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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Which Prompt Made This? \u2014 Baseline\n",
"\n",
"**Pelatnas IOAI 2026 \u00b7 Task Diffusion MCQA**\n",
"\n",
"Diberikan latent ter-noise `x_t` dari Stable Diffusion v1.4 pada timestep `t` yang\n",
"diketahui, plus 5 kandidat caption \u2014 pilih caption yang benar.\n",
"\n",
"Notebook ini menjalankan **satu** pendekatan sampai selesai dan menghasilkan\n",
"`submission.csv` yang valid. Pendekatan itu bekerja untuk sebagian soal.\n",
"Bagian menariknya ada di Section 5.\n",
"\n",
"| | |\n",
"|---|---|\n",
"| Metrik | Accuracy (chance = 20%) |\n",
"| Test | 250 soal |\n",
"| Train | 100 soal berlabel, **dengan kolom `type`** |\n",
"| Runtime | Colab **T4**, fp16 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip -q install diffusers==0.27.2 transformers accelerate safetensors\n",
"\n",
"import torch, numpy as np, pandas as pd\n",
"print(\"GPU:\", torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"TIDAK ADA GPU\")\n",
"print(\"torch:\", torch.__version__)\n",
"assert torch.cuda.is_available(), \"Aktifkan GPU: Runtime > Change runtime type > T4\"\n",
"\n",
"DEVICE, DTYPE = \"cuda\", torch.float16"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Muat data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import snapshot_download\n",
"\n",
"DATA = snapshot_download(repo_id=\"fassabilf/diffusion-mcqa-pelatnas-2026\", repo_type=\"dataset\")\n",
"print(\"data:\", DATA)\n",
"\n",
"train = pd.read_csv(f\"{DATA}/train/train.csv\")\n",
"test = pd.read_csv(f\"{DATA}/test/test.csv\")\n",
"print(train.shape, test.shape)\n",
"train.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Format latent \u2014 baca ini, hemat 20 menit\n",
"\n",
"- File: `train/latents/{id}.npy` dan `test/latents/{id}.npy`\n",
"- Shape `(4, 64, 64)`, dtype **float16**\n",
"- **Sudah dikalikan faktor skala VAE `0.18215`** \u2014 siap langsung masuk UNet.\n",
" Kamu **tidak** perlu mengalikannya lagi. Untuk VAE decode, bagi dulu dengan\n",
" faktor itu.\n",
"\n",
"4 channel itu **bukan** RGBA. Tidak ada channel yang berarti \"merah\" atau \"alpha\";\n",
"itu basis laten hasil belajar VAE dan urutannya arbitrer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def load_latent(split, i):\n",
" return np.load(f\"{DATA}/{split}/latents/{i}.npy\")\n",
"\n",
"x = load_latent(\"train\", 0)\n",
"print(x.shape, x.dtype, f\"mean={x.mean():.3f} std={x.std():.3f}\")\n",
"print(\"t =\", train.loc[0, \"t\"], \"| jawaban benar:\", train.loc[0, \"label\"])\n",
"for j in range(5):\n",
" print(f\" [{j}] {train.loc[0, f'choice_{j}']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Muat Stable Diffusion v1.4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from diffusers import AutoencoderKL, UNet2DConditionModel\n",
"from transformers import CLIPTextModel, CLIPTokenizer, CLIPModel\n",
"\n",
"SD = \"CompVis/stable-diffusion-v1-4\"\n",
"REV = \"133a221b8aa7292a167afc5127cb63fb5005638b\" # di-pin supaya semua orang dapat bobot yang sama\n",
"\n",
"vae = AutoencoderKL.from_pretrained(SD, subfolder=\"vae\", revision=REV,\n",
" variant=\"fp16\", torch_dtype=DTYPE).to(DEVICE).eval()\n",
"unet = UNet2DConditionModel.from_pretrained(SD, subfolder=\"unet\", revision=REV,\n",
" variant=\"fp16\", torch_dtype=DTYPE).to(DEVICE).eval()\n",
"# dtype tidak dilewatkan ke transformers: nama argumennya berbeda antara 4.x dan 5.x.\n",
"# .to(DEVICE, DTYPE) berlaku di keduanya.\n",
"text_encoder = CLIPTextModel.from_pretrained(SD, subfolder=\"text_encoder\", revision=REV,\n",
" variant=\"fp16\").to(DEVICE, DTYPE).eval()\n",
"tokenizer = CLIPTokenizer.from_pretrained(SD, subfolder=\"tokenizer\", revision=REV)\n",
"\n",
