Update app.py

app.py

@@ -1,154 +1,134 @@
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from diffusers import DiffusionPipeline
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+# -----------------------------
+# CPU MODE ONLY
+# -----------------------------
+device = "cpu"
+torch_dtype = torch.float32
+
+MODEL_CONFIGS = {
+    "FLUX.1-dev (CPU mode)": {
+        "repo_id": "black-forest-labs/FLUX.1-dev",
+        "width": 512,
+        "height": 512,
+        "guidance": 3.0,
+        "steps": 15,
+    },
+    "SDXL 1.0 (CPU mode)": {
+        "repo_id": "stabilityai/stable-diffusion-xl-base-1.0",
+        "width": 768,
+        "height": 768,
+        "guidance": 5.0,
+        "steps": 20,
+    },
+}
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+PIPELINES = {}
+MAX_SEED = np.iinfo(np.int32).max
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+def get_pipeline(model_label):
+    if model_label in PIPELINES:
+        return PIPELINES[model_label]
 
-    generator = torch.Generator().manual_seed(seed)
+    cfg = MODEL_CONFIGS[model_label]
 
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
+    pipe = DiffusionPipeline.from_pretrained(
+        cfg["repo_id"],
+        torch_dtype=torch_dtype,
+        low_cpu_mem_usage=True,
+    )
 
-    return image, seed
+    pipe.to(device)
+    pipe.enable_model_cpu_offload()
 
+    PIPELINES[model_label] = pipe
+    return pipe
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
+def build_prompt(prompt, style):
+    styles = {
+        "Tanpa gaya": "",
+        "Studio": "product photography, clean studio background, soft lighting",
+        "E-commerce": "white background, catalog photo, sharp, high quality",
+        "Pastel": "pastel colors, soft light, aesthetic instagram style",
+        "Lifestyle": "realistic lifestyle photography, natural light",
+    }
+    suffix = styles.get(style, "")
+    return f"{prompt}, {suffix}" if suffix else prompt
+
+
+def infer(prompt, negative_prompt, seed, randomize_seed,
+          width, height, guidance_scale, steps,
+          model_label, style, num_images):
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator(device=device).manual_seed(seed)
+    pipe = get_pipeline(model_label)
+
+    full_prompt = build_prompt(prompt, style)
+
+    images = []
+    for _ in range(num_images):
+        out = pipe(
+            prompt=full_prompt,
+            negative_prompt=negative_prompt or None,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            num_inference_steps=steps,
+            generator=generator,
+        )
+        images.append(out.images[0])
+
+    return images, seed
+
+
+with gr.Blocks(title="RuangAI CPU Mode") as demo:
+    gr.Markdown("# 🧴 RuangAI – CPU Mode Product Visualizer")
+
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt", placeholder="Deskripsi produk...")
+        run_btn = gr.Button("Generate")
+
+    with gr.Row():
+        model_label = gr.Dropdown(
+            list(MODEL_CONFIGS.keys()),
+            value="SDXL 1.0 (CPU mode)",
+            label="Model"
+        )
+        style = gr.Dropdown(
+            ["Tanpa gaya", "Studio", "E-commerce", "Pastel", "Lifestyle"],
+            value="Studio",
+            label="Gaya visual"
+        )
+        num_images = gr.Slider(1, 3, value=1, step=1, label="Jumlah gambar")
+
+    gallery = gr.Gallery(label="Hasil", columns=2, height=512)
+
+    with gr.Accordion("Advanced", open=False):
+        negative_prompt = gr.Textbox(label="Negative prompt")
+        seed = gr.Slider(0, MAX_SEED, value=0, step=1, label="Seed")
+        randomize_seed = gr.Checkbox(True, label="Randomize seed")
+        width = gr.Slider(256, 768, value=512, step=32, label="Width")
+        height = gr.Slider(256, 768, value=512, step=32, label="Height")
+        guidance_scale = gr.Slider(0, 10, value=5, step=0.5, label="Guidance")
+        steps = gr.Slider(5, 40, value=20, step=1, label="Steps")
+
+    run_btn.click(
+        infer,
         inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
+            prompt, negative_prompt, seed, randomize_seed,
+            width, height, guidance_scale, steps,
+            model_label, style, num_images
         ],
-        outputs=[result, seed],
+        outputs=[gallery, seed]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()