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import os
import random
import sys
from typing import Sequence, Mapping, Any, Union
from functools import partial
import torch
import gradio as gr
from PIL import Image
import numpy as np
import spaces
# Import FreeU for quality improvements
from comfy_extras.nodes_freelunch import FreeU_V2
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="stable-diffusion-v1-5/stable-diffusion-v1-5", filename="v1-5-pruned-emaonly.ckpt", local_dir="models/checkpoints")
hf_hub_download(repo_id="Lykon/DreamShaper", filename="DreamShaper_3.32_baked_vae_clip_fix_half.safetensors", local_dir="models/checkpoints")
hf_hub_download(repo_id="Lykon/DreamShaper", filename="DreamShaper_6.31_BakedVae_pruned.safetensors", local_dir="models/checkpoints")
hf_hub_download(repo_id="latentcat/latentcat-controlnet", filename="models/control_v1p_sd15_brightness.safetensors", local_dir="models/controlnet")
hf_hub_download(repo_id="comfyanonymous/ControlNet-v1-1_fp16_safetensors", filename="control_v11f1e_sd15_tile_fp16.safetensors", local_dir="models/controlnet")
hf_hub_download(repo_id="Lykon/dreamshaper-7", filename="vae/diffusion_pytorch_model.fp16.safetensors", local_dir="models")
hf_hub_download(repo_id="stabilityai/sd-vae-ft-mse-original", filename="vae-ft-mse-840000-ema-pruned.safetensors", local_dir="models/vae")
hf_hub_download(repo_id="lllyasviel/Annotators", filename="RealESRGAN_x4plus.pth", local_dir="models/upscale_models")
def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
"""Returns the value at the given index of a sequence or mapping.
If the object is a sequence (like list or string), returns the value at the given index.
If the object is a mapping (like a dictionary), returns the value at the index-th key.
Some return a dictionary, in these cases, we look for the "results" key
Args:
obj (Union[Sequence, Mapping]): The object to retrieve the value from.
index (int): The index of the value to retrieve.
Returns:
Any: The value at the given index.
Raises:
IndexError: If the index is out of bounds for the object and the object is not a mapping.
"""
try:
return obj[index]
except KeyError:
return obj["result"][index]
def find_path(name: str, path: str = None) -> str:
"""
Recursively looks at parent folders starting from the given path until it finds the given name.
Returns the path as a Path object if found, or None otherwise.
"""
# If no path is given, use the current working directory
if path is None:
path = os.getcwd()
# Check if the current directory contains the name
if name in os.listdir(path):
path_name = os.path.join(path, name)
print(f"{name} found: {path_name}")
return path_name
# Get the parent directory
parent_directory = os.path.dirname(path)
# If the parent directory is the same as the current directory, we've reached the root and stop the search
if parent_directory == path:
return None
# Recursively call the function with the parent directory
return find_path(name, parent_directory)
def add_comfyui_directory_to_sys_path() -> None:
"""
Add 'ComfyUI' to the sys.path
"""
comfyui_path = find_path("ComfyUI")
if comfyui_path is not None and os.path.isdir(comfyui_path):
sys.path.append(comfyui_path)
print(f"'{comfyui_path}' added to sys.path")
def add_extra_model_paths() -> None:
"""
Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path.
"""
try:
from main import load_extra_path_config
except ImportError:
print(
"Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead."
)
from utils.extra_config import load_extra_path_config
extra_model_paths = find_path("extra_model_paths.yaml")
if extra_model_paths is not None:
load_extra_path_config(extra_model_paths)
else:
print("Could not find the extra_model_paths config file.")
add_comfyui_directory_to_sys_path()
add_extra_model_paths()
def import_custom_nodes() -> None:
"""Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS
This function sets up a new asyncio event loop, initializes the PromptServer,
creates a PromptQueue, and initializes the custom nodes.
