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mrm987
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# ComfyUI Multi Prompt Generator
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Generate multiple images from a prompt list in a single run, with built-in upscaling and LUT support.
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## Features
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- **Batch Generation** — Input a list of prompts (one per line), generate all at once
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- **Auto Upscaling** — 2-pass upscale with model upscaler + img2img refinement
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- **64x Alignment** — Automatically adjusts dimensions to prevent edge artifacts
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- **LUT Support** — Apply .cube LUT files with adjustable strength
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- **Live Preview** — See results during generation (toggleable)
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- **Auto Save** — Images saved with auto-numbered filenames based on first tag
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## Installation
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### Option 1: ComfyUI Manager (Coming Soon)
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Search for "Multi Prompt Generator" in ComfyUI Manager.
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### Option 2: Manual Install
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```bash
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cd ComfyUI/custom_nodes
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git clone https://github.com/mrm987/ComfyUI_Multi_Prompt_Generator.git
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```
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Restart ComfyUI.
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## Usage
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1. Search for **"Multi Prompt Generator"** in the node menu
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2. Connect: Model, CLIP, VAE, Empty Latent, (optional) Upscale Model
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3. Set your base prompt and negative prompt
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4. Enter prompt list — one variation per line:
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```
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smile, happy, bright eyes
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angry, furrowed brow, clenched teeth
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sad, downcast eyes, closed mouth
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crying, tears, watery eyes
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```
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## Inputs
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| Input | Type | Description |
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|-------|------|-------------|
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| model | MODEL | Checkpoint model |
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| clip | CLIP | CLIP model |
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| vae | VAE | VAE model |
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| latent | LATENT | Empty latent image |
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| base_prompt | STRING | Base prompt (combined with each line) |
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| negative_prompt | STRING | Negative prompt |
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| prompt_list | STRING | One prompt variation per line |
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| seed | INT | Random seed |
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| steps | INT | Sampling steps (default: 30) |
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| cfg | FLOAT | CFG scale (default: 5.0) |
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| enable_upscale | BOOL | Enable 2-pass upscaling (default: True) |
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| enable_preview | BOOL | Show preview during generation (default: True) |
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| save_prefix | STRING | Output folder name (default: "MultiPrompt") |
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### Optional Inputs
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| Input | Type | Description |
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|-------|------|-------------|
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| upscale_model | UPSCALE_MODEL | Upscale model (e.g., 2x-AnimeSharp) |
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| scale_factor | FLOAT | Downscale after upscale (default: 0.7) |
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| upscale_steps | INT | 2nd pass steps (default: 15) |
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| upscale_cfg | FLOAT | 2nd pass CFG (default: 5.0) |
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| upscale_denoise | FLOAT | 2nd pass denoise (default: 0.5) |
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| lut_name | COMBO | LUT file from comfyui-propost/LUTs |
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| lut_strength | FLOAT | LUT intensity (default: 0.3) |
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## Output
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Images are saved to:
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```
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output/[save_prefix]/01_smile_00001.png
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output/[save_prefix]/02_angry_00001.png
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output/[save_prefix]/03_sad_00001.png
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...
