diff --git a/.lightning b/.lightning
new file mode 100644
index 0000000000000000000000000000000000000000..707676a31c0c079b2f4ec9c774b03236fc96eaf2
--- /dev/null
+++ b/.lightning
@@ -0,0 +1 @@
+name: famous-carson-8575
diff --git a/.lightningignore b/.lightningignore
new file mode 100644
index 0000000000000000000000000000000000000000..60945c31a5476e9963cf2cc6a5322b0f0d5cfef5
--- /dev/null
+++ b/.lightningignore
@@ -0,0 +1,32 @@
+__pycache__
+/ESRGAN/*
+/SwinIR/*
+/venv
+/tmp
+/GFPGANv1.3.pth
+/gfpgan/weights/*.pth
+/ui-config.json
+/outputs
+/log
+/webui.settings.bat
+/embeddings
+/styles.csv
+/params.txt
+/styles.csv.bak
+/interrogate
+/user.css
+/.idea
+notification.mp3
+/SwinIR
+/textual_inversion
+.vscode
+/extensions
+/test/stdout.txt
+/test/stderr.txt
+/cache.json
+.git
+*/chilloutmix_NiPrunedFp32Fix.safetensors
+*/vae-ft-mse-840000-ema-pruned.ckpt
+*/stLouisLuxuriousWheels_v1.safetensors
+*/taiwanDollLikeness_v10.safetensors
+*/koreanDollLikeness_v10.safetensors
diff --git a/README.md b/README.md
index f2906965b931418fad03988a7b80f69c0d1c07dd..5e8bdc659686e22cd9515d373bdab1943e7bebec 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,59 @@
# Chill Watcher
consider deploy on:
-- hugging-face inference point
+- huggingface inference point
- replicate api
+- lightning.ai
+
+# platform comparison
+> all support autoscaling
+
+|platform|prediction speed|charges|deploy handiness|
+|-|-|-|-|
+|huggingface|fast:20s|high:$0.6/hr (without autoscaling)|easy:git push|
+|replicate|fast if used frequently: 30s, slow if needs initialization: 5min|low: $0.02 per generation|difficult: build image and upload|
+|lightning.ai|fast with app running: 20s, slow if idle: XXs|low: free $30 per month, XX per run|easy: one command|
+
+# platform deploy options
+## huggingface
+> [docs](https://huggingface.co/docs/inference-endpoints/guides/custom_handler)
+
+- requirements: use pip packages in `requirements.txt`
+- `init()` and `predict()` function: use `handler.py`, implement the `EndpointHandler` class
+- more: modify `handler.py` for requests and inference and explore more highly-customized features
+- deploy: git (lfs) push to huggingface repository(the whole directory including models and weights, etc.), and use inference endpoints to deploy. Click and deploy automaticly, very simple.
+- call api: use the url provide by inference endpoints after endpoint is ready(build, initialize and in a "running" state), make a post request to the url using request schema definied in the `handler.py`
+
+## replicate
+> [docs](https://replicate.com/docs/guides/push-a-model)
+
+- requirements: specify all requirements(pip packages, system packages, python version, cuda, etc.) in `cog.yaml`
+- `init()` and `predict()` function: use `predict.py`, implement the `Predictor` class
+- more: modify `predict.py`
+- deploy:
+ 1. get a linux GPU machine with 60GB disk space;
+ 2. install [cog](https://replicate.com/docs/guides/push-a-model) and [docker](https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository)
+ 3. `git pull` the current repository from huggingface, including large model files
+ 4. after `predict.py` and `cog.yaml` is correctly coded, run `cog login`, `cog push`, then cog will build a docker image locally and push the image to replicate. As the image could take 30GB or so disk space, it would cost a lot network bandwidth.
+- call api: if everything runs successfully and the docker image is pushed to replicate, you will see a web-ui and an API example directly in your replicate repository
+
+## lightning.ai
+> docs: [code](https://lightning.ai/docs/app/stable/levels/basic/real_lightning_component_implementations.html), [deploy](https://lightning.ai/docs/app/stable/workflows/run_app_on_cloud/)
+
+- requirements:
+ - pip packages are listed in `requirements.txt`, note that some requirements are different from those in huggingface, and you need to modify some lines in `requirements.txt` according to the comment in the `requirements.txt`
+ - other pip packages, system packages and some big model weight files download commands, can be listed using a custom build config. Checkout `class CustomBuildConfig(BuildConfig)` in `app.py`. In a custom build config you can use many linux commands such as `wget` and `sudo apt-get update`. The custom build config will be executed on the `__init__()` of the `PythonServer` class
+- `init()` and `predict()` function: use `app.py`, implement the `PythonServer` class. Note:
+ - some packages haven't been installed when the file is called(these packages may be installed when `__init__()` is called), so some import code should be in the function, not at the top of the file, or you may get import errors.