"""
import asyncio
import execution
from nodes import init_extra_nodes
import server
# Creating a new event loop and setting it as the default loop
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# Creating an instance of PromptServer with the loop
server_instance = server.PromptServer(loop)
execution.PromptQueue(server_instance)
# Initializing custom nodes
init_extra_nodes()
from nodes import NODE_CLASS_MAPPINGS
# Initialize common nodes
checkpointloadersimple = NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]()
checkpointloadersimple_4 = checkpointloadersimple.load_checkpoint(
ckpt_name="DreamShaper_3.32_baked_vae_clip_fix_half.safetensors"
)
checkpointloadersimple_artistic = checkpointloadersimple.load_checkpoint(
ckpt_name="DreamShaper_6.31_BakedVae_pruned.safetensors"
)
emptylatentimage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()
cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
controlnetloader = NODE_CLASS_MAPPINGS["ControlNetLoader"]()
controlnetapplyadvanced = NODE_CLASS_MAPPINGS["ControlNetApplyAdvanced"]()
ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
import_custom_nodes()
comfy_qr_by_module_size = NODE_CLASS_MAPPINGS["comfy-qr-by-module-size"]()
tilepreprocessor = NODE_CLASS_MAPPINGS["TilePreprocessor"]()
# Load upscale model and additional nodes for artistic pipeline
upscalemodelloader = NODE_CLASS_MAPPINGS["UpscaleModelLoader"]()
upscalemodelloader_30 = upscalemodelloader.load_model(
model_name="RealESRGAN_x4plus.pth"
)
imageupscalewithmodel = NODE_CLASS_MAPPINGS["ImageUpscaleWithModel"]()
imagescale = NODE_CLASS_MAPPINGS["ImageScale"]()
latentupscaleby = NODE_CLASS_MAPPINGS["LatentUpscaleBy"]()
from comfy import model_management
# Add all the models that load a safetensors file
model_loaders = [checkpointloadersimple_4, checkpointloadersimple_artistic]
# Check which models are valid and how to best load them
valid_models = [
getattr(loader[0], 'patcher', loader[0])
for loader in model_loaders
if not isinstance(loader[0], dict) and not isinstance(getattr(loader[0], 'patcher', None), dict)
]
model_management.load_models_gpu(valid_models)
@spaces.GPU(duration=30)
def generate_qr_code_unified(prompt: str, text_input: str, input_type: str = "URL", image_size: int = 512, border_size: int = 4, error_correction: str = "Medium (15%)", module_size: int = 12, module_drawer: str = "Square", use_custom_seed: bool = False, seed: int = 0, pipeline: str = "standard"):
# Only manipulate the text if it's a URL input type
qr_text = text_input
if input_type == "URL":
if "https://" in qr_text:
qr_text = qr_text.replace("https://", "")
if "http://" in qr_text:
qr_text = qr_text.replace("http://", "")
# Use custom seed or random
actual_seed = seed if use_custom_seed else random.randint(1, 2**64)
with torch.inference_mode():
if pipeline == "standard":
yield from _pipeline_standard(prompt, qr_text, input_type, image_size, border_size, error_correction, module_size, module_drawer, actual_seed)
else: # artistic
yield from _pipeline_artistic(prompt, qr_text, input_type, image_size, border_size, error_correction, module_size, module_drawer, actual_seed)
def add_noise_to_border_only(image_tensor, seed: int, border_size: int, image_size: int, module_size: int = 12):
"""
Add QR-like cubic patterns ONLY to the border region of a QR code image.
Creates black squares that resemble QR modules for a smooth transition.
The density of border cubics automatically matches the QR code interior density.
Args:
image_tensor: ComfyUI image tensor (batch, height, width, channels) with values 0-1
seed: Random seed for reproducible noise
border_size: Border size in QR modules (from QR generation settings)
image_size: Image size in pixels
module_size: Size of QR modules in pixels (for cubic pattern)
Returns:
Modified tensor with QR-like cubic patterns in border region
"""
# Early return if no border
if border_size == 0:
return image_tensor
# Convert to numpy for manipulation
img_np = image_tensor.cpu().numpy()
# Set random seed for reproducibility (ensure it's within numpy's valid range)
np.random.seed(seed % (2**32))
# Work with first image in batch
img = img_np[0] # (height, width, channels)
height, width, channels = img.shape
# Calculate border region in pixels using exact QR parameters
border_thickness = border_size * module_size # Exact border size in pixels
# Create border mask (1 for border region, 0 for QR code interior)
border_mask = np.zeros((height, width), dtype=bool)
# Top border
border_mask[0:border_thickness, :] = True
# Bottom border
border_mask[height-border_thickness:height, :] = True
# Left border
border_mask[:, 0:border_thickness] = True
# Right border
border_mask[:, width-border_thickness:width] = True
# Only apply to white/light areas in the border (threshold > 240)
img_255 = (img * 255).astype(np.uint8)
white_mask = np.all(img_255 > 240, axis=-1)
# Combine: only border AND white areas
final_mask = border_mask & white_mask
# Calculate QR code interior density to determine border cubic density
interior_mask = ~border_mask # Inverse of border = QR interior
interior_pixels = img_255[interior_mask][:, 0] # Get first channel (grayscale)
black_count = (interior_pixels < 128).sum() # Count black pixels (< 128)
total_count = len(interior_pixels)
qr_density = float(black_count) / float(total_count) if total_count > 0 else 0.5
# Use QR interior density as probability for placing border cubics
# This creates a natural transition matching the QR pattern density
# Generate QR-like cubic pattern noise
# Create a grid based on module_size
for y in range(0, height, module_size):
for x in range(0, width, module_size):
