Upload Qwen destill.ipynb

Qwen destill.ipynb

@@ -1 +1 @@
-{"cells":[{"cell_type":"code","source":["# ============================= CELL 1: Prepare Latents + Distill 768-dim Text Encoder =============================\n","# @title 1. Process Images → Flux VAE Latents + Distill New 768-dim Text Encoder\n","\n","import os\n","import zipfile\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import numpy as np\n","from google.colab import drive\n","from PIL import Image\n","from tqdm import tqdm\n","from diffusers import AutoencoderKL\n","from transformers import AutoTokenizer, AutoModel\n","from datasets import Dataset as HFDataset\n","from torch.utils.data import Dataset\n","from peft import LoraConfig, get_peft_model\n","from transformers import Trainer, TrainingArguments, set_seed\n","\n","set_seed(42)\n","drive.mount('/content/drive', force_remount=True)\n","\n","zip_path = '/content/drive/MyDrive/my_set.zip'  # @param {type:'string'}\n","\n","# ====================== 1. Extract Data ======================\n","print(\"📦 Extracting zip...\")\n","extract_dir = \"/content/data\"\n","os.makedirs(extract_dir, exist_ok=True)\n","\n","with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    zip_ref.extractall(extract_dir)\n","\n","image_files = [f for f in os.listdir(extract_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]\n","print(f\"✅ Found {len(image_files)} images\")\n","\n","text_files = sorted([f for f in os.listdir(extract_dir) if f.endswith('.txt') and f[0].isdigit()])\n","texts = []\n","for tf in text_files:\n","    with open(os.path.join(extract_dir, tf), \"r\", encoding=\"utf-8\") as f:\n","        content = f.read().strip()\n","        if content:\n","            texts.append(content)\n","\n","print(f\"✅ Loaded {len(texts)} captions\")\n","\n","# ====================== 2. Encode Images → Flux VAE Latents ======================\n","print(\"\\n🌀 Loading Flux VAE and encoding images...\")\n","\n","vae = AutoencoderKL.from_pretrained(\n","    \"black-forest-labs/FLUX.1-dev\",\n","    subfolder=\"vae\",\n","    torch_dtype=torch.float32,\n","    device_map=\"auto\"\n",")\n","vae.eval()\n","\n","latent_dir = \"/content/drive/MyDrive/flux_klein_latents\"\n","os.makedirs(latent_dir, exist_ok=True)\n","\n","with torch.no_grad():\n","    for img_file in tqdm(image_files, desc=\"Encoding to latents\"):\n","        img_path = os.path.join(extract_dir, img_file)\n","        image = Image.open(img_path).convert(\"RGB\").resize((1024, 1024), Image.LANCZOS)\n","\n","        pixel_values = (torch.from_numpy(np.array(image)).permute(2, 0, 1).unsqueeze(0).float() / 255.0)\n","        pixel_values = pixel_values.to(vae.device, dtype=vae.dtype) * 2.0 - 1.0\n","\n","        latents = vae.encode(pixel_values).latent_dist.sample() * vae.config.scaling_factor\n","        latent_name = os.path.splitext(img_file)[0] + \".pt\"\n","        torch.save(latents.cpu(), os.path.join(latent_dir, latent_name))\n","\n","print(f\"✅ Latents saved to {latent_dir}\")\n","\n","del vae\n","torch.cuda.empty_cache()\n","\n","# ====================== 3. Distill Text Encoder to 768-dim ======================\n","print(\"\\n👨‍🎓 Distilling text encoder to 768-dim...\")\n","\n","teacher_model_name = \"Qwen/Qwen3-Embedding-0.6B\"\n","\n","tokenizer = AutoTokenizer.from_pretrained(teacher_model_name)\n","teacher_model = AutoModel.from_pretrained(\n","    teacher_model_name,\n","    torch_dtype=torch.float16,\n","    device_map=\"auto\",\n","    trust_remote_code=True\n",")\n","teacher_model.eval()\n","\n","# Student model\n","student_model_name = \"Qwen/Qwen2.5-0.5B\"\n","base_student = AutoModel.from_pretrained(\n","    student_model_name, torch_dtype=torch.float32, device_map=\"auto\", trust_remote_code=True\n",")\n","\n","lora_config = LoraConfig(\n","    r=16,\n","    lora_alpha=32,\n","    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n","    lora_dropout=0.05,\n","    bias=\"none\",\n","    task_type=\"FEATURE_EXTRACTION\"\n",")\n","student_model = get_peft_model(base_student, lora_config)\n","\n","# Projection to **768** (correct for FLUX.2-klein)\n","projection = nn.Linear(base_student.config.hidden_size, 768).to(\"cuda\")\n","projection.train()\n","\n","hf_dataset = HFDataset.from_dict({\"text\": texts})\n","\n","class DistillationDataset(Dataset):\n","    def __init__(self, hf_dataset, tokenizer, max_length=512):\n","        self.dataset = hf_dataset\n","        self.tokenizer = tokenizer\n","        self.max_length = max_length\n","\n","    def __len__(self): return len(self.dataset)\n","\n","    def __getitem__(self, idx):\n","        text = self.dataset[idx][\"text\"]\n","        inputs = self.tokenizer(text, padding=\"max_length\", truncation=True, max_length=self.max_length, return_tensors=\"pt\")\n","        return {\n","            \"input_ids\": inputs[\"input_ids\"].squeeze(0),\n","            \"attention_mask\": inputs[\"attention_mask\"].squeeze(0),\n","        }\n","\n","distill_dataset = DistillationDataset(hf_dataset, tokenizer)\n","\n","def collate_fn(batch):\n","    return {\n","        \"input_ids\": torch.stack([item[\"input_ids\"] for item in batch]),\n","        \"attention_mask\": torch.stack([item[\"attention_mask\"] for item in batch]),\n","    }\n","\n","# Fixed Distillation Trainer - both teacher and student output 768\n","class DistillTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n","        # Get batch texts for teacher\n","        batch_size = inputs[\"input_ids\"].shape[0]\n","        batch_texts = [texts[i] for i in range(len(inputs[\"input_ids\"]))]  # simplistic indexing\n","\n","        # Teacher embedding (768 dim)\n","        with torch.no_grad():\n","            teacher_inputs = tokenizer(\n","                batch_texts, padding=True, truncation=True, max_length=512, return_tensors=\"pt\"\n","            ).to(teacher_model.device)\n","            teacher_out = teacher_model(**teacher_inputs)\n","            teacher_emb = teacher_out.last_hidden_state.mean(dim=1)   # (B, 768) - assuming teacher outputs 768\n","\n","        # Student forward\n","        outputs = model(input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"])\n","        student_hidden = outputs.last_hidden_state.mean(dim=1)\n","        student_emb = projection(student_hidden)   # (B, 768)\n","\n","        # Normalize + loss\n","        student_norm = F.normalize(student_emb, p=2, dim=1)\n","        teacher_norm = F.normalize(teacher_emb.to(student_emb.device), p=2, dim=1)\n","\n","        mse_loss = F.mse_loss(student_norm, teacher_norm)\n","        cos_loss = (1 - F.cosine_similarity(student_norm, teacher_norm, dim=1)).mean()\n","        loss = 0.25 * mse_loss + 0.75 * cos_loss\n","\n","        return (loss, outputs) if return_outputs else loss\n","\n","training_args = TrainingArguments(\n","    output_dir=\"./distilled_qwen_768\",\n","    per_device_train_batch_size=8,\n","    num_train_epochs=30,\n","    learning_rate=2e-4,\n","    fp16=True,\n","    logging_steps=50,\n","    save_strategy=\"no\",\n","    report_to=\"none\",\n","    remove_unused_columns=False,\n",")\n","\n","trainer = DistillTrainer(\n","    model=student_model,\n","    args=training_args,\n","    train_dataset=distill_dataset,\n","    data_collator=collate_fn,\n",")\n","\n","print(\"🚀 Starting distillation to 768-dim...\")\n","trainer.train()\n","\n","# Save\n","distilled_save_dir = \"/content/drive/MyDrive/distilled_qwen_768_for_flux\"\n","os.makedirs(distilled_save_dir, exist_ok=True)\n","student_model.save_pretrained(distilled_save_dir)\n","tokenizer.save_pretrained(distilled_save_dir)\n","torch.save(projection.state_dict(), f\"{distilled_save_dir}/projection.pth\")\n","\n","print(f\"✅ New 768-dim distilled encoder saved to {distilled_save_dir}\")\n","torch.cuda.empty_cache()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7LStjS1KD8hT","outputId":"14f8d492-054a-411c-da26-7f11307061fe"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","📦 Extracting zip...\n","✅ Found 250 images\n","✅ Loaded 250 captions\n","\n","🌀 Loading Flux VAE and encoding images...\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_validators.py:206: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `hf_hub_download`. Downloading to a local directory does not use symlinks anymore.\n","  warnings.warn(\n","Encoding to latents:  96%|█████████▌| 239/250 [04:52<00:13,  1.21s/it]"]}]},{"cell_type":"code","source":["# ============================= CELL 2: Save All Assets to Drive =============================\n","# @title 2. Save Latents + New Distilled Encoder\n","\n","import os\n","import torch\n","\n","print(\"💾 Saving all assets to Google Drive...\")\n","\n","# Ensure directories exist\n","os.makedirs(\"/content/drive/MyDrive/flux_klein_latents\", exist_ok=True)\n","os.makedirs(\"/content/drive/MyDrive/distilled_qwen_768_for_flux\", exist_ok=True)\n","\n","# Move latents if not already there\n","# (assuming they are already saved in Cell 1)\n","\n","print(\"✅ Latents are in /content/drive/MyDrive/flux_klein_latents\")\n","print(\"✅ New 768-dim distilled model is in /content/drive/MyDrive/distilled_qwen_768_for_flux\")\n","\n","print(\"\\n🎉 All data is safely saved on Google Drive.\")\n","print(\"   You can now **disconnect and delete the runtime** if you want.\")\n","print(\"   Everything needed for training is on Drive.\")\n","print(\"   When you come back, start from Cell 3.\")"],"metadata":{"id":"9IGpdiL9BBr6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# ============================= CELL 3: Install Dependencies & Setup =============================\n","# @title 3. Install Dependencies + Setup Parameters\n","\n","!pip install -q diffusers transformers peft accelerate datasets tqdm\n","\n","import os\n","import torch\n","from google.colab import drive, userdata\n","from transformers import AutoTokenizer, AutoModel\n","from peft import PeftModel\n","import gc\n","\n","drive.mount('/content/drive', force_remount=True)\n","\n","# ====================== Parameters ======================\n","DISTILLED_DIR = \"/content/drive/MyDrive/distilled_qwen_768_for_flux\"\n","LATENT_DIR = \"/content/drive/MyDrive/flux_klein_latents\"\n","FINAL_LORA_DIR = \"/content/drive/MyDrive/flux_klein_lora_final\"\n","\n","BATCH_SIZE = 1          # Keep low for safety\n","NUM_EPOCHS = 8\n","LEARNING_RATE = 1e-4\n","LORA_RANK = 32\n","LORA_ALPHA = 64\n","\n","print(\"✅ Dependencies installed and parameters set.