├── README.md
├── model.json
├── Dockerfile
├── worker_runpod.py
├── schema.json
└── comfyui-ultralytics-upscale-tost.json
/README.md:
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1 | 🐣 Please follow me for new updates https://twitter.com/camenduru
2 | 🔥 Please join our discord server https://discord.gg/k5BwmmvJJU
3 | 🥳 Please join my patreon community https://patreon.com/camenduru
4 |
5 | ### 🥪 Tost
6 | https://tost.ai
7 |
8 | ### 🧬 Code
9 | https://github.com/comfyanonymous/ComfyUI
10 | https://github.com/Extraltodeus/ComfyUI-AutomaticCFG
11 | https://github.com/pythongosssss/ComfyUI-Custom-Scripts
12 | https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes
13 | https://github.com/ltdrdata/ComfyUI-Impact-Pack
14 | https://github.com/ltdrdata/ComfyUI-Inspire-Pack
15 | https://github.com/kijai/ComfyUI-KJNodes
16 | https://github.com/Fannovel16/comfyui_controlnet_aux
17 | https://github.com/shiimizu/ComfyUI-TiledDiffusion
18 | https://github.com/WASasquatch/was-node-suite-comfyui
19 |
20 | ### 🌐 Page
21 | https://www.reddit.com/r/StableDiffusion/comments/1e3v6jy/tile_controlnet_tiled_diffusion_very_realistic/
22 |
23 | ### 📦 Model
24 | https://civitai.com/models/334668/eye-detailersegmentation-adetailer
25 | https://huggingface.co/xinsir/controlnet-tile-sdxl-1.0
26 |
27 | ### 🖼 Output
28 |
29 | https://github.com/user-attachments/assets/dea63e09-67cc-495c-b5f9-dc4369b1cae6
30 |
31 | ### 🏢 Sponsor
32 | [https://runpod.io](https://runpod.io?ref=iqi9iy8y)
33 |
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/model.json:
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1 | {
2 | "input_image_check": "https://files.catbox.moe/frdc2j.jpg",
3 | "positive_prompt": "detailed, realistic, masterpiece, uhd, 8k",
4 | "negative_prompt": "blurry",
5 | "inspire_seed": 9,
6 | "inspire_steps": 4,
7 | "inspire_cfg": 1,
8 | "inspire_sampler_name": "dpmpp_sde",
9 | "inspire_scheduler": "karras",
10 | "inspire_denoise": 0.6,
11 | "inspire_noise_mode": "GPU(=A1111)",
12 | "inspire_batch_seed_mode": "incremental",
13 | "inspire_variation_seed": 0,
14 | "inspire_variation_strength": 0,
15 | "inspire_variation_method": "linear",
16 | "scale_factor": 4,
17 | "blur_strength": 1,
18 | "strength": 0.6,
19 | "start_percent": 0,
20 | "end_percent": 1,
21 | "tile_method": "Mixture of Diffusers",
22 | "tile_overlap": 128,
23 | "tile_size": 768,
24 | "threshold": 0.2,
25 | "dilation": 5,
26 | "crop_factor": 2,
27 | "drop_size": 10,
28 | "labels": "all",
29 | "detailer_guide_size": 1024,
30 | "detailer_guide_size_for_bbox": true,
31 | "detailer_max_size": 1280,
32 | "detailer_seed": 4,
33 | "detailer_steps": 4,
34 | "detailer_cfg": 1.5,
35 | "detailer_sampler_name": "dpmpp_sde",
36 | "detailer_scheduler": "karras",
37 | "detailer_denoise": 0.42,
38 | "detailer_feather": 5,
39 | "detailer_noise_mask": true,
40 | "detailer_force_inpaint": true,
41 | "detailer_cycle": 1,
42 | "detailer_inpaint_model": false,
43 | "detailer_noise_mask_feather": 20,
44 | "color_method": "hm-mvgd-hm",
45 | "blend_factor": 0.5,
46 | "blend_mode": "normal",
47 | "blending_mode": "color",
48 | "blending_blend_percentage": 1,
49 | "vram": 48,
50 | "upscale_mp": 4,
51 | "w_tiles": 4,
52 | "h_tiles": 4,
53 | "downscale_by": 1
54 | }
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/Dockerfile:
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1 | FROM runpod/pytorch:2.2.1-py3.10-cuda12.1.1-devel-ubuntu22.04
2 | WORKDIR /content
3 | ENV PATH="/home/camenduru/.local/bin:${PATH}"
4 | RUN adduser --disabled-password --gecos '' camenduru && \
5 | adduser camenduru sudo && \
6 | echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \
7 | chown -R camenduru:camenduru /content && \
8 | chmod -R 777 /content && \
9 | chown -R camenduru:camenduru /home && \
10 | chmod -R 777 /home
11 |
12 | RUN apt update -y && add-apt-repository -y ppa:git-core/ppa && apt update -y && apt install -y aria2 git git-lfs unzip ffmpeg
13 |
14 | USER camenduru
15 |
16 | RUN pip install opencv-python imageio imageio-ffmpeg ffmpeg-python av runpod \
17 | xformers==0.0.25 torchsde==0.2.6 einops==0.8.0 diffusers==0.28.0 transformers==4.41.2 accelerate==0.30.1 insightface==0.7.3 onnxruntime==1.20.1 onnxruntime-gpu==1.20.1 color-matcher==0.5.0 pilgram==1.2.1 \
18 | ultralytics==8.3.49 segment-anything==1.0 piexif==1.1.3 qrcode==7.4.2 requirements-parser==0.9.0 rembg==2.0.57 rich==13.7.1 rich-argparse==1.5.1 matplotlib==3.8.4 pillow spandrel==0.3.4 \
19 | scikit-image==0.24.0 opencv-python-headless==4.10.0.84 GitPython==3.1.43 scipy==1.14.0 numpy==1.26.4 cachetools==5.4.0 librosa==0.10.2.post1 importlib-metadata==8.0.0 PyYAML==6.0.1 filelock==3.15.4 \
