Update app.py

app.py

@@ -4,7 +4,7 @@ import random
 from diffusers import DiffusionPipeline, LCMScheduler
 import time
 
-# Optimized Load
+# Load Model
 model_id = "runwayml/stable-diffusion-v1-5"
 adapter_id = "latent-consistency/lcm-lora-sdv1-5"
 
@@ -13,94 +13,89 @@ pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 pipe.load_lora_weights(adapter_id)
 pipe.to("cpu")
 
-def get_auto_res(prompt):
-    # Logic: Analyze prompt for shape. Default 512.
+def analyze_auto_res(prompt):
+    # If the prompt implies a "scene" or "background", use full 512
     p = prompt.lower()
-    if any(x in p for x in ["tall", "portrait", "person", "tree"]):
-        return 448, 576  # Optimized portrait
-    elif any(x in p for x in ["wide", "landscape", "panorama", "horizon"]):
-        return 640, 384  # Optimized landscape
-    return 480, 480      # Tight square
+    scene_keywords = ["room", "forest", "city", "street", "landscape", "background", "many", "all"]
+    if any(k in p for k in scene_keywords):
+        return 512, 512
+    # If it's a single object, tighten the frame to 448 to save CPU time
+    return 448, 448
 
-def generate_process(prompt, neg_prompt, size, steps, is_random, manual_seed, bulk_count, auto_res, progress=gr.Progress()):
+def generate_process(prompt, neg_prompt, size, steps, is_random, manual_seed, bulk_count, auto_res):
     start_total = time.time()
     all_images = []
     
-    # Queue Detection Emulation
-    progress(0, desc="⌛ Checking Queue Status...")
-    time.sleep(0.5) 
-    progress(0, desc="🚀 Queue Clear: Initializing Generation...")
-
-    # Resolution Logic
-    if auto_res:
-        w, h = get_auto_res(prompt)
-    else:
-        w, h = int(size), int(size)
+    # Smarter Instruction Following: forcing the model to focus on prompt counts
+    enhanced_prompt = f"((centered single object)), {prompt}, high quality, sharp focus"
+    
+    # Auto-Res logic
+    w, h = analyze_auto_res(prompt) if auto_res else (int(size), int(size))
 
     for i in range(int(bulk_count)):
-        step_start = time.time()
-        progress((i/bulk_count), desc=f"🎨 Generating Image {i+1} of {bulk_count}...")
+        yield None, f"🤔 **Thinking...** (Processing Image {i+1})"
         
-        # Seed Logic: If bulk > 1, always randomize to prevent duplicates
         current_seed = random.randint(0, 10**6) if (is_random or bulk_count > 1) else int(manual_seed)
         gen = torch.Generator(device="cpu").manual_seed(current_seed)
         
+        # Guidance Scale 2.0 helps prevent "object duplication" (the 10 apples issue)
         img = pipe(
-            prompt=f"high quality, detailed, {prompt}", # Instruction enhancement
+            prompt=enhanced_prompt, 
             negative_prompt=neg_prompt,
             num_inference_steps=int(steps),
-            guidance_scale=1.5, # Slightly higher for better instruction following
+            guidance_scale=2.0, 
             width=w, height=h,
             generator=gen
         ).images[0]
         
         all_images.append(img)
+        yield all_images, f"🎨 **Generating...** ({i+1}/{bulk_count})"
     
     total_duration = round(time.time() - start_total, 2)
-    return all_images, f"✨ **Batch Complete:** {total_duration}s | **Res:** {w}x{h}"
+    yield all_images, f"✅ **Finished:** {total_duration}s | **Seed used:** {current_seed}"
 
 def update_estimate(steps, size, bulk, auto):
-    res = 480 if auto else int(size)
-    base = 13.5 * bulk
-    factor = (res / 512) ** 2
-    est = round(steps * base * factor, 1)
-    return f"⚡ **Estimated Total Time:** ~{est} seconds"
+    # 2-vCPU actual: ~14.2s per step for 512px. 
+    # Auto-res (448px) is ~25% faster.
+    res_val = 448 if auto else int(size)
+    time_per_step = 14.2 * ((res_val / 512) ** 2)
+    est = round(steps * time_per_step * bulk, 1)
+    return f"⚡ **Estimated Wait:** ~{est} seconds"
 
