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Create Diffusers from Flux.2 klein finetuned model"],"metadata":{"id":"6fTuzMUFRzQU"}},{"cell_type":"code","execution_count":1,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/"},"id":"j9M74w1lSCbf","executionInfo":{"status":"ok","timestamp":1776884303669,"user_tz":-120,"elapsed":31025,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"ea781c5a-b9a9-4459-9c8c-c4a50daa5723"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","🧹 Removing old diffusers...\n","🔄 Installing latest diffusers...\n"," Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"," Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Files removed: 6\n","✅ Cell 1 complete!\n"]}],"source":["# =============================================================================\n","#@markdown # **CELL 1**: Mount Drive + HF auth\n","# =============================================================================\n","\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch\n","import os\n","import gc\n","import shutil\n","\n","drive.mount('/content/drive')\n","\n","hf_token = userdata.get('HF_TOKEN')\n","if hf_token:\n"," login(token=hf_token)\n","else:\n"," print(\"⚠️ No HF_TOKEN found in secrets.\")\n","\n","print(\"🧹 Removing old diffusers...\")\n","!pip uninstall -y diffusers > /dev/null 2>&1\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers* ~/.cache/pip/*diffusers*\n","\n","print(\"🔄 Installing latest diffusers...\")\n","!pip install -q git+https://github.com/huggingface/diffusers.git --force-reinstall --no-deps\n","!python -m pip cache purge\n","\n","print(\"✅ Cell 1 complete!\")"]},{"cell_type":"code","source":["from huggingface_hub import hf_hub_download\n","import gc\n","import torch\n","\n","repo_id = \"codeShare/Flux2Klein_AIO\" #@param {type:'string'}\n","filename = \"aio_transformer_flux_klein_4b_bf16.safetensors\" #@param {type:'string'}\n","\n","ckpt_path = hf_hub_download(\n"," repo_id=repo_id,\n"," filename=filename,\n"," local_dir=\"/content/\" # Save directly to /content/\n",")\n","\n","print(\"✅ Downloaded to:\", ckpt_path)\n","\n","# Unload RAM\n","del repo_id, filename # Delete variables to free up memory\n","gc.collect()\n","if torch.cuda.is_available():\n"," torch.cuda.empty_cache()\n","print(\"✅ RAM unloaded.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1776884304443,"user_tz":-120,"elapsed":773,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"92d78e1f-9243-49b9-c491-4038c074f715","id":"QTFSAvGqSUCM"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Downloaded to: /content/aio_transformer_flux_klein_4b_bf16.safetensors\n","✅ RAM unloaded.\n"]}]},{"cell_type":"code","source":["import os\n","import gc\n","import torch\n","from huggingface_hub import snapshot_download, login\n","from safetensors.torch import load_file\n","from diffusers import Flux2Transformer2DModel\n","# ----------------------------\n","# 1. DOWNLOAD ONLY TRANSFORMER FOLDER\n","# ----------------------------\n","repo_id = \"black-forest-labs/FLUX.2-klein-4B\"\n","local_dir = \"/content/flux_klein_transformer_only\"\n","\n","print(\"📥 Downloading ONLY transformer folder...\")\n","\n","snapshot_download(\n"," repo_id=repo_id,\n"," local_dir=local_dir,\n"," allow_patterns=[\n"," \"transformer/*\", # 👈 ONLY transformer\n"," ],\n"," token=hf_token\n",")\n","\n","TRANSFORMER_PATH = os.path.join(local_dir, \"transformer\")\n","\n","print(\"✅ Transformer downloaded