Update

.pre-commit-config.yaml

@@ -0,0 +1,36 @@
+exclude: ^patch
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.2.0
+  hooks:
+  - id: check-executables-have-shebangs
+  - id: check-json
+  - id: check-merge-conflict
+  - id: check-shebang-scripts-are-executable
+  - id: check-toml
+  - id: check-yaml
+  - id: double-quote-string-fixer
+  - id: end-of-file-fixer
+  - id: mixed-line-ending
+    args: ['--fix=lf']
+  - id: requirements-txt-fixer
+  - id: trailing-whitespace
+- repo: https://github.com/myint/docformatter
+  rev: v1.4
+  hooks:
+  - id: docformatter
+    args: ['--in-place']
+- repo: https://github.com/pycqa/isort
+  rev: 5.12.0
+  hooks:
+    - id: isort
+- repo: https://github.com/pre-commit/mirrors-mypy
+  rev: v0.991
+  hooks:
+    - id: mypy
+      args: ['--ignore-missing-imports']
+- repo: https://github.com/google/yapf
+  rev: v0.32.0
+  hooks:
+  - id: yapf
+    args: ['--parallel', '--in-place']

README.md

@@ -4,9 +4,10 @@ emoji: 📊
 colorFrom: red
 colorTo: yellow
 sdk: gradio
-sdk_version: 3.0.17
+sdk_version: 3.36.1
 app_file: app.py
 pinned: false
+suggested_hardware: t4-small
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -2,9 +2,10 @@
 
 from __future__ import annotations
 
-import argparse
 import functools
 import os
+import random
+import shlex
 import subprocess
 import sys
 
@@ -16,16 +17,20 @@ from huggingface_hub import hf_hub_download
 
 if os.environ.get('SYSTEM') == 'spaces':
     with open('patch') as f:
-        subprocess.run('patch -p1'.split(), cwd='stylegan2-pytorch', stdin=f)
+        subprocess.run(shlex.split('patch -p1'),
+                       cwd='stylegan2-pytorch',
+                       stdin=f)
+    if not torch.cuda.is_available():
+        with open('patch-cpu') as f:
+            subprocess.run(shlex.split('patch -p1'),
+                           cwd='stylegan2-pytorch',
+                           stdin=f)
 
 sys.path.insert(0, 'stylegan2-pytorch')
 
 from model import Generator
 
-TITLE = 'TADNE (This Anime Does Not Exist) Interpolation'
-DESCRIPTION = '''The original TADNE site is https://thisanimedoesnotexist.ai/.
-
-Expected execution time on Hugging Face Spaces: 4s for one image
+DESCRIPTION = '''# [TADNE](https://thisanimedoesnotexist.ai/) (This Anime Does Not Exist) interpolation
 
 Related Apps:
 - [TADNE](https://huggingface.co/spaces/hysts/TADNE)
@@ -33,26 +38,19 @@ Related Apps:
 - [TADNE Image Selector](https://huggingface.co/spaces/hysts/TADNE-image-selector)
 - [TADNE Image Search with DeepDanbooru](https://huggingface.co/spaces/hysts/TADNE-image-search-with-DeepDanbooru)
 '''
-ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.tadne-interpolation" alt="visitor badge"/></center>'
+
+MAX_SEED = np.iinfo(np.int32).max
 
 
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    return parser.parse_args()
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
 
 
 def load_model(device: torch.device) -> nn.Module:
     model = Generator(512, 1024, 4, channel_multiplier=2)
-    path = hf_hub_download('hysts/TADNE',
+    path = hf_hub_download('public-data/TADNE',
                            'models/aydao-anime-danbooru2019s-512-5268480.pt')
     checkpoint = torch.load(path)
     model.load_state_dict(checkpoint['g_ema'])
@@ -86,14 +84,10 @@ def generate_interpolated_images(seed0: int, seed1: int, num_intermediate: int,
                                  psi0: float, psi1: float,
                                  randomize_noise: bool, model: nn.Module,
                                  device: torch.device) -> list[np.ndarray]:
-    seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
-    seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
+    seed0 = int(np.clip(seed0, 0, MAX_SEED))
+    seed1 = int(np.clip(seed1, 0, MAX_SEED))
 
     z0 = generate_z(model.style_dim, seed0, device)
-    if num_intermediate == -1:
-        out = generate_image(model, z0, psi0, randomize_noise)
-        return [out], None
-
     z1 = generate_z(model.style_dim, seed1, device)
     vec = z1 - z0
     dvec = vec / (num_intermediate + 1)
@@ -107,56 +101,75 @@ def generate_interpolated_images(seed0: int, seed1: int, num_intermediate: int,
     return res
 
