"workflow update"

test_prompt/app.py

@@ -0,0 +1,114 @@
+#This is an example that uses the websockets api and the SaveImageWebsocket node to get images directly without
+#them being saved to disk
+
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import json
+import urllib.request
+import urllib.parse
+import time
+import requests
+import random
+
+import sys
+import os
+
+# Add the parent directory to sys.path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+# 获取当前脚本文件所在的目录
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
+# 构建相对 ComfyUI 目录的路径
+comfyui_dir = os.path.abspath(os.path.join(script_dir, '..'))
+
+from ECCV2022_RIFE.InterpolatorInterface import InterpolatorInterface
+
+from generate_prompt import generate_prompt
+
+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 = {}
+    current_node = ""
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                if data['prompt_id'] == prompt_id:
+                    if data['node'] is None:
+                        break #Execution is done
+                    else:
+                        current_node = data['node']
+        else:
+            if current_node == 'save_image_websocket_node':
+                images_output = output_images.get(current_node, [])
+                images_output.append(out[8:])
+                output_images[current_node] = images_output
+
+    return output_images
+
+# prompt = json.loads(prompt_text)
+def main():
+    with open("workflow_api.json") as workflow_api:
+        prompt = json.load(workflow_api)
+
+    ws = websocket.WebSocket()
+    ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+    
+    interpolator = InterpolatorInterface() # Initialize image to video
+
+    temp = 300
+    while(1):
+        next = temp + 1
+        # 生prompt
+        prompt_text = generate_prompt()
+
+        # 生圖
+        prompt["6"]["inputs"]["text"] = prompt_text        # 更新 CLIPTextEncode 的文本提示
+        queue_prompt(prompt)['prompt_id']        # 将提示加入队列并获取 prompt_id
+
+        print(prompt_text)
+
+        # temp_str = f"output/ComfyUI_00{str(temp)}_.png"
+        # next_str = f"output/ComfyUI_00{str(next)}_.png"
+        temp_str = os.path.join(comfyui_dir, f"output/ComfyUI_00{str(temp)}_.png")
+        next_str = os.path.join(comfyui_dir, f"output/ComfyUI_00{str(next)}_.png")
+
+        # print(temp_str)
+
+        # image to video
+        results = interpolator.generate(
+            imgs = (temp_str, next_str),
+            exp=4,
+            # output_dir="interpolate_out"
+            # output_dir=os.path.join(comfyui_dir, "interpolate_out") 
+        )
+
+        temp = next
+
+        print("Output results to: ")
+        print(results)
+        print()
+
+if __name__ == "__main__":
+    main()

