texts

app.py

@@ -23,7 +23,6 @@ def GenerateImage(prompt,steps,progress,model):
         vae = pipe_txt2img.vae
         
         def latents_callback(i, t, latents):
-            print("Latents...");
             latents = 1 / 0.18215 * latents
             image = vae.decode(latents).sample[0]
             image = (image / 2 + 0.5).clamp(0, 1)
@@ -43,14 +42,11 @@ def GenerateImage(prompt,steps,progress,model):
         print("Waiting next item");
         nextItem = queue.get()
         
-        print(f"NextItem: {nextItem}");
-        
         if nextItem['type'] == 'end':
             break;
                  
         Image   = nextItem['image']
         Step    = nextItem['step']
-        print(f"Image: {Image}")
         yield [Image,Step];
 
     print("Waiting thread finish...");
@@ -61,9 +57,14 @@ def GenerateImage(prompt,steps,progress,model):
 
 
 with gr.Blocks() as demo:
+    gr.Markdown("""
+        This is a lab to demonstrate how we can implement a text-to-image generation using Gradio and Diffusers, showing the progress of each image produced at each step.  
+        Type a prompt, choose the maximum number of steps and the frequency (in steps) at which progress is shown. You will see the diffusion process live!  
+    """)
+    
     with gr.Row():
-        prompt          = gr.Text(label="Imagem prompt");
-        TotalSteps      = gr.Slider(label="Imagem prompt", minimum=1,maximum=150,value=10);
+        prompt          = gr.Text(label="prompt");
+        TotalSteps      = gr.Slider(label="Steps", minimum=1,maximum=150,value=10);
         ProgressSteps   = gr.Number(label="Progress steps", value = 2);
         model           = gr.Text(label="Model", value="dreamlike-art/dreamlike-photoreal-2.0")