Update app.py

app.py

@@ -24,26 +24,26 @@ del vae
 
 pipe.force_zeros_for_empty_prompt = False
 
-print("Optimizing BRIA 2.2 FAST - this could take a while")
-t=time.time()
-pipe.unet = torch.compile(
-    pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
-)
-with torch.no_grad():
-    outputs = pipe(
-        prompt="an apple",
-        num_inference_steps=8,
-    )
-
-    # This will avoid future compilations on different shapes
-    unet_compiled = torch._dynamo.run(pipe.unet)
-    unet_compiled.config=pipe.unet.config
-    unet_compiled.add_embedding = Dummy()
-    unet_compiled.add_embedding.linear_1 = Dummy()
-    unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
-    pipe.unet = unet_compiled
-
-print(f"Optimizing finished successfully after {time.time()-t} secs")
+# print("Optimizing BRIA 2.2 FAST - this could take a while")
+# t=time.time()
+# pipe.unet = torch.compile(
+#     pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
+# )
+# with torch.no_grad():
+#     outputs = pipe(
+#         prompt="an apple",
+#         num_inference_steps=8,
+#     )
+
+#     # This will avoid future compilations on different shapes
+#     unet_compiled = torch._dynamo.run(pipe.unet)
+#     unet_compiled.config=pipe.unet.config
+#     unet_compiled.add_embedding = Dummy()
+#     unet_compiled.add_embedding.linear_1 = Dummy()
+#     unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
+#     pipe.unet = unet_compiled
+
+# print(f"Optimizing finished successfully after {time.time()-t} secs")
 
 @spaces.GPU(enable_queue=True)
 def infer(prompt,seed,resolution):
@@ -89,13 +89,12 @@ with gr.Blocks(css=css) as demo:
           <p style="margin-bottom: 10px; font-size: 94%">
             This is a demo for 
             <a href="https://huggingface.co/briaai/BRIA-2.2-FAST" target="_blank">BRIA 2.2 FAST </a>. 
-            This is a fast version of BRIA 2.2 text-to-image model, still trained on licensed data, and so provides full legal liability coverage for copyright and privacy infringement.
-            Try it for free in our webapp demo <a href="https://labs.bria.ai/" </a>.
-
-            Are you a startup or a student? We encourage you to apply for our Startup Plan 
-            <a href="https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1*cqrl81*_ga*MTIxMDI2NzI5OC4xNjk5NTQ3MDAz*_ga_WRN60H46X4*MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..) </a> 
+            This is a fast version of BRIA 2.3 text-to-image model, still trained on licensed data, and so provides full legal liability coverage for copyright and privacy infringement.
+            Running time is 1.6s on A10 GPU. A demo for the newer version of BRIA-2.3 is also available <a href="https://huggingface.co/spaces/briaai/BRIA-2.3" target="_blank">HERE </a>. 
+            You can also try it for free in our <a href="https://labs.bria.ai/" target="_blank">webapp demo </a>.
+            Are you a startup or a student? We encourage you to apply for our 
+            <a href="https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1*cqrl81*_ga*MTIxMDI2NzI5OC4xNjk5NTQ3MDAz*_ga_WRN60H46X4*MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..)  target="_blank">Startup Plan  </a>
             This program are designed to support emerging businesses and academic pursuits with our cutting-edge technology.
-
           </p>
         ''')
         with gr.Group():