# Check if this module position is mostly in the border area
y_end = min(y + module_size, height)
x_end = min(x + module_size, width)
# Count how many pixels in this module are in the final_mask
module_region = final_mask[y:y_end, x:x_end]
# If at least 50% of the module is in the border, we can place a cubic here
if module_region.sum() > (module_size * module_size * 0.5):
# Randomly decide to place a black cubic based on QR interior density
if np.random.random() < qr_density:
# Place a black square (cubic) - set all channels to 0 (black)
for c in range(channels):
img[y:y_end, x:x_end, c] = 0
# Put modified image back into batch array
img_np[0] = img
# Convert back to tensor
return torch.from_numpy(img_np).to(image_tensor.device)
def _pipeline_standard(prompt: str, qr_text: str, input_type: str, image_size: int, border_size: int, error_correction: str, module_size: int, module_drawer: str, seed: int):
emptylatentimage_5 = emptylatentimage.generate(
width=image_size, height=image_size, batch_size=1
)
cliptextencode_6 = cliptextencode.encode(
text=prompt,
clip=get_value_at_index(checkpointloadersimple_4, 1),
)
cliptextencode_7 = cliptextencode.encode(
text="ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft, closed eyes, text, logo",
clip=get_value_at_index(checkpointloadersimple_4, 1),
)
controlnetloader_10 = controlnetloader.load_controlnet(
control_net_name="models/control_v1p_sd15_brightness.safetensors"
)
controlnetloader_12 = controlnetloader.load_controlnet(
control_net_name="control_v11f1e_sd15_tile_fp16.safetensors"
)
# Set protocol based on input type: None for plain text, Https for URLs
qr_protocol = "None" if input_type == "Plain Text" else "Https"
try:
comfy_qr_by_module_size_15 = comfy_qr_by_module_size.generate_qr(
protocol=qr_protocol,
text=qr_text,
module_size=module_size,
max_image_size=image_size,
fill_hexcolor="#000000",
back_hexcolor="#FFFFFF",
error_correction=error_correction,
border=border_size,
module_drawer=module_drawer,
)
except RuntimeError as e:
error_msg = (
f"Error generating QR code: {str(e)}\n"
"Try with a shorter text, increase the image size, or decrease the border size, module size, and error correction level under Advanced Settings."
)
yield None, error_msg
return
# 1) Yield the base QR image as the first intermediate result
base_qr_tensor = get_value_at_index(comfy_qr_by_module_size_15, 0)
base_qr_np = (base_qr_tensor.cpu().numpy() * 255).astype(np.uint8)
base_qr_np = base_qr_np[0]
base_qr_pil = Image.fromarray(base_qr_np)
yield base_qr_pil, "Generated base QR pattern… enhancing with AI (step 1/3)"
emptylatentimage_17 = emptylatentimage.generate(
width=image_size*2, height=image_size*2, batch_size=1
)
controlnetloader_19 = controlnetloader.load_controlnet(
control_net_name="control_v11f1e_sd15_tile_fp16.safetensors"
)
for q in range(1):
controlnetapplyadvanced_11 = controlnetapplyadvanced.apply_controlnet(
strength=0.45,
start_percent=0,
end_percent=1,
positive=get_value_at_index(cliptextencode_6, 0),
negative=get_value_at_index(cliptextencode_7, 0),
control_net=get_value_at_index(controlnetloader_10, 0),
image=get_value_at_index(comfy_qr_by_module_size_15, 0),
vae=get_value_at_index(checkpointloadersimple_4, 2),
)
tilepreprocessor_14 = tilepreprocessor.execute(
pyrUp_iters=3,
resolution=image_size,
image=get_value_at_index(comfy_qr_by_module_size_15, 0),
)
controlnetapplyadvanced_13 = controlnetapplyadvanced.apply_controlnet(
strength=0.45,
start_percent=0,
end_percent=1,
positive=get_value_at_index(controlnetapplyadvanced_11, 0),
negative=get_value_at_index(controlnetapplyadvanced_11, 1),
control_net=get_value_at_index(controlnetloader_12, 0),
image=get_value_at_index(tilepreprocessor_14, 0),
vae=get_value_at_index(checkpointloadersimple_4, 2),
)
ksampler_3 = ksampler.sample(
seed=seed,
steps=20,
cfg=7,
sampler_name="dpmpp_2m",
scheduler="karras",
denoise=1,
model=get_value_at_index(checkpointloadersimple_4, 0),
positive=get_value_at_index(controlnetapplyadvanced_13, 0),
negative=get_value_at_index(controlnetapplyadvanced_13, 1),
latent_image=get_value_at_index(emptylatentimage_5, 0),
)
vaedecode_8 = vaedecode.decode(
samples=get_value_at_index(ksampler_3, 0),
vae=get_value_at_index(checkpointloadersimple_4, 2),
)
# 2) Yield the first decoded image as a second intermediate result
mid_tensor = get_value_at_index(vaedecode_8, 0)
mid_np = (mid_tensor.cpu().numpy() * 255).astype(np.uint8)
mid_np = mid_np[0]
mid_pil = Image.fromarray(mid_np)
yield mid_pil, "First enhancement pass complete (step 2/3)… refining details"
controlnetapplyadvanced_20 = controlnetapplyadvanced.apply_controlnet(
strength=1,
start_percent=0,
end_percent=1,
positive=get_value_at_index(cliptextencode_6, 0),
negative=get_value_at_index(cliptextencode_7, 0),
control_net=get_value_at_index(controlnetloader_19, 0),
image=get_value_at_index(vaedecode_8, 0),
vae=get_value_at_index(checkpointloadersimple_4, 2),
)
ksampler_18 = ksampler.sample(
seed=seed + 1,
steps=20,
cfg=7,
sampler_name="dpmpp_2m",
scheduler="karras",
denoise=1,
model=get_value_at_index(checkpointloadersimple_4, 0),
positive=get_value_at_index(controlnetapplyadvanced_20, 0),
negative=get_value_at_index(controlnetapplyadvanced_20, 1),
latent_image=get_value_at_index(emptylatentimage_17, 0),
)
vaedecode_21 = vaedecode.decode(
samples=get_value_at_index(ksampler_18, 0),
vae=get_value_at_index(checkpointloadersimple_4, 2),
)
# 3) Yield the final enhanced image
image_tensor = get_value_at_index(vaedecode_21, 0)
image_np = (image_tensor.cpu().numpy() * 255).astype(np.uint8)
image_np = image_np[0]
pil_image = Image.fromarray(image_np)
yield pil_image, "No errors, all good! Final QR art generated."
