diff --git "a/klein_edit.ipynb" "b/klein_edit.ipynb"
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+++ "b/klein_edit.ipynb"
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+{"cells":[{"cell_type":"code","source":["# =============================================================================\n","# CELL 1: Mount Drive + HF auth + ULTRA-CLEAN diffusers install + load precompute components\n","# =============================================================================\n","\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch\n","from diffusers import AutoencoderKLFlux2\n","from transformers import Qwen2TokenizerFast, Qwen3ForCausalLM\n","import os\n","import gc\n","import shutil\n","\n","# Mount Google Drive\n","drive.mount('/content/drive')\n","\n","# HF token\n","hf_token = userdata.get('HF_TOKEN')\n","if hf_token:\n","    login(token=hf_token)\n","\n","# ====================== ULTRA-CLEAN DIFFUSERS INSTALL ======================\n","print(\"🧹 Removing ANY existing diffusers installation for a completely clean slate...\")\n","!pip uninstall -y diffusers\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers*\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers-*.dist-info\n","\n","print(\"🔄 Installing the absolute latest diffusers from GitHub (Flux2KleinPipeline + FLASHPACK_WEIGHTS_NAME)...\")\n","!pip install -q git+https://github.com/huggingface/diffusers.git --force-reinstall --no-deps --no-build-isolation\n","!python -m pip cache purge\n","\n","# Clear every possible stale cache\n","print(\"🗑️ Clearing all __pycache__ and .pyc files...\")\n","diffusers_dir = \"/usr/local/lib/python3.12/dist-packages/diffusers\"\n","if os.path.exists(diffusers_dir):\n","    shutil.rmtree(diffusers_dir, ignore_errors=True)\n","\n","print(\"✅ Latest diffusers (with Flux2KleinPipeline) successfully installed and cleaned.\")\n","\n","# Load OFFICIAL components for precomputation\n","print(\"Loading official FLUX.2 VAE + tokenizer + Qwen3 text encoder...\")\n","vae = AutoencoderKLFlux2.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-4B\",\n","    subfolder=\"vae\",\n","    torch_dtype=torch.bfloat16,\n",").to(\"cuda\")\n","\n","tokenizer = Qwen2TokenizerFast.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-4B\",\n","    subfolder=\"tokenizer\"\n",")\n","text_encoder = Qwen3ForCausalLM.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-4B\",\n","    subfolder=\"text_encoder\",\n","    torch_dtype=torch.bfloat16,\n",").to(\"cuda\")\n","\n","print(\"✅ Precompute components loaded.\")\n","\n","# =============================================================================\n","print(\"\\n🚨 RESTART THE RUNTIME NOW (Runtime → Restart session)\")\n","print(\"After restart, run Cell 1 again → Cell 2 → Cell 3 → Cell 4\")\n","print(\"This guarantees a 100% clean diffusers installation.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":877,"referenced_widgets":["28ec115bb808406ebfb5812bf68a1373","c713afe2a5f54bacb2a37264cca778f7","b7baf7990e03490db95b6df65865e73e","f5f4b8ec58fe47e0bc001af98f89f083","d50071efe37147e986250578ab66d032","318e4fa0cb80412181c28d632501884f","5bfe4eb9cde046a7b1d42432d3446ed5","2cee1854cd734b889bb339b50fcf13fc","a913d32ad52e4ccaa39b4eec86e4fcb7","e39fc35287a845409720edf6281bd8e7","052cba974dd848a49b36091213f9f61b","d686f3f8e8b44480a5cb5ce3026e6f16","c7489c43dad6497687c2c4d335cffa18","b3cc3c9da83741eab3abcde612ad67bd","a3e1c9df66a046869b96f53483b643d8","af3d01bc133a403795719a126d5efeed","cef37f715f62462f9d482c6d89c3c5b7","5f98ff83be3044dd91a6385d909b62ec","e242599e5ec54fe28c6ddf95f1fec5ad","58fe117509a64a24bde6d3617cc55c24","cc68086589884a779b0bcc78bdab1e24","af70ed41d68e4f46917d232af6362584","efc9b280b3174eba96f7616ec7734a89","90c986bd3b174985bb859ac4a2bc24be","fe0bf1f1546e46689e4cce9843dbfabc","b5c88e6af946461097c6f16df7aedb05","4abe075954fa48ec9240f2c1d8f97a84","c28e280065f343799f7df1ed51c90357","88fc92cf7997473d87d308ed26dac015","051c71ce8bd344748a73a3480f74d793","5fa40120ec6144daa2ee177b82f52606","af6bc07197f04046abb8c4423241a0ac","c443948f15bf43e6932052c2325e4dfd","bd6d457d91e8415490e8121d7e8e605e","a81161bb5fe24523913f5dd599f5c364","6ba839eff74a49cbb91a4db9d8e2eba5","c652f32ef2294cbd8a8b015c72a16f40","c314f1c2fa4e42989450