20 | mediapipe==0.10.14 svglib==1.5.1 fvcore==0.1.5.post20221221 yapf==0.40.2 omegaconf==2.3.0 ftfy==6.2.0 addict==2.4.0 yacs==0.1.8 albumentations==1.4.11 scikit-learn==1.5.1 fairscale==0.4.13 \
21 | git+https://github.com/WASasquatch/img2texture git+https://github.com/WASasquatch/cstr git+https://github.com/WASasquatch/ffmpy joblib==1.4.2 numba==0.60.0 timm==1.0.7 tqdm==4.66.4 kornia==0.7.4 && \
22 | git clone https://github.com/comfyanonymous/ComfyUI /content/ComfyUI && \
23 | git clone https://github.com/ltdrdata/ComfyUI-Manager /content/ComfyUI/custom_nodes/ComfyUI-Manager && \
24 | git clone https://github.com/ltdrdata/ComfyUI-Impact-Subpack /content/ComfyUI/custom_nodes/ComfyUI-Impact-Subpack && \
25 | git clone https://github.com/Extraltodeus/ComfyUI-AutomaticCFG /content/ComfyUI/custom_nodes/ComfyUI-AutomaticCFG && \
26 | git clone https://github.com/pythongosssss/ComfyUI-Custom-Scripts /content/ComfyUI/custom_nodes/ComfyUI-Custom-Scripts && \
27 | git clone https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes /content/ComfyUI/custom_nodes/Derfuu_ComfyUI_ModdedNodes && \
28 | git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack /content/ComfyUI/custom_nodes/ComfyUI-Impact-Pack && \
29 | git clone https://github.com/ltdrdata/ComfyUI-Inspire-Pack /content/ComfyUI/custom_nodes/ComfyUI-Inspire-Pack && \
30 | git clone https://github.com/kijai/ComfyUI-KJNodes /content/ComfyUI/custom_nodes/ComfyUI-KJNodes && \
31 | git clone https://github.com/Fannovel16/comfyui_controlnet_aux /content/ComfyUI/custom_nodes/comfyui_controlnet_aux && \
32 | git clone https://github.com/shiimizu/ComfyUI-TiledDiffusion /content/ComfyUI/custom_nodes/ComfyUI-TiledDiffusion && \
33 | git clone https://github.com/WASasquatch/was-node-suite-comfyui /content/ComfyUI/custom_nodes/was-node-suite-comfyui && \
34 | aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/ultralytics/resolve/main/PitEyeDetailer-v2-seg.pt -d /content/ComfyUI/models/ultralytics/segm -o PitEyeDetailer-v2-seg.pt && \
35 | aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/ultralytics/resolve/main/4xRealWebPhoto_v4_dat2.safetensors -d /content/ComfyUI/models/upscale_models -o 4xRealWebPhoto_v4_dat2.safetensors && \
36 | aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/ultralytics/resolve/main/dreamshaperXL_lightningDPMSDE.safetensors -d /content/ComfyUI/models/checkpoints -o dreamshaperXL_lightningDPMSDE.safetensors && \
37 | aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/ultralytics/resolve/main/xinsir-controlnet-tile-sdxl-1.0.safetensors -d /content/ComfyUI/models/controlnet -o xinsir-controlnet-tile-sdxl-1.0.safetensors
38 |
39 | COPY ./worker_runpod.py /content/ComfyUI/worker_runpod.py
40 | WORKDIR /content/ComfyUI
41 | CMD python worker_runpod.py
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/worker_runpod.py:
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1 | import os, json, requests, runpod
2 |
3 | import torch
4 | import random
5 | import comfy
6 | from comfy.sd import load_checkpoint_guess_config
7 | import nodes
8 | from nodes import NODE_CLASS_MAPPINGS
9 | from comfy_extras import nodes_post_processing, nodes_differential_diffusion, nodes_upscale_model
10 | import numpy as np
11 | from PIL import Image
12 | import asyncio
13 | import execution
14 | import server
15 | from nodes import load_custom_node
16 | from math import ceil, floor
17 |
18 | def download_file(url, save_dir='/content/ComfyUI/input'):
19 | os.makedirs(save_dir, exist_ok=True)
20 | file_name = url.split('/')[-1]
21 | file_path = os.path.join(save_dir, file_name)
22 | response = requests.get(url)
23 | response.raise_for_status()
24 | with open(file_path, 'wb') as file:
25 | file.write(response.content)
26 | return file_path
27 |
28 | loop = asyncio.new_event_loop()
29 | asyncio.set_event_loop(loop)
30 | server_instance = server.PromptServer(loop)
31 | execution.PromptQueue(server_instance)
32 |
33 | load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-AutomaticCFG")
34 | load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-Custom-Scripts")
35 | load_custom_node("/content/ComfyUI/custom_nodes/Derfuu_ComfyUI_ModdedNodes")
36 | load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-Impact-Pack")
37 | load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-Impact-Subpack")
38 | load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-Inspire-Pack")
39 | load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-KJNodes")
40 | load_custom_node("/content/ComfyUI/custom_nodes/comfyui_controlnet_aux")
41 | load_custom_node("/content/ComfyUI/custom_nodes/ComfyUI-TiledDiffusion")
42 | load_custom_node("/content/ComfyUI/custom_nodes/was-node-suite-comfyui")
43 |