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```
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Filename format: `[index]_[first_tag]_[counter].png`
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## Pipeline
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```
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For each prompt line:
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1. Combine: base_prompt + line
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2. KSampler (1st pass)
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3. Preview (if enabled)
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4. Upscale Model → Resize to 64x → KSampler (2nd pass)
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5. Apply LUT (if selected)
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6. Preview update (if enabled)
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7. Save
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```
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## Requirements
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- ComfyUI
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- (Optional) [comfyui-propost](https://github.com/Beinsezii/comfyui-propost) for LUT files
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## License
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MIT
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+466
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import os
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import glob
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import torch
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import numpy as np
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from PIL import Image
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import folder_paths
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import comfy.sd
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import comfy.utils
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import comfy.sample
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import comfy.samplers
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import comfy.model_management
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from server import PromptServer
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def get_lut_files():
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"""LUT 파일 목록 가져오기"""
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lut_paths = []
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# comfyui-propost LUTs 폴더
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propost_path = os.path.join(folder_paths.get_folder_paths("custom_nodes")[0] if folder_paths.get_folder_paths("custom_nodes") else "", "comfyui-propost", "LUTs")
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if os.path.exists(propost_path):
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lut_paths.append(propost_path)
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# models/luts 폴더
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models_lut_path = os.path.join(folder_paths.models_dir, "luts")
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if os.path.exists(models_lut_path):
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lut_paths.append(models_lut_path)
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# custom_nodes 폴더 내 LUTs
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custom_nodes_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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propost_luts = os.path.join(custom_nodes_path, "comfyui-propost", "LUTs")
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if os.path.exists(propost_luts) and propost_luts not in lut_paths:
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lut_paths.append(propost_luts)
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lut_files = ["None"]
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for path in lut_paths:
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if os.path.exists(path):
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for f in os.listdir(path):
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if f.lower().endswith(".cube"):
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lut_files.append(f)
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return list(set(lut_files))
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def find_lut_file(lut_name):
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"""LUT 파일 전체 경로 찾기"""
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if lut_name == "None":
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return None
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search_paths = []
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custom_nodes_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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search_paths.append(os.path.join(custom_nodes_path, "comfyui-propost", "LUTs"))
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search_paths.append(os.path.join(folder_paths.models_dir, "luts"))
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for path in search_paths:
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full_path = os.path.join(path, lut_name)
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if os.path.exists(full_path):
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return full_path
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return None
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def parse_cube_lut(lut_path):
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"""Parse .cube LUT file"""
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with open(lut_path, 'r') as f:
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lines = f.readlines()
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lut_size = 0
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lut_data = []
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for line in lines:
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line = line.strip()
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if line.startswith('#') or line == '':
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continue
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if line.startswith('TITLE'):
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continue
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if line.startswith('LUT_3D_SIZE'):
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lut_size = int(line.split()[1])
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continue
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if line.startswith('DOMAIN_MIN') or line.startswith('DOMAIN_MAX'):
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continue
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try:
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values = [float(x) for x in line.split()]
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if len(values) == 3:
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lut_data.append(values)
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except:
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continue
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if lut_size == 0 or len(lut_data) == 0:
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return None, 0
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return np.array(lut_data, dtype=np.float32), lut_size
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def apply_lut(image, lut_data, lut_size, strength=1.0):
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"""Apply 3D LUT to image"""
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if lut_data is None:
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return image
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# image: [B, H, W, C] tensor, 0-1 range
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img_np = image.cpu().numpy()
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result = np.zeros_like(img_np)