c4619812148e","51c965472fd84932bf8f9d644ee7210f","c2dc7f0e16934c20aae072cc13c87943","03f44309297f477f876bf2dfc701ec0a","447381535a094466be4b09550b7d2bdd","8e83c13b141f4b44aa692d0d2365edf9","b246aa04acfc4bb18208b67edc2fd08a","ea6f87dfad964d0688c46aba0c25c90d","2fadbe2985f64ec2865753229f095619","7310165903f24134b5bcb8b106131875","12556367dad944aaa41227b7db76ce4a","e62b424c5e7a41b28275f935ff1fbe1f","5ac390a096e7464a88acc619c169dd9e","bef1306ecf9e4b6f8ead6d1a86349bc0","c25553d6d6ea42fc9da82dc322323c72","59d59f3b13384a76b10456a246c39dd7","1bd186805fa84f61b1772071e55ea719","5f88f3ff97c746558b1fe75ff06a2419","bcd3810e377a4a6b889e101f573c1041","d1d322426eb845aaa4704fdd6b55692a","6c43e6a7451d4c7f9e2797b4c4c01e4b","0b7c6589ab97452dab6044842ca22a6f","de18b783c45547dfbaf668e2eded03ba","239cb882a37a45918af1f837a4874715","326f15a191874e61b3fff197d71f1696","6875641bb1ca4e43939b31f44fcbc6f6","c0e73ec3a25a462ea84ab05b8272d695","ec9e7c52c7f643a2bd294d9f5caaa9cc","0373fbf6c38a477dab7e7fa28552a57a","68c0c034c07341669a4b011968d10b78","0581e97409d3484e96a8c9f997f9da1f","fcadd42335364aebaf7caed4ec3d3d04","f15bc19cc0344339a7f94f30b7a5fce9","ff43e1fa4a6d42759377d5e9bd40c550","51eca9d10f79443db87c8386b978a499","f55900e117614ca39ecc4d7709823434","f8dd90cef0444a19a80ca05e9fc22d99","b20cc4a4fcf64155892ee6b707ca72c0","0e5d3463dc70445b9fa091230ea52ef2","98366de0d63d4f8b82d8c730a35f6795","f12167c361614307afbb43c4d0796d8c","f671c8ff5ecd4cb29146b5277d4353aa","5fbe5c5b1f3e443d9362e3e3fac9ccdc","eba869ada7674a81a165eadf69c99a63","b7c2995a5b1a4cc0a0b8d3d14be6f690","e69319aed9684979860e31928a63bd81","9254a9d8158b4f699c63c98349dc14d8","2272e746de914277add1156ee82736b4","72590b6a715c40f1a348ac045a0c26c3","8c1ef1b880e3435ab45cea9139e998e6","b1b1aa54207849e6ba8fbb11ff37c597","895bc6c56dea411e8817afde09c864d9","ef8b5b248cc64c8d8ea421d17d3a4f6d","b7aaa440e3ea43378407caec3c586b09","531ef68efa1b483daf68fbcc977a0d4b","073e84e156cc4430b97263a4263df7b9","d225adaeff1e481c8e24ae8f5a046b25","3391da3efd864db28585028b06facf5f","6ef10365b1e74382a185cd66e4ac1631","756beac9de2743aca48c8e2632f942d5","caf7feb373d24fe4bf658c58a078519f","44bab801f02046a6b6ea6e402ea21682","263b1281281b484db2914a6fc68f1c36","fb70becfdf2d411e8bac5435294f1e93","84126dce743b4c8dbdef1e0a729d98c8","e525596f1c9241d3a2ace6e9ba540906","5886b1f879664a129564fa4752bc6e52","76be14e10f7e4ce19a6fef95733a8bbf","2dcac4d4640941718ab7ae67c88d2f08","a920524281084f06b30bad403a4b82d0","5a08b4b23ea845619643b047809d155f","95364caaf2824c17beb54484c396f66c","b4251cc4acad43f9b7a83fcc1d189271","39de46908cb7454f84011b48c27787fc","2960719c8246488aa148f3ef7f1c43ef","84aba83d5c534280b23c87d9193ab6ff","562a35fd01e24bd5b9e95ef8df7b98cf","b7a34decc0c545d585d101b6f6d9ae25","af2c950c3cb041a78f06d62bb4703f47","d5aa9d1911c1404e852733cd1a115fd0","4514d8caf6204622b94a9460cb25041c","2a979585324142c0bd521407b4b2a41e","fca4eaef9aaa437299e1b93c0a3ed6a8","858aedd8a08f4811a605a8507df5fb9a","18b14e9e568e44fd8ff3fb92e55dbb9c","8c69c45a85044c098bd92d39d292ff34","23dfb73462a049ef96a69b214d6bb38d","021e9ff867c14f3babcfd1981df97d1f","229d312f1f314ede8185d35e1f3854cc","963233d7b84e4398a1aee143d1e00c8e","da97ecf5282748e7863e45054fe92c72","96de912daff940f0a8d1fec26d133e2c","c4788c943b85442897084d35cfd43ad7","508e1f20a7c84762b811b85d9abd3c9e","cc69f445c7924768b752f45035d87770","37843c2969c24d88aeb4530161aa20e6","fec48bb192f7435ebf90fa9ebc353a3b","b38f7c7a4bd045dba5fd72ba07796aaf","0dccb354fbb442fd8ab3dba7d1bb6e05","2d15181534df42cd8965f2483362ee61","98f0a27182a242b4a4c35da89a6b6bb9","957194a9f6f4462d8bf18f909d404af5","22421bcaf25f4070a46351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Name","userId":"10578412414437288386"}},"outputId":"30efc45e-5a55-49c7-b5e6-35ba21aff404"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","🧹 Removing ANY existing diffusers installation for a completely clean slate...\n","Found existing installation: diffusers 0.37.1\n","Uninstalling diffusers-0.37.1:\n","  Successfully uninstalled diffusers-0.37.1\n","🔄 Installing the absolute latest diffusers from GitHub (Flux2KleinPipeline + FLASHPACK_WEIGHTS_NAME)...\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","\u001b[33mWARNING: No matching packages\u001b[0m\u001b[33m\n","\u001b[0mFiles removed: 0\n","🗑️ Clearing all __pycache__ and .pyc files...