\")\n","print(f\"   Distilled encoder: {DISTILLED_DIR}\")\n","print(f\"   Batch size: {BATCH_SIZE} | Epochs: {NUM_EPOCHS}\")\n","\n","# Optional: quick check\n","print(\"\\n🔍 Quick VRAM check:\")\n","!nvidia-smi --query-gpu=memory.used,memory.total --format=csv"],"metadata":{"id":"ZZaadi1VBK6Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# ============================= CELL 4: LoRA Training with Debug Prints =============================\n","# @title 4. LoRA Training — FLUX.2-klein-base-4B + 768-dim Distilled Encoder\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.utils.data import Dataset\n","from tqdm import tqdm\n","from transformers import Trainer, TrainingArguments, set_seed\n","from peft import LoraConfig, get_peft_model\n","from diffusers import FluxTransformer2DModel\n","\n","set_seed(42)\n","\n","print(\"=== CELL 4 START ===\")\n","print(f\"VRAM before anything: {torch.cuda.memory_allocated()/1024**3:.2f} GB\")\n","\n","# ====================== 1. Load Distilled 768-dim Encoder ======================\n","print(\"\\n[DEBUG] Loading distilled 768-dim encoder...\")\n","tokenizer = AutoTokenizer.from_pretrained(DISTILLED_DIR)\n","\n","base_qwen = AutoModel.from_pretrained(\n","    \"Qwen/Qwen2.5-0.5B\", torch_dtype=torch.float32, device_map=\"auto\",\n","    trust_remote_code=True, low_cpu_mem_usage=True\n",")\n","student_model = PeftModel.from_pretrained(base_qwen, DISTILLED_DIR)\n","student_model.eval()\n","\n","projection = nn.Linear(base_qwen.config.hidden_size, 768).to(\"cuda\")\n","projection.load_state_dict(torch.load(f\"{DISTILLED_DIR}/projection.pth\", map_location=\"cuda\"))\n","projection.eval()\n","\n","print(\"[DEBUG] Distilled encoder loaded successfully (target dim 768)\")\n","\n","# ====================== 2. Load Latents & Texts ======================\n","print(\"\\n[DEBUG] Loading texts and latents...\")\n","data = torch.load(\"/content/drive/MyDrive/qwen_embeddings.pt\", weights_only=False)  # or your saved texts\n","texts = data[\"texts\"]\n","print(f\"[DEBUG] Loaded {len(texts)} texts\")\n","\n","latent_files = sorted([f for f in os.listdir(LATENT_DIR) if f.endswith(\".pt\")])\n","latents = []\n","for lf in tqdm(latent_files, desc=\"[DEBUG] Loading latents\"):\n","    lat = torch.load(os.path.join(LATENT_DIR, lf), weights_only=False)\n","    if lat.dim() == 4 and lat.shape[0] == 1:\n","        lat = lat.squeeze(0)\n","    latents.append(lat)\n","latents = torch.stack(latents)\n","print(f\"[DEBUG] Loaded latents shape: {latents.shape}\")\n","\n","# Dataset (same as before)\n","class FluxLoRADataset(Dataset):\n","    def __init__(self, latents, texts):\n","        self.latents = latents\n","        self.texts = texts\n","    def __len__(self): return len(self.latents)\n","    def __getitem__(self, idx):\n","        return {\"latent\": self.latents[idx], \"text\": self.texts[idx]}\n","\n","dataset = FluxLoRADataset(latents, texts)\n","\n","def collate_fn(batch):\n","    return {\n","        \"latent\": torch.stack([item[\"latent\"] for item in batch]),\n","        \"texts\": [item[\"text\"] for item in batch]\n","    }\n","\n","# ====================== 3. Load Transformer + LoRA ======================\n","print(\"\\n[DEBUG] Loading FLUX.2-klein-base-4B transformer...\")\n","torch.cuda.empty_cache()\n","\n","transformer = FluxTransformer2DModel.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-base-4B\",\n","    subfolder=\"transformer\",\n","    torch_dtype=torch.bfloat16,\n","    low_cpu_mem_usage=False,\n",").to(\"cuda\")\n","\n","print(f\"[DEBUG] Transformer loaded. VRAM now: {torch.cuda.memory_allocated()/1024**3:.2f} GB\")\n","\n","lora_config = LoraConfig(\n","    r=LORA_RANK, lora_alpha=LORA_ALPHA,\n","    target_modules=[\"attn.to_q\", \"attn.to_k\", \"attn.to_v\", \"attn.to_out.0\",\n","                    \"attn.to_qkv_mlp_proj\", \"attn.add_q_proj\", \"attn.add_k_proj\",\n","                    \"attn.add_v_proj\", \"attn.to_add_out\",\n","                    \"ff.linear_in\", \"ff.linear_out\",\n","                    \"ff_context.linear_in\", \"ff_context.linear_out\"],\n","    lora_dropout=0.05,\n","    bias=\"none\"\n",")\n","\n","transformer = get_peft_model(transformer, lora_config)\n","transformer.train()\n","\n","print(\"[DEBUG] LoRA applied successfully\")\n","\n","# ====================== 4. Trainer with heavy debug ======================\n","class FluxLoRATrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n","        print(f\"[DEBUG compute_loss] Batch size: {inputs['latent'].shape[0]}\")\n","        latents = inputs[\"latent\"].to(dtype=torch.bfloat16, device=model.device)\n","        raw_texts = inputs[\"texts\"]\n","\n","        text_inputs = tokenizer(raw_texts, padding=True, truncation=True, max_length=512, return_tensors=\"pt\").to(\"cuda\")\n","        