| import gradio as gr |
| import numpy as np |
| import random |
| import torch |
| from diffusers import DiffusionPipeline |
|
|
| |
| |
| |
| 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, |
| }, |
| } |
|
|
| PIPELINES = {} |
| MAX_SEED = np.iinfo(np.int32).max |
|
|
|
|
| def get_pipeline(model_label): |
| if model_label in PIPELINES: |
| return PIPELINES[model_label] |
|
|
| cfg = MODEL_CONFIGS[model_label] |
|
|
| pipe = DiffusionPipeline.from_pretrained( |
| cfg["repo_id"], |
| torch_dtype=torch_dtype, |
| low_cpu_mem_usage=True, |
| ) |
|
|
| pipe.to(device) |
| pipe.enable_model_cpu_offload() |
|
|
| PIPELINES[model_label] = pipe |
| return pipe |
|
|
|
|
| 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, steps, |
| model_label, style, num_images |
| ], |
| outputs=[gallery, seed] |
| ) |
|
|
| demo.launch() |
|
|