 
-def main():
-    args = parse_args()
-    device = torch.device(args.device)
-
-    model = load_model(device)
-
-    func = functools.partial(generate_interpolated_images,
-                             model=model,
-                             device=device)
-    func = functools.update_wrapper(func, generate_interpolated_images)
-
-    examples = [
-        [29703, 55376, 3, 0.7, 0.7, False],
-        [34141, 36864, 5, 0.7, 0.7, False],
-        [74650, 88322, 7, 0.7, 0.7, False],
-        [84314, 70317410, 9, 0.7, 0.7, False],
-        [55376, 55376, 5, 0.3, 1.3, False],
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+model = load_model(device)
+fn = functools.partial(generate_interpolated_images,
+                       model=model,
+                       device=device)
+
+examples = [
+    [29703, 55376, 3, 0.7, 0.7, False],
+    [34141, 36864, 5, 0.7, 0.7, False],
+    [74650, 88322, 7, 0.7, 0.7, False],
+    [84314, 70317410, 9, 0.7, 0.7, False],
+    [55376, 55376, 5, 0.3, 1.3, False],
+]
+
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        with gr.Column():
+            seed_1 = gr.Slider(label='Seed 1',
+                               minimum=0,
+                               maximum=MAX_SEED,
+                               step=1,
+                               value=29703)
+            seed_2 = gr.Slider(label='Seed 2',
+                               minimum=0,
+                               maximum=MAX_SEED,
+                               step=1,
+                               value=55376)
+            num_intermediate_frames = gr.Slider(
+                label='Number of Intermediate Frames',
+                minimum=1,
+                maximum=21,
+                step=1,
+                value=3,
+            )
+            psi_1 = gr.Slider(label='Truncation psi 1',
+                              minimum=0,
+                              maximum=2,
+                              step=0.05,
+                              value=0.7)
+            psi_2 = gr.Slider(label='Truncation psi 2',
+                              minimum=0,
+                              maximum=2,
+                              step=0.05,
+                              value=0.7)
+            randomize_noise = gr.Checkbox(label='Randomize Noise', value=False)
+            run_button = gr.Button('Run')
+        with gr.Column():
+            result = gr.Gallery(label='Output')
+
+    inputs = [
+        seed_1,
+        seed_2,
+        num_intermediate_frames,
+        psi_1,
+        psi_2,
+        randomize_noise,
     ]
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Number(default=29703, label='Seed 1'),
-            gr.inputs.Number(default=55376, label='Seed 2'),
-            gr.inputs.Slider(-1,
-                             21,
-                             step=1,
-                             default=3,
-                             label='Number of Intermediate Frames'),
-            gr.inputs.Slider(
-                0, 2, step=0.05, default=0.7, label='Truncation psi 1'),
-            gr.inputs.Slider(
-                0, 2, step=0.05, default=0.7, label='Truncation psi 2'),
-            gr.inputs.Checkbox(default=False, label='Randomize Noise'),
-        ],
-        gr.Gallery(type='numpy', label='Output Images'),
+    gr.Examples(
         examples=examples,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        theme=args.theme,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-        cache_examples=False,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
+        inputs=inputs,
+        outputs=result,
+        fn=fn,
+        cache_examples=os.getenv('CACHE_EXAMPLES') == '1',
     )
-
-
-if __name__ == '__main__':
-    main()
+    run_button.click(
+        fn=fn,
+        inputs=inputs,
+        outputs=result,
+        api_name='run',
+    )
+demo.queue(max_size=10).launch()