44 | Automatic_CFG = NODE_CLASS_MAPPINGS["Automatic CFG"]()
45 | ImageScaleToTotalPixels = nodes_post_processing.NODE_CLASS_MAPPINGS["ImageScaleToTotalPixels"]()
46 | GetImageSizeAndCount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
47 | TTPlanet_TileSimple = NODE_CLASS_MAPPINGS["TTPlanet_TileSimple_Preprocessor"]()
48 | TiledDiffusion = NODE_CLASS_MAPPINGS["TiledDiffusion"]()
49 | KSampler_inspire = NODE_CLASS_MAPPINGS["KSampler //Inspire"]()
50 | ControlNetApplyAdvanced = NODE_CLASS_MAPPINGS["ControlNetApplyAdvanced"]()
51 | UltralyticsDetectorProvider = NODE_CLASS_MAPPINGS["UltralyticsDetectorProvider"]()
52 | SegmDetectorSEGS = NODE_CLASS_MAPPINGS["SegmDetectorSEGS"]()
53 | DifferentialDiffusion = nodes_differential_diffusion.NODE_CLASS_MAPPINGS["DifferentialDiffusion"]()
54 | DetailerForEach = NODE_CLASS_MAPPINGS["DetailerForEach"]()
55 | VAEDecodeTiled = NODE_CLASS_MAPPINGS["VAEDecodeTiled"]()
56 | ColorMatch = NODE_CLASS_MAPPINGS["ColorMatch"]()
57 | ImageBlend = nodes_post_processing.NODE_CLASS_MAPPINGS["ImageBlend"]()
58 | WAS_Image_Blending_Mode = NODE_CLASS_MAPPINGS["Image Blending Mode"]()
59 | ImageScale = NODE_CLASS_MAPPINGS["ImageScale"]()
60 | ImageScaleBy = NODE_CLASS_MAPPINGS["ImageScaleBy"]()
61 | UpscaleModelLoader = nodes_upscale_model.NODE_CLASS_MAPPINGS["UpscaleModelLoader"]()
62 | ImageUpscaleWithModel = nodes_upscale_model.NODE_CLASS_MAPPINGS["ImageUpscaleWithModel"]()
63 |
64 | with torch.inference_mode():
65 | 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)
66 | tile_control_net = comfy.controlnet.load_controlnet("/content/ComfyUI/models/controlnet/xinsir-controlnet-tile-sdxl-1.0.safetensors")
67 | segm_detector = UltralyticsDetectorProvider.doit(model_name="segm/PitEyeDetailer-v2-seg.pt")
68 | upscale_model = UpscaleModelLoader.load_model(model_name="4xRealWebPhoto_v4_dat2.safetensors")[0]
69 | model_patcher = Automatic_CFG.patch(model=model_patcher, hard_mode=True, boost=True)[0]
70 |
71 | @torch.inference_mode()
72 | def generate(input):
73 | values = input["input"]
74 |
75 | input_image = values['input_image_check']
76 | input_image = download_file(input_image)
77 | positive_prompt = values['positive_prompt']
78 | negative_prompt = values['negative_prompt']
79 | inspire_seed = values['inspire_seed']
80 | inspire_steps = values['inspire_steps']
81 | inspire_cfg = values['inspire_cfg']
82 | inspire_sampler_name = values['inspire_sampler_name']
83 | inspire_scheduler = values['inspire_scheduler']
84 | inspire_denoise = values['inspire_denoise']
85 | inspire_noise_mode = values['inspire_noise_mode']
86 | inspire_batch_seed_mode = values['inspire_batch_seed_mode']
87 | inspire_variation_seed = values['inspire_variation_seed']
88 | inspire_variation_strength = values['inspire_variation_strength']
89 | inspire_variation_method = values['inspire_variation_method']
90 | scale_factor = values['scale_factor']
91 | blur_strength = values['blur_strength']
92 | strength = values['strength']
93 | start_percent = values['start_percent']
94 | end_percent = values['end_percent']
95 | tile_method = values['tile_method']
96 | tile_overlap = values['tile_overlap']
97 | tile_size = values['tile_size']
98 | threshold = values['threshold']
99 | dilation = values['dilation']
100 | crop_factor = values['crop_factor']
101 | drop_size = values['drop_size']
102 | labels = values['labels']
103 | detailer_guide_size = values['detailer_guide_size']
104 | detailer_guide_size_for_bbox = values['detailer_guide_size_for_bbox']
105 | detailer_max_size = values['detailer_max_size']
106 | detailer_seed = values['detailer_seed']
107 | detailer_steps = values['detailer_steps']
108 | detailer_cfg = values['detailer_cfg']
109 | detailer_sampler_name = values['detailer_sampler_name']
110 | detailer_scheduler = values['detailer_scheduler']
111 | detailer_denoise = values['detailer_denoise']
112 | detailer_feather = values['detailer_feather']
113 | detailer_noise_mask = values['detailer_noise_mask']
114 | detailer_force_inpaint = values['detailer_force_inpaint']
115 | detailer_cycle = values['detailer_cycle']
116 | detailer_inpaint_model = values['detailer_inpaint_model']
117 | detailer_noise_mask_feather = values['detailer_noise_mask_feather']
118 | color_method = values['color_method']
119 | blend_factor = values['blend_factor']
120 | blend_mode = values['blend_mode']
121 | blending_mode = values['blending_mode']
122 | blending_blend_percentage = values['blending_blend_percentage']
123 | vram = values['vram']
124 | upscale_mp = values['upscale_mp']
125 | w_tiles = values['w_tiles']
126 | h_tiles = values['h_tiles']
127 | downscale_by = values['downscale_by']
128 |
129 | output_image, output_mask = nodes.LoadImage().load_image(input_image)
