removed zero

app.py

@@ -1,92 +1,91 @@
-from diffusers import AutoPipelineForText2Image
-import torch
-import gradio as gr
-import threading
-import time;
-from queue import Queue
-import spaces
-
-
-@spaces.GPU(duration=120)
-def GenerateImage(prompt,steps,progress,model):
-    
-    data = []
-    
-    queue = Queue();
-    
-    def StartThread():
-        
-        pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
-            model, torch_dtype=torch.float16, use_safetensors=True
-        ).to("cuda")   
-        
-        vae = pipe_txt2img.vae
-        
-        def latents_callback(i, t, latents):
-            latents = 1 / 0.18215 * latents
-            image = vae.decode(latents).sample[0]
-            image = (image / 2 + 0.5).clamp(0, 1)
-            image = image.cpu().permute(1, 2, 0).numpy()
-            FinalImage = pipe_txt2img.numpy_to_pil(image)
-            queue.put({'type':'image', 'image':FinalImage[0], 'step': i})
-        
-        generator = torch.Generator(device="cpu").manual_seed(37)
-        FinalImage = pipe_txt2img(prompt, generator=generator,  num_inference_steps=steps,callback=latents_callback, callback_steps=progress).images[0]
-        queue.put({'type':'image', 'image':FinalImage, 'step': steps+1})
-        queue.put({'type':'end'})
-    
-    t = threading.Thread(target=StartThread)
-    t.start();
-    
-    while True:
-        print("Waiting next item");
-        nextItem = queue.get()
-        
-        if nextItem['type'] == 'end':
-            break;
-                 
-        Image   = nextItem['image']
-        Step    = nextItem['step']
-        yield [Image,Step];
-
-    print("Waiting thread finish...");
-    t.join()
-    
-    print("Finished!");
-
-
-
-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="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")
-    
-    with gr.Row():
-        with gr.Column():
-            btnRun      = gr.Button(value="Run!");
-            btnStop   = gr.Button(value="Stop!");
-        status  = gr.Text(label="Current Step");
-   
-        
-    image   = gr.Image();
-    
-    
-    GenerateEvent = btnRun.click( GenerateImage, [prompt,TotalSteps,ProgressSteps,model], [image,status] );
-    btnStop.click( None,None,None, cancels=[GenerateEvent] )
-
-if __name__ == "__main__":
-    demo.launch(show_api=True)
-
-
-    
-
-
-
-
+from diffusers import AutoPipelineForText2Image
+import torch
+import gradio as gr
+import threading
+import time;
+from queue import Queue
+
+
+# @spaces.GPU(duration=120)
+def GenerateImage(prompt,steps,progress,model):
+    
+    data = []
+    
+    queue = Queue();
+    
+    def StartThread():
+        
+        pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
+            model, torch_dtype=torch.float16, use_safetensors=True
+        ).to("cuda")   
+        
+        vae = pipe_txt2img.vae
+        
+        def latents_callback(i, t, latents):
+            latents = 1 / 0.18215 * latents
+            image = vae.decode(latents).sample[0]
+            image = (image / 2 + 0.5).clamp(0, 1)
+            image = image.cpu().permute(1, 2, 0).numpy()
+            FinalImage = pipe_txt2img.numpy_to_pil(image)
+            queue.put({'type':'image', 'image':FinalImage[0], 'step': i})
+        
+        generator = torch.Generator(device="cpu").manual_seed(37)
+        FinalImage = pipe_txt2img(prompt, generator=generator,  num_inference_steps=steps,callback=latents_callback, callback_steps=progress).images[0]
+        queue.put({'type':'image', 'image':FinalImage, 'step': steps+1})
+        queue.put({'type':'end'})
+    
+    t = threading.Thread(target=StartThread)
+    t.start();
+    
+    while True:
+        print("Waiting next item");
+        nextItem = queue.get()
+        
+        if nextItem['type'] == 'end':
+            break;
+                 
+        Image   = nextItem['image']
+        Step    = nextItem['step']
+        yield [Image,Step];
+
+    print("Waiting thread finish...");
+    t.join()
+    
+    print("Finished!");
+
+
+
+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="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")
+    
+    with gr.Row():
+        with gr.Column():
+            btnRun      = gr.Button(value="Run!");
+            btnStop   = gr.Button(value="Stop!");
+        status  = gr.Text(label="Current Step");
+   
+        
+    image   = gr.Image();
+    
+    
+    GenerateEvent = btnRun.click( GenerateImage, [prompt,TotalSteps,ProgressSteps,model], [image,status] );
+    btnStop.click( None,None,None, cancels=[GenerateEvent] )
+
+if __name__ == "__main__":
+    demo.launch(show_api=True)
+
+
+    
+
+
+
+