"text"!==s.type?this.tokens.links[n.tag]||(this.tokens.links[n.tag]={href:n.href,title:n.title}):(s.raw+="\n"+n.raw,s.text+="\n"+n.raw,this.inlineQueue[this.inlineQueue.length-1].src=s.text);else if(n=this.tokenizer.table(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.lheading(e))e=e.substring(n.raw.length),t.push(n);else{if(r=e,this.options.extensions&&this.options.extensions.startBlock){let t=1/0;const n=e.slice(1);let s;this.options.extensions.startBlock.forEach((e=>{s=e.call({lexer:this},n),"number"==typeof s&&s>=0&&(t=Math.min(t,s))})),t<1/0&&t>=0&&(r=e.substring(0,t+1))}if(this.state.top&&(n=this.tokenizer.paragraph(r)))s=t[t.length-1],i&&"paragraph"===s.type?(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=s.text):t.push(n),i=r.length!==e.length,e=e.substring(n.raw.length);else if(n=this.tokenizer.text(e))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===s.type?(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=s.text):t.push(n);else if(e){const t="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(t);break}throw new Error(t)}}return this.state.top=!0,t}inline(e,t=[]){return this.inlineQueue.push({src:e,tokens:t}),t}inlineTokens(e,t=[]){let n,s,r,i,l,o,a=e;if(this.tokens.links){const e=Object.keys(this.tokens.links);if(e.length>0)for(;null!=(i=this.tokenizer.rules.inline.reflinkSearch.exec(a));)e.includes(i[0].slice(i[0].lastIndexOf("[")+1,-1))&&(a=a.slice(0,i.index)+"["+"a".repeat(i[0].length-2)+"]"+a.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;null!=(i=this.tokenizer.rules.inline.blockSkip.exec(a));)a=a.slice(0,i.index)+"["+"a".repeat(i[0].length-2)+"]"+a.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;null!=(i=this.tokenizer.rules.inline.anyPunctuation.exec(a));)a=a.slice(0,i.index)+"++"+a.slice(this.tokenizer.rules.inline.anyPunctuation.lastIndex);for(;e;)if(l||(o=""),l=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some((s=>!!(n=s.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.escape(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.tag(e))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===n.type&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(n=this.tokenizer.link(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.reflink(e,this.tokens.links))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===n.type&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(n=this.tokenizer.emStrong(e,a,o))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.codespan(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.br(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.del(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.autolink(e))e=e.substring(n.raw.length),t.push(n);else if(this.state.inLink||!(n=this.tokenizer.url(e))){if(r=e,this.options.extensions&&this.options.extensions.startInline){let t=1/0;const n=e.slice(1);let s;this.options.extensions.startInline.forEach((e=>{s=e.call({lexer:this},n),"number"==typeof s&&s>=0&&(t=Math.min(t,s))})),t<1/0&&t>=0&&(r=e.substring(0,t+1))}if(n=this.tokenizer.inlineText(r))e=e.substring(n.raw.length),"_"!==n.raw.slice(-1)&&(o=n.raw.slice(-1)),l=!0,s=t[t.length-1],s&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(e){const t="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(t);break}throw new Error(t)}}else e=e.substring(n.raw.length),t.push(n);return t}}class se{options;constructor(t){this.options=t||e.defaults}code(e,t,n){const s=(t||"").match(/^\S*/)?.[0];return e=e.replace(/\n$/,"")+"\n",s?'