130 | output_image_s = ImageScaleToTotalPixels.upscale(image=output_image, upscale_method="nearest-exact", megapixels=1.0)[0]
131 | image_width = GetImageSizeAndCount.getsize(output_image_s)["result"][1]
132 | image_height = GetImageSizeAndCount.getsize(output_image_s)["result"][2]
133 | w_math = ceil((image_width * upscale_mp) / 8) * 8
134 | h_math = ceil((image_height * upscale_mp) / 8) * 8
135 | tile_width = ceil((w_math / w_tiles) / 8) * 8
136 | tile_height = ceil((h_math / h_tiles) / 8) * 8
137 | tile_batch_size = floor((vram-3) / ((tile_width*tile_height) / 1000000))
138 | upscale_image = ImageScaleBy.upscale(image=output_image, upscale_method="bilinear", scale_by=downscale_by)[0]
139 | upscaled_image = ImageUpscaleWithModel.upscale(upscale_model=upscale_model, image=upscale_image)[0]
140 | output_image = ImageScale.upscale(image=upscaled_image, upscale_method="bilinear", width=w_math, height=h_math, crop="disabled")[0]
141 |
142 | cond, pooled = clip.encode_from_tokens(clip.tokenize(positive_prompt), return_pooled=True)
143 | cond = [[cond, {"pooled_output": pooled}]]
144 | n_cond, n_pooled = clip.encode_from_tokens(clip.tokenize(negative_prompt), return_pooled=True)
145 | n_cond = [[n_cond, {"pooled_output": n_pooled}]]
146 | output_image_t = TTPlanet_TileSimple.execute(output_image, scale_factor=scale_factor, blur_strength=blur_strength)[0]
147 | 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)
148 | 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]
149 | latent_image = nodes.VAEEncode().encode(vae, output_image)[0]
150 | inspire_sample = KSampler_inspire.doit(model=tile_model,
151 | seed=inspire_seed,
152 | steps=inspire_steps,
153 | cfg=inspire_cfg,
154 | sampler_name=inspire_sampler_name,
155 | scheduler=inspire_scheduler,
156 | positive=positive,
157 | negative=negative,
158 | latent_image=latent_image,
159 | denoise=inspire_denoise,
160 | noise_mode=inspire_noise_mode,
161 | batch_seed_mode=inspire_batch_seed_mode,
162 | variation_seed=inspire_variation_seed,
163 | variation_strength=inspire_variation_strength,
164 | variation_method=inspire_variation_method)[0]
165 | tiled_decoded = VAEDecodeTiled.decode(vae=vae, samples=inspire_sample, tile_size=tile_size)[0]
166 | 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]
167 | dd_model_patcher = DifferentialDiffusion.apply(model_patcher)[0]
168 | detailer_image = DetailerForEach.do_detail(image=tiled_decoded,
169 | segs=segs,
170 | model=dd_model_patcher,
171 | clip=clip,
172 | vae=vae,
173 | guide_size=detailer_guide_size,
174 | guide_size_for_bbox=detailer_guide_size_for_bbox,
175 | max_size=detailer_max_size,
176 | seed=detailer_seed,
177 | steps=detailer_steps,
178 | cfg=detailer_cfg,
179 | sampler_name=detailer_sampler_name,
180 | scheduler=detailer_scheduler,
181 | positive=cond,
182 | negative=n_cond,
183 | denoise=detailer_denoise,
184 | feather=detailer_feather,
185 | noise_mask=detailer_noise_mask,
186 | force_inpaint=detailer_force_inpaint,
187 | cycle=detailer_cycle,
188 | inpaint_model=detailer_inpaint_model,
189 | noise_mask_feather=detailer_noise_mask_feather)[0]
190 | color_image = ColorMatch.colormatch(image_ref=output_image, image_target=detailer_image, method=color_method)[0]
191 | blend_image = ImageBlend.blend_images(image1=color_image, image2=detailer_image, blend_factor=blend_factor, blend_mode=blend_mode)[0]
192 | 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]
193 | Image.fromarray(np.array(blending_image*255, dtype=np.uint8)[0]).save("/content/ultralytics.png")
194 |
195 | result = "/content/ultralytics.png"
196 | try:
197 | notify_uri = values['notify_uri']
198 | del values['notify_uri']
199 | notify_token = values['notify_token']
200 | del values['notify_token']
201 | discord_id = values['discord_id']
202 | del values['discord_id']
203 | if(discord_id == "discord_id"):
204 | discord_id = os.getenv('com_camenduru_discord_id')
205 | discord_channel = values['discord_channel']
206 | del values['discord_channel']
207 | if(discord_channel == "discord_channel"):
208 | discord_channel = os.getenv('com_camenduru_discord_channel')
209 | discord_token = values['discord_token']
210 | del values['discord_token']
211 | if(discord_token == "discord_token"):
212 | discord_token = os.getenv('com_camenduru_discord_token')
213 | job_id = values['job_id']
214 | del values['job_id']
215 | default_filename = os.path.basename(result)
216 | with open(result, "rb") as file:
217 | files = {default_filename: file.read()}
218 | payload = {"content": f"{json.dumps(values)} <@{discord_id}>"}
219 | response = requests.post(
220 | f"https://discord.com/api/v9/channels/{discord_channel}/messages",
221 | data=payload,
222 | headers={"Authorization": f"Bot {discord_token}"},