+ - you can't save your own value to `PythonServer.self` unless it's predifined in the variables, so don't assign any self-defined variables to `self`
+ - if you use the custom build config, you should implement `PythonServer`'s `__init()__` yourself, so don't forget to use the correct function signature
+- more: ...
+- deploy:
+ - `pip install lightning`
+ - prepare the directory on your local computer(no need to have a GPU)
+ - list big files in the `.lightningignore` file to avoid big file upload and save deploy time cost
+ - run `lightning run app app.py --cloud` in the local terminal, and it will upload the files in the directory to lightning cloud, and start deploying on the cloud
+ - check error logs on the web-ui, use `all logs`
+- call api: only if the app starts successfully, you can see a valid url in the `settings` page of the web-ui. Open that url, and you can see the api
### some stackoverflow:
install docker:
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..0699f52d8a98c08aac5079908c9fd9d6314c7d6b
--- /dev/null
+++ b/app.py
@@ -0,0 +1,129 @@
+# inference handler for lightning ai
+
+import re
+import os
+import logging
+# import json
+from pydantic import BaseModel
+from typing import Any, Dict, Optional, TYPE_CHECKING
+from dataclasses import dataclass
+logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
+
+import lightning as L
+from lightning.app.components.serve import PythonServer, Text
+from lightning.app import BuildConfig
+
+
+class _DefaultInputData(BaseModel):
+ prompt: str
+
+class _DefaultOutputData(BaseModel):
+ prediction: str
+ parameters: str
+
+
+@dataclass
+class CustomBuildConfig(BuildConfig):
+ def build_commands(self):
+ dir_path = os.path.dirname(os.path.abspath(__file__))
+ model_path = os.path.join(dir_path, "models/Stable-diffusion")
+ model_url = "https://huggingface.co/Hardy01/chill_watcher/resolve/main/models/Stable-diffusion/chilloutmix_NiPrunedFp32Fix.safetensors"
+ download_cmd = "wget -P {} {}".format(str(model_path), model_url)
+ vae_url = "https://huggingface.co/Hardy01/chill_watcher/resolve/main/models/VAE/vae-ft-mse-840000-ema-pruned.ckpt"
+ vae_path = os.path.join(dir_path, "models/VAE")
+ down2 = "wget -P {} {}".format(str(vae_path), vae_url)
+ lora_url1 = "https://huggingface.co/Hardy01/chill_watcher/resolve/main/models/Lora/koreanDollLikeness_v10.safetensors"
+ lora_url2 = "https://huggingface.co/Hardy01/chill_watcher/resolve/main/models/Lora/taiwanDollLikeness_v10.safetensors"
+ lora_path = os.path.join(dir_path, "models/Lora")
+ down3 = "wget -P {} {}".format(str(lora_path), lora_url1)
+ down4 = "wget -P {} {}".format(str(lora_path), lora_url2)
+ # https://stackoverflow.com/questions/55313610/importerror-libgl-so-1-cannot-open-shared-object-file-no-such-file-or-directo
+ cmd1 = "pip3 install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117"
+ cmd2 = "pip3 install torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117"
+ cmd_31 = "sudo apt-get update"
+ cmd3 = "sudo apt-get install libgl1-mesa-glx"
+ cmd4 = "sudo apt-get install libglib2.0-0"
+ return [download_cmd, down2, down3, down4, cmd1, cmd2, cmd_31, cmd3, cmd4]
+
+
+class PyTorchServer(PythonServer):
+ def __init__(
+ self,
+ input_type: type = _DefaultInputData,
+ output_type: type = _DefaultOutputData,
+ **kwargs: Any,
+ ):
+ super().__init__(input_type=input_type, output_type=output_type, **kwargs)
+
+ # Use the custom build config
+ self.cloud_build_config = CustomBuildConfig()
+ def setup(self):
+ # need to install dependancies first to import packages
+ import torch
+ # Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors
+ if ".dev" in torch.__version__ or "+git" in torch.__version__:
+ torch.__long_version__ = torch.__version__
+ torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
+
+ from handler import initialize
+ initialize()
+
+ def predict(self, request):
+ from modules.api.api import encode_pil_to_base64
+ from modules import shared
+ from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+ args = {
+ # todo: don't output png
+ "outpath_samples": "C:\\Users\\wolvz\\Desktop",
