1. Change model download from the wget command to the requests module functionality.

2. Add inbrowser=True parameter to Gradio.

3. Upgrade the project's Gradio version to 5.8.0

4. Replace facexlib with the improved version implemented by sczhou.1. Add weights_only=True parameter to torch.load to avoid FutureWarning.

5. Modify GFPGAN to a custom version to avoid encountering the error: ModuleNotFoundError: No module named 'torchvision.transforms.functional_tensor'.

6. Change **basicsr** to the GitHub **BasicSR** version to avoid encountering the error: `ModuleNotFoundError: No module named 'torchvision.transforms.functional_tensor'`.

7. Face Restoration and RealESR can now be freely combined in various ways, or one can be set to "None" to use only the other model.

.gitignore

@@ -0,0 +1,143 @@
+# ignored folders
+datasets/*
+experiments/*
+results/*
+tb_logger/*
+wandb/*
+tmp/*
+
+version.py
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+.vs
+output
+weights
+*.jpg

README.md

@@ -4,7 +4,7 @@ emoji: 📈
 colorFrom: blue
 colorTo: gray
 sdk: gradio
-sdk_version: 3.1.7
+sdk_version: 5.8.0
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -1,55 +1,84 @@
 import os
-
+import gc
 import cv2
+import requests
+import numpy as np
 import gradio as gr
 import torch
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
+import traceback
+from tqdm import tqdm
+from realesrgan.archs.srvgg_arch import SRVGGNetCompact
 from gfpgan.utils import GFPGANer
 from realesrgan.utils import RealESRGANer
+from basicsr.archs.rrdbnet_arch import RRDBNet
 
-os.system("pip freeze")
-# download weights
-if not os.path.exists('realesr-general-x4v3.pth'):
-    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-if not os.path.exists('GFPGANv1.2.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-if not os.path.exists('GFPGANv1.3.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-if not os.path.exists('GFPGANv1.4.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
-if not os.path.exists('RestoreFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
-if not os.path.exists('CodeFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
-
-torch.hub.download_url_to_file(
-    'https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg',
-    'a1.jpg')
-torch.hub.download_url_to_file(
-    'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=',
-    'a2.jpg')
-torch.hub.download_url_to_file(
-    'https://i.guim.co.uk/img/media/06f614065ed82ca0e917b149a32493c791619854/0_0_3648_2789/master/3648.jpg?width=700&quality=85&auto=format&fit=max&s=05764b507c18a38590090d987c8b6202',
-    'a3.jpg')
-torch.hub.download_url_to_file(
-    'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg',
-    'a4.jpg')
-
-# background enhancer with RealESRGAN
-model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = 'realesr-general-x4v3.pth'
-half = True if torch.cuda.is_available() else False
-upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
+# Define URLs and their corresponding local storage paths
+face_model = {
+    "GFPGANv1.2.pth": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth",
+    "GFPGANv1.3.pth": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth",
+    "GFPGANv1.4.pth": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",
+    "RestoreFormer.pth": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth",
+    "CodeFormer.pth": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth",
+}
+realesr_model = {
+    "realesr-general-x4v3.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+    "realesr-animevideov3.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+    "RealESRGAN_x4plus_anime_6B.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+    "RealESRGAN_x2plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+    "RealESRNet_x4plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
+    "RealESRGAN_x4plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+    "4x-AnimeSharp.pth": "https://huggingface.co/utnah/esrgan/resolve/main/4x-AnimeSharp.pth?download=true",
+}
+files_to_download = [
+    ( "a1.jpg",
+      "https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg" ),
+    ( "a2.jpg",
+      "https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=" ),
+    ( "a3.jpg",
+      "https://i.guim.co.uk/img/media/06f614065ed82ca0e917b149a32493c791619854/0_0_3648_2789/master/3648.jpg?width=700&quality=85&auto=format&fit=max&s=05764b507c18a38590090d987c8b6202" ),
+    ( "a4.jpg",
+      "https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg" ),
+]
 
+# Ensure the target directory exists
+os.makedirs("weights", exist_ok=True)
 os.makedirs('output', exist_ok=True)
 