223 | files=files
224 | )
225 | response.raise_for_status()
226 | result_url = response.json()['attachments'][0]['url']
227 | notify_payload = {"jobId": job_id, "result": result_url, "status": "DONE"}
228 | web_notify_uri = os.getenv('com_camenduru_web_notify_uri')
229 | web_notify_token = os.getenv('com_camenduru_web_notify_token')
230 | if(notify_uri == "notify_uri"):
231 | requests.post(web_notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
232 | else:
233 | requests.post(web_notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
234 | requests.post(notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": notify_token})
235 | return {"jobId": job_id, "result": result_url, "status": "DONE"}
236 | except Exception as e:
237 | error_payload = {"jobId": job_id, "status": "FAILED"}
238 | try:
239 | if(notify_uri == "notify_uri"):
240 | requests.post(web_notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
241 | else:
242 | requests.post(web_notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
243 | requests.post(notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": notify_token})
244 | except:
245 | pass
246 | return {"jobId": job_id, "result": f"FAILED: {str(e)}", "status": "FAILED"}
247 | finally:
248 | if os.path.exists(result):
249 | os.remove(result)
250 |
251 | runpod.serverless.start({"handler": generate})
--------------------------------------------------------------------------------
/schema.json:
--------------------------------------------------------------------------------
1 | {
2 | "$schema": "http://json-schema.org/draft-04/hyper-schema#",
3 | "type": "object",
4 | "properties": {
5 | "readme": {
6 | "type": "string",
7 | "title": "Ultralytics Upscaler",
8 | "code": "https://github.com/camenduru/comfyui-ultralytics-upscaler-tost",
9 | "page": "https://www.reddit.com/r/StableDiffusion/comments/1e3v6jy/tile_controlnet_tiled_diffusion_very_realistic/",
10 | "api": "https://github.com/camenduru/tost-api-examples/blob/main/v1/comfyui-ultralytics-upscaler.ipynb",
11 | "tags": [
12 | "Image to Image"
13 | ],
14 | "widget": "readme"
15 | },
16 | "upload": {
17 | "type": "string",
18 | "widget": "upload",
19 | "upload_url": "https://litterbox.catbox.moe/resources/internals/api.php",
20 | "property": "input_image_check"
21 | },
22 | "input_image_check": {
23 | "type": "string",
24 | "description": "Input Image URL",
25 | "widget": "textarea"
26 | },
27 | "positive_prompt": {
28 | "type": "string",
29 | "description": "Positive Prompt",
30 | "widget": "textarea"
31 | },
32 | "negative_prompt": {
33 | "type": "string",
34 | "description": "Negative Prompt",
35 | "widget": "textarea"
36 | },
37 | "inspire_seed": {
38 | "type": "integer",
39 | "description": "inspire_seed"
40 | },
41 | "inspire_steps": {
42 | "type": "integer",
43 | "description": "inspire_steps"
44 | },
45 | "inspire_cfg": {
46 | "type": "number",
47 | "description": "inspire_cfg"
48 | },
49 | "inspire_sampler_name": {
50 | "type": "string",
51 | "widget": "select",
52 | "description": "inspire_sampler_name",
53 | "oneOf": [
54 | {
55 | "description": "euler",
56 | "enum": [
57 | "euler"
58 | ]
59 | },
60 | {
61 | "description": "euler_ancestral",
62 | "enum": [
63 | "euler_ancestral"
64 | ]
65 | },
66 | {
67 | "description": "heun",
68 | "enum": [
69 | "heun"
70 | ]
71 | },
72 | {
73 | "description": "heunpp2",
74 | "enum": [
75 | "heunpp2"
76 | ]
77 | },
78 | {
79 | "description": "dpm_2",
80 | "enum": [
81 | "dpm_2"
82 | ]
83 | },
84 | {
85 | "description": "dpm_2_ancestral",
86 | "enum": [
87 | "dpm_2_ancestral"
88 | ]
89 | },
90 | {
91 | "description": "lms",
92 | "enum": [
93 | "lms"
94 | ]
95 | },
96 | {
97 | "description": "dpm_fast",
98 | "enum": [
99 | "dpm_fast"
100 | ]
101 | },
102 | {
103 | "description": "dpm_adaptive",
104 | "enum": [
105 | "dpm_adaptive"
106 | ]
107 | },
108 | {
109 | "description": "dpmpp_2s_ancestral",
110 | "enum": [
111 | "dpmpp_2s_ancestral"
112 | ]
113 | },
114 | {
115 | "description": "dpmpp_sde",
116 | "enum": [
117 | "dpmpp_sde"
118 | ]
119 | },
120 | {
121 | "description": "dpmpp_sde_gpu",
122 | "enum": [
123 | "dpmpp_sde_gpu"
124 | ]
125 | },
126 | {
127 | "description": "dpmpp_2m",
128 | "enum": [
129 | "dpmpp_2m"
130 | ]
131 | },
132 | {
133 | "description": "dpmpp_2m_sde",
134 | "enum": [
135 | "dpmpp_2m_sde"
136 | ]
137 | },
138 | {
139 | "description": "dpmpp_2m_sde_gpu",
140 | "enum": [
141 | "dpmpp_2m_sde_gpu"
142 | ]
143 | },
144 | {
145 | "description": "dpmpp_3m_sde",
146 | "enum": [
147 | "dpmpp_3m_sde"
148 | ]
149 | },
150 | {
151 | "description": "dpmpp_3m_sde_gpu",
152 | "enum": [
153 | "dpmpp_3m_sde_gpu"
154 | ]
155 | },
156 | {
157 | "description": "ddpm",