def _pipeline_artistic(prompt: str, qr_text: str, input_type: str, image_size: int, border_size: int, error_correction: str, module_size: int, module_drawer: str, seed: int):
# Generate QR code
qr_protocol = "None" if input_type == "Plain Text" else "Https"
try:
comfy_qr = comfy_qr_by_module_size.generate_qr(
protocol=qr_protocol,
text=qr_text,
module_size=module_size,
max_image_size=image_size,
fill_hexcolor="#000000",
back_hexcolor="#FFFFFF",
error_correction=error_correction,
border=border_size,
module_drawer=module_drawer,
)
except RuntimeError as e:
error_msg = (
f"Error generating QR code: {str(e)}\n"
"Try with a shorter text, increase the image size, or decrease the border size, module size, and error correction level under Advanced Settings."
)
yield None, error_msg
return
# Show the base QR code
base_qr_tensor = get_value_at_index(comfy_qr, 0)
base_qr_np = (base_qr_tensor.cpu().numpy() * 255).astype(np.uint8)
base_qr_np = base_qr_np[0]
base_qr_pil = Image.fromarray(base_qr_np)
# Only add noise if there's a border (border_size > 0)
if border_size > 0:
yield base_qr_pil, "Generated base QR pattern... adding QR-like cubics to border (step 1/5)"
# Add QR-like cubic patterns ONLY to border region (extends QR structure into border)
# Density automatically matches QR code interior density for natural transition
qr_with_border_noise = add_noise_to_border_only(
get_value_at_index(comfy_qr, 0),
seed=seed + 100,
border_size=border_size,
image_size=image_size,
module_size=module_size, # Use same module size as QR code
)
# Show the noisy QR so you can see the border cubic pattern effect
noisy_qr_np = (qr_with_border_noise.cpu().numpy() * 255).astype(np.uint8)
noisy_qr_np = noisy_qr_np[0]
noisy_qr_pil = Image.fromarray(noisy_qr_np)
yield noisy_qr_pil, "Added QR-like cubics to border... enhancing with AI (step 2/5)"
else:
# No border, skip noise
qr_with_border_noise = get_value_at_index(comfy_qr, 0)
yield base_qr_pil, "Generated base QR pattern (no border)... enhancing with AI (step 1/4)"
# Generate latent image
latent_image = emptylatentimage.generate(
width=image_size, height=image_size, batch_size=1
)
# Encode text prompts
positive_prompt = cliptextencode.encode(
text=prompt,
clip=get_value_at_index(checkpointloadersimple_artistic, 1),
)
negative_prompt = cliptextencode.encode(
text="ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft, closed eyes, text, logo",
clip=get_value_at_index(checkpointloadersimple_artistic, 1),
)
# Load controlnets
brightness_controlnet = controlnetloader.load_controlnet(
control_net_name="models/control_v1p_sd15_brightness.safetensors"
)
tile_controlnet = controlnetloader.load_controlnet(
control_net_name="control_v11f1e_sd15_tile_fp16.safetensors"
)
# First ControlNet pass (using QR with border cubics)
controlnet_apply = controlnetapplyadvanced.apply_controlnet(
strength=0.45,
start_percent=0,
end_percent=1,
positive=get_value_at_index(positive_prompt, 0),
negative=get_value_at_index(negative_prompt, 0),
control_net=get_value_at_index(brightness_controlnet, 0),
image=qr_with_border_noise,
vae=get_value_at_index(checkpointloadersimple_artistic, 2),
)
# Tile preprocessor (using QR with border cubics)
tile_processed = tilepreprocessor.execute(
pyrUp_iters=3,
resolution=image_size,
image=qr_with_border_noise,
)
# Second ControlNet pass (using tile processed from QR with border cubics)
controlnet_apply = controlnetapplyadvanced.apply_controlnet(
strength=0.45,
start_percent=0,
end_percent=1,
positive=get_value_at_index(controlnet_apply, 0),
negative=get_value_at_index(controlnet_apply, 1),
control_net=get_value_at_index(tile_controlnet, 0),
image=get_value_at_index(tile_processed, 0),
vae=get_value_at_index(checkpointloadersimple_artistic, 2),
)
# Apply FreeU_V2 for enhanced quality (better detail, texture, and cleaner output)
base_model = get_value_at_index(checkpointloadersimple_artistic, 0)
freeu = FreeU_V2()
freeu_model = freeu.patch(
model=base_model,
b1=1.3, # Backbone feature enhancement - improves fine details (reduced for more blending)