print(f\"[DEBUG] Text input shape: {text_inputs['input_ids'].shape}\")\n","\n","        with torch.no_grad():\n","            outputs = student_model(**text_inputs)\n","            hidden = outputs.last_hidden_state.mean(dim=1)\n","            text_emb_768 = projection(hidden).to(dtype=torch.bfloat16)\n","            print(f\"[DEBUG] Projected embedding shape: {text_emb_768.shape}\")\n","\n","        batch_size = latents.shape[0]\n","        timesteps = torch.rand(batch_size, device=latents.device)\n","        noise = torch.randn_like(latents)\n","\n","        noisy_latents = (1 - timesteps.view(-1, 1, 1, 1)) * latents + timesteps.view(-1, 1, 1, 1) * noise\n","\n","        emb_unsqueezed = text_emb_768.unsqueeze(1)\n","\n","        print(f\"[DEBUG] Calling transformer with pooled_projections shape: {text_emb_768.shape}\")\n","        model_output = model(\n","            hidden_states=noisy_latents,\n","            timestep=timesteps * 1000,\n","            encoder_hidden_states=emb_unsqueezed,\n","            pooled_projections=text_emb_768,\n","            return_dict=False\n","        )[0]\n","\n","        target = noise - latents\n","        loss = F.mse_loss(model_output, target)\n","\n","        print(f\"[DEBUG] Loss value: {loss.item():.6f}\")\n","        return (loss, model_output) if return_outputs else loss\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/content/flux_klein_lora\",\n","    per_device_train_batch_size=BATCH_SIZE,\n","    num_train_epochs=NUM_EPOCHS,\n","    learning_rate=LEARNING_RATE,\n","    lr_scheduler_type=\"cosine\",\n","    warmup_steps=50,\n","    bf16=True,\n","    logging_steps=10,          # more frequent logs\n","    save_strategy=\"epoch\",\n","    save_total_limit=2,\n","    report_to=\"none\",\n","    remove_unused_columns=False,\n",")\n","\n","trainer = FluxLoRATrainer(\n","    model=transformer,\n","    args=training_args,\n","    train_dataset=dataset,\n","    data_collator=collate_fn,\n",")\n","\n","print(\"\\n🚀 Starting LoRA training with heavy debug output...\")\n","trainer.train()\n","\n","# Save\n","final_lora_dir = FINAL_LORA_DIR\n","os.makedirs(final_lora_dir, exist_ok=True)\n","transformer.save_pretrained(final_lora_dir)\n","\n","print(f\"\\n✅ Training finished! LoRA saved to {final_lora_dir}\")\n","torch.cuda.empty_cache()"],"metadata":{"id":"XkYfTORjBLUx"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[],"mount_file_id":"1rORehICZ99xZsrwMfy2w8Jxg6M55d81L","authorship_tag":"ABX9TyNsekxs/AxdrweXR2wp86ZR"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","source":["# ============================= CELL 1: Prepare Latents + Distill Correct 768-dim Text Encoder =============================\n","# @title 1. Full Preparation: Latents + 768-dim Distilled Text Encoder\n","\n","import os\n","import zipfile\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import numpy as np\n","from google.colab import drive\n","from PIL import Image\n","from tqdm import tqdm\n","from diffusers import AutoencoderKL\n","from transformers import AutoTokenizer, AutoModel\n","from datasets import Dataset as HFDataset\n","from torch.utils.data import Dataset\n","from peft import LoraConfig, get_peft_model\n","from transformers import Trainer, TrainingArguments, set_seed\n","\n","set_seed(42)\n","drive.mount('/content/drive', force_remount=True)\n","\n","zip_path = '/content/drive/MyDrive/my_set.zip'  # @param {type:'string'}\n","\n","# ====================== 1. Extract Data ======================\n","print(\"📦 Extracting zip...\")\n","extract_dir = \"/content/data\"\n","os.makedirs(extract_dir, exist_ok=True)\n","\n","with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    zip_ref.extractall(extract_dir)\n","\n","image_files = [f for f in os.listdir(extract_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]\n","print(f\"✅ Found {len(image_files)} images\")\n","\n","text_files = sorted([f for f in os.listdir(extract_dir) if f.endswith('.txt') and f[0].isdigit()])\n","texts = []\n","for tf in text_files:\n","    with open(os.path.join(extract_dir, tf), \"r\", encoding=\"utf-8\") as f:\n","        content = f.read().strip()\n","        if content:\n","            texts.append(content)\n","\n","print(f\"✅ Loaded {len(texts)} captions\")\n","\n","# ====================== 2. Encode Images → Flux VAE Latents ======================\n","latent_dir = \"/content/drive/MyDrive/flux_klein_latents\"\n","if os.path.exists(latent_dir) and len(os.listdir(latent_dir)) == len(image_files):\n","    print(f\"✅ Using existing latents from {latent_dir}\")\n","else:\n","    print(\"\\n🌀 Encoding images to Flux VAE latents...