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app.py

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+import websocket 
+import uuid
+import io
+import gradio as gr
+import numpy as np
+from PIL import Image
+import random
+import json
+import requests
+import urllib.parse
+
+client_id = str(uuid.uuid4())
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode('utf-8')
+    req =  requests.post("http://{}/prompt".format(server_address), data=data)
+    return req.json()
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with requests.get("http://{}/view?{}".format(server_address, url_values)) as response:
+        return response.content
+
+def get_history(prompt_id):
+    with requests.get("http://{}/history/{}".format(server_address, prompt_id)) as response:
+        return response.json()
+
+def get_images(prompt_id):
+    history = get_history(prompt_id)[prompt_id]
+    output_images = {}
+    for o in history['outputs']:
+        for node_id in history['outputs']:
+            node_output = history['outputs'][node_id]
+            if 'images' in node_output:
+                images_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 = json.load(open('workflow_api.json'))
+prompt["3"]["inputs"]["seed"] = random.randint(1, 1125899906842600)
+
+ws = websocket.WebSocket()
+ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+images = get_images(ws, prompt)
+for node_id in images:
+     for image_data in images[node_id]:
+         im = Image.open(io.BytesIO(image_data))
+         im.show()"""
+
+def image_mod(server_address,image_path,pr=gr.Progress()):
+    def queue_prompt(prompt):
+        p = {"prompt": prompt, "client_id": client_id}
+        data = json.dumps(p).encode('utf-8')
+        req =  requests.post("http://{}/prompt".format(server_address), data=data)
+        return req.json()
+    def get_image(filename, subfolder, folder_type):
+        data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+        url_values = urllib.parse.urlencode(data)
+        with requests.get("http://{}/view?{}".format(server_address, url_values)) as response:
+            return response.content
+    def get_history(prompt_id):
+        with requests.get("http://{}/history/{}".format(server_address, prompt_id)) as response:
+            return response.json()
+    def get_images(prompt_id):
+        history = get_history(prompt_id)[prompt_id]
+        output_images = {}
+        for o in history['outputs']:
+            for node_id in history['outputs']:
+                node_output = history['outputs'][node_id]
+                if 'images' in node_output:
+                    images_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
+    server_address = server_address
+    files = {"image":open(image_path, 'rb')}
+    data ={
+            "overwrite":None,
+            "subfolder":"",
+            "type":None
+          }
+    response = requests.post("http://{}/upload/image".format(server_address), files=files, data=data)
+    if response.status_code == 200:
+        response_json = response.json()
+        print("Image uploaded successfully!")
+    else:
+        print("Image upload failed:", response.text)
+        return Image.open(image_path)
+    
+    prompt = json.load(open('workflow_api.json'))
+    prompt["3"]["inputs"]["seed"] = random.randint(1, 1125899906842600)
+    prompt["12"]["inputs"]["image"] = response.json()["name"]
+    ws = websocket.WebSocket()
+    ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+    prompt_id = queue_prompt(prompt)['prompt_id']
+    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
+            if message['type'] == 'progress':
+                data = message['data']
+                pr((data['value'],data['max']))
+        else:
+            continue
+    images = get_images(prompt_id)
+    result = []
+    """for node,image_data in images.items():
+        im = Image.open(io.BytesIO(image_data))
+        result.append(im)"""
+    for node_id in images:
+     for image_data in images[node_id]:
+        im = Image.open(io.BytesIO(image_data))
+        result.append(im)
+    return result  
+            
+iface = gr.Interface(
+    fn=image_mod,
+    inputs=[gr.Textbox(label='Server Address'),gr.Image(type='filepath')],
+    outputs=gr.Gallery(),
+    title="Image Processor",
+)
+
+iface.queue().launch(share=True)

