Update app.py

app.py

@@ -10,6 +10,17 @@ from glide_text2im.model_creation import (
 )
 has_cuda = th.cuda.is_available()
 device = th.device('cpu' if not has_cuda else 'cuda')
+# Create base model.
+options = model_and_diffusion_defaults()
+options['use_fp16'] = has_cuda
+options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling
+model, diffusion = create_model_and_diffusion(**options)
+model.eval()
+if has_cuda:
+    model.convert_to_fp16()
+model.to(device)
+model.load_state_dict(load_checkpoint('base', device))
+print('total base parameters', sum(x.numel() for x in model.parameters()))
 # Create upsampler model.
 options_up = model_and_diffusion_defaults_upsampler()
 options_up['use_fp16'] = has_cuda
@@ -27,13 +38,14 @@ def show_images(batch: th.Tensor):
     reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])
     display(Image.fromarray(reshaped.numpy()))
 # Sampling parameters
-prompt = ""
+prompt = "an oil painting of a corgi"
 batch_size = 1
 guidance_scale = 3.0
 
 # Tune this parameter to control the sharpness of 256x256 images.
 # A value of 1.0 is sharper, but sometimes results in grainy artifacts.
 upsample_temp = 0.997
+
 import gradio as gr
 def generate_upsampled_image_from_text(prompt):
     # Set the prompt text
@@ -82,5 +94,5 @@ def generate_upsampled_image_from_text(prompt):
 
     # Show the output
     show_images(up_samples)
-demo = gr.Interface(fn =generate_upsampled_image_from_text,inputs ="text",outputs ="image")
-demo.launch()
+demo = gr.Interface(fn =generate_upsampled_image_from_text ="text",outputs ="image")
+demo.launch()