add docker file

README.md

@@ -1,3 +1,21 @@
+# Chill Watcher
+consider deploy on:
+- hugging-face inference point
+- replicate api
+
+### some stackoverflow:
+install docker:
+- https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository
+
+install git-lfs:
+- https://github.com/git-lfs/git-lfs/blob/main/INSTALLING.md
+linux:
+```
+curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
+
+sudo apt-get install git-lfs
+```
+
 ---
 license: apache-2.0
 ---

cog.yaml

@@ -0,0 +1,68 @@
+# Configuration for Cog ⚙️
+# https://replicate.com/docs/guides/push-a-model
+# prerequisite：https://docs.docker.com/engine/install/ubuntu/#set-up-the-repository  `dockerd` to start docker
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+# !!!! recommend 60G disk space for cog docker
+
+build:
+  # set to true if your model requires a GPU
+  gpu: true
+
+  # a list of ubuntu apt packages to install
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+
+  # python version in the form '3.8' or '3.8.12'
+  python_version: "3.10.4"
+
+  # a list of packages in the format <package-name>==<version>
+  python_packages:
+    - blendmodes==2022
+    - transformers==4.25.1
+    - accelerate==0.12.0
+    - basicsr==1.4.2
+    - gfpgan==1.3.8
+    - 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
+    - omegaconf==2.2.3
+    - pytorch_lightning==1.7.6
+    - scikit-image==0.19.2
+    - fonts
+    - font-roboto
+    - timm==0.6.7
+    - piexif==1.1.3
+    - einops==0.4.1
+    - jsonmerge==1.8.0
+    - clean-fid==0.1.29
+    - resize-right==0.0.2
+    - torchdiffeq==0.2.3
+    - kornia==0.6.7
+    - lark==1.1.2
+    - inflection==0.5.1
+    - GitPython==3.1.27
+    - torchsde==0.2.5
+    - safetensors==0.2.7
+    - httpcore<=0.15
+    - fastapi==0.90.1
+    # - open_clip_torch
+    - git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b
+    - git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
+  
+  # commands run after the environment is setup
+  run:
+    - "pip3 install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117"
+    - "pip3 install torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117"
+    - "echo env is ready!"
+
+# https://replicate.com/wolverinn/chill_watcher
+image: "r8.im/wolverinn/chill_watcher"
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"

handler.py

@@ -153,9 +153,11 @@ class EndpointHandler():
             "height": 768,
             "seed": -1,
         }
-        if "prompt" in data.keys():
-            print("get prompt from request: ", data["prompt"])
-            args["prompt"] = data["prompt"]
+        if data["inputs"]:
+             if "prompt" in data["inputs"].keys():
+                prompt = data["inputs"]["prompt"]
+                print("get prompt from request: ", prompt)
+                args["prompt"] = 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')

predict.py

@@ -0,0 +1,262 @@
+# Prediction interface for Cog ⚙️
+# https://github.com/replicate/cog/blob/main/docs/python.md
+
+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())
+
+from modules import errors
+from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
+
+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 modules import shared, devices, ui_tempdir
+import modules.codeformer_model as codeformer
+import modules.face_restoration
+import modules.gfpgan_model as gfpgan
+import modules.img2img
+
+import modules.lowvram
+import modules.paths
+import modules.scripts
+import modules.sd_hijack
+import modules.sd_models
+import modules.sd_vae
+import modules.txt2img
+import modules.script_callbacks
+import modules.textual_inversion.textual_inversion
+import modules.progress
+
+import modules.ui
+from modules import modelloader
+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()
+    except Exception as e:
+        errors.display(e, "loading stable diffusion model")
+        print("", file=sys.stderr)
+        print("Stable diffusion model failed to load, exiting", file=sys.stderr)
+        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}')
+        os._exit(0)
+
+    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"""
+        initialize()
+        self.shared = shared
+
+    def predict(
+        self,
+        prompt: str = Input(description="prompt en"),
+    ) -> Dict[str, Any]:
+        """Run a single prediction on the model"""
+        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 len(prompt) > 0:
+            print("get prompt from request: ", prompt)
+            args["prompt"] = 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', ""),
+        }