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README.md

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+## Swap Cloth
+
+This project demonstrates a Cloth Swap feature using ComfyUI, enabling users to change clothing on images seamlessly. This guide provides step-by-step instructions to set up, use, and contribute to the project.
+
+<div align="center">
+  <img width="500" alt="foduucom/stockmarket-pattern-detection-yolov8" src="https://huggingface.co/foduucom/stockmarket-pattern-detection-yolov8/resolve/main/thumbnail.jpg">
+</div>
+
+## Features
+
+- Swap clothing on images with precision.
+- Powered by ComfyUI's flexible architecture.
+- Upload JSON workflows for customization.
+- Simple, efficient, and open-source.
+- Setup Instructions
+
+
+## 1. Clone the Cloth Swap Repository
+Clone the repository containing the Cloth Swap JSON workflows and assets:
+
+```bash
+Copy code
+git clone <your-repo-url>
+cd <your-repo-folder>
+```
+
+## 2. Clone ComfyUI Repository
+
+Install ComfyUI by cloning its main repository:
+
+```bash
+Copy code
+git clone https://github.com/comfyanonymous/ComfyUI.git
+cd ComfyUI
+```
+Install dependencies:
+
+```bash
+Copy code
+pip install -r requirements.txt
+```
+
+Install ComfyUI Manager:
+
+goto ComfyUI/custom_nodes dir in terminal(cmd) and clone this repo:
+
+```bash
+Copy code
+git clone https://github.com/ltdrdata/ComfyUI-Manager.git
+```
+Restart ComfyUI
+
+To Start ComfyUI:
+
+```bash
+Copy code
+python3 main.py
+```
+Note: ComfyUI requires Python 3.9 or above. Ensure all required dependencies are installed.
+
+Now Go to Manager ->-> Custom Nodes Manager and install this two nodes "ComfyUI Layer Style" and "ComfyUI_CatVTON_Wrapper", restart and reload the page.
+
+<div align="center">
+  <img width="500" alt="foduucom/stockmarket-pattern-detection-yolov8" src="https://huggingface.co/foduucom/stockmarket-pattern-detection-yolov8/resolve/main/thumbnail.jpg">
+</div>
+
+Make sure you have "sam_vit_h_4b8939.pth" model inside ComfyUI/models/sams directory and "groundingdino_swint_ogc.pth" model in ComfyUI/models/grounding-dino directory if not download it.
+(If directory name not there in ComfyUI/models/ create new)
+
+- For Reference you can download model by below link:
+https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth
+https://huggingface.co/spaces/abhishek/StableSAM/resolve/main/sam_vit_h_4b8939.pth
+
+
+## 3. How to use
+
+- Start ComfyUI (by running python3 main.py)
+- Open ComfyUI in your browser (default: http://127.0.0.1:8188)
+- Click on Load button in menu bar and select the workflow.json file provided in this repository
+- Now click on Queue Prompt for generate output
+
+or you can use by python script provided in this repository:
+```bash 
+python3 main.py
+
+#Remember change the input paths in script here :
+#prompt["2"]["inputs"]["image"] = "\\ put your input person pose image"
+#prompt["3"]["inputs"]["image"] = "\\ put your input cloth image"
+```
+
+## 4. Using Cloth Swap
+
+-Prepare your input images (ensure proper resolution for better results).
+-Select the uploaded workflow in ComfyUI.
+-Provide necessary inputs as per the workflow:
+-Source Image: The base image where the clothing is to be swapped.
+-Cloth Image: The image of the clothing to be applied.
+-Start the process to generate swapped outputs.
+-Save the generated images for further use.
+
+
+## 5. Compute Infrastructure
+
+## Hardware
+
+NVIDIA GeForce RTX 3080 card
+
+## Model Card Contact
+
+For inquiries and contributions, please contact us at info@foduu.com.
+
+```bibtex
+@ModelCard{
+  author    = {Nehul Agrawal and
+               Priyal Mehta},
+  title     = {Cloth Swap},
+  year      = {2024}
+}
+```