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for b in range(img_np.shape[0]):
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img = img_np[b]
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h, w, c = img.shape
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# Reshape LUT
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lut_3d = lut_data.reshape((lut_size, lut_size, lut_size, 3))
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# Scale image to LUT indices
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img_scaled = img * (lut_size - 1)
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# Get integer indices and fractions for trilinear interpolation
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idx_low = np.floor(img_scaled).astype(np.int32)
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idx_high = np.ceil(img_scaled).astype(np.int32)
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frac = img_scaled - idx_low
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# Clamp indices
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idx_low = np.clip(idx_low, 0, lut_size - 1)
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idx_high = np.clip(idx_high, 0, lut_size - 1)
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# Trilinear interpolation
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r_low, g_low, b_low = idx_low[:,:,0], idx_low[:,:,1], idx_low[:,:,2]
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r_high, g_high, b_high = idx_high[:,:,0], idx_high[:,:,1], idx_high[:,:,2]
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r_frac, g_frac, b_frac = frac[:,:,0:1], frac[:,:,1:2], frac[:,:,2:3]
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# 8 corners of the cube
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c000 = lut_3d[r_low, g_low, b_low]
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c001 = lut_3d[r_low, g_low, b_high]
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c010 = lut_3d[r_low, g_high, b_low]
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c011 = lut_3d[r_low, g_high, b_high]
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c100 = lut_3d[r_high, g_low, b_low]
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c101 = lut_3d[r_high, g_low, b_high]
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c110 = lut_3d[r_high, g_high, b_low]
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c111 = lut_3d[r_high, g_high, b_high]
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# Interpolate
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c00 = c000 * (1 - r_frac) + c100 * r_frac
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c01 = c001 * (1 - r_frac) + c101 * r_frac
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c10 = c010 * (1 - r_frac) + c110 * r_frac
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c11 = c011 * (1 - r_frac) + c111 * r_frac
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c0 = c00 * (1 - g_frac) + c10 * g_frac
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c1 = c01 * (1 - g_frac) + c11 * g_frac
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lut_result = c0 * (1 - b_frac) + c1 * b_frac
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# Blend with original based on strength
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result[b] = img * (1 - strength) + lut_result * strength
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result = np.clip(result, 0, 1)
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return torch.from_numpy(result).to(image.device)
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class MultiPromptGenerator:
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"""
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프롬프트 리스트를 입력받아 한 번에 여러 이미지를 생성하고 업스케일하는 노드
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"""
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL",),
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"clip": ("CLIP",),
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"vae": ("VAE",),
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"latent": ("LATENT",),
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"base_prompt": ("STRING", {"multiline": True, "default": "1girl, solo,"}),
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"negative_prompt": ("STRING", {"multiline": True, "default": "lowres, bad quality,"}),
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"prompt_list": ("STRING", {"multiline": True, "default": "smile, happy\nangry, furrowed brow\nsad, crying"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"steps": ("INT", {"default": 30, "min": 1, "max": 200}),
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"cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.1}),
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"enable_upscale": ("BOOLEAN", {"default": True}),
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"enable_preview": ("BOOLEAN", {"default": True}),
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"save_prefix": ("STRING", {"default": "MultiPrompt"}),
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},
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"optional": {
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"upscale_model": ("UPSCALE_MODEL",),
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"scale_factor": ("FLOAT", {"default": 0.7, "min": 0.1, "max": 1.0, "step": 0.01}),
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"upscale_steps": ("INT", {"default": 15, "min": 1, "max": 200}),
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"upscale_cfg": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 30.0, "step": 0.1}),
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"upscale_denoise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"lut_name": (get_lut_files(),),
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"lut_strength": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "generate"
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CATEGORY = "image/generation"
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OUTPUT_NODE = True
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OUTPUT_IS_LIST = (True,)
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def encode_prompt(self, clip, text):
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"""CLIP 텍스트 인코딩"""
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tokens = clip.tokenize(text)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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return [[cond, {"pooled_output": pooled}]]
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def do_sample(self, model, seed, steps, cfg, positive, negative, latent_image, denoise=1.0):
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"""샘플링 실행"""
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device = comfy.model_management.get_torch_device()
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latent = latent_image.clone().to(device)
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noise = comfy.sample.prepare_noise(latent, seed, None)
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samples = comfy.sample.sample(
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model,
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noise,
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steps,
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cfg,
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"euler_ancestral",
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"normal",
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positive,
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negative,
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latent,
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denoise=denoise,
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disable_noise=False,
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start_step=None,
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last_step=None,