+def download_from_url(output_path, url):
+    try:
+        # Check if the file already exists
+        if os.path.exists(output_path):
+            print(f"File already exists, skipping download: {output_path}")
+            return
+
+        print(f"Downloading: {url}")
+        with requests.get(url, stream=True) as response, open(output_path, "wb") as f:
+            total_size = int(response.headers.get('content-length', 0))
+            with tqdm(total=total_size, unit='B', unit_scale=True) as pbar:
+                for chunk in response.iter_content(chunk_size=8192):
+                    f.write(chunk)
+                    pbar.update(len(chunk))
+        print(f"Download successful: {output_path}")
+    except requests.RequestException as e:
+        print(f"Download failed: {url}, Error: {e}")
+
+
+# Iterate through each file
+for output_path, url in files_to_download:
+    # Check if the file already exists
+    if os.path.exists(output_path):
+        print(f"File already exists, skipping download: {output_path}")
+        continue
+
+    # Start downloading
+    download_from_url(output_path, url)
+
 
-# def inference(img, version, scale, weight):
-def inference(img, version, scale):
-    # weight /= 100
+def inference(img, version, realesr, scale: float):
     print(img, version, scale)
     try:
-        extension = os.path.splitext(os.path.basename(str(img)))[1]
+        img_name = os.path.basename(str(img))
+        basename, extension = os.path.splitext(img_name)
         img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
         if len(img.shape) == 3 and img.shape[2] == 4:
             img_mode = 'RGBA'
@@ -62,50 +91,115 @@ def inference(img, version, scale):
         h, w = img.shape[0:2]
         if h < 300:
             img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
+            
+        if version:
+            download_from_url(os.path.join("weights", version), face_model[version])
+        if realesr:
+            download_from_url(os.path.join("weights", realesr), realesr_model[realesr])
 
-        if version == 'v1.2':
+        # background enhancer with RealESRGAN
+        if realesr == 'RealESRGAN_x4plus.pth':  # x4 RRDBNet model
+            netscale = 4
+            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=netscale)
+        elif realesr == 'RealESRNet_x4plus.pth':  # x4 RRDBNet model
+            netscale = 4
+            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=netscale)
+        elif realesr == 'RealESRGAN_x4plus_anime_6B.pth':  # x4 RRDBNet model with 6 blocks
+            netscale = 4
+            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=netscale)
+        elif realesr == 'RealESRGAN_x2plus.pth':  # x2 RRDBNet model
+            netscale = 2
+            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=netscale)
+        elif realesr == 'realesr-animevideov3.pth':  # x4 VGG-style model (XS size)
+            netscale = 4
+            model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=netscale, act_type='prelu')
+        elif realesr == 'realesr-general-x4v3.pth':  # x4 VGG-style model (S size)
+            netscale = 4
+            model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=netscale, act_type='prelu')
+        # elif realesr == '4x-AnimeSharp.pth':  # 4x-AnimeSharp
+        #     netscale = 4
+        #     model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=netscale)
+        
+        half = True if torch.cuda.is_available() else False
+        upsampler = RealESRGANer(scale=netscale, model_path=os.path.join("weights", realesr), model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
+
+        face_enhancer = None
+        if version == 'GFPGANv1.2.pth':
             face_enhancer = GFPGANer(
-            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'v1.3':
+            model_path='weights/GFPGANv1.2.pth', upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'GFPGANv1.3.pth':
             face_enhancer = GFPGANer(
-            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'v1.4':
+            model_path='weights/GFPGANv1.3.pth', upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'GFPGANv1.4.pth':
             face_enhancer = GFPGANer(
-            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'RestoreFormer':
+            model_path='weights/GFPGANv1.4.pth', upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'RestoreFormer.pth':
             face_enhancer = GFPGANer(
-            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'CodeFormer':
-             face_enhancer = GFPGANer(
-             model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        elif version == 'RealESR-General-x4v3':
+            model_path='weights/RestoreFormer.pth', upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'CodeFormer.pth':
              face_enhancer = GFPGANer(
-             model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler)
-
+             model_path='weights/CodeFormer.pth', upscale=scale, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
+             
+        files = []
+        outputs = []
         try:
-            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
-            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+            if face_enhancer:
+                cropped_faces, restored_aligned, restored_img = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+                # save faces
+                if cropped_faces and restored_aligned:
+                    for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_aligned)):
+                        # save cropped face
+                        save_crop_path = f"output/{basename}{idx:02d}_cropped_faces.png"
+                        cv2.imwrite(save_crop_path, cropped_face)
+                        # save restored face
+                        save_restore_path = f"output/{basename}{idx:02d}_restored_faces.png"
+                        cv2.imwrite(save_restore_path, restored_face)
+                        # save comparison image
+                        save_cmp_path = f"output/{basename}{idx:02d}_cmp.png"
+                        cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
+                        cv2.imwrite(save_cmp_path, cmp_img)
+                
+                        files.append(save_crop_path)
+                        files.append(save_restore_path)
+                        files.append(save_cmp_path)
+                        outputs.append(cv2.cvtColor(cropped_face, cv2.COLOR_BGR2RGB))
+                        outputs.append(cv2.cvtColor(restored_face, cv2.COLOR_BGR2RGB))
+                        outputs.append(cv2.cvtColor(cmp_img, cv2.COLOR_BGR2RGB))
+            else:
+                restored_img, _ = upsampler.enhance(img, outscale=scale)
         except RuntimeError as error:
+            print(traceback.format_exc())
             print('Error', error)
+        finally:
+            if face_enhancer:
+                face_enhancer._cleanup()
+            else:
+                # Free GPU memory and clean up resources
+                torch.cuda.empty_cache()
+                gc.collect()
+
 