b2=1.4, # Backbone feature enhancement (layer 2) - improves textures (reduced)
s1=0.9, # Skip connection dampening - reduces QR structure visibility (increased)
s2=0.2 # Skip connection dampening (layer 2) - hides more cubics (increased)
)[0]
# Apply SEG (Self-Attention Guidance) for improved structural coherence
# DISABLED: SEG blurs attention maps which rounds position marker corners, affecting scannability
# smoothed_energy = NODE_CLASS_MAPPINGS["SelfAttentionGuidance"]()
# enhanced_model = smoothed_energy.patch(
# model=freeu_model,
# scale=1.5,
# blur_sigma=0.5,
# )[0]
enhanced_model = freeu_model # Use only FreeU, skip SEG
# First sampling pass
samples = ksampler.sample(
seed=seed,
steps=30,
cfg=7,
sampler_name="dpmpp_3m_sde",
scheduler="karras",
denoise=1,
model=enhanced_model, # Using FreeU + SAG enhanced model
positive=get_value_at_index(controlnet_apply, 0),
negative=get_value_at_index(controlnet_apply, 1),
latent_image=get_value_at_index(latent_image, 0),
)
# First decode
decoded = vaedecode.decode(
samples=get_value_at_index(samples, 0),
vae=get_value_at_index(checkpointloadersimple_artistic, 2),
)
# Show first pass result
first_pass_tensor = get_value_at_index(decoded, 0)
first_pass_np = (first_pass_tensor.cpu().numpy() * 255).astype(np.uint8)
first_pass_np = first_pass_np[0]
first_pass_pil = Image.fromarray(first_pass_np)
step_msg = "First enhancement pass complete (step 3/5)... upscaling image" if border_size > 0 else "First enhancement pass complete (step 2/4)... upscaling image"
yield first_pass_pil, step_msg
# Upscale image with model
upscaled = imageupscalewithmodel.upscale(
upscale_model=get_value_at_index(upscalemodelloader_30, 0),
image=get_value_at_index(decoded, 0),
)
# Resize to target size
resized = imagescale.upscale(
upscale_method="area",
width=image_size*2,
height=image_size*2,
crop="disabled",
image=get_value_at_index(upscaled, 0),
)
# Show upscaled result
upscaled_tensor = get_value_at_index(resized, 0)
upscaled_np = (upscaled_tensor.cpu().numpy() * 255).astype(np.uint8)
upscaled_np = upscaled_np[0]
upscaled_pil = Image.fromarray(upscaled_np)
step_msg = "Image upscaled (step 4/5)... final refinement pass" if border_size > 0 else "Image upscaled (step 3/4)... final refinement pass"
yield upscaled_pil, step_msg
# Final ControlNet pass
controlnet_apply_final = controlnetapplyadvanced.apply_controlnet(
strength=0.7,
start_percent=0,
end_percent=1,
positive=get_value_at_index(positive_prompt, 0),
negative=get_value_at_index(negative_prompt, 0),
control_net=get_value_at_index(tile_controlnet, 0),
image=get_value_at_index(resized, 0),
vae=get_value_at_index(checkpointloadersimple_artistic, 2),
)
# Upscale latent
upscaled_latent = latentupscaleby.upscale(
upscale_method="area",
scale_by=2.0,
samples=get_value_at_index(samples, 0),
)
# Final sampling pass
final_samples = ksampler.sample(
seed=seed + 1,
steps=30,
cfg=7,
sampler_name="dpmpp_3m_sde",
scheduler="karras",
denoise=0.8,
model=enhanced_model, # Using FreeU + SAG enhanced model
positive=get_value_at_index(controlnet_apply_final, 0),
negative=get_value_at_index(controlnet_apply_final, 1),
latent_image=get_value_at_index(upscaled_latent, 0),
)
# Final decode
final_decoded = vaedecode.decode(
samples=get_value_at_index(final_samples, 0),
vae=get_value_at_index(checkpointloadersimple_artistic, 2),
)
# Convert to PIL Image and return
image_tensor = get_value_at_index(final_decoded, 0)
image_np = (image_tensor.cpu().numpy() * 255).astype(np.uint8)
image_np = image_np[0]
final_image = Image.fromarray(image_np)
step_msg = "No errors, all good! Final artistic QR code generated. (step 5/5)" if border_size > 0 else "No errors, all good! Final artistic QR code generated. (step 4/4)"
yield final_image, step_msg
if __name__ == "__main__":
# Start your Gradio app
with gr.Blocks() as app:
# Add a title and description
gr.Markdown("# QR Code Art Generator")
gr.Markdown("""
This is an AI-powered QR code generator that creates artistic QR codes using Stable Diffusion 1.5 and ControlNet models.