workflow_api.json

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+{
+  "3": {
+    "inputs": {
+      "seed": 2674941754,
+      "steps": 20,
+      "cfg": 7,
+      "sampler_name": "dpmpp_2m",
+      "scheduler": "karras",
+      "denoise": 1,
+      "model": [
+        "23",
+        0
+      ],
+      "positive": [
+        "11",
+        0
+      ],
+      "negative": [
+        "11",
+        1
+      ],
+      "latent_image": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "KSampler",
+    "_meta": {
+      "title": "KSampler"
+    }
+  },
+  "4": {
+    "inputs": {
+      "ckpt_name": "sd_xl_base_1.0.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "Load Checkpoint"
+    }
+  },
+  "5": {
+    "inputs": {
+      "width": 1024,
+      "height": 1024,
+      "batch_size": 1
+    },
+    "class_type": "EmptyLatentImage",
+    "_meta": {
+      "title": "Empty Latent Image"
+    }
+  },
+  "6": {
+    "inputs": {
+      "text": "digital painting style",
+      "clip": [
+        "28",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "7": {
+    "inputs": {
+      "text": "",
+      "clip": [
+        "28",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "8": {
+    "inputs": {
+      "samples": [
+        "3",
+        0
+      ],
+      "vae": [
+        "4",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "10": {
+    "inputs": {
+      "control_net_name": "controlnet-sd-xl-1.0-softedge-dexined.safetensors"
+    },
+    "class_type": "ControlNetLoader",
+    "_meta": {
+      "title": "Load ControlNet Model"
+    }
+  },
+  "11": {
+    "inputs": {
+      "strength": 1,
+      "start_percent": 0,
+      "end_percent": 1,
+      "positive": [
+        "6",
+        0
+      ],
+      "negative": [
+        "7",
+        0
+      ],
+      "control_net": [
+        "10",
+        0
+      ],
+      "image": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "ControlNetApplyAdvanced",
+    "_meta": {
+      "title": "Apply ControlNet (Advanced)"
+    }
+  },
+  "12": {
+    "inputs": {
+      "image": "244265560_10227611884548979_6104897418530269593_n.jpg",
+      "upload": "image"
+    },
+    "class_type": "LoadImage",
+    "_meta": {
+      "title": "Load Image"
+    }
+  },
+  "13": {
+    "inputs": {
+      "safe": "disable",
+      "resolution": 1024,
+      "image": [
+        "12",
+        0
+      ]
+    },
+    "class_type": "PiDiNetPreprocessor",
+    "_meta": {
+      "title": "PiDiNet Lines"
+    }
+  },
+  "23": {
+    "inputs": {
+      "lora_name": "digital_painting-000007.safetensors",
+      "strength_model": 1,
+      "strength_clip": 1,
+      "model": [
+        "4",
+        0
+      ],
+      "clip": [
+        "4",
+        1
+      ]
+    },
+    "class_type": "LoraLoader",
+    "_meta": {
+      "title": "Load LoRA"
+    }
+  },
+  "26": {
+    "inputs": {
+      "images": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "PreviewImage",
+    "_meta": {
+      "title": "Preview Image"
+    }
+  },
+  "27": {
+    "inputs": {
+      "enabled": true,
+      "swap_model": "inswapper_128.onnx",
+      "facedetection": "retinaface_resnet50",
+      "face_restore_model": "codeformer-v0.1.0.pth",
+      "face_restore_visibility": 1,
+      "codeformer_weight": 0.5,
+      "detect_gender_source": "no",
+      "detect_gender_input": "no",
+      "source_faces_index": "0",
+      "input_faces_index": "0",
+      "console_log_level": 1,
+      "input_image": [
+        "8",
+        0
+      ],
+      "source_image": [
+        "12",
+        0
+      ]
+    },
+    "class_type": "ReActorFaceSwap",
+    "_meta": {
+      "title": "ReActor - Fast Face Swap"
+    }
+  },
+  "28": {
+    "inputs": {
+      "stop_at_clip_layer": -2,
+      "clip": [
+        "23",
+        1
+      ]
+    },
+    "class_type": "CLIPSetLastLayer",
+    "_meta": {
+      "title": "CLIP Set Last Layer"
+    }
+  },
+  "29": {
+    "inputs": {
+      "filename_prefix": "digi_paint",
+      "images": [
+        "27",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  }
+}