Update app.py

app.py

@@ -1,9 +1,6 @@
 import torch
 torch.hub.download_url_to_file('http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.yaml', 'vqgan_imagenet_f16_16384.yaml')
 torch.hub.download_url_to_file('http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.ckpt', 'vqgan_imagenet_f16_16384.ckpt')
-# import torch
-# torch.hub.download_url_to_file('http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.yaml', 'vqgan_imagenet_f16_16384.yaml')
-# torch.hub.download_url_to_file('http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.ckpt', 'vqgan_imagenet_f16_16384.ckpt')
 import argparse
 import math
 from pathlib import Path
@@ -27,6 +24,9 @@ import imageio
 from PIL import ImageFile, Image
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 import gradio as gr
+
+torch.hub.download_url_to_file('https://i.imgur.com/WEHmKef.jpg', 'gpu.jpg')
+
 def sinc(x):
     return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))
 def lanczos(x, a):
@@ -183,7 +183,7 @@ args = argparse.Namespace(
     clip_model='ViT-B/32',
     vqgan_config=f'{model_name}.yaml',
     vqgan_checkpoint=f'{model_name}.ckpt',
-    step_size=0.1,
+    step_size=0.15,
     cutn=1,
     cut_pow=1.,
     display_freq=images_interval,
@@ -197,7 +197,7 @@ perceptor = clip.load(args.clip_model, jit=False)[0].eval().requires_grad_(False
 def inference(text):
     texts = text
     target_images = ""
-    max_iterations = 100
+    max_iterations = 150
     model_names={"vqgan_imagenet_f16_16384": 'ImageNet 16384',"vqgan_imagenet_f16_1024":"ImageNet 1024", 'vqgan_openimages_f16_8192':'OpenImages 8912',
                     "wikiart_1024":"WikiArt 1024", "wikiart_16384":"WikiArt 16384", "coco":"COCO-Stuff", "faceshq":"FacesHQ", "sflckr":"S-FLCKR"}
     name_model = model_names[model_name]
@@ -337,12 +337,18 @@ def inference(text):
 
 inferences_running = 0
 
+def load_image( infilename ) :
+    img = Image.open( infilename )
+    img.load()
+    data = np.asarray( img, dtype="int32" )
+    return data
+
 def throttled_inference(text):
     global inferences_running
     current = inferences_running
-    if current >= 2:
+    if current >= 3:
         print(f"Rejected inference when we already had {current} running")
-        return None
+        return load_image("./gpu.jpg")
 
     print(f"Inference starting when we already had {current} running")
     inferences_running += 1