patch

@@ -29,57 +29,3 @@ index 0134c39..3a7826c 100755
              style_t = []
  
              for style in styles:
-diff --git a/op/fused_act.py b/op/fused_act.py
-index 5d46e10..bc522ed 100755
---- a/op/fused_act.py
-+++ b/op/fused_act.py
-@@ -1,5 +1,3 @@
--import os
--
- import torch
- from torch import nn
- from torch.nn import functional as F
-@@ -7,16 +5,6 @@ from torch.autograd import Function
- from torch.utils.cpp_extension import load
- 
- 
--module_path = os.path.dirname(__file__)
--fused = load(
--    "fused",
--    sources=[
--        os.path.join(module_path, "fused_bias_act.cpp"),
--        os.path.join(module_path, "fused_bias_act_kernel.cu"),
--    ],
--)
--
--
- class FusedLeakyReLUFunctionBackward(Function):
-     @staticmethod
-     def forward(ctx, grad_output, out, bias, negative_slope, scale):
-diff --git a/op/upfirdn2d.py b/op/upfirdn2d.py
-index 67e0375..6c5840e 100755
---- a/op/upfirdn2d.py
-+++ b/op/upfirdn2d.py
-@@ -1,5 +1,4 @@
- from collections import abc
--import os
- 
- import torch
- from torch.nn import functional as F
-@@ -7,16 +6,6 @@ from torch.autograd import Function
- from torch.utils.cpp_extension import load
- 
- 
--module_path = os.path.dirname(__file__)
--upfirdn2d_op = load(
--    "upfirdn2d",
--    sources=[
--        os.path.join(module_path, "upfirdn2d.cpp"),
--        os.path.join(module_path, "upfirdn2d_kernel.cu"),
--    ],
--)
--
--
- class UpFirDn2dBackward(Function):
-     @staticmethod
-     def forward(

patch-cpu

@@ -0,0 +1,54 @@
+diff --git a/op/fused_act.py b/op/fused_act.py
+index 5d46e10..bc522ed 100755
+--- a/op/fused_act.py
++++ b/op/fused_act.py
+@@ -1,5 +1,3 @@
+-import os
+-
+ import torch
+ from torch import nn
+ from torch.nn import functional as F
+@@ -7,16 +5,6 @@ from torch.autograd import Function
+ from torch.utils.cpp_extension import load
+ 
+ 
+-module_path = os.path.dirname(__file__)
+-fused = load(
+-    "fused",
+-    sources=[
+-        os.path.join(module_path, "fused_bias_act.cpp"),
+-        os.path.join(module_path, "fused_bias_act_kernel.cu"),
+-    ],
+-)
+-
+-
+ class FusedLeakyReLUFunctionBackward(Function):
+     @staticmethod
+     def forward(ctx, grad_output, out, bias, negative_slope, scale):
+diff --git a/op/upfirdn2d.py b/op/upfirdn2d.py
+index 67e0375..6c5840e 100755
+--- a/op/upfirdn2d.py
++++ b/op/upfirdn2d.py
+@@ -1,5 +1,4 @@
+ from collections import abc
+-import os
+ 
+ import torch
+ from torch.nn import functional as F
+@@ -7,16 +6,6 @@ from torch.autograd import Function
+ from torch.utils.cpp_extension import load
+ 
+ 
+-module_path = os.path.dirname(__file__)
+-upfirdn2d_op = load(
+-    "upfirdn2d",
+-    sources=[
+-        os.path.join(module_path, "upfirdn2d.cpp"),
+-        os.path.join(module_path, "upfirdn2d_kernel.cu"),
+-    ],
+-)
+-
+-
+ class UpFirDn2dBackward(Function):
+     @staticmethod
+     def forward(

requirements.txt

@@ -1,4 +1,4 @@
-numpy==1.22.3
-Pillow==9.0.1
-torch==1.11.0
-torchvision==0.12.0
+numpy==1.23.5
+Pillow==10.0.0
+torch==2.0.1
+torchvision==0.15.2

style.css

@@ -0,0 +1,7 @@
+h1 {
+  text-align: center;
+}
+
+#duplicate-button {
+  margin: auto;
+}