chatglm3接入，可实现函数调用，代码执行等功能

tool/comfyUI_api.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 requests
+import urllib
+import random
+import io
+from PIL import Image
+
+
+class ComfyUIApi():
+    def __init__(self, server_address="127.0.0.1:8188"):
+        self.server_address=server_address
+        self.client_id=str(uuid.uuid4())
+        self.ws = websocket.WebSocket()
+        self.ws.connect("ws://{}/ws?clientId={}".format(server_address, self.client_id))
+
+    def queue_prompt(self,prompt):
+        p = {"prompt": prompt, "client_id": self.client_id}
+        data = json.dumps(p).encode('utf-8')
+        req =  requests.post("http://{}/prompt".format(self.server_address), data=data)
+        print(req.text)
+        return json.loads(req.text)
+
+    def get_image(self,filename, subfolder, folder_type):
+        data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+        url_values = urllib.parse.urlencode(data)
+        with requests.get("http://{}/view?{}".format(self.server_address, url_values)) as response:
+            image = Image.open(io.BytesIO(response.content))
+            return image
+
+    def get_image_url(self,filename, subfolder, folder_type):
+        data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+        url_values = urllib.parse.urlencode(data)
+        return "http://{}/view?{}".format(self.server_address, url_values)
+
+    def get_history(self,prompt_id):
+        with requests.get("http://{}/history/{}".format(self.server_address, prompt_id)) as response:
+            return json.loads(response.text)
+
+    def get_images(self,prompt,isUrl=False):
+        prompt_id = self.queue_prompt(prompt)['prompt_id']
+        output_images = []
+        while True:
+            out = self.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 = self.get_history(prompt_id)[prompt_id]
+        for o in history['outputs']:
+            for node_id in history['outputs']:
+                node_output = history['outputs'][node_id]
+                if 'images' in node_output:
+                    for image in node_output['images']:
+                        image_data =self.get_image_url(image['filename'], image['subfolder'], image['type']) if isUrl else self.get_image(image['filename'], image['subfolder'], image['type'])
+                        image['image']=image_data
+                        output_images.append(image)
+
+        return output_images
+
+prompt_text = """
+{
+    "3": {
+        "class_type": "KSampler",
+        "inputs": {
+            "cfg": 8,
+            "denoise": 1,
+            "latent_image": [
+                "5",
+                0
+            ],
+            "model": [
+                "4",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "seed": 8566257,
+            "steps": 20
+        }
+    },
+    "4": {
+        "class_type": "CheckpointLoaderSimple",
+        "inputs": {
+            "ckpt_name": "chilloutmix_NiPrunedFp32Fix.safetensors"
+        }
+    },
+    "5": {
+        "class_type": "EmptyLatentImage",
+        "inputs": {
+            "batch_size": 1,
+            "height": 512,
+            "width": 512
+        }
+    },
+    "6": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "masterpiece best quality girl"
+        }
+    },
+    "7": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "bad hands"
+        }
+    },
+    "8": {
+        "class_type": "VAEDecode",
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        }
+    },
+    "9": {
+        "class_type": "SaveImage",
+        "inputs": {
+            "filename_prefix": "ComfyUI",
+            "images": [
+                "8",
+                0
+            ]
+        }
+    }
+}
+"""
+if __name__ == '__main__':
+    prompt = json.loads(prompt_text)
+    #set the text prompt for our positive CLIPTextEncode
+    prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
+
+    #set the seed for our KSampler node
+    prompt["3"]["inputs"]["seed"] = ''.join(random.sample('123456789012345678901234567890',14))
+
+    cfui=ComfyUIApi()
+    images = cfui.get_images(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.show()
+