+ "prompt": "lora:koreanDollLikeness_v15:0.66, best quality, ultra high res, (photorealistic:1.4), 1girl, beige sweater, black choker, smile, laughing, bare shoulders, solo focus, ((full body), (brown hair:1), looking at viewer",
+ "negative_prompt": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, (ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, 3hands,4fingers,3arms, bad anatomy, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts,poorly drawn face,mutation,deformed",
+ "sampler_name": "DPM++ SDE Karras",
+ "steps": 20, # 25
+ "cfg_scale": 8,
+ "width": 512,
+ "height": 768,
+ "seed": -1,
+ }
+ print("&&&&&&&&&&&&&&&&&&&&&&&&",request)
+ if request.prompt:
+ prompt = request.prompt
+ print("get prompt from request: ", prompt)
+ args["prompt"] = prompt
+ p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
+ processed = process_images(p)
+ single_image_b64 = encode_pil_to_base64(processed.images[0]).decode('utf-8')
+ return {
+ "prediction": single_image_b64,
+ "parameters": processed.images[0].info.get('parameters', ""),
+ }
+
+
+component = PyTorchServer(
+ cloud_compute=L.CloudCompute('gpu', disk_size=20, idle_timeout=30)
+)
+app = L.LightningApp(component)
+
+# class Flow(L.LightningFlow):
+# # 1. Define the state
+# def __init__(self):
+# self.cloud_build_config = CustomBuildConfig()
+# super().__init__()
+# self.component = PyTorchServer(
+# input_type=Text, output_type=Text, cloud_compute=L.CloudCompute('gpu', disk_size=20, idle_timeout=30)
+# )
+
+# # 2. Optional, but used to validate names
+# def run(self):
+# self.component.run()
+
+# # 3. Method executed when a request is received.
+# def handle_post(self, prompt: str):
+# return f'The name {name} was registered'
+
+# # 4. Defines this Component's Restful API. You can have several routes.
+# def configure_api(self):
+# return [Post(route="/name", method=self.handle_post)]
+
+
+# app = L.LightningApp(Flow())
+
diff --git a/handler.py b/handler.py
index 219750425e8b52507f6a38271e156db512297b79..c6e76834473977d85725ae97765ef1f15916264e 100644
--- a/handler.py
+++ b/handler.py
@@ -1,3 +1,4 @@
+# inference handler for huggingface
import os
import sys
import time
diff --git a/play.py b/play.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2bc9c4836628909fee190d2300f589a5793c52a
--- /dev/null
+++ b/play.py
@@ -0,0 +1,19 @@
+import requests
+import random
+import time
+import base64
+import hashlib
+import json
+
+def lightning():
+ start = int(time.time())
+ url = "https://wsoqr-01gwy9mc1gzh3b4ce9b708vp31.litng-ai-03.litng.ai/predict"
+ form = {
+ "prompt": "extremely detailed CG unity 8k wallpaper, masterpiece, best quality, ultra-detailed, best illustration, best shadow, photorealistic:1.4, 1 gorgeous girls,oversize pink_hoodie,under eiffel tower,grey_hair:1.1, collarbone,puffy breasts:1.5,full body shot,shiny eyes,enjoyable expression,evil smile,slim legs,narrow waist,detailed face, looking at viewer,looking back,gorgeous skin,short curly hair,kneeling,puffy ass up,climbing,lying,rosy pussy,nsfw,insert left_hand into pussy",
+ }
+ resp = requests.post(url, json=form)
+ print(resp.status_code, '\n', resp.content)
+ print("time cost(ms): ", int(time.time())*1e3-start*1e3)
+
+
+lightning()
\ No newline at end of file
diff --git a/predict.py b/predict.py
index cb7bd5f7ebdc73efacfedf4d1bf7dc44a4d71fdd..3c31229245984cb92fe0a0e9b57fe4ba1c57e7eb 100644
--- a/predict.py
+++ b/predict.py
@@ -5,15 +5,9 @@ from cog import BasePredictor, Input, Path
import os
import sys
-import time
-import importlib
import signal
import re
from typing import Dict, List, Any
-# from fastapi import FastAPI
-# from fastapi.middleware.cors import CORSMiddleware
-# from fastapi.middleware.gzip import GZipMiddleware
-from packaging import version
import logging
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
@@ -29,6 +23,7 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__:
torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
from modules import shared, devices, ui_tempdir
+from modules.api.api import encode_pil_to_base64
import modules.codeformer_model as codeformer
import modules.face_restoration
import modules.gfpgan_model as gfpgan