"clip = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\", revision=\"32bd64288804d66eefd0ccbe215aa642df71cc41\").to(DEVICE).eval()\n",
"clip_tok = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", revision=\"32bd64288804d66eefd0ccbe215aa642df71cc41\")\n",
"\n",
"VAE_SCALE = 0.18215\n",
"print(\"model siap\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Schedule noise\n",
"\n",
"Forward process Stable Diffusion:\n",
"\n",
"$$x_t = \\sqrt{\\bar\\alpha_t}\\, x_0 + \\sqrt{1-\\bar\\alpha_t}\\, \\varepsilon,\n",
"\\qquad \\varepsilon \\sim \\mathcal N(0, I)$$\n",
"\n",
"`alphas_cumprod[t]` di bawah adalah $\\bar\\alpha_t$. Semakin besar `t`, semakin\n",
"kecil $\\bar\\alpha_t$, semakin sedikit sisa sinyal gambarnya."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"betas = torch.linspace(0.00085**0.5, 0.012**0.5, 1000, dtype=torch.float64)**2\n",
"alphas_cumprod = torch.cumprod(1.0 - betas, dim=0)\n",
"\n",
"for t in sorted(train[\"t\"].unique()):\n",
" a = alphas_cumprod[t].item()\n",
" print(f\"t={t:4d} abar={a:.4f} sqrt(abar)={a**0.5:.4f} \"\n",
" f\"sqrt(1-abar)={(1-a)**0.5:.4f} SNR={a/(1-a):.3f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preview murah: latent2rgb\n",
"\n",
"Proyeksi linear `4 -> 3` yang dipakai ComfyUI/A1111 untuk live preview. Kasar,\n",
"tapi cukup untuk melihat dengan mata apa yang tersisa di tiap level noise \u2014\n",
"dan jauh lebih cepat daripada VAE decode."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"L2RGB = torch.tensor([[ 0.298, 0.207, 0.208],\n",
" [ 0.187, 0.286, 0.173],\n",
" [-0.158, 0.189, 0.264],\n",
" [-0.184,-0.271,-0.473]])\n",
"\n",
"def latent2rgb(lat):\n",
" x = torch.from_numpy(np.asarray(lat, dtype=np.float32)) / VAE_SCALE\n",
" img = torch.einsum(\"chw,cr->hwr\", x, L2RGB)\n",
" return ((img - img.min()) / (img.max() - img.min() + 1e-8)).numpy()\n",
"\n",
"fig, axes = plt.subplots(1, len(train[\"t\"].unique()), figsize=(4*len(train[\"t\"].unique()), 4))\n",
"for ax, t in zip(np.atleast_1d(axes), sorted(train[\"t\"].unique())):\n",
" i = int(train[train[\"t\"] == t][\"id\"].iloc[0])\n",
" ax.imshow(latent2rgb(load_latent(\"train\", i))); ax.set_title(f\"t = {t}\"); ax.axis(\"off\")\n",
"plt.tight_layout(); plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Baseline \u2014 decode lalu cocokkan dengan CLIP\n",
"\n",
"Ide paling langsung: `x_t` adalah latent, VAE bisa mengubah latent jadi gambar,\n",
"CLIP bisa mencocokkan gambar dengan teks. Rangkai ketiganya."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"CLIP_MEAN = torch.tensor([0.48145466, 0.4578275, 0.40821073], device=DEVICE).view(1,3,1,1)\n",
"CLIP_STD = torch.tensor([0.26862954, 0.26130258, 0.27577711], device=DEVICE).view(1,3,1,1)\n",
"\n",
"def _feat(o): # transformers baru mengembalikan objek, bukan tensor\n",
" return o if torch.is_tensor(o) else o.pooler_output\n",
"\n",
"@torch.no_grad()\n",
"def decode(latents):\n",
" img = vae.decode(latents / VAE_SCALE).sample\n",
" return (img / 2 + 0.5).clamp(0, 1)\n",
"\n",
"@torch.no_grad()\n",
"def clip_image(images):\n",
" x = torch.nn.functional.interpolate(images.float(), size=224, mode=\"bicubic\",\n",
" antialias=True, align_corners=False)\n",
" f = _feat(clip.get_image_features(pixel_values=(x - CLIP_MEAN) / CLIP_STD))\n",
" return torch.nn.functional.normalize(f.float(), dim=-1)\n",
"\n",
"@torch.no_grad()\n",
"def clip_text(captions):\n",
" tok = clip_tok(captions, padding=True, truncation=True, max_length=77,\n",
" return_tensors=\"pt\").to(DEVICE)\n",
" f = _feat(clip.get_text_features(**tok))\n",
" return torch.nn.functional.normalize(f.float(), dim=-1)\n",
"\n",
"@torch.no_grad()\n",
"def baseline_scores(df, split, batch=16):\n",
" out = []\n",
" for i in range(0, len(df), batch):\n",
" grp = df.iloc[i:i+batch]\n",
" lat = torch.from_numpy(np.stack([load_latent(split, int(r)) for r in grp[\"id\"]]))\n",