\n","✅ Latest diffusers (with Flux2KleinPipeline) successfully installed and cleaned.\n","Loading official FLUX.2 VAE + tokenizer + Qwen3 text encoder...\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_validators.py:205: 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":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/821 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"28ec115bb808406ebfb5812bf68a1373"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vae/diffusion_pytorch_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d686f3f8e8b44480a5cb5ce3026e6f16"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"efc9b280b3174eba96f7616ec7734a89"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bd6d457d91e8415490e8121d7e8e605e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ea6f87dfad964d0688c46aba0c25c90d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer/tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bcd3810e377a4a6b889e101f573c1041"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["added_tokens.json:   0%|          | 0.00/707 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"68c0c034c07341669a4b011968d10b78"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/613 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f12167c361614307afbb43c4d0796d8c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["chat_template.jinja: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"895bc6c56dea411e8817afde09c864d9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"263b1281281b484db2914a6fc68f1c36"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"39de46908cb7454f84011b48c27787fc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (incomplete total...): 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"18b14e9e568e44fd8ff3fb92e55dbb9c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"37843c2969c24d88aeb4530161aa20e6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/398 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a68cc1479f24ccf87ac81bf473c1555"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/214 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aa60a1903e3a4a5c895a8d975fc05f79"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["✅ Precompute components loaded.\n","\n","🚨 RESTART THE RUNTIME NOW (Runtime → Restart session)\n","After restart, run Cell 1 again → Cell 2 → Cell 3 → Cell 4\n","This guarantees a 100% clean diffusers installation.\n"]}]},{"cell_type":"code","source":["# =============================================================================\n","# CELL 2: Pure @param inputs\n","# =============================================================================\n","\n","edit_prompt = 'remove the background. the background is dark gray. ' #@param {type:\"string\"}\n","zip_path = '/content/drive/MyDrive/lora_test.zip' #@param {type:\"string\"}\n","resolution = '1024 x 1024 (Square)' #@param [\"1024 x 1024 (Square)\", \"512 x 1024 (Portrait)\", \"768 x 1024 (Slight Portrait)\", \"1536 x 1024 (Landscape)\", \"2048 x 1024 (Wide Landscape)\"] {type:\"string\"}\n","debug = True #@param {type:\"boolean\"}\n","\n","print(\"✅ Settings ready.\")\n","print(\"Now run Cell 3.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1slE6sTeFK5Y","executionInfo":{"status":"ok","timestamp":1776692247332,"user_tz":-120,"elapsed":10,"user":{"displayName":"No Name","userId":"10578412414437288386"}},"outputId":"3642ab83-f9bd-4ecb-b26f-d1447f0a9c3c"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Settings ready.\n","Now run Cell 3.\n"]}]},{"cell_type":"code","source":["# =============================================================================\n","# CELL 3: Precompute prompt embedding + ALL image latents\n","# =============================================================================\n","\n","import os\n","import zipfile\n","from PIL import Image\n","import torch\n","import numpy as np\n","\n","if 'tokenizer' not in globals() or 'text_encoder' not in globals() or 'vae' not in globals():\n","    raise NameError(\"❌ Run Cell 1 first!