I've added a script to execute a default pipeline and updated the README.

This update introduces a new Python script, `script_examples/execute_default_pipeline.py`, that allows you to execute a predefined ComfyUI pipeline.

The script:
- Defines a default pipeline structure in JSON format.
- Sends the pipeline to a running ComfyUI server instance for execution.
- Prints a success message upon queuing the prompt.

The `README.md` has been updated to include:
- A new section "Running a Default Pipeline".
- Step-by-step instructions on how to run the new script.
- Information on where to find the generated output images.
- A note about the required checkpoint file (`v1-5-pruned-emaonly.safetensors`).

README.md

@@ -34,6 +34,24 @@
 This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
 ### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
 
+## Running a Default Pipeline
+
+A script to execute a default pipeline is available in `script_examples/execute_default_pipeline.py`.
+
+To run this script:
+
+1.  **Ensure ComfyUI Server is Running:**
+    Start the ComfyUI server by running `python main.py` in your terminal from the root directory of this project. By default, the server runs at `http://127.0.0.1:8188`.
+2.  **Execute the Script:**
+    Open another terminal, navigate to the `script_examples/` directory, and run the script:
+    ```bash
+    python execute_default_pipeline.py
+    ```
+3.  **Find the Output:**
+    If the execution is successful, the script will print "Prompt queued successfully!". The generated image will be saved in the `output/` directory with a filename starting with `ComfyUI` (e.g., `ComfyUI_00001_.png`).
+
+**Note:** The default pipeline uses the `v1-5-pruned-emaonly.safetensors` checkpoint. Make sure this checkpoint is available in your `models/checkpoints/` directory. If not, you can either download it or modify the `ckpt_name` in the `execute_default_pipeline.py` script to use a checkpoint you have.
+
 ### [Installing ComfyUI](#installing)
 
 ## Features

script_examples/execute_default_pipeline.py

@@ -0,0 +1,115 @@
+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}
+    data = json.dumps(p).encode('utf-8')
+    req =  request.Request("http://127.0.0.1:8188/prompt", data=data)
+    request.urlopen(req)
+    print("Prompt queued successfully!")
+
+if __name__ == "__main__":
+    prompt = json.loads(prompt_text)
+    # You can modify the prompt here if needed, for example:
+    # prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
+    # prompt["3"]["inputs"]["seed"] = 5
+
+    queue_prompt(prompt)