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import os, shutil, json, requests, random, time, runpod
from urllib.parse import urlsplit
import torch
from PIL import Image
import numpy as np
from nodes import NODE_CLASS_MAPPINGS, load_custom_node
from comfy_extras import nodes_flux, nodes_differential_diffusion, nodes_model_advanced, nodes_custom_sampler
load_custom_node("/content/ComfyUI/custom_nodes/comfyui-art-venture")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI_LayerStyle")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI_essentials")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-Advanced-ControlNet")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI_SLK_joy_caption_two")
CheckpointLoaderSimple = NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]()
LoraLoader = NODE_CLASS_MAPPINGS["LoraLoader"]()
ACN_ControlNet = NODE_CLASS_MAPPINGS["ACN_ControlNet++LoaderSingle"]()
LoadBiRefNetModel = NODE_CLASS_MAPPINGS["LayerMask: LoadBiRefNetModel"]()
LoadImage = NODE_CLASS_MAPPINGS["LoadImage"]()
ImageBatch = NODE_CLASS_MAPPINGS["ImageBatch"]()
AV_IPAdapter = NODE_CLASS_MAPPINGS["AV_IPAdapter"]()
ImageScaleToMegapixels = NODE_CLASS_MAPPINGS["ImageScaleToMegapixels"]()
BiRefNetUltraV2 = NODE_CLASS_MAPPINGS["LayerMask: BiRefNetUltraV2"]()
ImageRemoveAlpha = NODE_CLASS_MAPPINGS["LayerUtility: ImageRemoveAlpha"]()
ImageDesaturate = NODE_CLASS_MAPPINGS["ImageDesaturate+"]()
AV_ControlNetPreprocessor = NODE_CLASS_MAPPINGS["AV_ControlNetPreprocessor"]()
ControlNetApplyAdvanced = NODE_CLASS_MAPPINGS["ControlNetApplyAdvanced"]()
CLIPTextEncode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
Joy_caption_two_load = NODE_CLASS_MAPPINGS["Joy_caption_two_load"]()
Joy_caption_two = NODE_CLASS_MAPPINGS["Joy_caption_two"]()
EmptyLatentImage = NODE_CLASS_MAPPINGS["EmptyLatentImage"]()
GetImageSize = NODE_CLASS_MAPPINGS["GetImageSize+"]()
KSampler = NODE_CLASS_MAPPINGS["KSampler"]()
VAEDecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
with torch.inference_mode():
unet, clip, vae = CheckpointLoaderSimple.load_checkpoint("sdxl/leosamsHelloworldXL_helloworldXL70.safetensors")
lora_unet, lora_clip = LoraLoader.load_lora(unet, clip, "sdxl/araminta_k_midsommar_cartoon.safetensors", 0.80, 1.0)
control_net = ACN_ControlNet.load_controlnet_plusplus("sdxl/controlnet-union-sdxl-1.0-promax.safetensors", "canny/lineart/mlsd")[0]
birefnet_model = LoadBiRefNetModel.load_birefnet_model("BiRefNet-general-epoch_244.pth")[0]
joy_two_pipeline = Joy_caption_two_load.generate("Llama-3.1-8B-Lexi-Uncensored-V2")[0]
def download_file(url, save_dir, file_name):
os.makedirs(save_dir, exist_ok=True)
file_suffix = os.path.splitext(urlsplit(url).path)[1]
file_name_with_suffix = file_name + file_suffix
file_path = os.path.join(save_dir, file_name_with_suffix)
response = requests.get(url)
response.raise_for_status()
with open(file_path, 'wb') as file:
file.write(response.content)
return file_path
@torch.inference_mode()
def generate(input):
values = input["input"]
input_image = values['input_image']
input_image = download_file(url=input_image, save_dir='/content/ComfyUI/input', file_name='input_image')
style_image1 = values['style_image1']
style_image1 = download_file(url=style_image1, save_dir='/content/ComfyUI/input', file_name='style_image1')
style_image2 = values['style_image2']
style_image2 = download_file(url=style_image2, save_dir='/content/ComfyUI/input', file_name='style_image2')
style_image3 = values['style_image3']
style_image3 = download_file(url=style_image3, save_dir='/content/ComfyUI/input', file_name='style_image3')
style_image4 = values['style_image4']
style_image4 = download_file(url=style_image4, save_dir='/content/ComfyUI/input', file_name='style_image4')
negative_prompt = values['negative_prompt']
seed = values['seed']
steps = values['steps']
cfg = values['cfg']
sampler_name = values['sampler_name']
scheduler = values['scheduler']
width = values['width']
height = values['height']
enable_image_caption = values['enable_image_caption']
if seed == 0:
random.seed(int(time.time()))
seed = random.randint(0, 18446744073709551615)
input_image = LoadImage.load_image(input_image)[0]
input_image = ImageScaleToMegapixels.image_scale_down_to_total_pixels(input_image, megapixels=1.0)[0]
