mehu

Dockerfile

@@ -1,90 +0,0 @@
-# Download LFS content while building in order to make this step cacheable
-# #===== LFS =====
-# FROM alpine/git:2.36.2 AS lfs
-# WORKDIR /app
-# COPY --link .lfs.hf.co .
-# RUN --mount=type=secret,id=SPACE_REPOSITORY,mode=0444,required=true \
-#     git init \
-#  && git remote add origin $(cat /run/secrets/SPACE_REPOSITORY) \
-#  && git add --all \
-#  && git config user.email "name@mail.com" \
-#  && git config user.name "Name" \
-#  && git commit -m "lfs" \
-#  && git lfs pull \
-#  && rm -rf .git .gitattributes
-# #===============
-
-FROM nvidia/cuda:11.8.0-runtime-ubuntu18.04
-# BEGIN Static part
-ENV DEBIAN_FRONTEND=noninteractive \
-	TZ=Europe/Paris
-
-RUN apt-get update && apt-get install -y \
-        git \
-        make build-essential libssl-dev zlib1g-dev \
-        libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
-        libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev git-lfs  \
-    	ffmpeg libsm6 libxext6 cmake libgl1-mesa-glx \
-		&& rm -rf /var/lib/apt/lists/* \
-    	&& git lfs install
-
-# User
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-WORKDIR /home/user/app
-
-# Pyenv
-RUN curl https://pyenv.run | bash
-ENV PATH=$HOME/.pyenv/shims:$HOME/.pyenv/bin:$PATH
-
-ARG PIP_VERSION=22.3.1
-ARG PYTHON_VERSION=3.10
-# Python
-RUN pyenv install $PYTHON_VERSION && \
-    pyenv global $PYTHON_VERSION && \
-    pyenv rehash && \
-    pip install --no-cache-dir --upgrade pip==${PIP_VERSION} setuptools wheel && \
-    pip install --no-cache-dir \
-        datasets \
-        "huggingface-hub>=0.12.1" "protobuf<4" "click<8.1"
-
-#^ Waiting for https://github.com/huggingface/huggingface_hub/pull/1345/files to be merge
-
-USER root
-# User Debian packages
-## Security warning : Potential user code executed as root (build time)
-RUN --mount=target=/root/packages.txt,source=packages.txt \
-	apt-get update && \
-    xargs -r -a /root/packages.txt apt-get install -y \
-    && rm -rf /var/lib/apt/lists/*
-    
-USER user
-
-# Pre requirements (e.g. upgrading pip)
-RUN --mount=target=pre-requirements.txt,source=pre-requirements.txt \
-	pip install --no-cache-dir -r pre-requirements.txt
-
-# Python packages
-RUN pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu117
-RUN --mount=target=requirements.txt,source=requirements.txt \
-	pip install --no-cache-dir -r requirements.txt
-
-ARG SDK=gradio \
-	SDK_VERSION=3.27.0
-RUN pip install --no-cache-dir \
-        ${SDK}==${SDK_VERSION}
-
-# App
-# COPY --link --chown=1000 --from=lfs /app /home/user/app
-COPY --link --chown=1000 ./ /home/user/app
-ENV PYTHONPATH=$HOME/app \
-	PYTHONUNBUFFERED=1 \
-	GRADIO_ALLOW_FLAGGING=never \
-	GRADIO_NUM_PORTS=1 \
-	GRADIO_SERVER_NAME=0.0.0.0 \
-	GRADIO_THEME=huggingface \
-	SYSTEM=spaces
-
-CMD ["python", "app.py"]

README.md

@@ -3,7 +3,8 @@ title: IF
 emoji: 🔥
 colorFrom: pink
 colorTo: red
-sdk: docker
+sdk: gradio
+sdk_version: 3.27.0
 python_version: 3.10.11
 app_file: app.py
 pinned: false

app.py

@@ -6,9 +6,6 @@ import json
 import os
 import random
 import tempfile
-import shortuuid
-from apscheduler.schedulers.background import BackgroundScheduler
-import shutil
 
 import gradio as gr
 import torch
@@ -108,18 +105,14 @@ def upload_stage1_result(stage1_param_path: str, stage1_result_path: str,
     if not UPLOAD_REPO_ID:
         return
     try:
-        folder_params = "tmp/results/stage1_params"
-        folder_results = "tmp/results/stage1_results"
-        
-        path_params = f"{folder_params}/{save_name}.json"
-        path_results = f"{folder_results}/{save_name}.pth"
-        
-        os.makedirs(folder_params, exist_ok=True)
-        os.makedirs(folder_results, exist_ok=True)
-        
-        shutil.copy(stage1_param_path, path_params)
-        shutil.copy(stage1_result_path, path_results)
-
+        hf_api.upload_file(path_or_fileobj=stage1_param_path,
+                           path_in_repo=f'stage1_params/{save_name}.json',
+                           repo_id=UPLOAD_REPO_ID,
+                           repo_type='dataset')
+        hf_api.upload_file(path_or_fileobj=stage1_result_path,
+                           path_in_repo=f'stage1_results/{save_name}.pth',
+                           repo_id=UPLOAD_REPO_ID,
+                           repo_type='dataset')
     except Exception as e:
         print(e)
 
@@ -146,20 +139,17 @@ def upload_stage2_info(stage1_param_file_hash_name: str,
         param_file.write(json.dumps(stage2_params))
     stage2_param_file_hash_name = get_param_file_hash_name(param_file.name)
     save_name = f'{stage1_param_file_hash_name}_{stage2_param_file_hash_name}'
-    
-    try:
-        folder_params = "tmp/results/stage2_params"
 