         try:
             if scale != 2:
                 interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
                 h, w = img.shape[0:2]
-                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
+                restored_img = cv2.resize(restored_img, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
         except Exception as error:
-            print('wrong scale input.', error)
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
-            extension = 'png'
-        else:
-            extension = 'jpg'
-        save_path = f'output/out.{extension}'
-        cv2.imwrite(save_path, output)
+            print(traceback.format_exc())
+            print("wrong scale input.", error)
+
+        if not extension:
+            extension = ".png" if img_mode == "RGBA" else ".jpg" # RGBA images should be saved in png format
+        save_path = f"output/{basename}{extension}"
+        cv2.imwrite(save_path, restored_img)
 
-        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
-        return output, save_path
+        restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
+        files.append(save_path)
+        outputs.append(restored_img)
+        return outputs, files
     except Exception as error:
-        print('global exception', error)
+        print(traceback.format_exc())
+        print("global exception", error)
         return None, None
 
 
@@ -122,21 +216,34 @@ article = r"""
 """
 demo = gr.Interface(
     inference, [
-        gr.inputs.Image(type="filepath", label="Input"),
-        # gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", default='v1.4', label='version'),
-        gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer','CodeFormer','RealESR-General-x4v3'], type="value", default='v1.4', label='version'),
-        gr.inputs.Number(label="Rescaling factor", default=2),
-        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', default=50)
+        gr.Image(type="filepath", label="Input", format="png"),
+        gr.Dropdown(["GFPGANv1.2.pth",
+                     "GFPGANv1.3.pth",
+                     "GFPGANv1.4.pth",
+                     "RestoreFormer.pth",
+                     # "CodeFormer.pth",
+                     None], type="value", value='GFPGANv1.4.pth', label='Face Restoration version', info="Face Restoration and RealESR can be freely combined in different ways, or one can be set to \"None\" to use only the other model. Face Restoration is primarily used for face restoration in real-life images, while RealESR serves as a background restoration model."),
+        gr.Dropdown(["realesr-general-x4v3.pth",
+                     "realesr-animevideov3.pth",
+                     "RealESRGAN_x4plus_anime_6B.pth",
+                     "RealESRGAN_x2plus.pth",
+                     "RealESRNet_x4plus.pth",
+                     "RealESRGAN_x4plus.pth",
+                     # "4x-AnimeSharp.pth",
+                     None], type="value", value='realesr-general-x4v3.pth', label='RealESR version'),
+        gr.Number(label="Rescaling factor", value=2),
+        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
     ], [
-        gr.outputs.Image(type="numpy", label="Output (The whole image)"),
-        gr.outputs.File(label="Download the output image")
+        gr.Gallery(type="numpy", label="Output (The whole image)", format="png"),
+        gr.File(label="Download the output image")
     ],
     title=title,
     description=description,
     article=article,
-    # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
-    #           ['10045.png', 'v1.4', 2, 50]]).launch()
-    examples=[['a1.jpg', 'v1.4', 2], ['a2.jpg', 'v1.4', 2], ['a3.jpg', 'v1.4', 2],['a4.jpg', 'v1.4', 2]])
+    examples=[['a1.jpg', 'GFPGANv1.4.pth', "realesr-general-x4v3.pth", 2], 
+              ['a2.jpg', 'GFPGANv1.4.pth', "realesr-general-x4v3.pth", 2], 
+              ['a3.jpg', 'GFPGANv1.4.pth', "realesr-general-x4v3.pth", 2],
+              ['a4.jpg', 'GFPGANv1.4.pth', "realesr-general-x4v3.pth", 2]])
     