test_prompt/generate_prompt.py

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+# Input：溫度, 情緒向量 < 4, 0, 0, 0, 1, 0, 0 >
+import requests
+import time
+import random
+
+def fetch_data():
+    # Function to continuously fetch temperature data
+    while True:
+        url = "http://140.119.108.248:89/ComfyUI_Data/"
+        response = requests.get(url)
+        data = response.json()
+        time.sleep(1)  # Fetch temperature every 1 second
+        if data:
+            temperature = data['temperature']
+            emotions_vec = data['dominant_emotion']
+            yield temperature, emotions_vec
+            
+
+def get_dominant_emotion(emotions_vec):
+    # 找到最大值的索引
+    max_index = emotions_vec.index(max(emotions_vec))
+
+    # 用索引來查找對應的情緒
+    emotions = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
+    dominant_emotion = emotions[max_index]
+
+    return dominant_emotion
+
+
+# cloud_types_and_color
+def choose_cloud_and_color(dominant_emotion):
+
+    if(dominant_emotion == "angry"):
+        cloud_type = "cumulus, "
+        color = random.choice(["Red, ", "Dark red, "])
+
+    elif(dominant_emotion == "disgust"):
+        cloud_type = "stratus, "
+        color = random.choice(["Grown, ", "Dark yellow, "])
+
+    elif(dominant_emotion == "fear"):
+        cloud_type = "cumulonimbus, "
+        color = random.choice(["Red, ", "Black, "])
+
+    elif(dominant_emotion == "happy"):
+        cloud_type = random.choice(["cirrus, ", "cirrostratus, "])
+        color = random.choice(["Yellow, ", "Bright green, "])
+
+    elif(dominant_emotion == "sad"):
+        cloud_type = random.choice(["altostratus, ", "nimbostratus, "])
+        color = random.choice(["Gray, ", "Black, "])
+
+    elif(dominant_emotion == "surprise"):
+        cloud_type = random.choice(["cirroscumulus, ", "altocumulus, "])
+        color = random.choice(["Yellow, ", "Bright Green, ", "Bright Pink, "])
+
+    else:
+        cloud_type = "stratocumulus"
+        color = "No color, "
+
+    return cloud_type, color
+
+# cloud_types_and_color
+def choose_cloud_and_color_chinese(dominant_emotion):
+
+    if(dominant_emotion == "angry"):
+        cloud_type = "積雲, "
+        color = random.choice(["紅色, ", "暗紅色, "])
+
+    elif(dominant_emotion == "disgust"):
+        cloud_type = "層雲, "
+        color = random.choice(["棕色, ", "深黃色, "])
+
+    elif(dominant_emotion == "fear"):
+        cloud_type = "積雨雲, "
+        color = random.choice(["紅色, ", "黑色, "])
+
+    elif(dominant_emotion == "happy"):
+        cloud_type = random.choice(["捲雲, ", "卷積雲, "])
+        color = random.choice(["深黃色, ", "亮綠色, "])
+
+    elif(dominant_emotion == "sad"):
+        cloud_type = random.choice(["高層雲, ", "雨層雲, "])
+        color = random.choice(["灰色, ", "黑色, "])
+
+    elif(dominant_emotion == "surprise"):
+        cloud_type = random.choice(["卷積雲, ", "高積雲, "])
+        color = random.choice(["黃色, ", "亮綠色, ", "亮粉色, "])
+
+    else:
+        cloud_type = "層積雲"
+        color = "沒有顏色, "
+
+    return cloud_type, color
+
+# timing
+season_list = ['spring ', 'summer ', 'fall ', 'winter ', 'unknown ']
+timing_list = ['morning, ', 'evening, ','noon, ', 'night, ', 'unknown, ' ]
+
+season_chinese = ['春天', '夏天', '秋天', '冬天', '季節皆可']
+timing_chinese = ['早上, ', '傍晚, ', '中午, ', '晚上, ', '時間皆可, ']
+
+def choose_date(temperature):
+    season_num = 0
+    timing_num = 0
+    if 0 <= temperature < 10: 
+        season_num = random.randint(0, 3)
+        timing_num = 3
+    elif 10 <= temperature < 20:
+        season_num = random.randint(2, 3)
+        timing_num = random.randint(0, 1)
+    elif 20 <= temperature < 30:
+        season_num = random.randint(0, 1)
+        timing_num = random.randint(0, 1)
+    elif 30 <= temperature <= 40:
+        season_num = random.randint(0, 3)
+        timing_num = 2
+    else: 
+        season_num = 4
+        timing_num = 4
+
+    return season_num, timing_num
+
+def generate_prompt():
+
+    for temperature, emotions_vec in fetch_data():
+        # print(f"Temperature: {temperature}, Emotions: {emotions_vec}")
+        temperature_str = str(temperature) #str
+        dominant_emotion = get_dominant_emotion(emotions_vec)
+        cloud_types, color = choose_cloud_and_color(dominant_emotion) # str
+        season_num, timing_num = choose_date(temperature) 
+        season = season_list[season_num] #str
+        timing = timing_list[timing_num] #str
+
+        prompt = "weather forecast, no people, only show the sky, fantasy style, " + temperature_str + " degrees Celsius, " + cloud_types + color + season + timing 
+
+        print(prompt)
+
+        # weather forecast, no people, only show the sky, fantasy style, 33 degrees Celsius, cirrus, Bright green, spring noon, 
+        return prompt
+
+def generate_chinese_prompt():
+
+    for temperature, emotions_vec in fetch_data():
+        # print(f"Temperature: {temperature}, Emotions: {emotions_vec}")
+        temperature_str = str(temperature) #str
+        dominant_emotion = get_dominant_emotion(emotions_vec)
+        cloud_types, color = choose_cloud_and_color_chinese(dominant_emotion) # str
+        season_num, timing_num = choose_date(temperature) 
+        season = season_chinese[season_num] #str
+        timing = timing_chinese[timing_num] #str
+
+        prompt = "天氣預報, 沒有人物, 只顯示雲層, 奇幻風格, " + temperature_str + " 攝氏度, " + cloud_types + color + season + timing 
+
+        print(prompt)
+
+        # weather forecast, no people, only show the sky, fantasy style, 33 degrees Celsius, cirrus, Bright green, spring noon, 
+        return prompt
+
+
+# if __name__ == "__main__":
+#     generate_prompt()