main.py

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+#This is an example that uses the websockets api to know when a prompt execution is done
+#Once the prompt execution is done it downloads the images using the /history endpoint
+
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import json
+import urllib.request
+import urllib.parse
+
+server_address = "127.0.0.1:8188"
+client_id = str(uuid.uuid4())
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode('utf-8')
+    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
+    return json.loads(urllib.request.urlopen(req).read())
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
+        return response.read()
+
+def get_history(prompt_id):
+    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
+        return json.loads(response.read())
+
+def get_images(ws, prompt):
+    prompt_id = queue_prompt(prompt)['prompt_id']
+    output_images = {}
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                if data['node'] is None and data['prompt_id'] == prompt_id:
+                    break #Execution is done
+        else:
+            continue #previews are binary data
+
+    history = get_history(prompt_id)[prompt_id]
+    for node_id in history['outputs']:
+        node_output = history['outputs'][node_id]
+        images_output = []
+        if 'images' in node_output:
+            for image in node_output['images']:
+                image_data = get_image(image['filename'], image['subfolder'], image['type'])
+                images_output.append(image_data)
+        output_images[node_id] = images_output
+
+    return output_images
+
+prompt_text = """
+{
+  "1": {
+    "inputs": {
+      "sam_model": "sam_vit_h (2.56GB)",
+      "grounding_dino_model": "GroundingDINO_SwinT_OGC (694MB)",
+      "threshold": 0.3,
+      "detail_method": "VITMatte",
+      "detail_erode": 6,
+      "detail_dilate": 6,
+      "black_point": 0.01,
+      "white_point": 0.99,
+      "process_detail": false,
+      "prompt": "shirt",
+      "device": "cuda",
+      "max_megapixels": 2,
+      "cache_model": true,
+      "image": [
+        "2",
+        0
+      ]
+    },
+    "class_type": "LayerMask: SegmentAnythingUltra V2",
+    "_meta": {
+      "title": "LayerMask: SegmentAnythingUltra V2"
+    }
+  },
+  "2": {
+    "inputs": {
+      "image": "q.jpg",
+      "upload": "image"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "Load Image"
+    }
+  },
+  "3": {
+    "inputs": {
+      "image": "tshirt.jpeg",
+      "upload": "image"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "Load Image"
+    }
+  },
+  "5": {
+    "inputs": {
+      "mask_grow": 25,
+      "mixed_precision": "fp16",
+      "seed": 95593377186337,
+      "steps": 40,
+      "cfg": 2.5,
+      "image": [
+        "2",
+        0
+      ],
+      "mask": [
+        "1",
+        1
+      ],
+      "refer_image": [
+        "3",
+        0
+      ]
+    },
+    "class_type": "CatVTONWrapper",
+    "_meta": {
+      "title": "CatVTON Wrapper"
+    }
+  },
+  "6": {
+    "inputs": {
+      "images": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "PreviewImage",
+    "_meta": {
+      "title": "Preview Image"
+    }
+  }
+}"""
+
+prompt = json.loads(prompt_text)
+
+prompt["2"]["inputs"]["image"] = "\\ put your input person pose image"
+prompt["3"]["inputs"]["image"] = "\\ put your input cloth image"
+
+ws = websocket.WebSocket()
+ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+images = get_images(ws, prompt)
+
+# Commented out code to display the output images:
+
+for node_id in images:
+    for image_data in images[node_id]:
+        from PIL import Image
+        import io
+        image = Image.open(io.BytesIO(image_data))
+        image.save("output.jpg")
+        # image.show()
+
+

workflow.json

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+{
+  "last_node_id": 6,
+  "last_link_id": 6,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "LayerMask: SegmentAnythingUltra V2",
+      "pos": {
+        "0": 400,
+        "1": 184
+      },
+      "size": [
+        320.8495699999994,
+        366
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": null
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "links": [
+            2,
+            6
+          ],
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerMask: SegmentAnythingUltra V2"
+      },
+      "widgets_values": [
+        "sam_vit_h (2.56GB)",
+        "GroundingDINO_SwinT_OGC (694MB)",
+        0.3,
+        "VITMatte",
+        6,
+        6,
+        0.01,
+        0.99,
+        false,
+        "shirt",
+        "cuda",
+        2,
+        true
+      ],
+      "color": "rgba(27, 80, 119, 0.7)"
+    },
+    {
+      "id": 4,
+      "type": "LayerMask: MaskPreview",
+      "pos": {
+        "0": 749,
+        "1": 293
+      },
+      "size": [
+        241.8495699999994,
+        249.08422000000064
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 2
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "LayerMask: MaskPreview"
+      },
+      "widgets_values": [],
+      "color": "rgba(27, 80, 119, 0.7)"
+    },
+    {
+      "id": 5,
+      "type": "CatVTONWrapper",
+      "pos": {
+        "0": 742,
+        "1": 22
+      },
+      "size": {
+        "0": 315,
+        "1": 218
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 4
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 6
+        },
+        {
+          "name": "refer_image",
+          "type": "IMAGE",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            5
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CatVTONWrapper"
+      },
+      "widgets_values": [
+        25,
+        "fp16",
+        595025660139604,
+        "randomize",
+        40,
+        2.5
+      ]
+    },
+    {
+      "id": 6,
+      "type": "PreviewImage",
+      "pos": {
+        "0": 1079,
+        "1": 33
+      },
+      "size": [
+        333.8495699999994,
+        431.08422000000064
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 5
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 2,
+      "type": "LoadImage",
+      "pos": {
+        "0": 12,
+        "1": -81
+      },
+      "size": [
+        357.84956999999935,
+        383.08422000000064
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            1,
+            4
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "q.jpg",
+        "image"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "LoadImage",
+      "pos": {
+        "0": 58,
+        "1": 297
+      },
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            3
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "tshirt.jpeg",
+        "image"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      1,
+      0,
+      "IMAGE"
+    ],
+    [
+      2,
+      1,
+      1,
+      4,
+      0,
+      "MASK"
+    ],
+    [
+      3,
+      3,
+      0,
+      5,
+      2,
+      "IMAGE"
+    ],
+    [
+      4,
+      2,
+      0,
+      5,
+      0,
+      "IMAGE"
+    ],
+    [
+      5,
+      5,
+      0,
+      6,
+      0,
+      "IMAGE"
+    ],
+    [
+      6,
+      1,
+      1,
+      5,
+      1,
+      "MASK"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.9090909090909091,
+      "offset": [
+        -31.689569999999357,
+        95.6457799999993
+      ]
+    }
+  },
+  "version": 0.4
+}