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import torch
import random
import comfy
from comfy.sd import load_checkpoint_guess_config
import nodes
from nodes import NODE_CLASS_MAPPINGS
from comfy_extras import nodes_post_processing, nodes_differential_diffusion, nodes_upscale_model
import numpy as np
from PIL import Image
import asyncio
import execution
import server
from nodes import load_custom_node
from math import ceil, floor
import os, json, requests, runpod
discord_token = os.getenv('com_camenduru_discord_token')
web_uri = os.getenv('com_camenduru_web_uri')
web_token = os.getenv('com_camenduru_web_token')
def download_file(url, save_dir='/content/ComfyUI/input'):
os.makedirs(save_dir, exist_ok=True)
file_name = url.split('/')[-1]
file_path = os.path.join(save_dir, file_name)
response = requests.get(url)
response.raise_for_status()
with open(file_path, 'wb') as file:
file.write(response.content)
return file_path
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
server_instance = server.PromptServer(loop)
execution.PromptQueue(server_instance)
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-AutomaticCFG")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-Custom-Scripts")
load_custom_node("/content/ComfyUI/custom_nodes/Derfuu_ComfyUI_ModdedNodes")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-Impact-Pack")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-Inspire-Pack")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-KJNodes")
load_custom_node("/content/ComfyUI/custom_nodes/comfyui_controlnet_aux")
load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-TiledDiffusion")
load_custom_node("/content/ComfyUI/custom_nodes/was-node-suite-comfyui")
Automatic_CFG = NODE_CLASS_MAPPINGS["Automatic CFG"]()
ImageScaleToTotalPixels = nodes_post_processing.NODE_CLASS_MAPPINGS["ImageScaleToTotalPixels"]()
GetImageSizeAndCount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
TTPlanet_TileSimple = NODE_CLASS_MAPPINGS["TTPlanet_TileSimple_Preprocessor"]()
TiledDiffusion = NODE_CLASS_MAPPINGS["TiledDiffusion"]()
KSampler_inspire = NODE_CLASS_MAPPINGS["KSampler //Inspire"]()
ControlNetApplyAdvanced = NODE_CLASS_MAPPINGS["ControlNetApplyAdvanced"]()
UltralyticsDetectorProvider = NODE_CLASS_MAPPINGS["UltralyticsDetectorProvider"]()
SegmDetectorSEGS = NODE_CLASS_MAPPINGS["SegmDetectorSEGS"]()
DifferentialDiffusion = nodes_differential_diffusion.NODE_CLASS_MAPPINGS["DifferentialDiffusion"]()
DetailerForEach = NODE_CLASS_MAPPINGS["DetailerForEach"]()
VAEDecodeTiled = NODE_CLASS_MAPPINGS["VAEDecodeTiled"]()
ColorMatch = NODE_CLASS_MAPPINGS["ColorMatch"]()
ImageBlend = nodes_post_processing.NODE_CLASS_MAPPINGS["ImageBlend"]()
WAS_Image_Blending_Mode = NODE_CLASS_MAPPINGS["Image Blending Mode"]()
ImageScale = NODE_CLASS_MAPPINGS["ImageScale"]()
ImageScaleBy = NODE_CLASS_MAPPINGS["ImageScaleBy"]()
UpscaleModelLoader = nodes_upscale_model.NODE_CLASS_MAPPINGS["UpscaleModelLoader"]()
ImageUpscaleWithModel = nodes_upscale_model.NODE_CLASS_MAPPINGS["ImageUpscaleWithModel"]()
with torch.inference_mode():
model_patcher, clip, vae, clipvision = load_checkpoint_guess_config("/content/ComfyUI/models/checkpoints/dreamshaperXL_lightningDPMSDE.safetensors", output_vae=True, output_clip=True, embedding_directory=None)
tile_control_net = comfy.controlnet.load_controlnet("/content/ComfyUI/models/controlnet/xinsir-controlnet-tile-sdxl-1.0.safetensors")
segm_detector = UltralyticsDetectorProvider.doit(model_name="segm/PitEyeDetailer-v2-seg.pt")
upscale_model = UpscaleModelLoader.load_model(model_name="4xRealWebPhoto_v4_dat2.safetensors")[0]