@@ -51,38 +46,19 @@ from modules.shared import cmd_opts, opts
import modules.hypernetworks.hypernetwork
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-import base64
-import io
-from fastapi import HTTPException
-from io import BytesIO
-import piexif
-import piexif.helper
-from PIL import PngImagePlugin,Image
def initialize():
- # check_versions()
-
- # extensions.list_extensions()
- # localization.list_localizations(cmd_opts.localizations_dir)
-
- # if cmd_opts.ui_debug_mode:
- # shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
- # modules.scripts.load_scripts()
- # return
-
modelloader.cleanup_models()
modules.sd_models.setup_model()
codeformer.setup_model(cmd_opts.codeformer_models_path)
gfpgan.setup_model(cmd_opts.gfpgan_models_path)
modelloader.list_builtin_upscalers()
- # modules.scripts.load_scripts()
modelloader.load_upscalers()
modules.sd_vae.refresh_vae_list()
- # modules.textual_inversion.textual_inversion.list_textual_inversion_templates()
try:
modules.sd_models.load_model()
@@ -93,35 +69,11 @@ def initialize():
exit(1)
shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
-
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
- # shared.reload_hypernetworks()
-
- # ui_extra_networks.intialize()
- # ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion())
- # ui_extra_networks.register_page(ui_extra_networks_hypernets.ExtraNetworksPageHypernetworks())
- # ui_extra_networks.register_page(ui_extra_networks_checkpoints.ExtraNetworksPageCheckpoints())
-
- # extra_networks.initialize()
- # extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet())
-
- # if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
-
- # try:
- # if not os.path.exists(cmd_opts.tls_keyfile):
- # print("Invalid path to TLS keyfile given")
- # if not os.path.exists(cmd_opts.tls_certfile):
- # print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
- # except TypeError:
- # cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
- # print("TLS setup invalid, running webui without TLS")
- # else:
- # print("Running with TLS")
-
# make the program just exit at ctrl+c without waiting for anything
def sigint_handler(sig, frame):
print(f'Interrupted with signal {sig} in {frame}')
@@ -129,104 +81,6 @@ def initialize():
signal.signal(signal.SIGINT, sigint_handler)
-
-class EndpointHandler():
- def __init__(self, path=""):
- # Preload all the elements you are going to need at inference.
- # pseudo:
- # self.model= load_model(path)
- initialize()
- self.shared = shared
-
- def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
- """
- data args:
- inputs (:obj: `str` | `PIL.Image` | `np.array`)
- kwargs
- Return:
- A :obj:`list` | `dict`: will be serialized and returned
- """
- args = {
- # todo: don't output png
- "outpath_samples": "C:\\Users\\wolvz\\Desktop",
- "prompt": "lora:koreanDollLikeness_v15:0.66, best quality, ultra high res, (photorealistic:1.4), 1girl, beige sweater, black choker, smile, laughing, bare shoulders, solo focus, ((full body), (brown hair:1), looking at viewer",
- "negative_prompt": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, (ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, 3hands,4fingers,3arms, bad anatomy, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts,poorly drawn face,mutation,deformed",
- "sampler_name": "DPM++ SDE Karras",
- "steps": 20, # 25
- "cfg_scale": 8,
- "width": 512,
- "height": 768,
- "seed": -1,
- }
- if "prompt" in data.keys():
- print("get prompt from request: ", data["prompt"])
- args["prompt"] = data["prompt"]
- p = StableDiffusionProcessingTxt2Img(sd_model=self.shared.sd_model, **args)
- processed = process_images(p)
- single_image_b64 = encode_pil_to_base64(processed.images[0]).decode('utf-8')
- return {
- "img_data": single_image_b64,
- "parameters": processed.images[0].info.get('parameters', ""),
- }
-
-
-def manual_hack():
- initialize()
- args = {
- # todo: don't output res
- "outpath_samples": "C:\\Users\\wolvz\\Desktop",
- "prompt": "lora:koreanDollLikeness_v15:0.66, best quality, ultra high res, (photorealistic:1.4), 1girl, beige sweater, black choker, smile, laughing, bare shoulders, solo focus, ((full body), (brown hair:1), looking at viewer",