The application uses a custom ComfyUI workflow to generate QR codes.
### Tips:
- Use detailed prompts for better results
- Include style keywords like 'photorealistic', 'detailed', '8k'
- Choose **URL** mode for web links or **Plain Text** mode for VCARD, WiFi credentials, calendar events, etc.
- Try the examples below for inspiration
### Two Modes:
- **Standard QR**: Stable, accurate QR code generation (faster, more scannable)
- **Artistic QR**: More artistic and creative results with upscaling (slower, more creative)
### Note:
Feel free to share your suggestions or feedback on how to improve the app! Thanks!
""")
# Add tabs for different generation methods
with gr.Tabs():
# STANDARD QR TAB
with gr.TabItem("Standard QR"):
with gr.Row():
with gr.Column():
# Add input type selector
input_type = gr.Radio(
choices=["URL", "Plain Text"],
value="URL",
label="Input Type",
info="URL: For web links (auto-removes https://). Plain Text: For VCARD, WiFi, calendar, location, etc. (no manipulation)"
)
# Add inputs
prompt_input = gr.Textbox(
label="Prompt",
placeholder="Describe the image you want to generate (check examples below for inspiration)",
value="Enter your prompt here... For example: 'a beautiful sunset over mountains, photorealistic, detailed landscape'",
lines=3
)
text_input = gr.Textbox(
label="QR Code Content",
placeholder="Enter URL or plain text",
value="Enter your URL or text here... For example: https://github.com",
lines=3
)
with gr.Accordion("Advanced Settings", open=False):
# Add image size slider
image_size = gr.Slider(
minimum=512,
maximum=1024,
step=64,
value=512,
label="Image Size",
info="Base size of the generated image. Final output will be 2x this size (e.g., 512 → 1024) due to the two-step enhancement process. Higher values use more VRAM and take longer to process."
)
# Add border size slider
border_size = gr.Slider(
minimum=0,
maximum=8,
step=1,
value=4,
label="QR Code Border Size",
info="Number of modules (squares) to use as border around the QR code. Higher values add more whitespace."
)
# Add error correction dropdown
error_correction = gr.Dropdown(
choices=["Low (7%)", "Medium (15%)", "Quartile (25%)", "High (30%)"],
value="Medium (15%)",
label="Error Correction Level",
info="Higher error correction makes the QR code more scannable when damaged or obscured, but increases its size and complexity. Medium (15%) is a good starting point for most uses."
)
# Add module size slider
module_size = gr.Slider(
minimum=4,
maximum=16,
step=1,
value=12,
label="QR Module Size",
info="Pixel width of the smallest QR code unit. Larger values improve readability but require a larger image size. 12 is a good starting point."
)
# Add module drawer dropdown with style examples
module_drawer = gr.Dropdown(
choices=["Square", "Gapped Square", "Circle", "Rounded", "Vertical bars", "Horizontal bars"],
value="Square",
label="QR Code Style",
info="Select the style of the QR code modules (squares). See examples below. Different styles can give your QR code a unique look while maintaining scannability."
)
# Add seed controls
use_custom_seed = gr.Checkbox(
label="Use Custom Seed",
value=False,
info="Enable to use a specific seed for reproducible results"
)
seed = gr.Slider(
minimum=0,
maximum=2000000,
step=1,
value=0,
label="Seed",
info="Seed value for reproducibility. Same seed with same settings will produce the same result."