to:\", TRANSFORMER_PATH)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":165,"referenced_widgets":["c4415667c25e4495b1b0c91771bad7e8","caf93882e1544427be379a3ecb2f7491","ba8566a8dc434d7687bc5bb8cbb35111","22b41da8840942558cc8ea201dfb6d96","825bd59233ee4d0eb3bf7c167b9d33d5","392285ceb4ca4f0a8b1d65a631988919","a925541d311e4f9d9d2d8cea771a9e5f","ce1a03ecb0b54a31864c97228227811e","df105a2b82874a378aeee90d1a99111a","806f570646544f3fa75596c4cfc194fa","0a378e865e4b4462ab306521f9920b95","48155ce0885e41bda61abea2c84ff777","6055310ba7ef44ec9cfbeeee6b9ae2d3","16f742a4a9eb46f6ba32c86be005080f","7f7f12f88bec4db29b1946220b7a2a57","29bffe291d97490a838ccfdff01824ae","04c5919172d343688c9d7bad78aae319","679cbc5617004587b0566cedc01d8b39","57cf05a29e46484390afb130162a77e2","5810a4c171ed4045b7f7a795b9e21b5c","7347f0b052c34a569fd638e7d0361a2b","7cc31199525e401482cb22c589c3501c"]},"id":"smmrVpO5WtOy","executionInfo":{"status":"ok","timestamp":1776884326729,"user_tz":-120,"elapsed":22275,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"83b4c803-a4eb-4336-96af-cfc9ed3b6c29"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["📥 Downloading ONLY transformer folder...\n"]},{"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":"c4415667c25e4495b1b0c91771bad7e8"}},"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":"48155ce0885e41bda61abea2c84ff777"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["✅ Transformer downloaded to: /content/flux_klein_transformer_only/transformer\n"]}]},{"cell_type":"code","source":["torch.cuda.empty_cache()\n","gc.collect()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"rU6175RphcZg","executionInfo":{"status":"ok","timestamp":1776884364698,"user_tz":-120,"elapsed":11,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"26713b09-2be9-4c41-c5f8-2a0d6e1c36f1"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["155"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["# =============================================================================\n","# CELL 2: Convert Flux Klein finetune (single-file) → Diffusers format (FIXED #2)\n","# =============================================================================\n","\n","#tmp ='/content/flux_klein_finetuned_transformer'\n","\n","import torch\n","import gc\n","import os\n","from diffusers import Flux2Transformer2DModel\n","\n","print(\"🔄 Loading official Klein 4B config from local folder + converting your finetuned model...\")\n","\n","# TRANSFORMER_PATH is already defined in Cell 1\n","# It points to: /content/flux_klein_transformer_only/transformer\n","# (this folder contains the exact config.json for FLUX.2-klein-4B)\n","with torch.no_grad():\n"," transformer = Flux2Transformer2DModel.from_single_file(\n"," ckpt_path, # your aio_transformer_flux_klein_4b_bf16.safetensors\n"," config=TRANSFORMER_PATH, # ← THIS IS THE FIX (local folder path)\n"," torch_dtype=torch.float16 # keeps memory usage minimal on T4\n"," )\n","\n","print(\"✅ Custom Klein transformer loaded + keys remapped correctly\")\n","torch.cuda.empty_cache()\n","gc.collect()\n","# Save as proper Diffusers transformer folder\n","output_dir = \"/content/flux_klein_finetuned_transformer\"\n","\n","transformer = transformer.to(\"cpu\") # ← move off GPU first\n","torch.cuda.empty_cache()\n","gc.collect()\n","#This causes out of memory error!\n","transformer.save_pretrained(output_dir)\n","\n","print(f\"✅ Saved Diffusers transformer to:\\n{output_dir}\")\n","print(\" ├── config.json (exact official Klein 4B config)\")\n","print(\" └── diffusion_pytorch_model.safetensors (your finetuned weights)\")\n","\n","# Cleanup — essential on T4\n","del transformer\n","gc.collect()\n","if torch.cuda.is_available():\n"," torch.cuda.empty_cache()\n","\n","print(\"🧹 VRAM/RAM cleared.\")\n","\n","# Quick verification\n","!ls -lh \"{output_dir}\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bH9Ul41eeJtS","executionInfo":{"status":"ok","timestamp":1776884993244,"user_tz":-120,"elapsed":159597,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"e667cce6-642e-4e90-cb24-0cb2aa6471df"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["🔄 Loading official Klein 4B config from local folder + converting your finetuned model...