Update app.py

app.py

@@ -151,22 +151,6 @@ if torch.cuda.is_available():
         pipe2.unet = torch.compile(pipe2.unet, mode="reduce-overhead", fullgraph=True)
         print("Model RealVisXL_V4.0 Compiled!")
     
-    # Turbo v3 model
-    pipe3 = StableDiffusionXLPipeline.from_pretrained(
-        "SG161222/RealVisXL_V3.0_Turbo",
-        torch_dtype=dtype,
-        use_safetensors=True,
-        add_watermarker=False,
-    ).to(device)
-    pipe3.text_encoder = pipe3.text_encoder.half()
-    if ENABLE_CPU_OFFLOAD:
-        pipe3.enable_model_cpu_offload()
-    else:
-        pipe3.to(device)
-        print("Loaded RealVisXL_V3.0_Turbo on Device!")
-    if USE_TORCH_COMPILE:
-        pipe3.unet = torch.compile(pipe3.unet, mode="reduce-overhead", fullgraph=True)
-        print("Model RealVisXL_V3.0_Turbo Compiled!")
 else:
     pipe = StableDiffusionXLPipeline.from_pretrained(
         "SG161222/RealVisXL_V5.0_Lightning",
@@ -180,20 +164,13 @@ else:
         use_safetensors=True,
         add_watermarker=False,
     ).to(device)
-    pipe3 = StableDiffusionXLPipeline.from_pretrained(
-        "SG161222/RealVisXL_V3.0_Turbo",
-        torch_dtype=dtype,
-        use_safetensors=True,
-        add_watermarker=False,
-    ).to(device)
     print("Running on CPU; models loaded in float32.")
 
 DEFAULT_MODEL = "Lightning 5"
-MODEL_CHOICES = [DEFAULT_MODEL, "Lightning 4", "Turbo v3"]
+MODEL_CHOICES = [DEFAULT_MODEL, "Lightning 4"]
 models = {
     "Lightning 5": pipe,
-    "Lightning 4": pipe2,
-    "Turbo v3": pipe3
+    "Lightning 4": pipe2
 }
 
 def save_image(img: Image.Image) -> str:
@@ -248,8 +225,7 @@ def generate(
 
     # IMAGE GENERATION BRANCH (Stable Diffusion models)
     if (lower_text.startswith("@lightningv5") or 
-        lower_text.startswith("@lightningv4") or 
-        lower_text.startswith("@turbov3")):
+        lower_text.startswith("@lightningv4")):
         
         # Determine model choice based on flag.
         model_choice = None
@@ -257,13 +233,10 @@ def generate(
             model_choice = "Lightning 5"
         elif "@lightningv4" in lower_text:
             model_choice = "Lightning 4"
-        elif "@turbov3" in lower_text:
-            model_choice = "Turbo v3"
         
         # Remove the model flag from the prompt.
         prompt_clean = re.sub(r"@lightningv5", "", text, flags=re.IGNORECASE)
         prompt_clean = re.sub(r"@lightningv4", "", prompt_clean, flags=re.IGNORECASE)
-        prompt_clean = re.sub(r"@turbov3", "", prompt_clean, flags=re.IGNORECASE)
         prompt_clean = prompt_clean.strip().strip('"')
         
         # Default parameters for single image generation.
@@ -481,7 +454,6 @@ demo = gr.ChatInterface(
         gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2),
     ],
     examples=[
-
         [{"text": "@gemma3-4b Explain the Image", "files": ["examples/3.jpg"]}],
         [{"text": "@video-infer Explain the content of the Advertisement", "files": ["examples/videoplayback.mp4"]}],
         [{"text": "@video-infer Explain the content of the video in detail", "files": ["examples/breakfast.mp4"]}],
@@ -495,14 +467,13 @@ demo = gr.ChatInterface(
         ["Python Program for Array Rotation"],
         ["@tts1 Who is Nikola Tesla, and why did he die?"],
         ['@lightningv4 Cat holding a sign that says hello world'],
-        ['@turbov3 Anime illustration of a wiener schnitzel'],
         ["@tts2 What causes rainbows to form?"],
     ],
     cache_examples=False,
     type="messages",
     description="# **Gemma 3 `@gemma3-4b, @video-infer for video understanding`**",
     fill_height=True,
-    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image", "video"], file_count="multiple", placeholder="@gemma3-4b for multimodal, @video-infer for video, @lightningv5, @lightningv4, @turbov3 for image gen !"),
+    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image", "video"], file_count="multiple", placeholder="@gemma3-4b for multimodal, @video-infer for video, @lightningv5, @lightningv4 for image gen !"),
     stop_btn="Stop Generation",
     multimodal=True,
 )