- "negative_prompt": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans",
- "sampler_name": "DPM++ SDE Karras",
- "steps": 20, # 25
- "cfg_scale": 8,
- "width": 512,
- "height": 768,
- "seed": -1,
- }
- p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
- processed = process_images(p)
-
-
-def decode_base64_to_image(encoding):
- if encoding.startswith("data:image/"):
- encoding = encoding.split(";")[1].split(",")[1]
- try:
- image = Image.open(BytesIO(base64.b64decode(encoding)))
- return image
- except Exception as err:
- raise HTTPException(status_code=500, detail="Invalid encoded image")
-
-def encode_pil_to_base64(image):
- with io.BytesIO() as output_bytes:
-
- if opts.samples_format.lower() == 'png':
- use_metadata = False
- metadata = PngImagePlugin.PngInfo()
- for key, value in image.info.items():
- if isinstance(key, str) and isinstance(value, str):
- metadata.add_text(key, value)
- use_metadata = True
- image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
-
- elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
- parameters = image.info.get('parameters', None)
- exif_bytes = piexif.dump({
- "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
- })
- if opts.samples_format.lower() in ("jpg", "jpeg"):
- image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality)
- else:
- image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality)
-
- else:
- raise HTTPException(status_code=500, detail="Invalid image format")
-
- bytes_data = output_bytes.getvalue()
-
- return base64.b64encode(bytes_data)
-
-
class Predictor(BasePredictor):
def setup(self):
"""Load the model into memory to make running multiple predictions efficient"""
diff --git a/repositories/BLIP/BLIP.gif b/repositories/BLIP/BLIP.gif
deleted file mode 100644
index f97959778a4d3a9c1d5c06793c96d96204fe2081..0000000000000000000000000000000000000000
--- a/repositories/BLIP/BLIP.gif
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7757a1a1133807158ec4e696a8187f289e64c30a86aa470d8e0a93948a02be22
-size 6707660
diff --git a/repositories/CodeFormer/assets/CodeFormer_logo.png b/repositories/CodeFormer/assets/CodeFormer_logo.png
deleted file mode 100644
index 4cce53bd4945d43aef405f40279c184511a67f07..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/assets/CodeFormer_logo.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9f47e6d67d4aabffe5f3794d9e46c301b953f59c3328dd7dcafd94ccb615d29c
-size 5259
diff --git a/repositories/CodeFormer/assets/color_enhancement_result1.png b/repositories/CodeFormer/assets/color_enhancement_result1.png
deleted file mode 100644
index 9ffb42b76445130cad0f67754a0e41cdfe40f689..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/assets/color_enhancement_result1.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0e5fd836661f974b8f691d2a779d15b0f1419ffc6be1e57ce864b9c3562754a2
-size 684585
diff --git a/repositories/CodeFormer/assets/color_enhancement_result2.png b/repositories/CodeFormer/assets/color_enhancement_result2.png
deleted file mode 100644
index 4fe33ea5edab942e3b40f1540ee334ce45a6d537..0000000000000000000000000000000000000000
--- a/repositories/CodeFormer/assets/color_enhancement_result2.png
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/requirements.txt b/requirements.txt
index 00b071afa896cf5ef3583a8b1ecc40ab9be69a6c..fffe46ed9fab48066f574bf9e66514119fa9e122 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,10 +7,13 @@ gradio==3.16.2
numpy==1.23.3
Pillow==9.4.0
realesrgan==0.3.0
-torch==1.13.1+cu117
---extra-index-url https://download.pytorch.org/whl/cu117
-torchvision==0.14.1+cu117
---extra-index-url https://download.pytorch.org/whl/cu117
+# !!!!option1: if you use huggingface, you should use the following lines
+# !!!!option2: if you use lightning ai, you should comment the following lines
+lightning-api-access
+# torch==1.13.1+cu117
+# --extra-index-url https://download.pytorch.org/whl/cu117
+# torchvision==0.14.1+cu117
+# --extra-index-url https://download.pytorch.org/whl/cu117
omegaconf==2.2.3
pytorch_lightning==1.7.6
scikit-image==0.19.2