Update app.py

app.py

@@ -38,14 +38,14 @@ vae = vae.to(torch_device)
 text_encoder = text_encoder.to(torch_device)
 unet = unet.to(torch_device)
 
-style_files = ['stable_diffusion/learned_embeddings/arcane-style-jv.bin', 'stable_diffusion/learned_embeddings/birb-style.bin',
-                     'stable_diffusion/learned_embeddings/dr-strange.bin', 'stable_diffusion/learned_embeddings/midjourney-style.bin',
-                     'stable_diffusion/learned_embeddings/oil_style.bin']
+style_files = ['stable_diffusion/learned_embeddings/bird_style.bin', 'stable_diffusion/learned_embeddings/mid_journey_style.bin',
+                     'stable_diffusion/learned_embeddings/hitokumoru.bin', 'stable_diffusion/learned_embeddings/depthmap.bin',
+                     'stable_diffusion/learned_embeddings/arcane_style.bin']
 
 images_without_loss = []
 images_with_loss = []
 
-seed_values = [8,16,50,80,128]
+seed_values = [10,20,30,40,50]
 height = 512                        # default height of Stable Diffusion
 width = 512                         # default width of Stable Diffusion
 num_inference_steps = 5            # Number of denoising steps
@@ -277,10 +277,10 @@ def image_generator(prompt = "dog", loss_function=None):
 
   return display_images_in_rows(generated_sd_images, titles)
 
-description = "Generate an image with a prompt and apply vibrance loss if you wish to. Note that the app is hosted on a cpu and it takes atleast 15 minutes for generating images without loss. Please feel free to clone the space and use it with a GPU after increase the inference steps to more than 10 for better results"
+description = "Generate an image with a prompt"
 
 demo = gr.Interface(image_generator,
-                    inputs=[gr.Textbox(label="Enter prompt for generation", type="text", value="dog sitting on a bench"),
-                            gr.Radio(["Yes", "No"], value="No"  , label="Apply vibrance loss")],
-                    outputs=gr.Plot(label="Generated Images"), title = "Stable Diffusion using Textual Inversion", description=description)
-demo.launch()
+                    inputs=[gr.Textbox(label="prompt", type="text", value="cat fight"),
+                            gr.Radio(["Yes", "No"], value="No"  , label="loss checkbox")],
+                    outputs=gr.Plot(label="Generated Images"), title = "Stable Diffusion", description=description)
+demo.launch(debug=True)