)
# Add style examples with labels
gr.Markdown("### Style Examples:")
# First row of examples
with gr.Row():
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Square**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/square.png", width=100, show_label=False, show_download_button=False)
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Gapped Square**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/gapped_square.png", width=100, show_label=False, show_download_button=False)
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Circle**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/circle.png", width=100, show_label=False, show_download_button=False)
# Second row of examples
with gr.Row():
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Rounded**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/rounded.png", width=100, show_label=False, show_download_button=False)
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Vertical Bars**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/vertical-bars.png", width=100, show_label=False, show_download_button=False)
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Horizontal Bars**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/horizontal-bars.png", width=100, show_label=False, show_download_button=False)
# The generate button
generate_btn = gr.Button("Generate Standard QR", variant="primary")
with gr.Column():
# The output image
output_image = gr.Image(label="Generated Standard QR Code")
error_message = gr.Textbox(
label="Status / Errors",
interactive=False,
lines=3,
)
# When clicking the button, it will trigger the main function
generate_btn.click(
fn=partial(generate_qr_code_unified, pipeline="standard"),
inputs=[prompt_input, text_input, input_type, image_size, border_size, error_correction, module_size, module_drawer, use_custom_seed, seed],
outputs=[output_image, error_message]
)
# Add examples
examples = [
[
"some clothes spread on ropes, realistic, great details, out in the open air sunny day realistic, great details,absence of people, Detailed and Intricate, CGI, Photoshoot,rim light, 8k, 16k, ultra detail",
"https://www.google.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"some cards on poker tale, realistic, great details, realistic, great details,absence of people, Detailed and Intricate, CGI, Photoshoot,rim light, 8k, 16k, ultra detail",
"https://store.steampowered.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"a beautiful sunset over mountains, photorealistic, detailed landscape, golden hour, dramatic lighting, 8k, ultra detailed",
"https://github.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"underwater scene with coral reef and tropical fish, photorealistic, detailed, crystal clear water, sunlight rays, 8k, ultra detailed",
"https://twitter.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"futuristic cityscape with flying cars and neon lights, cyberpunk style, detailed architecture, night scene, 8k, ultra detailed",
"https://linkedin.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"vintage camera on wooden table, photorealistic, detailed textures, soft lighting, bokeh background, 8k, ultra detailed",
"https://instagram.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"business card design, professional, modern, clean layout, corporate style, detailed, 8k, ultra detailed",
"BEGIN:VCARD\nVERSION:3.0\nFN:John Doe\nORG:Acme Corporation\nTITLE:Software Engineer\nTEL:+1-555-123-4567\nEMAIL:john.doe@example.com\nEND:VCARD",
"Plain Text",
832,
4,
"Medium (15%)",
12,
"Square"
],
[
"wifi network symbol, modern tech, digital art, glowing blue, detailed, 8k, ultra detailed",
"WIFI:T:WPA;S:MyNetwork;P:MyPassword123;;",
"Plain Text",
576,
4,
"Medium (15%)",
12,
"Square"
],
[
"calendar appointment reminder, organized planner, professional office, detailed, 8k, ultra detailed",
"BEGIN:VEVENT\nSUMMARY:Team Meeting\nDTSTART:20251115T140000Z\nDTEND:20251115T150000Z\nLOCATION:Conference Room A\nEND:VEVENT",
"Plain Text",
832,
4,
"Medium (15%)",
12,
"Square"
],
[
"location pin on map, travel destination, scenic view, detailed cartography, 8k, ultra detailed",
"geo:37.7749,-122.4194",
"Plain Text",
512,
4,
"Medium (15%)",
12,
"Square"
]
]
gr.Examples(
examples=examples,
inputs=[
prompt_input,
text_input,
input_type,
image_size,
border_size,
error_correction,
module_size,
module_drawer
],
outputs=[output_image, error_message],
fn=partial(generate_qr_code_unified, pipeline="standard"),
cache_examples=False
)
# ARTISTIC QR TAB
with gr.TabItem("Artistic QR"):
with gr.Row():
with gr.Column():
# Add input type selector for artistic QR
artistic_input_type = gr.Radio(
choices=["URL", "Plain Text"],
value="URL",
label="Input Type",
info="URL: For web links (auto-removes https://). Plain Text: For VCARD, WiFi, calendar, location, etc. (no manipulation)"
)
# Add inputs for artistic QR
artistic_prompt_input = gr.Textbox(
label="Prompt",
placeholder="Describe the image you want to generate (check examples below for inspiration)",
value="Enter your prompt here... For example: 'a beautiful sunset over mountains, photorealistic, detailed landscape'",
lines=3
)
artistic_text_input = gr.Textbox(
label="QR Code Content",
placeholder="Enter URL or plain text",
value="Enter your URL or text here... For example: https://github.com",
lines=3
)
with gr.Accordion("Advanced Settings", open=False):
# Add image size slider for artistic QR
artistic_image_size = gr.Slider(
minimum=512,
maximum=1024,
step=64,
value=512,
label="Image Size",
info="Base size of the generated image. Final output will be 2x this size (e.g., 512 → 1024) due to the two-step enhancement process. Higher values use more VRAM and take longer to process."
)
# Add border size slider for artistic QR
artistic_border_size = gr.Slider(
minimum=0,
maximum=8,
step=1,
value=4,
label="QR Code Border Size",
info="Number of modules (squares) to use as border around the QR code. Higher values add more whitespace."
)
# Add error correction dropdown for artistic QR
artistic_error_correction = gr.Dropdown(
choices=["Low (7%)", "Medium (15%)", "Quartile (25%)", "High (30%)"],
value="Medium (15%)",
label="Error Correction Level",
info="Higher error correction makes the QR code more scannable when damaged or obscured, but increases its size and complexity. Medium (15%) is a good starting point for most uses."
)
# Add module size slider for artistic QR
artistic_module_size = gr.Slider(
minimum=4,
maximum=16,
step=1,
value=12,
label="QR Module Size",
info="Pixel width of the smallest QR code unit. Larger values improve readability but require a larger image size. 12 is a good starting point."
)
# Add module drawer dropdown with style examples for artistic QR
artistic_module_drawer = gr.Dropdown(
choices=["Square", "Gapped Square", "Circle", "Rounded", "Vertical bars", "Horizontal bars"],
value="Square",
label="QR Code Style",
info="Select the style of the QR code modules (squares). See examples below. Different styles can give your QR code a unique look while maintaining scannability."