-        os.makedirs(folder_params, exist_ok=True)
-        path_params = f"{folder_params}/{save_name}.json"
-        shutil.copy(param_file.name, path_params)
-        
+    try:
+        hf_api.upload_file(path_or_fileobj=param_file.name,
+                           path_in_repo=f'stage2_params/{save_name}.json',
+                           repo_id=UPLOAD_REPO_ID,
+                           repo_type='dataset')
         if UPLOAD_RESULT_IMAGE:
-            folder_results = "tmp/results/stage2_results"
-            os.makedirs(folder_results, exist_ok=True)
-            path_results = f"{folder_results}/{save_name}.png"
-            shutil.copy(stage2_output_path, path_results)
-
+            hf_api.upload_file(path_or_fileobj=stage2_output_path,
+                               path_in_repo=f'stage2_results/{save_name}.png',
+                               repo_id=UPLOAD_REPO_ID,
+                               repo_type='dataset')
     except Exception as e:
         print(e)
 
@@ -196,16 +186,16 @@ def upload_stage2_3_info(stage1_param_file_hash_name: str,
     save_name = f'{stage1_param_file_hash_name}_{stage2_3_param_file_hash_name}'
 
     try:
-        folder_params = "tmp/results/stage2_3_params"
-        os.makedirs(folder_params, exist_ok=True)
-        path_params = f"{folder_params}/{save_name}.json"
-        shutil.copy(param_file.name, path_params)
-
+        hf_api.upload_file(path_or_fileobj=param_file.name,
+                           path_in_repo=f'stage2_3_params/{save_name}.json',
+                           repo_id=UPLOAD_REPO_ID,
+                           repo_type='dataset')
         if UPLOAD_RESULT_IMAGE:
-            folder_results = "tmp/results/stage2_3_results"
-            os.makedirs(folder_results, exist_ok=True)
-            path_results = f"{folder_results}/{save_name}.png"
-            shutil.copy(stage2_3_output_path, path_results)
+            hf_api.upload_file(
+                path_or_fileobj=stage2_3_output_path,
+                path_in_repo=f'stage2_3_results/{save_name}.png',
+                repo_id=UPLOAD_REPO_ID,
+                repo_type='dataset')
     except Exception as e:
         print(e)
 
@@ -228,22 +218,6 @@ def show_gallery_view() -> tuple[dict, dict]:
 def show_upscaled_view() -> tuple[dict, dict]:
     return _update_result_view(False)
 
-def upload_files():
-    """Zips files and uploads to dataset. Local data is deleted
-    """
-    if os.path.exists("tmp/results") and os.path.isdir("tmp/results"):
-        try:
-            random_folder = random.randint(0,1000)
-            shutil.make_archive("tmp/results", 'zip', "tmp/results")
-            hf_api.upload_file(
-                path_or_fileobj="tmp/results.zip",
-                path_in_repo=f"{random_folder}/results_{shortuuid.uuid()}.zip",
-                repo_id=UPLOAD_REPO_ID,
-                repo_type="dataset",
-            )
-            shutil.rmtree("tmp/results")
-        except Exception as e:
-            print(e)
 
 examples = [
     'high quality dslr photo, a photo product of a lemon inspired by natural and organic materials, wooden accents, intricately decorated with glowing vines of led lights, inspired by baroque luxury',
@@ -692,10 +666,5 @@ with gr.Blocks(css='style.css') as demo:
         ],
         queue=False,
     )
-    
-    if UPLOAD_REPO_ID:
-        scheduler = BackgroundScheduler()
-        scheduler.add_job(func=upload_files, trigger="interval", seconds=60*20)
-        scheduler.start()
 
 demo.queue(api_open=False, max_size=MAX_QUEUE_SIZE).launch(debug=DEBUG)

model.py

@@ -59,10 +59,6 @@ class Model:
         if DISABLE_AUTOMATIC_CPU_OFFLOAD:
             self.pipe.to(self.device)
             self.super_res_1_pipe.to(self.device)
-
-            self.pipe.unet.to(memory_format=torch.channels_last)
-            self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
-
             if not DISABLE_SD_X4_UPSCALER:
                 self.super_res_2_pipe.to(self.device)
         else:

requirements.txt

@@ -1,7 +1,7 @@
 accelerate==0.18.0
 beautifulsoup4==4.12.2
 bitsandbytes==0.38.1
-git+https://github.com/huggingface/diffusers
+diffusers==0.16.0
 ftfy==6.1.1
 gradio==3.27.0
 huggingface_hub==0.14.1
@@ -10,7 +10,7 @@ Pillow==9.5.0
 safetensors==0.3.0
 sentencepiece==0.1.98
 tokenizers==0.13.3
+torch==2.0.0
+torchvision==0.15.1
 tqdm==4.65.0
 transformers==4.28.1
-shortuuid==1.0.11
-apscheduler==3.10.1

settings.py

@@ -51,7 +51,6 @@ if os.getenv('IS_PUBLIC_DEMO') == '1':
     SHOW_NUM_IMAGES = False
     # parameters
     DEFAULT_CUSTOM_TIMESTEPS_1 = 'smart50'
-    UPLOAD_RESULT_IMAGE = True
     # model
     DISABLE_AUTOMATIC_CPU_OFFLOAD = True
     RUN_GARBAGE_COLLECTION = False