\")\n","    vae = AutoencoderKL.from_pretrained(\n","        \"black-forest-labs/FLUX.1-dev\",\n","        subfolder=\"vae\",\n","        torch_dtype=torch.float32,\n","        device_map=\"auto\"\n","    )\n","    vae.eval()\n","\n","    os.makedirs(latent_dir, exist_ok=True)\n","\n","    with torch.no_grad():\n","        for img_file in tqdm(image_files, desc=\"Encoding latents\"):\n","            img_path = os.path.join(extract_dir, img_file)\n","            image = Image.open(img_path).convert(\"RGB\").resize((1024, 1024), Image.LANCZOS)\n","\n","            pixel_values = (torch.from_numpy(np.array(image)).permute(2, 0, 1).unsqueeze(0).float() / 255.0)\n","            pixel_values = pixel_values.to(vae.device, dtype=vae.dtype) * 2.0 - 1.0\n","\n","            latents = vae.encode(pixel_values).latent_dist.sample() * vae.config.scaling_factor\n","            latent_name = os.path.splitext(img_file)[0] + \".pt\"\n","            torch.save(latents.cpu(), os.path.join(latent_dir, latent_name))\n","\n","    del vae\n","    torch.cuda.empty_cache()\n","    print(f\"✅ Latents saved to {latent_dir}\")\n","\n","# ====================== 3. Compute & Project Teacher Embeddings to 768 ======================\n","print(\"\\n📝 Computing teacher embeddings and projecting to 768-dim...\")\n","\n","teacher_model_name = \"Qwen/Qwen3-Embedding-0.6B\"\n","tokenizer = AutoTokenizer.from_pretrained(teacher_model_name)\n","teacher_model = AutoModel.from_pretrained(\n","    teacher_model_name,\n","    torch_dtype=torch.float16,\n","    device_map=\"auto\",\n","    trust_remote_code=True\n",")\n","teacher_model.eval()\n","\n","teacher_embeddings_1024 = []\n","with torch.no_grad():\n","    for text in tqdm(texts, desc=\"Teacher encoding\"):\n","        inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors=\"pt\").to(teacher_model.device)\n","        outputs = teacher_model(**inputs)\n","        emb = outputs.last_hidden_state.mean(dim=1).squeeze(0).cpu()\n","        teacher_embeddings_1024.append(emb)\n","\n","teacher_embeddings_1024 = torch.stack(teacher_embeddings_1024)\n","print(f\"✅ Teacher embeddings (1024): {teacher_embeddings_1024.shape}\")\n","\n","# Project teacher to 768-dim (correct target for FLUX.2-klein)\n","teacher_proj = nn.Linear(1024, 768).to(\"cuda\")\n","with torch.no_grad():\n","    teacher_embeddings_768 = teacher_proj(teacher_embeddings_1024.to(\"cuda\")).cpu()\n","\n","print(f\"✅ Projected teacher embeddings (768): {teacher_embeddings_768.shape}\")\n","\n","# Save projected teacher embeddings\n","torch.save({\n","    \"embeddings\": teacher_embeddings_768,\n","    \"texts\": texts,\n","    \"dim\": 768\n","}, \"/content/drive/MyDrive/qwen_embeddings_768.pt\")\n","\n","del teacher_model, teacher_proj\n","torch.cuda.empty_cache()\n","\n","# ====================== 4. Distill Student to 768-dim ======================\n","print(\"\\n👨‍🎓 Distilling student model to 768-dim...\")\n","\n","student_model_name = \"Qwen/Qwen2.5-0.5B\"\n","base_student = AutoModel.from_pretrained(\n","    student_model_name, torch_dtype=torch.float32, device_map=\"auto\", trust_remote_code=True\n",")\n","\n","lora_config = LoraConfig(\n","    r=16,\n","    lora_alpha=32,\n","    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n","    lora_dropout=0.05,\n","    bias=\"none\",\n","    task_type=\"FEATURE_EXTRACTION\"\n",")\n","student_model = get_peft_model(base_student, lora_config)\n","\n","projection = nn.Linear(base_student.config.hidden_size, 768).to(\"cuda\")  # Correct dimension\n","projection.train()\n","\n","hf_dataset = HFDataset.from_dict({\"text\": texts})\n","\n","class DistillationDataset(Dataset):\n","    def __init__(self, hf_dataset, tokenizer, teacher_embs, max_length=512):\n","        self.dataset = hf_dataset\n","        self.tokenizer = tokenizer\n","        self.teacher_embs = teacher_embs\n","        self.max_length = max_length\n","\n","    def __len__(self): return len(self.dataset)\n","\n","    def __getitem__(self, idx):\n","        text = self.dataset[idx][\"text\"]\n","        inputs = self.tokenizer(text, padding=\"max_length\", truncation=True, max_length=self.max_length, return_tensors=\"pt\")\n","        return {\n","            \"input_ids\": inputs[\"input_ids\"].squeeze(0),\n","            \"attention_mask\": inputs[\"attention_mask\"].squeeze(0),\n","            \"labels\": self.teacher_embs[idx],\n","        }\n","\n","distill_dataset = DistillationDataset(hf_dataset, tokenizer, teacher_embeddings_768)\n","\n","def collate_fn(batch):\n","    return {\n","        \"input_ids\": torch.stack([item[\"input_ids\"] for item in batch]),\n","        \"attention_mask\": torch.stack([item[\"attention_mask\"] for item in batch]),\n","        \"labels\": torch.stack([item[\"labels\"] for item in batch])\n","    }\n","\n","class DistillTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n","        labels = inputs.pop(\"labels\").to(\"cuda\")   # 768-dim\n","\n","        outputs = model(input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"])\n","        hidden = outputs.last_hidden_state.mean(dim=1)\n","        student_emb = projection(hidden)   # 768-dim\n","\n","        student_norm = F.normalize(student_emb, p=2, dim=1)\n","        teacher_norm = F.normalize(labels, p=2, dim=1)\n","\n","        mse_loss = F.mse_loss(student_norm, teacher_norm)\n","        cos_loss = (1 - F.cosine_similarity(student_norm, teacher_norm, dim=1)).mean()\n","        loss = 0.25 * mse_loss + 0.75 * cos_loss\n","\n","        return (loss, outputs) if return_outputs else loss\n","\n","training_args = TrainingArguments(\n","    output_dir=\"./distilled_qwen_768\",\n","    per_device_train_batch_size=4,      # safe for Colab\n","    num_train_epochs=30,\n","    learning_rate=2e-4,\n","    fp16=True,\n","    logging_steps=50,\n","    save_strategy=\"no\",\n","    report_to=\"none\",\n","    remove_unused_columns=False,\n",")\n","\n","trainer = DistillTrainer(\n","    model=student_model,\n","    args=training_args,\n","    train_dataset=distill_dataset,\n","    data_collator=collate_fn,\n",")\n","\n","print(\"🚀 Starting distillation with correct 768-dim targets...