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README.md

@@ -1,12 +1,6 @@
 ---
-title: Image Generator Using Sd Turbo
-emoji: 🦀
-colorFrom: blue
-colorTo: indigo
+title: image_generator_using_sd_turbo
+app_file: image.py
 sdk: gradio
 sdk_version: 5.38.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

image.py

@@ -0,0 +1,95 @@
+import gradio as gr
+import torch
+from diffusers import AutoPipelineForText2Image
+import time
+import numpy as np # The only library we need for this fix
+
+# --- 1. Load the SD-Turbo Model (Optimized for CPU) ---
+# (No changes here)
+print("Loading the SD-Turbo model for CPU...")
+pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sd-turbo")
+device = "cpu"
+pipe = pipe.to(device)
+print("Model loaded successfully on CPU!")
+
+# --- 2. Pre-warm the Model ---
+# (No changes here)
+print("Pre-warming the pipeline...")
+_ = pipe(prompt="A photo of a cat", width=512, height=512, num_inference_steps=1).images[0]
+print("Pipeline is warmed up and ready!")
+
+
+# --- 3. The NumPy Array Solution ---
+def generate_and_return_numpy(prompt, seed, width, height):
+    """
+    Generates an image and returns it as a raw NumPy array. This is the most
+    stable method to avoid Gradio/Windows bugs, though a UI delay will exist.
+    """
+    start_time = time.time()
+    try:
+        width = int(width)
+        height = int(height)
+        generator = torch.Generator(device=pipe.device).manual_seed(int(seed))
+
+        # The model generates the PIL Image
+        pil_image = pipe(
+            prompt=prompt,
+            width=width,
+            height=height,
+            num_inference_steps=2,
+            guidance_scale=0.0,
+            generator=generator,
+        ).images[0]
+        
+        # --- THE FIX: Convert to NumPy Array ---
+        numpy_array = np.array(pil_image)
+        # --- END OF FIX ---
+
+        end_time = time.time()
+        # This time will be the FAST backend time. The UI will take longer.
+        generation_time = f"Backend generation time: {end_time - start_time:.2f} seconds"
+        
+        # We return the raw array. Gradio will handle the slow encoding now.
+        return numpy_array, generation_time, None
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return None, "Generation failed", str(e)
+
+
+# --- 4. Create the Gradio Interface ---
+# The UI code is identical. gr.Image can handle NumPy arrays.
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+    gr.Markdown(
+        """
+        # 💯 Stable CPU Generator (NumPy Version) 💯
+        ### This is the most robust version to prevent crashes on Windows.
+        """
+    )
+    with gr.Row():
+        with gr.Column(scale=3):
+            prompt_input = gr.Textbox(
+                label="Prompt", placeholder="A wizard casting a spell", lines=3,
+            )
+            with gr.Row():
+                width_slider = gr.Slider(
+                    label="Width", minimum=256, maximum=768, value=512, step=64,
+                )
+                height_slider = gr.Slider(
+                    label="Height", minimum=256, maximum=768, value=512, step=64,
+                )
+            seed_input = gr.Number(label="Seed", value=100)
+            generate_button = gr.Button("Generate", variant="primary")
+        with gr.Column(scale=2):
+            image_output = gr.Image(label="Generated Image", show_label=False)
+            info_output = gr.Textbox(label="Status", show_label=False, interactive=False)
+            error_output = gr.Textbox(label="Error", visible=False)
+
+    generate_button.click(
+        fn=generate_and_return_numpy,
+        inputs=[prompt_input, seed_input, width_slider, height_slider],
+        outputs=[image_output, info_output, error_output],
+    )
+
+# --- 5. Launch the App ---
+app.launch(share=True)

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+torch
+diffusers
+numpy
+accelerate