Update app.py

app.py

@@ -101,7 +101,7 @@ def generate_with_embs(text_embeddings, text_input, seed,num_inference_steps,gui
     height = 512                        # default height of Stable Diffusion
     width = 512                         # default width of Stable Diffusion
     num_inference_steps = num_inference_steps # 10            # Number of denoising steps
-    guidance_scale = guidance_scale # 7.5                # Scale for classifier-free guidance
+    guidance_scale = 7.5                # Scale for classifier-free guidance
     generator = torch.manual_seed(seed)   # Seed generator to create the inital latent noise
     batch_size = 1
 
@@ -376,7 +376,7 @@ def blue_loss_variant(images, use_mean=False, alpha=1.0):
     
     return loss
 
-def generate_with_prompt_style_guidance(prompt, style, seed,num_inference_steps,guidance_scale,loss_function):
+def generate_with_prompt_style_guidance(prompt, style, seed,num_inference_steps,loss_function):
 
     prompt = prompt + ' in style of s'
     
@@ -385,7 +385,7 @@ def generate_with_prompt_style_guidance(prompt, style, seed,num_inference_steps,
     height = 512                        # default height of Stable Diffusion
     width = 512                         # default width of Stable Diffusion
     num_inference_steps = num_inference_steps  #           # Number of denoising steps
-    guidance_scale = guidance_scale #               # Scale for classifier-free guidance
+    # guidance_scale = guidance_scale #               # Scale for classifier-free guidance
     generator = torch.manual_seed(seed)   # Seed generator to create the inital latent noise
     batch_size = 1
     
@@ -521,37 +521,30 @@ def inference(prompt, seed, style,num_inference_steps,guidance_scale,loss_functi
     else:
         return None
 
-title = "Stable Diffusion and Textual Inversion"
-description = "Gradio interface to apply style to Stable Diffusion outputs"
-examples = [["Pink Ferrari Car", 24041975,"Manga"], ["A man sipping tea wearing a spacesuit on the moon",24041975, "GTA-5"]]  # Added valid styles
+title = "Stable Diffusion with text input"
+description = "Apply various stable diffusion styles with text prompt as input"
+examples = [["a hyper-realistic high definition render of bear sitting on a red rug, 4k", 24041975,"Manga"], ["A man dancing on bhutan costume",24041975, "GTA-5"]]  # Added valid styles
 
 demo = gr.Interface(inference,
-                    inputs = [gr.Textbox(label='Prompt', value='Pink Ferrari Car'),    gr.Textbox(label='Seed', value=24041975),                           
+                    inputs = [gr.Textbox(label='Prompt', value='A man dancing on bhutan costume'),    gr.Textbox(label='Seed', value=24041975),                           
                               gr.Dropdown(['Dr Strange', 'GTA-5', 'Manga', 'Pokemon','Illustration','Matrix','Oil Painting'], label='Style', value='Dr Strange'),
                               gr.Slider(
                                 minimum=5,
                                 maximum=20,
                                 value=10,
                                 step=5,
-                                label="Select Number of Steps",
+                                label="Number of Steps",
                                 interactive=True,
                                 ),
-                                gr.Slider(
-                                minimum=0,
-                                maximum=10,
-                                value=8,
-                                step=8,
-                                label="Select Guidance Scale",
-                                interactive=True,
-                                ),gr.Radio(["contrast", "blue_original", "blue_modified","ymca","cmyk"], label="loss-function", info="loss-function" , value="ymca"),
+                                gr.Radio(["contrast", "blue_original", "blue_modified","ymca","cmyk"], label="loss-function", info="loss-function" , value="ymca"),
                               ],
                     outputs = [
                               gr.Image(label="Stable Diffusion Output"),
                               ],
                     title = title,
                     description = description,
-                    # examples = examples, 
-                    # cache_examples=True
+                    examples = examples, 
+                    cache_examples=True
                     )
 demo.launch()