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force_full_denoise=True,
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noise_mask=None,
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callback=None,
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disable_pbar=False,
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seed=seed
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)
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return samples
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def decode_vae(self, vae, samples):
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"""VAE 디코딩"""
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return vae.decode(samples)
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def encode_vae(self, vae, image):
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"""VAE 인코딩"""
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return vae.encode(image[:, :, :, :3])
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def upscale_with_model(self, upscale_model, image):
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"""업스케일 모델로 이미지 확대"""
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device = comfy.model_management.get_torch_device()
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upscale_model.to(device)
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in_img = image.movedim(-1, -3).to(device)
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tile = 512
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overlap = 32
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oom = True
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while oom:
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try:
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steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(
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in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap
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)
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pbar = comfy.utils.ProgressBar(steps)
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s = comfy.utils.tiled_scale(
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in_img,
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lambda a: upscale_model(a),
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tile_x=tile,
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tile_y=tile,
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overlap=overlap,
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upscale_amount=upscale_model.scale,
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pbar=pbar
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)
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oom = False
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except comfy.model_management.OOM_EXCEPTION as e:
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tile //= 2
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if tile < 128:
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raise e
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upscale_model.to("cpu")
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s = torch.clamp(s.movedim(-3, -1), min=0, max=1.0)
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return s
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def resize_to_64(self, image, scale_factor):
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"""이미지를 64배수 크기로 리사이즈"""
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batch, height, width, channels = image.shape
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new_width = int(width * scale_factor)
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new_height = int(height * scale_factor)
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# 64배수로 반올림
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new_width = ((new_width + 32) // 64) * 64
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new_height = ((new_height + 32) // 64) * 64
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# 최소 크기 보장
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new_width = max(64, new_width)
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new_height = max(64, new_height)
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# BCHW로 변환 후 리사이즈
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samples = image.movedim(-1, 1)
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s = comfy.utils.common_upscale(samples, new_width, new_height, "lanczos", "disabled")
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s = s.movedim(1, -1)
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return s
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def save_image(self, image, prefix, filename, counter):
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"""이미지 저장"""
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full_output_folder = os.path.join(self.output_dir, prefix)
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os.makedirs(full_output_folder, exist_ok=True)
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file = f"{filename}_{counter:05d}.png"
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filepath = os.path.join(full_output_folder, file)
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img = image[0].cpu().numpy()
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img = (img * 255).astype(np.uint8)
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pil_img = Image.fromarray(img)
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pil_img.save(filepath, compress_level=4)
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print(f"[MultiPrompt] Saved: {filepath}")
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return filepath
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def send_preview(self, image, unique_id, idx):
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"""생성 중 프리뷰를 UI로 전송"""
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temp_dir = folder_paths.get_temp_directory()
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# temp 폴더에 프리뷰 저장
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preview_filename = f"multiprompt_preview_{unique_id}_{idx}.png"
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preview_path = os.path.join(temp_dir, preview_filename)
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img = image[0].cpu().numpy()
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img = (img * 255).astype(np.uint8)
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pil_img = Image.fromarray(img)
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pil_img.save(preview_path, compress_level=4)
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|
||||
# UI로 프리뷰 전송
|
||||
PromptServer.instance.send_sync("executed", {
|
||||
"node": unique_id,
|
||||
"output": {
|
||||
"images": [{
|
||||
"filename": preview_filename,
|
||||
"subfolder": "",
|
||||
"type": "temp"
|
||||
}]
|
||||
}
|
||||
})
|
||||
|
||||
def generate(self, model, clip, vae, latent, base_prompt, negative_prompt, prompt_list,
|
||||
seed, steps, cfg, enable_upscale, enable_preview, save_prefix,
|
||||
upscale_model=None, scale_factor=0.7, upscale_steps=15,
|
||||
upscale_cfg=5.0, upscale_denoise=0.5,
|
||||
lut_name="None", lut_strength=0.3, unique_id=None):
|
||||
|
||||
# prompt_list 파싱
|
||||
lines = [line.strip() for line in prompt_list.strip().split("\n") if line.strip()]
|
||||
|
||||
if not lines:
|
||||
raise ValueError("prompt_list가 비어있습니다.")
|
||||
|
||||
# LUT 로드
|
||||
lut_data = None
|
||||
lut_size = 0
|
||||
if lut_name != "None":
|
||||
lut_path = find_lut_file(lut_name)
|
||||
if lut_path:
|
||||
lut_data, lut_size = parse_cube_lut(lut_path)
|
||||
print(f"[MultiPrompt] LUT loaded: {lut_name} (size: {lut_size})")
|
||||
|
||||
# negative 인코딩 (한 번만)
|
||||
negative_cond = self.encode_prompt(clip, negative_prompt)
|
||||
|
||||
all_images = []
|
||||
counter = self._get_next_counter(save_prefix)
|
||||
|
||||
for idx, line in enumerate(lines):
|
||||
print(f"[MultiPrompt] Processing {idx + 1}/{len(lines)}: {line[:40]}...")