model_patcher = Automatic_CFG.patch(model=model_patcher, hard_mode=True, boost=True)[0]
@torch.inference_mode()
def generate(input):
values = input["input"]
input_image = values['input_image_check']
input_image = download_file(input_image)
positive_prompt = values['positive_prompt']
negative_prompt = values['negative_prompt']
inspire_seed = values['inspire_seed']
inspire_steps = values['inspire_steps']
inspire_cfg = values['inspire_cfg']
inspire_sampler_name = values['inspire_sampler_name']
inspire_scheduler = values['inspire_scheduler']
inspire_denoise = values['inspire_denoise']
inspire_noise_mode = values['inspire_noise_mode']
inspire_batch_seed_mode = values['inspire_batch_seed_mode']
inspire_variation_seed = values['inspire_variation_seed']
inspire_variation_strength = values['inspire_variation_strength']
inspire_variation_method = values['inspire_variation_method']
scale_factor = values['scale_factor']
blur_strength = values['blur_strength']
strength = values['strength']
start_percent = values['start_percent']
end_percent = values['end_percent']
tile_method = values['tile_method']
tile_overlap = values['tile_overlap']
tile_size = values['tile_size']
threshold = values['threshold']
dilation = values['dilation']
crop_factor = values['crop_factor']
drop_size = values['drop_size']
labels = values['labels']
detailer_guide_size = values['detailer_guide_size']
detailer_guide_size_for_bbox = values['detailer_guide_size_for_bbox']
detailer_max_size = values['detailer_max_size']
detailer_seed = values['detailer_seed']
detailer_steps = values['detailer_steps']
detailer_cfg = values['detailer_cfg']
detailer_sampler_name = values['detailer_sampler_name']
detailer_scheduler = values['detailer_scheduler']
detailer_denoise = values['detailer_denoise']
detailer_feather = values['detailer_feather']
detailer_noise_mask = values['detailer_noise_mask']
detailer_force_inpaint = values['detailer_force_inpaint']
detailer_cycle = values['detailer_cycle']
detailer_inpaint_model = values['detailer_inpaint_model']
detailer_noise_mask_feather = values['detailer_noise_mask_feather']
color_method = values['color_method']
blend_factor = values['blend_factor']
blend_mode = values['blend_mode']
blending_mode = values['blending_mode']
blending_blend_percentage = values['blending_blend_percentage']
output_image, output_mask = nodes.LoadImage().load_image(input_image)
output_image_s = ImageScaleToTotalPixels.upscale(image=output_image, upscale_method="nearest-exact", megapixels=1.0)[0]
image_width = GetImageSizeAndCount.getsize(output_image_s)["result"][1]
image_height = GetImageSizeAndCount.getsize(output_image_s)["result"][2]
upscale_mp = 4
w_math = ceil((image_width * upscale_mp) / 8) * 8
h_math = ceil((image_height * upscale_mp) / 8) * 8
w_tiles = 4
tile_width = ceil((w_math / w_tiles) / 8) * 8
h_tiles = 4
tile_height = ceil((h_math / h_tiles) / 8) * 8
vram = 8
tile_batch_size = floor((vram-3) / ((tile_width*tile_height) / 1000000))
upscale_image = ImageScaleBy.upscale(image=output_image, upscale_method="bilinear", scale_by=1.0)[0]
upscaled_image = ImageUpscaleWithModel.upscale(upscale_model=upscale_model, image=upscale_image)[0]
output_image = ImageScale.upscale(image=upscaled_image, upscale_method="bilinear", width=w_math, height=h_math, crop="disabled")[0]
cond, pooled = clip.encode_from_tokens(clip.tokenize(positive_prompt), return_pooled=True)
cond = [[cond, {"pooled_output": pooled}]]
n_cond, n_pooled = clip.encode_from_tokens(clip.tokenize(negative_prompt), return_pooled=True)
n_cond = [[n_cond, {"pooled_output": n_pooled}]]
output_image_t = TTPlanet_TileSimple.execute(output_image, scale_factor=scale_factor, blur_strength=blur_strength)[0]