158 | "enum": [
159 | "ddpm"
160 | ]
161 | },
162 | {
163 | "description": "lcm",
164 | "enum": [
165 | "lcm"
166 | ]
167 | },
168 | {
169 | "description": "ddim",
170 | "enum": [
171 | "ddim"
172 | ]
173 | },
174 | {
175 | "description": "uni_pc",
176 | "enum": [
177 | "uni_pc"
178 | ]
179 | },
180 | {
181 | "description": "uni_pc_bh2",
182 | "enum": [
183 | "uni_pc_bh2"
184 | ]
185 | }
186 | ],
187 | "default": "dpmpp_2m"
188 | },
189 | "inspire_scheduler": {
190 | "type": "string",
191 | "widget": "select",
192 | "description": "inspire_scheduler",
193 | "oneOf": [
194 | {
195 | "description": "karras",
196 | "enum": [
197 | "karras"
198 | ]
199 | },
200 | {
201 | "description": "exponential",
202 | "enum": [
203 | "exponential"
204 | ]
205 | },
206 | {
207 | "description": "heunpp2",
208 | "enum": [
209 | "heunpp2"
210 | ]
211 | },
212 | {
213 | "description": "sgm_uniform",
214 | "enum": [
215 | "sgm_uniform"
216 | ]
217 | },
218 | {
219 | "description": "simple",
220 | "enum": [
221 | "simple"
222 | ]
223 | },
224 | {
225 | "description": "ddim_uniform",
226 | "enum": [
227 | "ddim_uniform"
228 | ]
229 | }
230 | ],
231 | "default": "karras"
232 | },
233 | "inspire_denoise": {
234 | "type": "number",
235 | "description": "inspire_denoise"
236 | },
237 | "inspire_noise_mode": {
238 | "type": "string",
239 | "description": "inspire_noise_mode"
240 | },
241 | "inspire_batch_seed_mode": {
242 | "type": "string",
243 | "description": "inspire_batch_seed_mode"
244 | },
245 | "inspire_variation_seed": {
246 | "type": "number",
247 | "description": "inspire_variation_seed"
248 | },
249 | "inspire_variation_strength": {
250 | "type": "integer",
251 | "description": "inspire_variation_strength"
252 | },
253 | "inspire_variation_method": {
254 | "type": "string",
255 | "description": "inspire_variation_method"
256 | },
257 | "scale_factor": {
258 | "type": "number",
259 | "description": "scale_factor"
260 | },
261 | "blur_strength": {
262 | "type": "number",
263 | "description": "blur_strength"
264 | },
265 | "strength": {
266 | "type": "number",
267 | "description": "strength"
268 | },
269 | "start_percent": {
270 | "type": "number",
271 | "description": "start_percent"
272 | },
273 | "end_percent": {
274 | "type": "number",
275 | "description": "end_percent"
276 | },
277 | "tile_method": {
278 | "type": "string",
279 | "description": "tile_method"
280 | },
281 | "tile_overlap": {
282 | "type": "integer",
283 | "description": "tile_overlap"
284 | },
285 | "tile_size": {
286 | "type": "integer",
287 | "description": "tile_size"
288 | },
289 | "threshold": {
290 | "type": "number",
291 | "description": "threshold"
292 | },
293 | "dilation": {
294 | "type": "integer",
295 | "description": "dilation"
296 | },
297 | "crop_factor": {
298 | "type": "number",
299 | "description": "crop_factor"
300 | },
301 | "drop_size": {
302 | "type": "integer",
303 | "description": "drop_size"
304 | },
305 | "labels": {
306 | "type": "string",
307 | "description": "labels"
308 | },
309 | "detailer_guide_size": {
310 | "type": "integer",
311 | "description": "detailer_guide_size"
312 | },
313 | "detailer_guide_size_for_bbox": {
314 | "type": "boolean",
315 | "description": "detailer_guide_size_for_bbox"
316 | },
317 | "detailer_max_size": {
318 | "type": "integer",
319 | "description": "detailer_max_size"
320 | },
321 | "detailer_seed": {
322 | "type": "integer",
323 | "description": "detailer_seed"
324 | },
325 | "detailer_steps": {
326 | "type": "integer",
327 | "description": "detailer_steps"
328 | },
329 | "detailer_cfg": {
330 | "type": "number",
331 | "description": "detailer_cfg"
332 | },
333 | "detailer_sampler_name": {
334 | "type": "string",
335 | "widget": "select",
336 | "description": "detailer_sampler_name",
337 | "oneOf": [
338 | {
339 | "description": "euler",
340 | "enum": [
341 | "euler"
342 | ]
343 | },
344 | {
345 | "description": "euler_ancestral",
346 | "enum": [
347 | "euler_ancestral"
348 | ]
349 | },
350 | {
351 | "description": "heun",
352 | "enum": [
353 | "heun"
354 | ]
355 | },
356 | {
357 | "description": "heunpp2",
358 | "enum": [
359 | "heunpp2"
360 | ]
361 | },
362 | {
363 | "description": "dpm_2",
364 | "enum": [
365 | "dpm_2"
366 | ]
367 | },
368 | {
369 | "description": "dpm_2_ancestral",
370 | "enum": [
371 | "dpm_2_ancestral"
372 | ]
373 | },
374 | {
375 | "description": "lms",
376 | "enum": [
377 | "lms"
378 | ]
379 | },
380 | {
381 | "description": "dpm_fast",
382 | "enum": [
383 | "dpm_fast"
384 | ]
385 | },
386 | {
387 | "description": "dpm_adaptive",
388 | "enum": [
389 | "dpm_adaptive"
390 | ]
391 | },
392 | {
393 | "description": "dpmpp_2s_ancestral",
394 | "enum": [
395 | "dpmpp_2s_ancestral"
