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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,97 @@ | ||
| #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 json | ||
|
|
||
| server_address = "127.0.0.1:7864" | ||
| 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 = {} | ||
| 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 | ||
|
|
||
| def query_sd35(ckpt_name: str = "sd3.5_medium.safetensors", | ||
| prompt: str = "a capybara", | ||
| negative_prompt: str = "ugly, disfigured, deformed", | ||
| width: int = 1024, | ||
| height: int = 1024, | ||
| batch_size: int = 1, | ||
| seed: int = 77498386, | ||
| cfg: float = 3.0, | ||
| step: int = 20): | ||
|
|
||
| with open('stuffs/comfyui_workflow_api/sd3_5_workflow_api.json') as f: | ||
| prompt_config = json.load(f) | ||
|
|
||
| prompt_config["3"]["inputs"]["seed"] = seed | ||
| prompt_config["3"]["inputs"]["cfg"] = cfg | ||
| prompt_config["3"]["inputs"]["step"] = step | ||
| prompt_config["4"]["inputs"]["ckpt_name"] = ckpt_name | ||
| prompt_config["16"]["inputs"]["text"] = prompt | ||
| prompt_config["40"]["inputs"]["text"] = negative_prompt | ||
| prompt_config["53"]["inputs"]["width"] = width | ||
| prompt_config["53"]["inputs"]["height"] = height | ||
| prompt_config["53"]["inputs"]["batch_size"] = batch_size | ||
|
|
||
| ws = websocket.WebSocket() | ||
| ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) | ||
| images = get_images(ws, prompt_config) | ||
| 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: | ||
|
|
||
| output_images = [] | ||
| for node_id in images: | ||
| for image_data in images[node_id]: | ||
| from PIL import Image | ||
| import io | ||
| output_images.append(Image.open(io.BytesIO(image_data))) | ||
| return output_images | ||
|
|
||
|
|
||
| # query_sd35(prompt="a cat") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,139 @@ | ||
| { | ||
| "3": { | ||
| "inputs": { | ||
| "seed": 715383868423439, | ||
| "steps": 25, | ||
| "cfg": 3, | ||
| "sampler_name": "euler", | ||
| "scheduler": "sgm_uniform", | ||
| "denoise": 1, | ||
| "model": [ | ||
| "4", | ||
| 0 | ||
| ], | ||
| "positive": [ | ||
| "16", | ||
| 0 | ||
| ], | ||
| "negative": [ | ||
| "40", | ||
| 0 | ||
| ], | ||
| "latent_image": [ | ||
| "53", | ||
| 0 | ||
| ] | ||
| }, | ||
| "class_type": "KSampler", | ||
| "_meta": { | ||
| "title": "KSampler" | ||
| } | ||
| }, | ||
| "4": { | ||
| "inputs": { | ||
| "ckpt_name": "sd3.5_medium.safetensors" | ||
| }, | ||
| "class_type": "CheckpointLoaderSimple", | ||
| "_meta": { | ||
| "title": "Load Checkpoint" | ||
| } | ||
| }, | ||
| "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": "PreviewImage", | ||
| "_meta": { | ||
| "title": "Save Image" | ||
| } | ||
| }, | ||
| "16": { | ||
| "inputs": { | ||
| "text": "Painting of Taylor Swift in the style of Vincent van Gogh.", | ||
| "clip": [ | ||
| "43", | ||
| 0 | ||
| ] | ||
| }, | ||
| "class_type": "CLIPTextEncode", | ||
| "_meta": { | ||
| "title": "Positive Prompt" | ||
| } | ||
| }, | ||
| "40": { | ||
| "inputs": { | ||
| "text": "ugly, disfigured, deformed", | ||
| "clip": [ | ||
| "43", | ||
| 0 | ||
| ] | ||
| }, | ||
| "class_type": "CLIPTextEncode", | ||
| "_meta": { | ||
| "title": "Negative Prompt" | ||
| } | ||
| }, | ||
| "41": { | ||
| "inputs": { | ||
| "clip_name": "t5xxl_fp16.safetensors", | ||
| "type": "sd3" | ||
| }, | ||
| "class_type": "CLIPLoader", | ||
| "_meta": { | ||
| "title": "Load CLIP" | ||
| } | ||
| }, | ||
| "42": { | ||
| "inputs": { | ||
| "clip_name1": "clip_l.safetensors", | ||
| "clip_name2": "clip_g.safetensors", | ||
| "type": "sd3" | ||
| }, | ||
| "class_type": "DualCLIPLoader", | ||
| "_meta": { | ||
| "title": "DualCLIPLoader" | ||
| } | ||
| }, | ||
| "43": { | ||
| "inputs": { | ||
| "clip_name1": "clip_l.safetensors", | ||
| "clip_name2": "clip_g.safetensors", | ||
| "clip_name3": "t5xxl_fp16.safetensors" | ||
| }, | ||
| "class_type": "TripleCLIPLoader", | ||
| "_meta": { | ||
| "title": "TripleCLIPLoader" | ||
| } | ||
| }, | ||
| "53": { | ||
| "inputs": { | ||
| "width": 1024, | ||
| "height": 1024, | ||
| "batch_size": 1 | ||
| }, | ||
| "class_type": "EmptySD3LatentImage", | ||
| "_meta": { | ||
| "title": "EmptySD3LatentImage" | ||
| } | ||
| } | ||
| } |
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