<pre><code class="language-'+c(s)+'">'+(n?e:c(e,!0))+"</code></pre>\n":"<pre><code>"+(n?e:c(e,!0))+"</code></pre>\n"}blockquote(e){return`<blockquote>\n${e}</blockquote>\n`}html(e,t){return e}heading(e,t,n){return`<h${t}>${e}</h${t}>\n`}hr(){return"<hr>\n"}list(e,t,n){const s=t?"ol":"ul";return"<"+s+(t&&1!==n?' start="'+n+'"':"")+">\n"+e+"</"+s+">\n"}listitem(e,t,n){return`<li>${e}</li>\n`}checkbox(e){return"<input "+(e?'checked="" ':"")+'disabled="" type="checkbox">'}paragraph(e){return`<p>${e}</p>\n`}table(e,t){return t&&(t=`<tbody>${t}</tbody>`),"<table>\n<thead>\n"+e+"</thead>\n"+t+"</table>\n"}tablerow(e){return`<tr>\n${e}</tr>\n`}tablecell(e,t){const n=t.header?"th":"td";return(t.align?`<${n} align="${t.align}">`:`<${n}>`)+e+`</${n}>\n`}strong(e){return`<strong>${e}</strong>`}em(e){return`<em>${e}</em>`}codespan(e){return`<code>${e}</code>`}br(){return"<br>"}del(e){return`<del>${e}</del>`}link(e,t,n){const s=g(e);if(null===s)return n;let r='<a href="'+(e=s)+'"';return t&&(r+=' title="'+t+'"'),r+=">"+n+"</a>",r}image(e,t,n){const s=g(e);if(null===s)return n;let r=`<img src="${e=s}" alt="${n}"`;return t&&(r+=` title="${t}"`),r+=">",r}text(e){return e}}class re{strong(e){return e}em(e){return e}codespan(e){return e}del(e){return e}html(e){return e}text(e){return e}link(e,t,n){return""+n}image(e,t,n){return""+n}br(){return""}}class ie{options;renderer;textRenderer;constructor(t){this.options=t||e.defaults,this.options.renderer=this.options.renderer||new se,this.renderer=this.options.renderer,this.renderer.options=this.options,this.textRenderer=new re}static parse(e,t){return new ie(t).parse(e)}static parseInline(e,t){return new ie(t).parseInline(e)}parse(e,t=!0){let n="";for(let s=0;s<e.length;s++){const r=e[s];if(this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[r.type]){const e=r,t=this.options.extensions.renderers[e.type].call({parser:this},e);if(!1!==t||!["space","hr","heading","code","table","blockquote","list","html","paragraph","text"].includes(e.type)){n+=t||"";continue}}switch(r.type){case"space":continue;case"hr":n+=this.renderer.hr();continue;case"heading":{const e=r;n+=this.renderer.heading(this.parseInline(e.tokens),e.depth,p(this.parseInline(e.tokens,this.textRenderer)));continue}case"code":{const e=r;n+=this.renderer.code(e.text,e.lang,!!e.escaped);continue}case"table":{const e=r;let t="",s="";for(let t=0;t<e.header.length;t++)s+=this.renderer.tablecell(this.parseInline(e.header[t].tokens),{header:!0,align:e.align[t]});t+=this.renderer.tablerow(s);let i="";for(let t=0;t<e.rows.length;t++){const n=e.rows[t];s="";for(let t=0;t<n.length;t++)s+=this.renderer.tablecell(this.parseInline(n[t].tokens),{header:!1,align:e.align[t]});i+=this.renderer.tablerow(s)}n+=this.renderer.table(t,i);continue}case"blockquote":{const e=r,t=this.parse(e.tokens);n+=this.renderer.blockquote(t);continue}case"list":{const e=r,t=e.ordered,s=e.start,i=e.loose;let l="";for(let t=0;t<e.items.length;t++){const n=e.items[t],s=n.checked,r=n.task;let o="";if(n.task){const e=this.renderer.checkbox(!!s);i?n.tokens.length>0&&"paragraph"===n.tokens[0].type?