input_image, input_mask = BiRefNetUltraV2.birefnet_ultra_v2(input_image, birefnet_model, detail_method="VITMatte", detail_erode=4, detail_dilate=2, black_point=0.01, white_point=0.99, process_detail=False, device="cuda", max_megapixels=2.0)
input_image = ImageRemoveAlpha.image_remove_alpha(input_image, fill_background=True, background_color="#FFFFFF", mask=input_mask)[0]
input_image = ImageDesaturate.execute(input_image, factor=1.0, method="luminance (Rec.601)")[0]
if enable_image_caption:
caption_type = values['caption_type']
caption_length = values['caption_length']
low_vram = values['low_vram']
positive_prompt = Joy_caption_two.generate(joy_two_pipeline, input_image, caption_type, caption_length, low_vram)[0]
else:
positive_prompt = values['positive_prompt']
style_image1 = LoadImage.load_image(style_image1)[0]
style_image2 = LoadImage.load_image(style_image2)[0]
style_image3 = LoadImage.load_image(style_image3)[0]
style_image4 = LoadImage.load_image(style_image4)[0]
batch_image1 = ImageBatch.batch(style_image1, style_image2)[0]
batch_image2 = ImageBatch.batch(style_image3, style_image4)[0]
batch_image3 = ImageBatch.batch(batch_image1, batch_image2)[0]
ip_unet = AV_IPAdapter.apply_ip_adapter("ip-adapter_sdxl_vit-h.safetensors", "CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors", lora_unet, batch_image3, weight=1.5, weight_type="style transfer", start_at=0, end_at=1)[0]
canny_image = AV_ControlNetPreprocessor.detect_controlnet(input_image, preprocessor="canny", sd_version="sdxl", resolution=640, preprocessor_override="None")[0]
positive = CLIPTextEncode.encode(clip, positive_prompt)[0]
negative = CLIPTextEncode.encode(clip, negative_prompt)[0]
positive, negative = ControlNetApplyAdvanced.apply_controlnet(positive, negative, control_net, canny_image, strength=0.65, start_percent=0.0, end_percent=0.91, vae=vae)
latent_image = EmptyLatentImage.generate(width, height, batch_size=1)[0]
samples = KSampler.sample(ip_unet, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0)[0]
decoded = VAEDecode.decode(vae, samples)[0].detach()
Image.fromarray(np.array(decoded*255, dtype=np.uint8)[0]).save(f"/content/sdxl-line-art-style-transfer-{seed}-tost.png")
result = f"/content/sdxl-line-art-style-transfer-{seed}-tost.png"
try:
notify_uri = values['notify_uri']
del values['notify_uri']
notify_token = values['notify_token']
del values['notify_token']
discord_id = values['discord_id']
del values['discord_id']
if(discord_id == "discord_id"):
discord_id = os.getenv('com_camenduru_discord_id')
discord_channel = values['discord_channel']
del values['discord_channel']
if(discord_channel == "discord_channel"):
discord_channel = os.getenv('com_camenduru_discord_channel')
discord_token = values['discord_token']
del values['discord_token']
if(discord_token == "discord_token"):
discord_token = os.getenv('com_camenduru_discord_token')
job_id = values['job_id']
del values['job_id']
default_filename = os.path.basename(result)
with open(result, "rb") as file:
files = {default_filename: file.read()}
payload = {"content": f"{json.dumps(values)} <@{discord_id}>"}
response = requests.post(
f"https://discord.com/api/v9/channels/{discord_channel}/messages",
data=payload,
headers={"Authorization": f"Bot {discord_token}"},
files=files
)
response.raise_for_status()
result_url = response.json()['attachments'][0]['url']
notify_payload = {"jobId": job_id, "result": result_url, "status": "DONE"}
web_notify_uri = os.getenv('com_camenduru_web_notify_uri')
web_notify_token = os.getenv('com_camenduru_web_notify_token')
if(notify_uri == "notify_uri"):
requests.post(web_notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
else:
requests.post(web_notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
requests.post(notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": notify_token})
return {"jobId": job_id, "result": result_url, "status": "DONE"}
except Exception as e:
error_payload = {"jobId": job_id, "status": "FAILED"}
try:
if(notify_uri == "notify_uri"):
requests.post(web_notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
else:
requests.post(web_notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
requests.post(notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": notify_token})
except:
pass
return {"jobId": job_id, "result": f"FAILED: {str(e)}", "status": "FAILED"}
finally:
if os.path.exists(result):
os.remove(result)
runpod.serverless.start({"handler": generate}) |