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| import gradio as gr | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| import os | |
| import time | |
| # Konfigurasi model | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model_repo_id = "cagliostrolab/animagine-xl-3.1" | |
| pipe = DiffusionPipeline.from_pretrained( | |
| model_repo_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| use_safetensors=True, | |
| ) | |
| pipe.to(device) | |
| # Fungsi inference | |
| def infer(prompt, negative_prompt, width, height, guidance_scale, num_inference_steps): | |
| try: | |
| # Generate image | |
| image = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=int(width), | |
| height=int(height), | |
| guidance_scale=float(guidance_scale), | |
| num_inference_steps=int(num_inference_steps), | |
| ).images[0] | |
| # Simpan hasil gambar di folder output dengan nama unik berdasarkan timestamp | |
| os.makedirs("./output", exist_ok=True) | |
| output_path = f"./output/generated_image_{int(time.time())}.png" | |
| image.save(output_path) | |
| return image | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| # Pesan pemberitahuan jika menggunakan CPU | |
| gr.Markdown( | |
| "### ⚠ Sorry for the inconvenience. The Space is currently running on the CPU, which might affect performance. We appreciate your understanding." | |
| ) | |
| gr.Markdown("## Text-to-Image Generator with animagine-xl-3.1") | |
| # Output gambar di atas | |
| result_image = gr.Image(label="Generated Image", elem_id="result-image") | |
| # Input parameter di bawah | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| placeholder="Masukkan prompt Anda di sini", | |
| value="1girl, souryuu asuka langley, neon genesis evangelion, solo, upper body, v, smile, looking at viewer, outdoors, night", | |
| ) | |
| negative_prompt = gr.Textbox( | |
| label="Negative Prompt", | |
| placeholder="Masukkan negative prompt untuk menghindari elemen tidak diinginkan", | |
| value="nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]" | |
| ) | |
| # Accordion untuk pengaturan lanjutan | |
| with gr.Accordion("Advanced Settings", open=False): | |
| width = gr.Dropdown( | |
| label="Width", | |
| choices=["256", "512", "768", "832", "896", "1024"], | |
| value="832", | |
| ) | |
| height = gr.Dropdown( | |
| label="Height", | |
| choices=["256", "512", "768", "832", "896", "1216", "1024"], | |
| value="1216", | |
| ) | |
| guidance_scale = gr.Dropdown( | |
| label="Guidance Scale", | |
| choices=[str(i / 10) for i in range(0, 201, 10)], # 0.0 to 20.0 | |
| value="7.0", | |
| ) | |
| num_inference_steps = gr.Dropdown( | |
| label="Number of Inference Steps", | |
| choices=[str(i) for i in range(1, 101)], # 1 to 100 | |
| value="28", | |
| ) | |
| run_button = gr.Button("Generate Image") | |
| # Hubungkan fungsi infer ke UI | |
| run_button.click( | |
| fn=infer, | |
| inputs=[prompt, negative_prompt, width, height, guidance_scale, num_inference_steps], | |
| outputs=result_image, | |
| ) | |
| # Jalankan aplikasi | |
| if __name__ == "__main__": | |
| demo.launch() |