positive, negative = ControlNetApplyAdvanced.apply_controlnet(positive=cond, negative=n_cond, control_net=tile_control_net, image=output_image_t, strength=strength, start_percent=start_percent, end_percent=end_percent)
tile_model = TiledDiffusion.apply(model=model_patcher, method=tile_method, tile_width=tile_width, tile_height=tile_height, tile_overlap=tile_overlap, tile_batch_size=tile_batch_size)[0]
latent_image = nodes.VAEEncode().encode(vae, output_image)[0]
inspire_sample = KSampler_inspire.doit(model=tile_model,
seed=inspire_seed,
steps=inspire_steps,
cfg=inspire_cfg,
sampler_name=inspire_sampler_name,
scheduler=inspire_scheduler,
positive=positive,
negative=negative,
latent_image=latent_image,
denoise=inspire_denoise,
noise_mode=inspire_noise_mode,
batch_seed_mode=inspire_batch_seed_mode,
variation_seed=inspire_variation_seed,
variation_strength=inspire_variation_strength,
variation_method=inspire_variation_method)[0]
tiled_decoded = VAEDecodeTiled.decode(vae=vae, samples=inspire_sample, tile_size=tile_size)[0]
segs = SegmDetectorSEGS.doit(segm_detector=segm_detector[1], image=output_image, threshold=threshold, dilation=dilation, crop_factor=crop_factor, drop_size=drop_size, labels=labels)[0]
dd_model_patcher = DifferentialDiffusion.apply(model_patcher)[0]
detailer_image = DetailerForEach.do_detail(image=tiled_decoded,
segs=segs,
model=dd_model_patcher,
clip=clip,
vae=vae,
guide_size=detailer_guide_size,
guide_size_for_bbox=detailer_guide_size_for_bbox,
max_size=detailer_max_size,
seed=detailer_seed,
steps=detailer_steps,
cfg=detailer_cfg,
sampler_name=detailer_sampler_name,
scheduler=detailer_scheduler,
positive=cond,
negative=n_cond,
denoise=detailer_denoise,
feather=detailer_feather,
noise_mask=detailer_noise_mask,
force_inpaint=detailer_force_inpaint,
cycle=detailer_cycle,
inpaint_model=detailer_inpaint_model,
noise_mask_feather=detailer_noise_mask_feather)[0]
color_image = ColorMatch.colormatch(image_ref=output_image, image_target=detailer_image, method=color_method)[0]
blend_image = ImageBlend.blend_images(image1=color_image, image2=detailer_image, blend_factor=blend_factor, blend_mode=blend_mode)[0]
blending_image = WAS_Image_Blending_Mode.image_blending_mode(image_a=blend_image, image_b=output_image, mode=blending_mode, blend_percentage=blending_blend_percentage)[0]
Image.fromarray(np.array(blending_image*255, dtype=np.uint8)[0]).save("/content/ultralytics.png")
result = "/content/ultralytics.png"
response = None
try:
source_id = values['source_id']
del values['source_id']
source_channel = values['source_channel']
del values['source_channel']
job_id = values['job_id']
del values['job_id']
default_filename = os.path.basename(result)
files = {default_filename: open(result, "rb").read()}
payload = {"content": f"{json.dumps(values)} <@{source_id}>"}
response = requests.post(
f"https://discord.com/api/v9/channels/{source_channel}/messages",
data=payload,
headers={"authorization": f"Bot {discord_token}"},
files=files
)
response.raise_for_status()
except Exception as e:
print(f"An unexpected error occurred: {e}")
finally:
if os.path.exists(result):
os.remove(result)
if response and response.status_code == 200:
try:
payload = {"jobId": job_id, "result": response.json()['attachments'][0]['url']}
requests.post(f"{web_uri}/api/notify", data=json.dumps(payload), headers={'Content-Type': 'application/json', "authorization": f"{web_token}"})
except Exception as e:
print(f"An unexpected error occurred: {e}")
finally:
return {"result": response.json()['attachments'][0]['url']}
else:
return {"result": "ERROR"}
runpod.serverless.start({"handler": generate}) |