Delete script_examples

script_examples/basic_api_example.py

@@ -1,127 +0,0 @@
-import json
-from urllib import request
-
-#This is the ComfyUI api prompt format.
-
-#If you want it for a specific workflow you can "enable dev mode options"
-#in the settings of the UI (gear beside the "Queue Size: ") this will enable
-#a button on the UI to save workflows in api format.
-
-#keep in mind ComfyUI is pre alpha software so this format will change a bit.
-
-#this is the one for the default workflow
-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": "v1-5-pruned-emaonly.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
-            ]
-        }
-    }
-}
-"""
-
-def queue_prompt(prompt):
-    p = {"prompt": prompt}
-
-    # If the workflow contains API nodes, you can add a Comfy API key to the `extra_data`` field of the payload.
-    # p["extra_data"] = {
-    #     "api_key_comfy_org": "comfyui-87d01e28d*******************************************************"  # replace with real key
-    # }
-    # See: https://docs.comfy.org/tutorials/api-nodes/overview
-    # Generate a key here: https://platform.comfy.org/login
-
-    data = json.dumps(p).encode('utf-8')
-    req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
-    request.urlopen(req)
-
-
-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"] = 5
-
-
-queue_prompt(prompt)
-
-

script_examples/websockets_api_example.py

@@ -1,167 +0,0 @@
-#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, prompt_id):
-    p = {"prompt": prompt, "client_id": client_id, "prompt_id": prompt_id}
-    data = json.dumps(p).encode('utf-8')
-    req = urllib.request.Request("http://{}/prompt".format(server_address), data=data)
-    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 = str(uuid.uuid4())
-    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:
-            # If you want to be able to decode the binary stream for latent previews, here is how you can do it:
-            # bytesIO = BytesIO(out[8:])
-            # preview_image = Image.open(bytesIO) # This is your preview in PIL image format, store it in a global
-            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 = """
-{
-    "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": "v1-5-pruned-emaonly.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
-            ]
-        }
-    }
-}
-"""
-
-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"] = 5
-
-ws = websocket.WebSocket()
-ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
-images = get_images(ws, prompt)
-ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts
-#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()
-

script_examples/websockets_api_example_ws_images.py

@@ -1,159 +0,0 @@
-#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
-
-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 = """
-{
-    "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": "v1-5-pruned-emaonly.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
-            ]
-        }
-    },
-    "save_image_websocket_node": {
-        "class_type": "SaveImageWebsocket",
-        "inputs": {
-            "images": [
-                "8",
-                0
-            ]
-        }
-    }
-}
-"""
-
-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"] = 5
-
-ws = websocket.WebSocket()
-ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
-images = get_images(ws, prompt)
-ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts
-#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()
-