Add files

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "stylegan2-pytorch"]
+	path = stylegan2-pytorch
+	url = https://github.com/rosinality/stylegan2-pytorch

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import subprocess
+import sys
+
+import gradio as gr
+import numpy as np
+import torch
+import torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+if os.environ.get('SYSTEM') == 'spaces':
+    subprocess.call('git apply ../patch'.split(), cwd='stylegan2-pytorch')
+
+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/.'
+ARTICLE = None
+
+TOKEN = os.environ['TOKEN']
+
+
+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')
+    parser.add_argument('--allow-screenshot', action='store_true')
+    return parser.parse_args()
+
+
+def load_model(device: torch.device) -> nn.Module:
+    model = Generator(512, 1024, 4, channel_multiplier=2)
+    path = hf_hub_download('hysts/TADNE',
+                           'models/aydao-anime-danbooru2019s-512-5268480.pt',
+                           use_auth_token=TOKEN)
+    checkpoint = torch.load(path)
+    model.load_state_dict(checkpoint['g_ema'])
+    model.eval()
+    model.to(device)
+    model.latent_avg = checkpoint['latent_avg'].to(device)
+    with torch.inference_mode():
+        z = torch.zeros((1, model.style_dim)).to(device)
+        model([z], truncation=0.7, truncation_latent=model.latent_avg)
+    return model
+
+
+def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor:
+    return torch.from_numpy(np.random.RandomState(seed).randn(
+        1, z_dim)).to(device).float()
+
+
+@torch.inference_mode()
+def generate_image(model: nn.Module, z: torch.Tensor, truncation_psi: float,
+                   randomize_noise: bool) -> np.ndarray:
+    out, _ = model([z],
+                   truncation=truncation_psi,
+                   truncation_latent=model.latent_avg,
+                   randomize_noise=randomize_noise)
+    out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+    return out[0].cpu().numpy()
+
+
+@torch.inference_mode()
+def generate_interpolated_images(seed0: int, seed1: int, num_intermediate: int,
+                                 psi0: float, psi1: float,
+                                 randomize_noise: bool, model: nn.Module,
+                                 device: torch.device) -> np.ndarray:
+    seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
+    seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
+
+    z0 = generate_z(model.style_dim, seed0, device)
+    if num_intermediate == -1:
+        out = generate_image(model, z0, psi0, randomize_noise)
+        return out
+
+    z1 = generate_z(model.style_dim, seed1, device)
+    vec = z1 - z0
+    dvec = vec / (num_intermediate + 1)
+    zs = [z0 + dvec * i for i in range(num_intermediate + 2)]
+    dpsi = (psi1 - psi0) / (num_intermediate + 1)
+    psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)]
+    res = []
+    for z, psi in zip(zs, psis):
+        out = generate_image(model, z, psi, randomize_noise)
+        res.append(out)
+    res = np.hstack(res)
+    return res
+
+
+def main():
+    gr.close_all()
+
+    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],
+    ]
+
+    gr.Interface(
+        func,
+        [
+            gr.inputs.Number(default=29703, label='Seed 1'),
+            gr.inputs.Number(default=55376, label='Seed 2'),
+            gr.inputs.Slider(-1,
+                             11,
+                             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.outputs.Image(type='numpy', label='Output'),
+        examples=examples,
+        title=TITLE,
+        description=DESCRIPTION,
+        article=ARTICLE,
+        theme=args.theme,
+        allow_screenshot=args.allow_screenshot,
+        allow_flagging=args.allow_flagging,
+        live=args.live,
+    ).launch(
+        enable_queue=args.enable_queue,
+        server_port=args.port,
+        share=args.share,
+    )
+
+
+if __name__ == '__main__':
+    main()

patch

@@ -0,0 +1,85 @@
+diff --git a/model.py b/model.py
+index 0134c39..3a7826c 100755
+--- a/model.py
++++ b/model.py
+@@ -395,6 +395,7 @@ class Generator(nn.Module):
+         style_dim,
+         n_mlp,
+         channel_multiplier=2,
++        additional_multiplier=2,
+         blur_kernel=[1, 3, 3, 1],
+         lr_mlp=0.01,
+     ):
+@@ -426,6 +427,9 @@ class Generator(nn.Module):
+             512: 32 * channel_multiplier,
+             1024: 16 * channel_multiplier,
+         }
++        if additional_multiplier > 1:
++            for k in list(self.channels.keys()):
++                self.channels[k] *= additional_multiplier
+ 
+         self.input = ConstantInput(self.channels[4])
+         self.conv1 = StyledConv(
+@@ -518,7 +522,7 @@ class Generator(nn.Module):
+                     getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
+                 ]
+ 
+-        if truncation < 1:
++        if truncation_latent is not None:
+             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(

requirements.txt

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+numpy==1.22.3
+Pillow==9.0.1
+torch==1.11.0
+torchvision==0.12.0

stylegan2-pytorch

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+Subproject commit bef283a1c24087da704d16c30abc8e36e63efa0e