|
||||
|
||||
# 파일명: 순번 + 첫 태그
|
||||
first_tag = line.split(",")[0].strip().replace(" ", "_")
|
||||
filename = f"{idx + 1:02d}_{first_tag}"
|
||||
|
||||
# 프롬프트 결합
|
||||
full_prompt = f"{base_prompt}, {line}"
|
||||
positive_cond = self.encode_prompt(clip, full_prompt)
|
||||
|
||||
# latent 복사
|
||||
latent_samples = latent["samples"].clone()
|
||||
|
||||
# 1단계: 기본 샘플링
|
||||
samples = self.do_sample(
|
||||
model, seed, steps, cfg,
|
||||
positive_cond, negative_cond,
|
||||
latent_samples, denoise=1.0
|
||||
)
|
||||
image = self.decode_vae(vae, samples)
|
||||
|
||||
# 1차 결과 프리뷰 전송
|
||||
if enable_preview and unique_id is not None:
|
||||
self.send_preview(image, unique_id, idx)
|
||||
|
||||
# 2단계: 업스케일
|
||||
if enable_upscale and upscale_model is not None:
|
||||
# 업스케일 모델 적용
|
||||
upscaled = self.upscale_with_model(upscale_model, image)
|
||||
|
||||
# 64배수로 리사이즈
|
||||
resized = self.resize_to_64(upscaled, scale_factor)
|
||||
|
||||
# VAE 인코딩 → 2차 샘플링 → 디코딩
|
||||
latent_up = self.encode_vae(vae, resized)
|
||||
|
||||
samples_up = self.do_sample(
|
||||
model, seed, upscale_steps, upscale_cfg,
|
||||
positive_cond, negative_cond,
|
||||
latent_up, denoise=upscale_denoise
|
||||
)
|
||||
image = self.decode_vae(vae, samples_up)
|
||||
|
||||
# 3단계: LUT 적용
|
||||
if lut_data is not None and lut_strength > 0:
|
||||
image = apply_lut(image, lut_data, lut_size, lut_strength)
|
||||
|
||||
# 최종 결과 프리뷰 교체
|
||||
if enable_preview and unique_id is not None:
|
||||
self.send_preview(image, unique_id, idx)
|
||||
|
||||
# 저장
|
||||
self.save_image(image, save_prefix, filename, counter)
|
||||
all_images.append(image)
|
||||
|
||||
print(f"[MultiPrompt] Complete! Generated {len(all_images)} images.")
|
||||
return {"ui": {"images": []}, "result": (all_images,)}
|
||||
|
||||
def _get_next_counter(self, prefix):
|
||||
"""다음 카운터 번호"""
|
||||
full_output_folder = os.path.join(self.output_dir, prefix)
|
||||
|
||||
if not os.path.exists(full_output_folder):
|
||||
return 1
|
||||
|
||||
existing_files = os.listdir(full_output_folder)
|
||||
if not existing_files:
|
||||
return 1
|
||||
|
||||
max_counter = 0
|
||||
for f in existing_files:
|
||||
if f.endswith(".png"):
|
||||
try:
|
||||
counter_str = f.rsplit("_", 1)[-1].replace(".png", "")
|
||||
counter_val = int(counter_str)
|
||||
max_counter = max(max_counter, counter_val)
|
||||
except:
|
||||
pass
|
||||
|
||||
return max_counter + 1
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"MultiPromptGenerator": MultiPromptGenerator
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"MultiPromptGenerator": "Multi Prompt Generator"
|
||||
}
|
||||
Reference in New Issue
Block a user