\")\n","\n","# Get @param values\n","resolution_map = {\n","    '1024 x 1024 (Square)': (1024, 1024),\n","    '512 x 1024 (Portrait)': (512, 1024),\n","    '768 x 1024 (Slight Portrait)': (768, 1024),\n","    '1536 x 1024 (Landscape)': (1536, 1024),\n","    '2048 x 1024 (Wide Landscape)': (2048, 1024),\n","}\n","target_width, target_height = resolution_map[resolution]\n","\n","output_folder = '/content/drive/MyDrive/FLUX_KLEIN_EDITED'\n","\n","# Unzip\n","temp_folder = '/content/input_images'\n","os.makedirs(temp_folder, exist_ok=True)\n","with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    zip_ref.extractall(temp_folder)\n","\n","image_extensions = ('.png', '.jpg', '.jpeg', '.webp', '.bmp')\n","image_files = [f for f in os.listdir(temp_folder) if f.lower().endswith(image_extensions)]\n","\n","print(f\"Found {len(image_files)} images.\")\n","\n","if debug:\n","    print(f\"Initial VRAM: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n","\n","# Precompute prompt embedding (once)\n","print(\"Precomputing prompt embedding...\")\n","with torch.no_grad():\n","    text_inputs = tokenizer(\n","        edit_prompt,\n","        padding=\"max_length\",\n","        max_length=512,\n","        truncation=True,\n","        return_tensors=\"pt\"\n","    ).to(\"cuda\")\n","    outputs = text_encoder(**text_inputs, output_hidden_states=True)\n","    prompt_embeds = torch.cat([outputs.hidden_states[i] for i in (9, 18, 27)], dim=-1)\n","\n","if debug:\n","    print(f\"Prompt embeds shape: {prompt_embeds.shape}\")\n","\n","# Precompute image latents (NO resize)\n","print(\"Precomputing image latents...\")\n","precomputed_latents = []\n","for i, filename in enumerate(image_files):\n","    input_path = os.path.join(temp_folder, filename)\n","    input_image = Image.open(input_path).convert(\"RGB\")\n","\n","    image_np = np.array(input_image).astype(np.float32)\n","    image_tensor = (image_np / 127.5) - 1.0\n","    image_tensor = torch.from_numpy(image_tensor).permute(2, 0, 1).unsqueeze(0).to(\"cuda\", torch.bfloat16)\n","\n","    with torch.no_grad():\n","        encoder_output = vae.encode(image_tensor)\n","        latents = encoder_output.latent_dist.mode()   # FLUX.2 VAE already in correct space\n","\n","    precomputed_latents.append(latents)\n","    if debug:\n","        print(f\"  [{i+1}/{len(image_files)}] Latent shape for {filename}: {latents.shape}\")\n","\n","if debug:\n","    print(f\"VRAM after precompute: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n","\n","print(\"\\n✅ PRECOMPUTATION COMPLETE! Ready for Cell 4.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VanC2YOmFUt_","executionInfo":{"status":"ok","timestamp":1776692258147,"user_tz":-120,"elapsed":6671,"user":{"displayName":"No Name","userId":"10578412414437288386"}},"outputId":"18c1c84a-3080-4fbe-faa7-2819eb6aa7cb"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 2 images.\n","Initial VRAM: 8.28 GB\n","Precomputing prompt embedding...\n","Prompt embeds shape: torch.Size([1, 512, 7680])\n","Precomputing image latents...\n","  [1/2] Latent shape for 000.jpeg: torch.Size([1, 32, 128, 128])\n","  [2/2] Latent shape for 001.jpeg: torch.Size([1, 32, 128, 128])\n","VRAM after precompute: 8.63 GB\n","\n","✅ PRECOMPUTATION COMPLETE! Ready for Cell 4.\n"]}]},{"cell_type":"code","source":["# =============================================================================\n","# CELL 3.5: QUICK FIX – Nuclear reinstall of diffusers (fixes \"No module named 'diffusers.pipelines'\")\n","# Run this AFTER Cell 3 and BEFORE Cell 4\n","# =============================================================================\n","\n","import os\n","import shutil\n","import gc\n","import torch\n","import importlib\n","\n","print(\"🔥 QUICK FIX: Nuclear clean + reinstall of diffusers (fixes pipelines import error)\")\n","\n","# 1. Force-remove everything diffusers-related\n","print(\"🗑️ Completely removing diffusers package...\")\n","!pip uninstall -y diffusers\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers*\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers-*.dist-info\n","!rm -rf ~/.cache/pip/*diffusers*\n","\n","# 2. Fresh install from official GitHub (this is what brings Flux2KleinPipeline + pipelines module)\n","print(\"🔄 Installing latest diffusers from GitHub (with Flux2KleinPipeline)...