396 | ]
397 | },
398 | {
399 | "description": "dpmpp_sde",
400 | "enum": [
401 | "dpmpp_sde"
402 | ]
403 | },
404 | {
405 | "description": "dpmpp_sde_gpu",
406 | "enum": [
407 | "dpmpp_sde_gpu"
408 | ]
409 | },
410 | {
411 | "description": "dpmpp_2m",
412 | "enum": [
413 | "dpmpp_2m"
414 | ]
415 | },
416 | {
417 | "description": "dpmpp_2m_sde",
418 | "enum": [
419 | "dpmpp_2m_sde"
420 | ]
421 | },
422 | {
423 | "description": "dpmpp_2m_sde_gpu",
424 | "enum": [
425 | "dpmpp_2m_sde_gpu"
426 | ]
427 | },
428 | {
429 | "description": "dpmpp_3m_sde",
430 | "enum": [
431 | "dpmpp_3m_sde"
432 | ]
433 | },
434 | {
435 | "description": "dpmpp_3m_sde_gpu",
436 | "enum": [
437 | "dpmpp_3m_sde_gpu"
438 | ]
439 | },
440 | {
441 | "description": "ddpm",
442 | "enum": [
443 | "ddpm"
444 | ]
445 | },
446 | {
447 | "description": "lcm",
448 | "enum": [
449 | "lcm"
450 | ]
451 | },
452 | {
453 | "description": "ddim",
454 | "enum": [
455 | "ddim"
456 | ]
457 | },
458 | {
459 | "description": "uni_pc",
460 | "enum": [
461 | "uni_pc"
462 | ]
463 | },
464 | {
465 | "description": "uni_pc_bh2",
466 | "enum": [
467 | "uni_pc_bh2"
468 | ]
469 | }
470 | ],
471 | "default": "dpmpp_2m"
472 | },
473 | "detailer_scheduler": {
474 | "type": "string",
475 | "widget": "select",
476 | "description": "detailer_scheduler",
477 | "oneOf": [
478 | {
479 | "description": "karras",
480 | "enum": [
481 | "karras"
482 | ]
483 | },
484 | {
485 | "description": "exponential",
486 | "enum": [
487 | "exponential"
488 | ]
489 | },
490 | {
491 | "description": "heunpp2",
492 | "enum": [
493 | "heunpp2"
494 | ]
495 | },
496 | {
497 | "description": "sgm_uniform",
498 | "enum": [
499 | "sgm_uniform"
500 | ]
501 | },
502 | {
503 | "description": "simple",
504 | "enum": [
505 | "simple"
506 | ]
507 | },
508 | {
509 | "description": "ddim_uniform",
510 | "enum": [
511 | "ddim_uniform"
512 | ]
513 | }
514 | ],
515 | "default": "karras"
516 | },
517 | "detailer_denoise": {
518 | "type": "number",
519 | "description": "detailer_denoise"
520 | },
521 | "detailer_feather": {
522 | "type": "integer",
523 | "description": "detailer_feather"
524 | },
525 | "detailer_noise_mask": {
526 | "type": "boolean",
527 | "description": "detailer_noise_mask"
528 | },
529 | "detailer_force_inpaint": {
530 | "type": "boolean",
531 | "description": "detailer_force_inpaint"
532 | },
533 | "detailer_cycle": {
534 | "type": "integer",
535 | "description": "detailer_cycle"
536 | },
537 | "detailer_inpaint_model": {
538 | "type": "boolean",
539 | "description": "detailer_inpaint_model"
540 | },
541 | "detailer_noise_mask_feather": {
542 | "type": "integer",
543 | "description": "detailer_noise_mask_feather"
544 | },
545 | "color_method": {
546 | "type": "string",
547 | "description": "color_method"
548 | },
549 | "blend_factor": {
550 | "type": "number",
551 | "description": "blend_factor"
552 | },
553 | "blend_mode": {
554 | "type": "string",
555 | "description": "blend_mode"
556 | },
557 | "blending_mode": {
558 | "type": "string",
559 | "description": "blending_mode"
560 | },
561 | "blending_blend_percentage": {
562 | "type": "number",
563 | "description": "blending_blend_percentage"
564 | },
565 | "vram": {
566 | "type": "integer",
567 | "description": "vram"
568 | },
569 | "upscale_mp": {
570 | "type": "integer",
571 | "description": "upscale_mp"
572 | },
573 | "w_tiles": {
574 | "type": "integer",
575 | "description": "w_tiles"
576 | },
577 | "h_tiles": {
578 | "type": "integer",
579 | "description": "h_tiles"
580 | },
581 | "downscale_by": {
582 | "type": "number",
583 | "description": "downscale_by"
584 | },
585 | "example": {
586 | "type": "string",
587 | "image1": "https://fakeimg.pl/1024x1024/282828/eae0d0/?text=input#.png",
588 | "image2": "https://files.catbox.moe/frdc2j.jpg",
589 | "image3": "https://fakeimg.pl/1024x1024/282828/eae0d0/?text=output#.png",
590 | "image4": "https://files.catbox.moe/n7qml5.png",
591 | "widget": "example"
592 | }
593 | },
594 | "buttons": [
595 | {
596 | "id": "enter",
597 | "label": "🥪 Enter"
598 | }
599 | ]
600 | }
--------------------------------------------------------------------------------
/comfyui-ultralytics-upscale-tost.json:
--------------------------------------------------------------------------------
1 | {
2 | "last_node_id": 283,
3 | "last_link_id": 626,
4 | "nodes": [
5 | {
6 | "id": 97,
7 | "type": "UltralyticsDetectorProvider",
8 | "pos": [
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11 | ],
12 | "size": {
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14 | "1": 78
15 | },
16 | "flags": {},
17 | "order": 0,
18 | "mode": 0,
19 | "outputs": [
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21 | "name": "BBOX_DETECTOR",