-demo.queue(concurrency_count=4)
-demo.launch()
+demo.queue(default_concurrency_limit=4)
+demo.launch(inbrowser=True)

requirements.txt

@@ -1,11 +1,19 @@
-torch>=1.7
-basicsr>=1.4.2
-facexlib>=0.2.5
-gfpgan>=1.3.7
-realesrgan>=0.2.5
+--extra-index-url https://download.pytorch.org/whl/cu124
+
+gradio==5.8.0
+
+basicsr @ git+https://github.com/XPixelGroup/BasicSR
+facexlib @ git+https://github.com/avan06/facexlib
+gfpgan @ git+https://github.com/avan06/GFPGAN
+realesrgan @ git+https://github.com/avan06/Real-ESRGAN
 numpy
 opencv-python
-torchvision
+
+torch==2.5.0+cu124; sys_platform != 'darwin'
+torchvision==0.20.0+cu124; sys_platform != 'darwin'
+torch==2.5.0; sys_platform == 'darwin'
+torchvision==0.20.0; sys_platform == 'darwin'
+
 scipy
 tqdm
 lmdb

webui.bat

@@ -0,0 +1,72 @@
+@echo off
+
+:: The source of the webui.bat file is stable-diffusion-webui
+
+if not defined PYTHON (set PYTHON=python)
+if not defined VENV_DIR (set "VENV_DIR=%~dp0%venv")
+
+mkdir tmp 2>NUL
+
+%PYTHON% -c "" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :check_pip
+echo Couldn't launch python
+goto :show_stdout_stderr
+
+:check_pip
+%PYTHON% -mpip --help >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :start_venv
+if "%PIP_INSTALLER_LOCATION%" == "" goto :show_stdout_stderr
+%PYTHON% "%PIP_INSTALLER_LOCATION%" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :start_venv
+echo Couldn't install pip
+goto :show_stdout_stderr
+
+:start_venv
+if ["%VENV_DIR%"] == ["-"] goto :skip_venv
+if ["%SKIP_VENV%"] == ["1"] goto :skip_venv
+
+dir "%VENV_DIR%\Scripts\Python.exe" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :activate_venv
+
+for /f "delims=" %%i in ('CALL %PYTHON% -c "import sys; print(sys.executable)"') do set PYTHON_FULLNAME="%%i"
+echo Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME%
+%PYTHON_FULLNAME% -m venv "%VENV_DIR%" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :activate_venv
+echo Unable to create venv in directory "%VENV_DIR%"
+goto :show_stdout_stderr
+
+:activate_venv
+set PYTHON="%VENV_DIR%\Scripts\Python.exe"
+echo venv %PYTHON%
+
+:skip_venv
+goto :launch
+
+:launch
+%PYTHON% app.py %COMMANDLINE_ARGS% %*
+pause
+exit /b
+
+:show_stdout_stderr
+
+echo.
+echo exit code: %errorlevel%
+
+for /f %%i in ("tmp\stdout.txt") do set size=%%~zi
+if %size% equ 0 goto :show_stderr
+echo.
+echo stdout:
+type tmp\stdout.txt
+
+:show_stderr
+for /f %%i in ("tmp\stderr.txt") do set size=%%~zi
+if %size% equ 0 goto :show_stderr
+echo.
+echo stderr:
+type tmp\stderr.txt
+
+:endofscript
+
+echo.
+echo Launch unsuccessful. Exiting.
+pause