(n.tokens[0].text=e+" "+n.tokens[0].text,n.tokens[0].tokens&&n.tokens[0].tokens.length>0&&"text"===n.tokens[0].tokens[0].type&&(n.tokens[0].tokens[0].text=e+" "+n.tokens[0].tokens[0].text)):n.tokens.unshift({type:"text",text:e+" "}):o+=e+" "}o+=this.parse(n.tokens,i),l+=this.renderer.listitem(o,r,!!s)}n+=this.renderer.list(l,t,s);continue}case"html":{const e=r;n+=this.renderer.html(e.text,e.block);continue}case"paragraph":{const e=r;n+=this.renderer.paragraph(this.parseInline(e.tokens));continue}case"text":{let i=r,l=i.tokens?this.parseInline(i.tokens):i.text;for(;s+1<e.length&&"text"===e[s+1].type;)i=e[++s],l+="\n"+(i.tokens?this.parseInline(i.tokens):i.text);n+=t?this.renderer.paragraph(l):l;continue}default:{const e='Token with "'+r.type+'" type was not found.';if(this.options.silent)return console.error(e),"";throw new Error(e)}}}return n}parseInline(e,t){t=t||this.renderer;let n="";for(let s=0;s<e.length;s++){const r=e[s];if(this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[r.type]){const e=this.options.extensions.renderers[r.type].call({parser:this},r);if(!1!==e||!["escape","html","link","image","strong","em","codespan","br","del","text"].includes(r.type)){n+=e||"";continue}}switch(r.type){case"escape":{const e=r;n+=t.text(e.text);break}case"html":{const e=r;n+=t.html(e.text);break}case"link":{const e=r;n+=t.link(e.href,e.title,this.parseInline(e.tokens,t));break}case"image":{const e=r;n+=t.image(e.href,e.title,e.text);break}case"strong":{const e=r;n+=t.strong(this.parseInline(e.tokens,t));break}case"em":{const e=r;n+=t.em(this.parseInline(e.tokens,t));break}case"codespan":{const e=r;n+=t.codespan(e.text);break}case"br":n+=t.br();break;case"del":{const e=r;n+=t.del(this.parseInline(e.tokens,t));break}case"text":{const e=r;n+=t.text(e.text);break}default:{const e='Token with "'+r.type+'" type was not found.';if(this.options.silent)return console.error(e),"";throw new Error(e)}}}return n}}class le{options;constructor(t){this.options=t||e.defaults}static passThroughHooks=new Set(["preprocess","postprocess","processAllTokens"]);preprocess(e){return e}postprocess(e){return e}processAllTokens(e){return e}}class oe{defaults={async:!1,breaks:!1,extensions:null,gfm:!0,hooks:null,pedantic:!1,renderer:null,silent:!1,tokenizer:null,walkTokens:null};options=this.setOptions;parse=this.#e(ne.lex,ie.parse);parseInline=this.#e(ne.lexInline,ie.parseInline);Parser=ie;Renderer=se;TextRenderer=re;Lexer=ne;Tokenizer=w;Hooks=le;constructor(...e){this.use(...e)}walkTokens(e,t){let n=[];for(const s of e)switch(n=n.concat(t.call(this,s)),s.type){case"table":{const e=s;for(const s of e.header)n=n.concat(this.walkTokens(s.tokens,t));for(const s of e.rows)for(const e of s)n=n.concat(this.walkTokens(e.tokens,t));break}case"list":{const e=s;n=n.concat(this.walkTokens(e.items,t));break}default:{const e=s;this.defaults.extensions?.childTokens?.[e.type]?this.defaults.extensions.childTokens[e.type].forEach((s=>{const r=e[s].flat(1/0);n=n.concat(this.walkTokens(r,t))})):e.tokens&&(n=n.concat(this.walkTokens(e.tokens,t)))}}return n}use(...e){const t=this.defaults.extensions||{renderers:{},childTokens:{}};return e.forEach((e=>{const n={...e};if(n.async=this.defaults.async||n.async||!1,e.extensions&&(e.extensions.forEach((e=>{if(!e.name)throw new Error("extension name required");if("renderer"in e){const n=t.renderers[e.name];t.renderers[e.name]=n?function(...t){let s=e.renderer.apply(this,t);return!1===s&&(s=n.apply(this,t)),s}:e.renderer}if("tokenizer"in e){if(!e.level||"block"!==e.level&&"inline"!==e.level)throw new Error("extension level must be 'block' or 'inline'");const n=t[e.level];n?n.unshift(e.tokenizer):t[e.level]=[e.tokenizer],e.start&&("block"===e.level?t.startBlock?t.startBlock.push(e.start):t.startBlock=[e.start]:"inline"===e.level&&(t.startInline?t.startInline.push(e.start):t.startInline=[e.start]))}"childTokens"in e&&e.childTokens&&(t.childTokens[e.name]=e.childTokens)})),n.extensions=t),e.renderer){const t=this.defaults.renderer||new se(this.defaults);for(const n in e.renderer){if(!