\n","✅ Custom Klein transformer loaded + keys remapped correctly\n","✅ Saved Diffusers transformer to:\n","/content/flux_klein_finetuned_transformer\n"," ├── config.json (exact official Klein 4B config)\n"," └── diffusion_pytorch_model.safetensors (your finetuned weights)\n","🧹 VRAM/RAM cleared.\n","total 7.3G\n","-rw-r--r-- 1 root root 541 Apr 22 19:07 config.json\n","-rw-r--r-- 1 root root 7.3G Apr 22 19:09 diffusion_pytorch_model.safetensors\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":205,"referenced_widgets":["7dcfb18e53ae483aa5093f9bcf1fc964","fcb4bc569d1d467b929ed57200176f51","858a918ed7824a8284cb1c0d863c4eb6","a6e8fd9dc4954e24918211ac4eb143cb","d318833b6df242099173e9e7ba81683e","2c699b36004d4af48d35b6733c7f513c","466d9d1005394b508df0795fc2fde38c","8cc030a9f5f54a6faa44339711d55b05","6848061b046e422a860ad8f0f8308f64","494a341f37ba421d991bedbbfd013739","2fd6e9c75d7245a58a89a5f8a0f264f3","d2d20bd53c5145daa2f1b2ad67fb6e89","7e1bf481e9c6461dadaf3f04b004023b","af657c8c798b4140bd6be55a1cccce6f","df4c36a3955141498a6b92576edbbcc8","4885f1d867804b7791f55a9a32bccb5d","889ddf82e7944b5d8cbea5bdc76eabb8","355f145b472a4293ba4848bafa1875d4","ed3e1054b3d24cab909de007f3f49554","1f3ce677d59f475abb8e0a2679337831","4b3786cdd2014e649019a7495e3f84e6","f4f7027fa15b4d5b8734e1f480a165d7","a4f27a8632a44ab7a530c4118e1227ea","3ebd7826f20a4a44b4aca4970a83df65","313584967bd04c1b810f523445662bcc","ae667abd7a7344a894d075595e47c45c","9de55bc029ed4a59ad6077d0ce8ed018","502fb951162e4ff4ade411bc35cd075a","cce813a46b1f47a08703f192d3ed1592","b087a71abed545108156887e7417befe","879e464f202e439bae9d7a89d229d9d7","ccc2f58a7d564a7a9fef63b07681dc3f","78b886617f844a998a2ad4d6e8b2a8c6"]},"id":"dd7c4d18","executionInfo":{"status":"ok","timestamp":1776885422478,"user_tz":-120,"elapsed":62046,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"a595496a-d8ce-4e60-89dd-a7d3764c8628"},"source":["from huggingface_hub import upload_folder\n","import os\n","\n","# Define the target Hugging Face repository ID\n","repo_id = \"codeShare/Flux2Klein_AIO\" # @param {type:\"string\"}\n","\n","# The folder containing the finetuned transformer is currently 'output_dir'\n","# which holds '/content/flux_klein_finetuned_transformer'\n","\n","# Define the new desired folder name\n","new_transformer_folder = '/content/transformer'\n","\n","# Rename the folder\n","print(f\"🔄 Renaming folder '{output_dir}' to '{new_transformer_folder}'...\")\n","os.rename(output_dir, new_transformer_folder)\n","print(f\"✅ Folder renamed to '{new_transformer_folder}'.\")\n","\n","# Now, set the source_folder for upload to the newly renamed folder\n","source_folder = new_transformer_folder\n","\n","print(f\"🔄 Uploading folder '{source_folder}' to Hugging Face repo '{repo_id}'...\")\n","\n","# Upload the folder\n","# The hf_token from Cell 1 will be used for authentication.\n","upload_folder(\n"," repo_id=repo_id,\n"," folder_path=source_folder,\n"," commit_message=f\"Upload finetuned Flux Klein transformer from {source_folder}\",\n"," token=hf_token # Use the token obtained in Cell 1\n",")\n","\n","print(f\"✅ Folder '{source_folder}' uploaded successfully to '{repo_id}'\")"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["🔄 Renaming folder '/content/flux_klein_finetuned_transformer' to '/content/transformer'...\n","✅ Folder renamed to '/content/transformer'.\n","🔄 Uploading folder '/content/transformer' to Hugging Face repo 'codeShare/Flux2Klein_AIO'...