test_prompt/test.py

@@ -0,0 +1,101 @@
+#This is an example that uses the websockets api and the SaveImageWebsocket node to get images directly without
+#them being saved to disk
+
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import json
+import urllib.request
+import urllib.parse
+import time
+import requests
+import random
+
+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 = {}
+    current_node = ""
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                if data['prompt_id'] == prompt_id:
+                    if data['node'] is None:
+                        break #Execution is done
+                    else:
+                        current_node = data['node']
+        else:
+            if current_node == 'save_image_websocket_node':
+                images_output = output_images.get(current_node, [])
+                images_output.append(out[8:])
+                output_images[current_node] = images_output
+
+    return output_images
+
+
+# prompt_text = """
+# {
+# ...
+# }
+# """
+
+# prompt = json.loads(prompt_text)
+def main():
+    with open("workflow_api.json") as workflow_api:
+        prompt = json.load(workflow_api)
+
+    ws = websocket.WebSocket()
+    ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+
+
+    prompt_text = "23  degrees Celsius, no people, only show the sky, weather forecast, cloudy"
+
+    # 更新 CLIPTextEncode 的文本提示
+    prompt["6"]["inputs"]["text"] = prompt_text
+
+    # 将提示加入队列并获取 prompt_id
+    queue_prompt(prompt)['prompt_id']
+
+    # 打印生成的提示文本
+    print(prompt_text)
+
+    
+    #set the seed for our KSampler node
+    # prompt["3"]["inputs"]["seed"] = 5
+
+    # prompt["6"]["inputs"]["text"] = "green sky"
+    # queue_prompt(prompt)['prompt_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.show()
+
+if __name__ == "__main__":
+    main()

test_prompt/workflow_api.json

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+{
+    "3": {
+      "inputs": {
+        "seed": 787475629082271,
+        "steps": 20,
+        "cfg": 8,
+        "sampler_name": "euler",
+        "scheduler": "normal",
+        "denoise": 1,
+        "model": [
+          "4",
+          0
+        ],
+        "positive": [
+          "6",
+          0
+        ],
+        "negative": [
+          "7",
+          0
+        ],
+        "latent_image": [
+          "5",
+          0
+        ]
+      },
+      "class_type": "KSampler",
+      "_meta": {
+        "title": "KSampler"
+      }
+    },
+    "4": {
+      "inputs": {
+        "ckpt_name": "absolutereality_v181.safetensors"
+      },
+      "class_type": "CheckpointLoaderSimple",
+      "_meta": {
+        "title": "Load Checkpoint"
+      }
+    },
+    "5": {
+      "inputs": {
+        "width": 512,
+        "height": 512,
+        "batch_size": 1
+      },
+      "class_type": "EmptyLatentImage",
+      "_meta": {
+        "title": "Empty Latent Image"
+      }
+    },
+    "6": {
+      "inputs": {
+        "text" : "", 
+        "clip": [
+          "4",
+          1
+        ]
+      },
+      "class_type": "CLIPTextEncode",
+      "_meta": {
+        "title": "CLIP Text Encode (Prompt)"
+      }
+    },
+    "7": {
+      "inputs": {
+        "text": "text, watermark, people",
+        "clip": [
+          "4",
+          1
+        ]
+      },
+      "class_type": "CLIPTextEncode",
+      "_meta": {
+        "title": "CLIP Text Encode (Prompt)"
+      }
+    },
+    "8": {
+      "inputs": {
+        "samples": [
+          "3",
+          0
+        ],
+        "vae": [
+          "4",
+          2
+        ]
+      },
+      "class_type": "VAEDecode",
+      "_meta": {
+        "title": "VAE Decode"
+      }
+    },
+    "9": {
+      "inputs": {
+        "filename_prefix": "ComfyUI",
+        "images": [
+          "8",
+          0
+        ]
+      },
+      "class_type": "SaveImage",
+      "_meta": {
+        "title": "Save Image"
+      }
+    }
+  }