(n in t))throw new Error(`renderer '${n}' does not exist`);if("options"===n)continue;const s=n,r=e.renderer[s],i=t[s];t[s]=(...e)=>{let n=r.apply(t,e);return!1===n&&(n=i.apply(t,e)),n||""}}n.renderer=t}if(e.tokenizer){const t=this.defaults.tokenizer||new w(this.defaults);for(const n in e.tokenizer){if(!(n in t))throw new Error(`tokenizer '${n}' does not exist`);if(["options","rules","lexer"].includes(n))continue;const s=n,r=e.tokenizer[s],i=t[s];t[s]=(...e)=>{let n=r.apply(t,e);return!1===n&&(n=i.apply(t,e)),n}}n.tokenizer=t}if(e.hooks){const t=this.defaults.hooks||new le;for(const n in e.hooks){if(!(n in t))throw new Error(`hook '${n}' does not exist`);if("options"===n)continue;const s=n,r=e.hooks[s],i=t[s];le.passThroughHooks.has(n)?t[s]=e=>{if(this.defaults.async)return Promise.resolve(r.call(t,e)).then((e=>i.call(t,e)));const n=r.call(t,e);return i.call(t,n)}:t[s]=(...e)=>{let n=r.apply(t,e);return!1===n&&(n=i.apply(t,e)),n}}n.hooks=t}if(e.walkTokens){const t=this.defaults.walkTokens,s=e.walkTokens;n.walkTokens=function(e){let n=[];return n.push(s.call(this,e)),t&&(n=n.concat(t.call(this,e))),n}}this.defaults={...this.defaults,...n}})),this}setOptions(e){return this.defaults={...this.defaults,...e},this}lexer(e,t){return ne.lex(e,t??this.defaults)}parser(e,t){return ie.parse(e,t??this.defaults)}#e(e,t){return(n,s)=>{const r={...s},i={...this.defaults,...r};!0===this.defaults.async&&!1===r.async&&(i.silent||console.warn("marked(): The async option was set to true by an extension. The async: false option sent to parse will be ignored."),i.async=!0);const l=this.#t(!!i.silent,!!i.async);if(null==n)return l(new Error("marked(): input parameter is undefined or null"));if("string"!=typeof n)return l(new Error("marked(): input parameter is of type "+Object.prototype.toString.call(n)+", string expected"));if(i.hooks&&(i.hooks.options=i),i.async)return Promise.resolve(i.hooks?i.hooks.preprocess(n):n).then((t=>e(t,i))).then((e=>i.hooks?i.hooks.processAllTokens(e):e)).then((e=>i.walkTokens?Promise.all(this.walkTokens(e,i.walkTokens)).then((()=>e)):e)).then((e=>t(e,i))).then((e=>i.hooks?i.hooks.postprocess(e):e)).catch(l);try{i.hooks&&(n=i.hooks.preprocess(n));let s=e(n,i);i.hooks&&(s=i.hooks.processAllTokens(s)),i.walkTokens&&this.walkTokens(s,i.walkTokens);let r=t(s,i);return i.hooks&&(r=i.hooks.postprocess(r)),r}catch(e){return l(e)}}}#t(e,t){return n=>{if(n.message+="\nPlease report this to https://github.com/markedjs/marked.",e){const e="<p>An error occurred:</p><pre>"+c(n.message+"",!0)+"</pre>";return t?Promise.resolve(e):e}if(t)return Promise.reject(n);throw n}}}const ae=new oe;function ce(e,t){return ae.parse(e,t)}ce.options=ce.setOptions=function(e){return ae.setOptions(e),ce.defaults=ae.defaults,n(ce.defaults),ce},ce.getDefaults=t,ce.defaults=e.defaults,ce.use=function(...e){return ae.use(...e),ce.defaults=ae.defaults,n(ce.defaults),ce},ce.walkTokens=function(e,t){return ae.walkTokens(e,t)},ce.parseInline=ae.parseInline,ce.Parser=ie,ce.parser=ie.parse,ce.Renderer=se,ce.TextRenderer=re,ce.Lexer=ne,ce.lexer=ne.lex,ce.Tokenizer=w,ce.Hooks=le,ce.parse=ce;const he=ce.options,pe=ce.setOptions,ue=ce.use,ke=ce.walkTokens,ge=ce.parseInline,fe=ce,de=ie.parse,xe=ne.lex;e.Hooks=le,e.Lexer=ne,e.Marked=oe,e.Parser=ie,e.Renderer=se,e.TextRenderer=re,e.Tokenizer=w,e.getDefaults=t,e.lexer=xe,e.marked=ce,e.options=he,e.parse=fe,e.parseInline=ge,e.parser=de,e.setOptions=pe,e.use=ue,e.walkTokens=ke}));