\")\n","!pip install -q git+https://github.com/huggingface/diffusers.git --force-reinstall --no-deps --no-build-isolation\n","!python -m pip cache purge\n","\n","# 3. Nuke any remaining stale files\n","print(\"🧹 Clearing all __pycache__ and leftover files...\")\n","diffusers_dir = \"/usr/local/lib/python3.12/dist-packages/diffusers\"\n","if os.path.exists(diffusers_dir):\n","    shutil.rmtree(diffusers_dir, ignore_errors=True)\n","\n","# 4. Force Python to forget any cached/old imports\n","print(\"🔄 Forcing Python to reload diffusers module...\")\n","if 'diffusers' in globals():\n","    importlib.reload(diffusers)\n","else:\n","    import diffusers  # trigger fresh import\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"✅ DIFFUSERS FULLY REINSTALLED AND FIXED!\")\n","print(f\"   Current diffusers version: {diffusers.__version__}\")\n","print(\"   pipelines module should now be available.\")\n","\n","if 'debug' in globals() and debug:\n","    print(f\"VRAM after quick fix: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n","\n","print(\"\\n🚀 Now run Cell 4 (it should import Flux2KleinPipeline without error).\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"csG1zsEUHj7V","executionInfo":{"status":"ok","timestamp":1776692421064,"user_tz":-120,"elapsed":29020,"user":{"displayName":"No Name","userId":"10578412414437288386"}},"outputId":"e43fc3fc-f0d9-4d9e-beca-655e8586fe2b"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["🔥 QUICK FIX: Nuclear clean + reinstall of diffusers (fixes pipelines import error)\n","🗑️ Completely removing diffusers package...\n","Found existing installation: diffusers 0.38.0.dev0\n","Uninstalling diffusers-0.38.0.dev0:\n","  Successfully uninstalled diffusers-0.38.0.dev0\n","🔄 Installing latest diffusers from GitHub (with Flux2KleinPipeline)...\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","\u001b[33mWARNING: No matching packages\u001b[0m\u001b[33m\n","\u001b[0mFiles removed: 0\n","🧹 Clearing all __pycache__ and leftover files...\n","🔄 Forcing Python to reload diffusers module...\n","✅ DIFFUSERS FULLY REINSTALLED AND FIXED!\n","   Current diffusers version: 0.37.1\n","   pipelines module should now be available.\n","VRAM after quick fix: 0.36 GB\n","\n","🚀 Now run Cell 4 (it should import Flux2KleinPipeline without error).\n"]}]},{"cell_type":"code","source":["# =============================================================================\n","# CELL 4: Unload precompute components + load ONLY DiT + run edits\n","# =============================================================================\n","\n","import os\n","import gc\n","\n","# Safe unload\n","print(\"Unloading text encoder + VAE to free VRAM...\")\n","for var_name in ['text_encoder', 'vae', 'tokenizer']:\n","    if var_name in globals():\n","        del globals()[var_name]\n","        print(f\"   • Deleted {var_name}\")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","if debug:\n","    print(f\"VRAM after unload: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n","\n","# ====================== LOAD ONLY DiT ======================\n","print(\"Loading DiT transformer + scheduler + lightweight VAE...\")\n","\n","from diffusers import Flux2KleinPipeline, FlowMatchEulerDiscreteScheduler\n","from diffusers.models import Flux2Transformer2DModel\n","from diffusers import AutoencoderKLFlux2   # explicit import for safety\n","\n","# Reload VAE (lightweight)\n","vae = AutoencoderKLFlux2.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-4B\",\n","    subfolder=\"vae\",\n","    torch_dtype=torch.bfloat16\n",")\n","\n","transformer = Flux2Transformer2DModel.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-4B\",\n","    subfolder=\"transformer\",\n","    torch_dtype=torch.bfloat16\n",")\n","scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(\n","    \"black-forest-labs/FLUX.2-klein-4B\",\n","    subfolder=\"scheduler\"\n",")\n","\n","# Create pipeline with precomputed components\n","pipe = Flux2KleinPipeline(\n","    vae=vae,\n","    text_encoder=None,\n","    tokenizer=None,\n","    transformer=transformer,\n","    scheduler=scheduler,\n",")\n","pipe.to(\"cuda\")\n","\n","if debug:\n","    print(f\"DiT loaded – VRAM: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n","print(\"✅ Pipeline ready!