\")\n","trainer.train()\n","\n","# ====================== Save Distilled Model ======================\n","distilled_save_dir = \"/content/drive/MyDrive/distilled_qwen_768_for_flux\"\n","os.makedirs(distilled_save_dir, exist_ok=True)\n","\n","student_model.save_pretrained(distilled_save_dir)\n","tokenizer.save_pretrained(distilled_save_dir)\n","torch.save(projection.state_dict(), f\"{distilled_save_dir}/projection.pth\")\n","\n","print(f\"\\n✅ SUCCESS! 768-dim distilled encoder saved to: {distilled_save_dir}\")\n","print(\"   Latents are in: /content/drive/MyDrive/flux_klein_latents\")\n","print(\"   You can now safely run Cell 2 to save everything and disconnect.\")\n","\n","torch.cuda.empty_cache()"],"metadata":{"id":"oluEkFV0KMXo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# ============================= CELL 2: Save All Assets to Drive =============================\n","# @title 2. Save Latents + New Distilled Encoder\n","\n","import os\n","import torch\n","\n","print(\"💾 Saving all assets to Google Drive...\")\n","\n","# Ensure directories exist\n","os.makedirs(\"/content/drive/MyDrive/flux_klein_latents\", exist_ok=True)\n","os.makedirs(\"/content/drive/MyDrive/distilled_qwen_768_for_flux\", exist_ok=True)\n","\n","# Move latents if not already there\n","# (assuming they are already saved in Cell 1)\n","\n","print(\"✅ Latents are in /content/drive/MyDrive/flux_klein_latents\")\n","print(\"✅ New 768-dim distilled model is in /content/drive/MyDrive/distilled_qwen_768_for_flux\")\n","\n","print(\"\\n🎉 All data is safely saved on Google Drive.\")\n","print(\"   You can now **disconnect and delete the runtime** if you want.\")\n","print(\"   Everything needed for training is on Drive.\")\n","print(\"   When you come back, start from Cell 3.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9IGpdiL9BBr6","executionInfo":{"status":"ok","timestamp":1774999215889,"user_tz":-120,"elapsed":11,"user":{"displayName":"fukU Google","userId":"02763165356193834046"}},"outputId":"28cffe5f-fa50-4572-bd91-c36ed16bd507"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["💾 Saving all assets to Google Drive...\n","✅ Latents are in /content/drive/MyDrive/flux_klein_latents\n","✅ New 768-dim distilled model is in /content/drive/MyDrive/distilled_qwen_768_for_flux\n","\n","🎉 All data is safely saved on Google Drive.\n","   You can now **disconnect and delete the runtime** if you want.\n","   Everything needed for training is on Drive.\n","   When you come back, start from Cell 3.\n"]}]},{"cell_type":"markdown","source":["You can disconnect the colab past thispoint. All data from cells 1  and 2 are saved to drive."],"metadata":{"id":"cfshTDIFM5ND"}},{"cell_type":"code","source":["# ============================= CELL 3: Install Dependencies & Setup =============================\n","# @title 3. Install Dependencies + Setup Parameters\n","\n","!pip install -q diffusers transformers peft accelerate datasets tqdm\n","\n","import os\n","import torch\n","from google.colab import drive, userdata\n","from transformers import AutoTokenizer, AutoModel\n","from peft import PeftModel\n","import gc\n","\n","drive.mount('/content/drive', force_remount=True)\n","\n","# ====================== Parameters ======================\n","DISTILLED_DIR = \"/content/drive/MyDrive/distilled_qwen_768_for_flux\"\n","LATENT_DIR = \"/content/drive/MyDrive/flux_klein_latents\"\n","FINAL_LORA_DIR = \"/content/drive/MyDrive/flux_klein_lora_final\"\n","\n","BATCH_SIZE = 1          # Keep low for safety\n","NUM_EPOCHS = 8\n","LEARNING_RATE = 1e-4\n","LORA_RANK = 32\n","LORA_ALPHA = 64\n","\n","print(\"✅ Dependencies installed and parameters set.\")\n","print(f\"   Distilled encoder: {DISTILLED_DIR}\")\n","print(f\"   Batch size: {BATCH_SIZE} | Epochs: {NUM_EPOCHS}\")\n","\n","# Optional: quick check\n","print(\"\\n🔍 Quick VRAM check:\")\n","!nvidia-smi --query-gpu=memory.used,memory.total --format=csv"],"metadata":{"id":"ZZaadi1VBK6Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# ============================= CELL 4: LoRA Training with Debug Prints =============================\n","# @title 4. LoRA Training — FLUX.2-klein-base-4B + 768-dim Distilled Encoder\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.utils.data import Dataset\n","from tqdm import tqdm\n","from transformers import Trainer, TrainingArguments, set_seed\n","from peft import LoraConfig, get_peft_model\n","from diffusers import FluxTransformer2DModel\n","\n","set_seed(42)\n","\n","print(\"=== CELL 4 START ===\")\n","print(f\"VRAM before anything: {torch.cuda.memory_allocated()/1024**3:.2f} GB\")\n","\n","# ====================== 1. Load Distilled 768-dim Encoder ======================\n","print(\"\\n[DEBUG] Loading distilled 768-dim encoder...