)
# Add seed controls for artistic QR
artistic_use_custom_seed = gr.Checkbox(
label="Use Custom Seed",
value=False,
info="Enable to use a specific seed for reproducible results"
)
artistic_seed = gr.Slider(
minimum=0,
maximum=2000000,
step=1,
value=0,
label="Seed",
info="Seed value for reproducibility. Same seed with same settings will produce the same result."
)
# Add style examples with labels
gr.Markdown("### Style Examples:")
# First row of examples
with gr.Row():
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Square**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/square.png", width=100, show_label=False, show_download_button=False)
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Gapped Square**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/gapped_square.png", width=100, show_label=False, show_download_button=False)
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Circle**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/circle.png", width=100, show_label=False, show_download_button=False)
# Second row of examples
with gr.Row():
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Rounded**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/rounded.png", width=100, show_label=False, show_download_button=False)
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Vertical Bars**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/vertical-bars.png", width=100, show_label=False, show_download_button=False)
with gr.Column(scale=1, min_width=0):
gr.Markdown("**Horizontal Bars**", show_label=False)
gr.Image("custom_nodes/ComfyQR/img/horizontal-bars.png", width=100, show_label=False, show_download_button=False)
# The generate button for artistic QR
artistic_generate_btn = gr.Button("Generate Artistic QR", variant="primary")
with gr.Column():
# The output image for artistic QR
artistic_output_image = gr.Image(label="Generated Artistic QR Code")
artistic_error_message = gr.Textbox(
label="Status / Errors",
interactive=False,
lines=3,
)
# When clicking the button, it will trigger the artistic function
artistic_generate_btn.click(
fn=partial(generate_qr_code_unified, pipeline="artistic"),
inputs=[artistic_prompt_input, artistic_text_input, artistic_input_type, artistic_image_size, artistic_border_size, artistic_error_correction, artistic_module_size, artistic_module_drawer, artistic_use_custom_seed, artistic_seed],
outputs=[artistic_output_image, artistic_error_message]
)
# Add examples for artistic QR
artistic_examples = [
[
"some clothes spread on ropes, realistic, great details, out in the open air sunny day realistic, great details, absence of people, Detailed and Intricate, CGI, Photoshoot, rim light, 8k, 16k, ultra detail",
"https://www.google.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"some cards on poker tale, realistic, great details, realistic, great details,absence of people, Detailed and Intricate, CGI, Photoshoot,rim light, 8k, 16k, ultra detail",
"https://store.steampowered.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"a beautiful sunset over mountains, photorealistic, detailed landscape, golden hour, dramatic lighting, 8k, ultra detailed",
"https://github.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"underwater scene with coral reef and tropical fish, photorealistic, detailed, crystal clear water, sunlight rays, 8k, ultra detailed",
"https://twitter.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"futuristic cityscape with flying cars and neon lights, cyberpunk style, detailed architecture, night scene, 8k, ultra detailed",
"https://linkedin.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"vintage camera on wooden table, photorealistic, detailed textures, soft lighting, bokeh background, 8k, ultra detailed",
"https://instagram.com",
"URL",
512,
4,
"Medium (15%)",
12,
"Square"
],
[
"business card design, professional, modern, clean layout, corporate style, detailed, 8k, ultra detailed",
"BEGIN:VCARD\nVERSION:3.0\nFN:John Doe\nORG:Acme Corporation\nTITLE:Software Engineer\nTEL:+1-555-123-4567\nEMAIL:john.doe@example.com\nEND:VCARD",
"Plain Text",
832,
4,
"Medium (15%)",
12,
"Square"
],
[
"wifi network symbol, modern tech, digital art, glowing blue, detailed, 8k, ultra detailed",
"WIFI:T:WPA;S:MyNetwork;P:MyPassword123;;",
"Plain Text",
576,
4,
"Medium (15%)",
12,
"Square"
],
[
"calendar appointment reminder, organized planner, professional office, detailed, 8k, ultra detailed",
"BEGIN:VEVENT\nSUMMARY:Team Meeting\nDTSTART:20251115T140000Z\nDTEND:20251115T150000Z\nLOCATION:Conference Room A\nEND:VEVENT",
"Plain Text",
832,
4,
"Medium (15%)",
12,
"Square"
],
[
"location pin on map, travel destination, scenic view, detailed cartography, 8k, ultra detailed",
"geo:37.7749,-122.4194",
"Plain Text",
512,
4,
"Medium (15%)",
12,
"Square"
]
]
gr.Examples(
examples=artistic_examples,
inputs=[
artistic_prompt_input,
artistic_text_input,
artistic_input_type,
artistic_image_size,
artistic_border_size,
artistic_error_correction,
artistic_module_size,
artistic_module_drawer
],
outputs=[artistic_output_image, artistic_error_message],
fn=partial(generate_qr_code_unified, pipeline="artistic"),
cache_examples=False
)
app.launch(share=False, mcp_server=True)
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