SEND JOB TO COMFY UI - Githubissues

Install ComfyUI using PINOKIO AI Browser. Having it running as server backend for AI Generative.

I am using Mac Mini M2 Pro 32 GB. Seems good base model, not the fastest, but silent and speedy anough. Make sure you have 2 TB or more.

# SDXL Turbo model is used, but LoRA LCM was also fast

import bpy
import json
import requests
import random

# ComfyUI server address
comfyui_address = "http://127.0.0.1:8188"

def queue_prompt(prompt_workflow):
    p = {"prompt": prompt_workflow}

    # Convert prompt workflow to JSON
    workflow_json = json.dumps(p).encode('utf-8')

    # Send POST request to ComfyUI server
    response = requests.post(comfyui_address + "/prompt", data=workflow_json)

    # Check if request was successful
    if response.status_code != 200:
        print("Error sending prompt to ComfyUI:", response.status_code)
        print(response.text)
    else:
        print("Prompt successfully sent to ComfyUI")

# Read workflow API data from file and convert it into dictionary
prompt_workflow = json.load(open("/Users/jimmygunawan/Downloads/sdxl_turbo_api.json"))

# Create a list of prompts

prompt_list = []

# Access the text block and append for ComfyUI
# Get the text datablock
text_datablock = bpy.data.texts['MonkeyRobot']

# Get the text content
text_content = text_datablock.lines

if text_datablock:
    # Read the contents of the text block
    for i in text_content:
        print(i.body)
        prompt_list.append(i.body)
else:
    print(f"Text block '{text_block_name}' not found.")

#prompt_list = []
#prompt_list.append("photo of a bearded man Mario wearing a red hat sitting alone in a cafe")
#prompt_list.append("oil painting of a llama standing in the middle of a busy street")
#prompt_list.append("water colour art of a fish sitting in a tree")
#prompt_list.append("beautiful mushroom glass bottle landscape, purple galaxy bottle")
#prompt_list.append("princess peach on abandoned castle reading book at night, fireflies")
#prompt_list.append("head totem of monkey head, made of mecha garbage recycle, in the middle of empty park")

# Give some easy-to-remember names to the nodes
prompt_pos_node = prompt_workflow["2"]
seed_node = prompt_workflow["4"]
#save_image_node = prompt_workflow["1"]
#empty_latent_img_node = prompt_workflow["1"]

# Set image dimensions and batch size in EmptyLatentImage node
#empty_latent_img_node["inputs"]["width"] = 512
#empty_latent_img_node["inputs"]["height"] = 640
#empty_latent_img_node["inputs"]["num_images"] = 1

# For every prompt in prompt_list...
for index, prompt in enumerate(prompt_list):
    # Set the text prompt for positive CLIPTextEncode node
    prompt_pos_node["inputs"]["text"] = prompt + " made of garbage, waste recycles, wabisabi, imperfect"

    # Set a random seed in KSampler node
    seed_node["inputs"]["noise_seed"] = random.randint(1, 18446744073709551614)

    # Set filename prefix to be the same as prompt
    # (truncate to first 100 chars if necessary)
    #fileprefix = prompt
    #if len(fileprefix) > 100:
    #    fileprefix = fileprefix[:100]

    #save_image_node["inputs"]["filename_prefix"] = fileprefix

    # Everything set, add entire workflow to queue.
    queue_prompt(prompt_workflow)

sdxl_turbo_api.json.zip

{ "1": { "inputs": { "ckpt_name": "sd_xl_turbo_1.0_fp16.safetensors" }, "class_type": "CheckpointLoaderSimple" }, "2": { "inputs": { "text": "oil painting style of ", "clip": [ "1", 1 ] }, "class_type": "CLIPTextEncode" }, "3": { "inputs": { "text": "", "clip": [ "1", 1 ] }, "class_type": "CLIPTextEncode" }, "4": { "inputs": { "add_noise": true, "noise_seed": 340269611110808, "cfg": 1, "model": [ "1", 0 ], "positive": [ "2", 0 ], "negative": [ "3", 0 ], "sampler": [ "13", 0 ], "sigmas": [ "9", 0 ], "latent_image": [ "10", 0 ] }, "class_type": "SamplerCustom" }, "9": { "inputs": { "steps": 1, "model": [ "1", 0 ] }, "class_type": "SDTurboScheduler" }, "10": { "inputs": { "width": 512, "height": 512, "batch_size": 1 }, "class_type": "EmptyLatentImage" }, "11": { "inputs": { "samples": [ "4", 0 ], "vae": [ "1", 2 ] }, "class_type": "VAEDecode" }, "12": { "inputs": { "images": [ "11", 0 ] }, "class_type": "PreviewImage" }, "13": { "inputs": { "sampler_name": "euler" }, "class_type": "KSamplerSelect" }, "19": { "inputs": { "pixels": [ "21", 0 ], "vae": [ "1", 2 ] }, "class_type": "VAEEncode" }, "21": { "inputs": { "upscale_method": "nearest-exact", "scale_by": 2, "image": [ "11", 0 ] }, "class_type": "ImageScaleBy" }, "25": { "inputs": { "samples": [ "27", 0 ], "vae": [ "1", 2 ] }, "class_type": "VAEDecode" }, "26": { "inputs": { "images": [ "25", 0 ] }, "class_type": "PreviewImage" }, "27": { "inputs": { "seed": 340269611110554, "steps": 2, "cfg": 1, "sampler_name": "ddim", "scheduler": "ddim_uniform", "denoise": 1, "model": [ "1", 0 ], "positive": [ "2", 0 ], "negative": [ "3", 0 ], "latent_image": [ "19", 0 ] }, "class_type": "KSampler" }, "28": { "inputs": { "filename_prefix": "ComfyUI", "images": [ "25", 0 ] }, "class_type": "SaveImage" }, "29": { "inputs": { "filename_prefix": "ComfyUI", "images": [ "11", 0 ] }, "class_type": "SaveImage" } }

enzyme69 / blendersushi

SEND JOB TO COMFY UI #1347