22 | "type": "BBOX_DETECTOR",
23 | "links": null,
24 | "shape": 3
25 | },
26 | {
27 | "name": "SEGM_DETECTOR",
28 | "type": "SEGM_DETECTOR",
29 | "links": [
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31 | ],
32 | "shape": 3
33 | }
34 | ],
35 | "properties": {
36 | "Node name for S&R": "UltralyticsDetectorProvider"
37 | },
38 | "widgets_values": [
39 | "segm/PitEyeDetailer-v2-seg.pt"
40 | ]
41 | },
42 | {
43 | "id": 245,
44 | "type": "ColorMatch",
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55 | "mode": 0,
56 | "inputs": [
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58 | "name": "image_ref",
59 | "type": "IMAGE",
60 | "link": 615,
61 | "label": "img_model"
62 | },
63 | {
64 | "name": "image_target",
65 | "type": "IMAGE",
66 | "link": 499,
67 | "label": "img_upscaled"
68 | }
69 | ],
70 | "outputs": [
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73 | "type": "IMAGE",
74 | "links": [
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76 | ],
77 | "shape": 3,
78 | "slot_index": 0
79 | }
80 | ],
81 | "properties": {
82 | "Node name for S&R": "ColorMatch"
83 | },
84 | "widgets_values": [
85 | "hm-mvgd-hm"
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87 | },
88 | {
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90 | "type": "ImageBlend",
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95 | "size": {
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98 | },
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102 | "inputs": [
103 | {
104 | "name": "image1",
105 | "type": "IMAGE",
106 | "link": 494
107 | },
108 | {
109 | "name": "image2",
110 | "type": "IMAGE",
111 | "link": 501,
112 | "label": "img_upscaled"
113 | }
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115 | "outputs": [
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118 | "type": "IMAGE",
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126 | "properties": {
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128 | },
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131 | "normal"
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136 | "type": "DifferentialDiffusion",
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162 | ],
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166 | ],
167 | "properties": {
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169 | }
170 | },
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173 | "type": "SegmDetectorSEGS",
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188 | "type": "SEGM_DETECTOR",
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190 | "slot_index": 0
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192 | {
193 | "name": "image",
194 | "type": "IMAGE",
195 | "link": 619,
196 | "label": "img_saved"
197 | },
198 | {
199 | "name": "detailer_hook",
200 | "type": "DETAILER_HOOK",
201 | "link": null,
202 | "slot_index": 2
203 | }
204 | ],
205 | "outputs": [
206 | {
207 | "name": "SEGS",
208 | "type": "SEGS",
209 | "links": [
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211 | 613
212 | ],
213 | "shape": 3,
214 | "slot_index": 0
215 | }
216 | ],
217 | "properties": {
218 | "Node name for S&R": "SegmDetectorSEGS"
219 | },
220 | "widgets_values": [
221 | 0.2,
222 | 5,
223 | 2,
224 | 10,
225 | "all"
226 | ]
227 | },
228 | {
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230 | "type": "CheckpointLoaderSimple",
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234 | ],
235 | "size": {
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237 | "1": 100
238 | },
239 | "flags": {},
240 | "order": 1,
241 | "mode": 0,
242 | "outputs": [
243 | {
244 | "name": "MODEL",
245 | "type": "MODEL",
246 | "links": [
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248 | ],
249 | "shape": 3,
250 | "slot_index": 0
251 | },
252 | {
253 | "name": "CLIP",
254 | "type": "CLIP",
255 | "links": [
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257 | 602,
258 | 603
259 | ],
260 | "shape": 3,
261 | "slot_index": 1
262 | },
263 | {
264 | "name": "VAE",
265 | "type": "VAE",
266 | "links": [
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268 | 606,
269 | 607
270 | ],
271 | "shape": 3,
272 | "slot_index": 2
273 | }
274 | ],
275 | "properties": {
276 | "Node name for S&R": "CheckpointLoaderSimple"
277 | },
278 | "widgets_values": [
279 | "dreamshaperXL_lightningDPMSDE.safetensors"
280 | ]
281 | },
282 | {
283 | "id": 99,
284 | "type": "SEGSPreview",
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293 | "flags": {},
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295 | "mode": 0,
296 | "inputs": [
297 | {
298 | "name": "segs",
299 | "type": "SEGS",
300 | "link": 613
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