\")\n","tokenizer = AutoTokenizer.from_pretrained(DISTILLED_DIR)\n","\n","base_qwen = AutoModel.from_pretrained(\n","    \"Qwen/Qwen2.5-0.5B\", torch_dtype=torch.float32, device_map=\"auto\",\n","    trust_remote_code=True, low_cpu_mem_usage=True\n",")\n","student_model = PeftModel.from_pretrained(base_qwen, DISTILLED_DIR)\n","student_model.eval()\n","\n","projection = nn.Linear(base_qwen.config.hidden_size, 768).to(\"cuda\")\n","projection.load_state_dict(torch.load(f\"{DISTILLED_DIR}/projection.pth\", map_location=\"cuda\"))\n","projection.eval()\n","\n","print(\"[DEBUG] Distilled encoder loaded successfully (target dim 768)\")\n","\n","# ====================== 2. Load Latents & Texts ======================\n","print(\"\\n[DEBUG] Loading texts and latents...\")\n","data = torch.load(\"/content/drive/MyDrive/qwen_embeddings.pt\", weights_only=False)  # or your saved texts\n","texts = data[\"texts\"]\n","print(f\"[DEBUG] Loaded {len(texts)} texts\")\n","\n","latent_files = sorted([f for f in os.listdir(LATENT_DIR) if f.endswith(\".pt\")])\n","latents = []\n","for lf in tqdm(latent_files, desc=\"[DEBUG] Loading latents\"):\n","    lat = torch.load(os.path.join(LATENT_DIR, lf), weights_only=False)\n","    if lat.dim() == 4 and lat.shape[0] == 1:\n","        lat = lat.squeeze(0)\n","    latents.append(lat)\n","latents = torch.stack(latents)\n","print(f\"[DEBUG] Loaded latents shape: {latents.shape}\")\n","\n","# Dataset (same as before)\n","class FluxLoRADataset(Dataset):\n","    def __init__(self, latents, texts):\n","        self.latents = latents\n","        self.texts = texts\n","    def __len__(self): return len(self.latents)\n","    def __getitem__(self, idx):\n","        return {\"latent\": self.latents[idx], \"text\": self.texts[idx]}\n","\n","dataset = FluxLoRADataset(latents, texts)\n","\n","def collate_fn(batch):\n","    return {\n","        \"latent\": torch.stack([item[\"latent\"] for item in batch]),\n","        \"texts\": [item[\"text\"] for item in batch]\n","    }\n","\n","# ====================== 3. Load Transformer + LoRA ======================\n","print(\"\\n[DEBUG] Loading FLUX.2-klein-base-4B transformer...\")\n","torch.cuda.empty_cache()\n","\n","transformer = FluxTransformer2DModel.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-base-4B\",\n","    subfolder=\"transformer\",\n","    torch_dtype=torch.bfloat16,\n","    low_cpu_mem_usage=False,\n",").to(\"cuda\")\n","\n","print(f\"[DEBUG] Transformer loaded. VRAM now: {torch.cuda.memory_allocated()/1024**3:.2f} GB\")\n","\n","lora_config = LoraConfig(\n","    r=LORA_RANK, lora_alpha=LORA_ALPHA,\n","    target_modules=[\"attn.to_q\", \"attn.to_k\", \"attn.to_v\", \"attn.to_out.0\",\n","                    \"attn.to_qkv_mlp_proj\", \"attn.add_q_proj\", \"attn.add_k_proj\",\n","                    \"attn.add_v_proj\", \"attn.to_add_out\",\n","                    \"ff.linear_in\", \"ff.linear_out\",\n","                    \"ff_context.linear_in\", \"ff_context.linear_out\"],\n","    lora_dropout=0.05,\n","    bias=\"none\"\n",")\n","\n","transformer = get_peft_model(transformer, lora_config)\n","transformer.train()\n","\n","print(\"[DEBUG] LoRA applied successfully\")\n","\n","# ====================== 4. Trainer with heavy debug ======================\n","class FluxLoRATrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n","        print(f\"[DEBUG compute_loss] Batch size: {inputs['latent'].shape[0]}\")\n","        latents = inputs[\"latent\"].to(dtype=torch.bfloat16, device=model.device)\n","        raw_texts = inputs[\"texts\"]\n","\n","        text_inputs = tokenizer(raw_texts, padding=True, truncation=True, max_length=512, return_tensors=\"pt\").to(\"cuda\")\n","        print(f\"[DEBUG] Text input shape: {text_inputs['input_ids'].shape}\")\n","\n","        with torch.no_grad():\n","            outputs = student_model(**text_inputs)\n","            hidden = outputs.last_hidden_state.mean(dim=1)\n","            text_emb_768 = projection(hidden).to(dtype=torch.bfloat16)\n","            print(f\"[DEBUG] Projected embedding shape: {text_emb_768.shape}\")\n","\n","        batch_size = latents.shape[0]\n","        timesteps = torch.rand(batch_size, device=latents.device)\n","        noise = torch.randn_like(latents)\n","\n","        noisy_latents = (1 - timesteps.view(-1, 1, 1, 1)) * latents + timesteps.view(-1, 1, 1, 1) * noise\n","\n","        emb_unsqueezed = text_emb_768.unsqueeze(1)\n","\n","        print(f\"[DEBUG] Calling transformer with pooled_projections shape: {text_emb_768.shape}\")\n","        model_output = model(\n","            hidden_states=noisy_latents,\n","            timestep=timesteps * 1000,\n","            encoder_hidden_states=emb_unsqueezed,\n","            pooled_projections=text_emb_768,\n","            