\")\n","\n","# ====================== RUN BATCH EDITS ======================\n","os.makedirs(output_folder, exist_ok=True)\n","print(f\"Starting batch edit on {len(image_files)} images...\")\n","\n","for i, (filename, latents) in enumerate(zip(image_files, precomputed_latents)):\n","    print(f\"[{i+1}/{len(image_files)}] Editing: {filename}\")\n","\n","    result = pipe(\n","        prompt_embeds=prompt_embeds,\n","        image=latents,\n","        height=target_height,\n","        width=target_width,\n","        guidance_scale=1.0,\n","        num_inference_steps=4,\n","        max_sequence_length=512,\n","        generator=torch.Generator(\"cuda\").manual_seed(42),\n","    ).images[0]\n","\n","    output_filename = f\"edited_{filename}\"\n","    output_path = os.path.join(output_folder, output_filename)\n","    result.save(output_path)\n","\n","    if debug:\n","        print(f\"   ��� Saved {output_filename}\")\n","        torch.cuda.empty_cache()\n","\n","print(\"\\n✅ ALL DONE!\")\n","print(f\"Edited images saved to: {output_folder}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":711},"id":"x84OaABRFYq_","executionInfo":{"status":"error","timestamp":1776692425180,"user_tz":-120,"elapsed":440,"user":{"displayName":"No Name","userId":"10578412414437288386"}},"outputId":"e4537601-0dcf-4873-a102-14973d4c8ca1"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Unloading text encoder + VAE to free VRAM...\n","VRAM after unload: 0.36 GB\n","Loading DiT transformer + scheduler + lightweight VAE...\n"]},{"output_type":"error","ename":"RuntimeError","evalue":"Failed to import diffusers.pipelines because of the following error (look up to see its traceback):\nNo module named 'diffusers.pipelines'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/diffusers/utils/import_utils.py\u001b[0m in \u001b[0;36m_get_module\u001b[0;34m(self, module_name)\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'diffusers.pipelines'","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_1252/3972182759.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loading DiT transformer + scheduler + lightweight VAE...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdiffusers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFlux2KleinPipeline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFlowMatchEulerDiscreteScheduler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdiffusers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFlux2Transformer2DModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdiffusers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoencoderKLFlux2\u001b[0m   \u001b[0;31m# explicit import for safety\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_handle_fromlist\u001b[0;34m(module, fromlist, import_, recursive)\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/diffusers/utils/import_utils.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/diffusers/utils/import_utils.py\u001b[0m in \u001b[0;36m_get_module\u001b[0;34m(self, module_name)\u001b[0m\n","\u001b[0;31mRuntimeError\u001b[0m: Failed to import diffusers.pipelines because of the following error (look up to see its traceback):\nNo module named 'diffusers.pipelines'"]}]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/vertical_slice_prepper.ipynb","timestamp":1776687886662},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/vertical_slice_prepper.ipynb","timestamp":1776366149549},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776287741995},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776178739426},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776027716448},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1773663661932},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773663290922},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773264797996},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773163850245},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773090196076},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773089575687},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773080355474},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive 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