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Runtime error
| import streamlit as st | |
| from transformers import pipeline | |
| from transformers import MarianMTModel, MarianTokenizer | |
| from diffusers import StableDiffusionPipeline | |
| from accelerate import Accelerator | |
| import torch | |
| # Inisialisasi accelerator | |
| accelerator = Accelerator() | |
| # Inisialisasi model terjemahan | |
| model_name = "Helsinki-NLP/opus-mt-id-en" | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| model = MarianMTModel.from_pretrained(model_name) | |
| # Instalisasi Diffusion User | |
| model_id = "runwayml/stable-diffusion-v1-5" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32) | |
| if torch.cuda.is_available(): | |
| # Pipe akan dipindahkan ke GPU jika tersedia | |
| pipe = pipe.to(accelerator.device) | |
| st.session_state.model_initialized = True | |
| st.markdown("<span style='color: green'>Model dipindahkan ke GPU.</span>", unsafe_allow_html=True) | |
| else: | |
| # Memeriksa apakah menggunakan accelerate di CPU | |
| if accelerator.device.type == 'cpu': | |
| with accelerator.device: | |
| st.session_state.model_initialized = True | |
| st.markdown("<span style='color: green'>Model dijalankan di CPU dengan Accelerate.</span>", unsafe_allow_html=True) | |
| else: | |
| # Jika tidak menggunakan accelerate, tetap di CPU | |
| pipe = pipe.to("cpu") | |
| st.session_state.model_initialized = True | |
| st.markdown("<span style='color: red'>GPU tidak ditemukan. Model tetap di CPU.</span>", unsafe_allow_html=True) | |
| st.title("AI Diffusion Bahasa Indonesia") | |
| st.write("Ini adalah pembuat gambar menggunakan AI, saya menggunakan model runwayml/stable-diffusion-v1.5 dan Helsinki-NLP/opus-mt-id-e. Berkerja dengan baik, tetapi karena saya menggunakan kapasitas geratisan jadi prosesnya akan sangat lama") | |
| text = st.text_area('Masukkan teks dalam bahasa Indonesia') | |
| if st.button("Proses"): | |
| if text: | |
| with st.spinner("Sedang memproses..."): | |
| # Terjemahkan teks ke bahasa Inggris | |
| inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) | |
| outputs = model.generate(**inputs) | |
| translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Membuat Gambar | |
| image = pipe(translated_text).images[0] | |
| # Tampilkan hasil | |
| st.image(image, caption='Hasil Diffusion User', use_column_width=True) | |
| else: | |
| st.write("Silakan masukkan teks terlebih dahulu.") | |