" feats = clip_image(decode(lat.to(DEVICE, DTYPE)))\n",
" caps = [r[f\"choice_{j}\"] for _, r in grp.iterrows() for j in range(5)]\n",
" txt = clip_text(caps).view(len(grp), 5, -1)\n",
" out.append((feats.unsqueeze(1) * txt).sum(-1).cpu().numpy())\n",
" return np.concatenate(out)\n",
"\n",
"train_scores = baseline_scores(train, \"train\")\n",
"train_pred = train_scores.argmax(1)\n",
"print(\"akurasi train (keseluruhan):\", f\"{(train_pred == train['label']).mean():.1%}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Evaluasi \u2014 dipecah per tipe soal\n",
"\n",
"Satu angka akurasi hampir tidak pernah memberi tahu apa yang harus diperbaiki.\n",
"`train.csv` punya kolom `type` (test tidak) \u2014 pakai itu."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ev = train.copy()\n",
"ev[\"pred\"] = train_pred\n",
"ev[\"correct\"] = (ev[\"pred\"] == ev[\"label\"]).astype(float)\n",
"\n",
"tab = ev.groupby(\"type\").agg(t=(\"t\", \"first\"), n=(\"id\", \"size\"), akurasi=(\"correct\", \"mean\"))\n",
"tab[\"akurasi\"] = tab[\"akurasi\"].map(lambda v: f\"{v:.0%}\")\n",
"print(tab.to_string())\n",
"print(f\"\\nchance level = 20%\")\n",
"\n",
"ev.groupby(\"type\")[\"correct\"].mean().plot(kind=\"bar\", figsize=(6,3),\n",
" title=\"Akurasi baseline per tipe soal (chance = 20%)\")\n",
"plt.axhline(0.2, color=\"r\", ls=\"--\", label=\"chance\"); plt.legend()\n",
"plt.ylabel(\"akurasi\"); plt.tight_layout(); plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Berhenti sebentar di sini.** Lihat tabelnya, dan lihat kolom `t`-nya.\n",
"\n",
"Ada kelompok soal yang diselesaikan baseline ini dengan mudah, dan ada kelompok di\n",
"mana ia praktis menebak. Sebelum lanjut menulis kode, jawab dulu untuk diri sendiri:\n",
"*apa tepatnya yang hilang di kelompok yang gagal itu?*\n",
"\n",
"Tulis dugaanmu. Nanti dicek waktu debrief."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Submission"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_scores = baseline_scores(test, \"test\")\n",
"sub = pd.DataFrame({\"id\": test[\"id\"], \"label\": test_scores.argmax(1)})\n",
"sub.to_csv(\"submission.csv\", index=False)\n",
"print(sub.shape)\n",
"print(sub[\"label\"].value_counts().sort_index().to_dict())\n",
"sub.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Ke mana selanjutnya\n",
"\n",
"Tiga pertanyaan. Tidak ada jawabannya di notebook ini \u2014 itu memang bagian\n",
"soalnya.\n",
"\n",
"---\n",
"\n",
"**1.** VAE decode gagal total pada `t` tinggi. Tapi UNet **justru dilatih** pada\n",
"input persis seperti itu \u2014 latent ter-noise pada level noise sembarang. Kenapa\n",
"kamu membuang satu-satunya komponen yang memang dirancang untuk membaca input ini?\n",
"\n",
"---\n",
"\n",
"**2.** `unet(x_t, t, encoder_hidden_states=cond)` mengembalikan sesuatu. Apa\n",
"tepatnya benda itu, dalam persamaan di Section 3? Dan apa yang berubah pada\n",
"keluaran itu kalau kamu menukar caption yang dikondisikan \u2014 sementara `x_t` dan\n",
"`t` dibiarkan sama persis?\n",
"\n",
"---\n",
"\n",
"**3.** Kamu tidak tahu `\u03b5` yang dipakai untuk membuat `x_t`, dan kami tidak akan\n",
"memberikannya. Tapi tidak ada yang melarangmu **menambahkan noise-mu sendiri** ke\n",
"`x_t` untuk naik ke level noise yang lebih tinggi \u2014 dan noise yang itu, kamu tahu\n",
"persis nilainya.\n",
"\n",
"Kalau kamu menempuh jalan ini: pastikan kamu memakai **pasangan noise yang sama\n",
"untuk kelima caption** pada setiap sampel. Alasannya layak kamu pikirkan sendiri;\n",
"efeknya pada akurasi jauh lebih besar dari yang kamu duga.\n",
"\n",
"---\n",
"\n",
"*Anggaran: UNet fp16 di T4 kira-kira 20 forward pass/detik. 250 soal x 5 caption\n",
"x K sampel. Hitung K yang muat di waktumu sebelum menulis loop-nya.*"
]
}
],
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