just use gradio interface for TADNE

README.md

@@ -1,5 +1,5 @@
 ---
-title: TADNE
+title: SafteyWaifu
 emoji: 🏃
 colorFrom: green
 colorTo: red

app.py

@@ -1,129 +1,37 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import functools
-import os
-import subprocess
-import sys
-
+from asyncio import constants
 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)'
-DESCRIPTION = '''The original TADNE site is https://thisanimedoesnotexist.ai/.
-The model used here is the one converted from the model provided in [this site](https://www.gwern.net/Faces) using [this repo](https://github.com/rosinality/stylegan2-pytorch).
-
-Expected execution time on Hugging Face Spaces: 4s
-
-Based off of [hysts/TADNE](https://huggingface.co/spaces/hysts/TADNE)
-'''
-SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/TADNE/resolve/main/samples'
-ARTICLE = f'''## Generated images
-- size: 512x512
-- truncation: 0.7
-- seed: 0-99
-![samples]({SAMPLE_IMAGE_DIR}/sample.jpg)
-
-'''
-
-TOKEN = os.environ['TOKEN']
+import requests
+import os 
+import random
 
+def desc_to_image(desc):
+  
+  random.seed(desc)
+  tadneSeed=random.randint(0,2**256)
+  psi=0.7  
+  
+  iface = gr.Interface.load("spaces/hysts/TADNE")
+  print("about to die",iface,dir(iface))
 
-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()
 
+  img=iface(tadneSeed,psi)
+  return img
 
-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
+demo = gr.Blocks()
 
-
-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(seed: str, truncation_psi: float, randomize_noise: bool,
-                   model: nn.Module, device: torch.device) -> np.ndarray:
-    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
-
-    arr=bytearray.fromhex(seed[2:])
-    z = np.random.RandomState(arr).randn(n_sample, inputSize).astype("float32")
-
-    #z = generate_z(model.style_dim, seed, device)
-    
-    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()
-
-
-def main():
-    args = parse_args()
-    device = torch.device(args.device)
-
-    model = load_model(device)
-
-    func = functools.partial(generate_image, model=model, device=device)
-    func = functools.update_wrapper(func, generate_image)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.String(default="0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984", label='Seed'),
-            gr.inputs.Slider(
-                0, 2, step=0.05, default=0.7, label='Truncation psi'),
-            gr.inputs.Checkbox(default=False, label='Randomize Noise'),
-        ],
-        gr.outputs.Image(type='numpy', label='Output'),
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        theme=args.theme,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
+with demo:
+  gr.Markdown("<h1><center>NPC Generator</center></h1>")
+  gr.Markdown(
+        "based on <a href=https://huggingface.co/spaces/hysts/TADNE> TADNE</a>."
     )
 
+  with gr.Row():
+    desc_txt = gr.Textbox(label="description",lines=7)
+    output_image = gr.Image(label="portrait",type="filepath", shape=(256,256))
+
+  b0 = gr.Button("Randomize name,race and class")
+  
+  b0.click(desc_to_image,desc_txt,output_image)
+  #examples=examples
 
-if __name__ == '__main__':
-    main()
+demo.launch(enable_queue=True, debug=True)

requirements.txt

@@ -1,5 +0,0 @@
-ninja==1.10.2
-numpy==1.22.3
-Pillow==9.0.1
-torch==1.11.0
-torchvision==0.12.0

stylegan2-pytorch

@@ -1 +0,0 @@
-Subproject commit bef283a1c24087da704d16c30abc8e36e63efa0e