-# CSS for "Creeper" aesthetic
 css = """
 .gradio-container { background-color: #0b0f19 !important; color: #00ffcc !important; }
-.generate-btn { background: linear-gradient(90deg, #00ffcc, #0077ff) !important; font-weight: bold !important; }
-.side-panel { border: 1px solid #00ffcc !important; padding: 15px; border-radius: 10px; background: rgba(0,255,204,0.05); }
+.generate-btn { background: linear-gradient(90deg, #00ffcc, #0077ff) !important; color: white !important; }
+.side-panel { border: 1px solid #00ffcc; padding: 15px; border-radius: 12px; background: rgba(0,255,204,0.05); }
 """
 
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    gr.Markdown("# 👾 CREEPER AI - V2 BULK ENGINE", elem_id="title")
+    gr.Markdown("# 👾 CREEPER AI - V2.1 CORE", elem_id="title")
     
     with gr.Row():
         with gr.Column(elem_classes="side-panel"):
-            prompt = gr.Textbox(label="Instruction Prompt", placeholder="Describe precisely...", lines=3)
-            neg_prompt = gr.Textbox(label="Negative Prompt", value="blurry, distorted, lowres")
+            prompt = gr.Textbox(label="Instruction", placeholder="Example: One red apple on a table", lines=3)
+            neg_prompt = gr.Textbox(label="Negative Prompt", placeholder="Avoid: blurry, many objects, distorted...")
             
-            with gr.Accordion("⚙️ Advanced Engine", open=True):
-                bulk_slider = gr.Slider(1, 3, value=1, step=1, label="Bulk Generation (Images)")
-                auto_res = gr.Checkbox(label="Auto-Resolution (Speed Optimize)", value=False)
-                size_choice = gr.Radio(choices=[512, 768], label="Fixed Resolution", value=512)
-                step_slider = gr.Slider(1, 6, value=4, step=1, label="Steps")
+            with gr.Accordion("⚙️ Engine Parameters", open=True):
+                bulk_slider = gr.Slider(1, 3, value=1, step=1, label="Bulk Count")
+                auto_res = gr.Checkbox(label="Auto-Optimize Resolution", value=False)
+                size_choice = gr.Radio(choices=, label="Fixed Resolution", value=512)
+                step_slider = gr.Slider(1, 6, value=4, step=1, label="Iteration Steps")
                 
                 with gr.Row():
                     random_seed = gr.Checkbox(label="Random Seed", value=True)
-                    seed_val = gr.Number(label="Seed", visible=False, value=42)
+                    seed_val = gr.Number(label="Manual Seed", visible=False, value=42)
             
-            est_box = gr.Markdown("⚡ **Estimated Total Time:** ~54.0 seconds")
-            gen_btn = gr.Button("INITIALIZE CLUSTER", variant="primary", elem_classes="generate-btn")
+            est_box = gr.Markdown("⚡ **Estimated Wait:** ~56.8 seconds")
+            gen_btn = gr.Button("INITIALIZE GENERATION", variant="primary", elem_classes="generate-btn")
 
         with gr.Column():
             status = gr.Markdown("🟢 **System Ready**")
-            gallery = gr.Gallery(label="Output Buffer", columns=1, height="auto")
+            gallery = gr.Gallery(label="Output", columns=1, height="auto", preview=True)
 
-    # Event Logic
     random_seed.change(lambda x: gr.update(visible=not x), random_seed, seed_val)
     
+    # Update timer live
     for ctrl in [bulk_slider, auto_res, size_choice, step_slider]:
         ctrl.change(update_estimate, [step_slider, size_choice, bulk_slider, auto_res], est_box)