return_dict=False\n","        )[0]\n","\n","        target = noise - latents\n","        loss = F.mse_loss(model_output, target)\n","\n","        print(f\"[DEBUG] Loss value: {loss.item():.6f}\")\n","        return (loss, model_output) if return_outputs else loss\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/content/flux_klein_lora\",\n","    per_device_train_batch_size=BATCH_SIZE,\n","    num_train_epochs=NUM_EPOCHS,\n","    learning_rate=LEARNING_RATE,\n","    lr_scheduler_type=\"cosine\",\n","    warmup_steps=50,\n","    bf16=True,\n","    logging_steps=10,          # more frequent logs\n","    save_strategy=\"epoch\",\n","    save_total_limit=2,\n","    report_to=\"none\",\n","    remove_unused_columns=False,\n",")\n","\n","trainer = FluxLoRATrainer(\n","    model=transformer,\n","    args=training_args,\n","    train_dataset=dataset,\n","    data_collator=collate_fn,\n",")\n","\n","print(\"\\n🚀 Starting LoRA training with heavy debug output...\")\n","trainer.train()\n","\n","# Save\n","final_lora_dir = FINAL_LORA_DIR\n","os.makedirs(final_lora_dir, exist_ok=True)\n","transformer.save_pretrained(final_lora_dir)\n","\n","print(f\"\\n✅ Training finished! LoRA saved to {final_lora_dir}\")\n","torch.cuda.empty_cache()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467,"referenced_widgets":["f5426f03975f49b2a93f604987c64dc3","d2382d8fc5e5433abad641f819cce43e","8ea05da221a54022825a0381986c8621","0692869e648b434aa481ff49529b7b31","98c2762a0ac4488ebc8bd0dcc53e6fe7","39affee5f9b54cd08d749e9fe46b109b","b78bf2314db94f9fbc036e9c5a91a575","011209a9700b4e4281a4858f399bf228","fdb8f46f6c3243a5b7ea023efdd3dcb3","e4852a656d1a4c17914f1fdf156776f3","055c4accb3e042a8a1f269497e44a8fa","c82bb026aa1441df99b3b7ac245e8612","e613a9e1c6994c4fa9f35a192074b3e9","8ea55b68c5fb4c349d19ac437d6dde74","066168bd3f5b46b0a5ae9cb40be1e4fa","e5532252ec4f4c79b63be14d8e7aea75","28344fb5da7447b299380df82b370875","9e2b781644134a07b5059f9dc58cd44d","8b6c648d448c4b9da3da5548e4c9372c","eedb204240ae4b13abe1fcd29df1c414","c6801c5bb0e64fa0bea1781fe3f09f91","dcb46e33f39e4d8e8873c6ee8e5e7f4b","67f8c39c8c734cb486431e72ad75310e","e683458145154dbf8bcc2908f7a07ae2","f2bd32402ecf412da05d4c9249028fed","eed1688e42c34402bc33421173afcbba","2982ff7c18af4941818dac32709ebb6d","91a05c4b65b1425a8aa196b6e7b5d3e8","e8b52160d70948bc8d1982118312e8a3","493dafb22fb64215ba9a3402a0518103","a5bc9e73a2c84798ba8867c0aab23e15","4fba1d416afc4981bb0523708b375f13","3f866988334a47209b69f4c32386838a","87fc7f4731a6444d82cf9da34833d2f2","bf09a57095e64c108f873f2a5ccda30a","7666863459674bb4991e8e851032e10b","bcc1ced87ece460dac6660998b0a6778","17a5f0546d51484dbe7c90608ba0193a","297443c83ae7418fa3cfe8dc324ab464","481a871f0d684de084eea0e3e839beaa","42f42cb139df4920b8fcd7841d75ddc5","8b6a171afe9f434da21ce3b25bef3c4e","7f9fdb5f1f9a475c8faad5cc64753f9d","532afd6199f14dcd97387265c0f19725","1c5e32b4c9804fd09f0dd74c38a252da","aa5145cfbeb44d9f9e78ea02c98d994e","77216bbf5ded432fa3b2a6e01c77ae7e","9b24cc31084a4d358fac3bf039867949","4ca8823cab464d889aeef2905d232a36","99fca8c1e87a4c1182d5f0618ca1f3d5","6bba194b06c842b1bff912422e2b3b0d","85b2c2e86a9b43f5853f331990648e7a","5ebff3cbf8434dc19465f8d661048fdc","22a8a0da10434cd58c70ea0f033349da","853e82961ecd49cd91ce2dea6faaf65e"]},"id":"XkYfTORjBLUx","outputId":"e8da5c02-753d-4209-9047-a122982aaa73"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["=== CELL 4 START ===\n","VRAM before anything: 0.00 GB\n","\n","[DEBUG] Loading distilled 768-dim encoder...\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/681 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f5426f03975f49b2a93f604987c64dc3"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["`torch_dtype` is deprecated! Use `dtype` instead!\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/988M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c82bb026aa1441df99b3b7ac245e8612"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/290 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"67f8c39c8c734cb486431e72ad75310e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[DEBUG] Distilled encoder loaded successfully (target dim 768)\n","\n","[DEBUG] Loading texts and latents...\n","[DEBUG] Loaded 250 texts\n"]},{"output_type":"stream","name":"stderr","text":["[DEBUG] Loading latents: 100%|██████████| 250/250 [00:09<00:00, 26.61it/s]\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_validators.py:206: UserWarning: The `local_dir_use_symlinks` argument is deprecated and ignored in `hf_hub_download`. Downloading to a local directory does not use symlinks anymore.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["[DEBUG] Loaded latents shape: torch.Size([250, 16, 128, 128])\n","\n","[DEBUG] Loading FLUX.2-klein-base-4B transformer...\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/531 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"87fc7f4731a6444d82cf9da34833d2f2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["transformer/diffusion_pytorch_model.safe(…):   0%|          | 0.00/7.75G [00:00<?, 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