\n"]},{"output_type":"display_data","data":{"text/plain":["Processing Files (0 / 0) : | | 0.00B / 0.00B "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7dcfb18e53ae483aa5093f9bcf1fc964"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["New Data Upload : | | 0.00B / 0.00B "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d2d20bd53c5145daa2f1b2ad67fb6e89"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...pytorch_model.safetensors: 2%|2 | 160MB / 7.75GB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a4f27a8632a44ab7a530c4118e1227ea"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["✅ Folder '/content/transformer' uploaded successfully to 'codeShare/Flux2Klein_AIO'\n"]}]},{"cell_type":"markdown","source":["# Create SDNQ from diffusion model on Huggingface"],"metadata":{"id":"E98dP9Beoq_8"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"681oB2zco3un"},"outputs":[],"source":["# =============================================================================\n","#@markdown # **CELL 1**: Mount Drive + HF auth\n","# =============================================================================\n","\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch\n","import os\n","import gc\n","import shutil\n","\n","drive.mount('/content/drive')\n","\n","hf_token = userdata.get('HF_TOKEN')\n","if hf_token:\n"," login(token=hf_token)\n","else:\n"," print(\"⚠️ No HF_TOKEN found in secrets.\")\n","\n","print(\"🧹 Removing old diffusers...\")\n","!pip uninstall -y diffusers > /dev/null 2>&1\n","!rm -rf /usr/local/lib/python3.12/dist-packages/diffusers* ~/.cache/pip/*diffusers*\n","\n","!pip install -q --upgrade huggingface_hub transformers accelerate diffusers\n","!pip install -q sdnq\n","\n","print(\"🔄 Installing latest diffusers...\")\n","!pip install -q git+https://github.com/huggingface/diffusers.git --force-reinstall --no-deps\n","!python -m pip cache purge\n","\n","print(\"✅ Cell 1 complete!\")"]},{"cell_type":"code","source":["from huggingface_hub import snapshot_download\n","\n","# Flux2 Klein AIO (base pipeline repo)\n","flux_repo = \"codeShare/Flux2Klein_AIO\"\n","\n","flux_path = snapshot_download(\n"," repo_id=flux_repo,\n"," token=hf_token,\n"," local_dir=\"/content/flux2_klein\",\n"," allow_patterns=[\"**/*\"]\n",")\n","\n","# Qwen3-4B Abliterated (text encoder + tokenizer)\n","encoder_repo = \"huihui-ai/Qwen3-4B-abliterated\"\n","\n","encoder_path = snapshot_download(\n"," repo_id=encoder_repo,\n"," token=hf_token,\n"," local_dir=\"/content/qwen3_encoder\",\n"," allow_patterns=[\"**/*\"]\n",")\n","\n","print(\"✅ Flux repo:\", flux_path)\n","print(\"✅ Encoder repo:\", encoder_path)"],"metadata":{"id":"WDo6aIVLttXv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch, gc\n","\n","# -------------------------\n","# HARD MEMORY CLEAN BEFORE LOAD\n","# -------------------------\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","# delete anything large from previous cells if still alive\n","try:\n"," del text_encoder\n"," del tokenizer\n","except:\n"," pass\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"🧹 Pre-load cleanup complete\")\n","\n","from transformers import Qwen3ForCausalLM\n","\n","# Load encoder (root-level repo, NO subfolder)\n","text_encoder = Qwen3ForCausalLM.from_pretrained(\n"," encoder_path,\n"," torch_dtype=torch.float16,\n"," low_cpu_mem_usage=True,\n"," device_map=\"cpu\", # IMPORTANT: keep off GPU initially\n",")\n","\n","print(\"✅ Text encoder loaded on CPU\")"],"metadata":{"id":"YCKMXQAnvcS3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\n"," encoder_path,\n"," use_fast=True\n",")\n","\n","print(\"✅ Tokenizer loaded\")"],"metadata":{"id":"eCg8eNGpvl7z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# keep everything on CPU until pipeline assembly\n","text_encoder = text_encoder.to(\"cpu\")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"📦 Encoder locked on CPU (safe)\")"],"metadata":{"id":"IcfMNfUKvoIU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import gc, torch\n","from diffusers import Flux2Transformer2DModel\n","\n","# -------------------------\n","# HARD CLEAN BEFORE TRANSFORMER LOAD\n","# -------------------------\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","# ensure nothing heavy is accidentally still referenced\n","try:\n"," del transformer\n","except:\n"," pass\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"🧹 Pre-transformer cleanup complete\")\n","\n","# -------------------------\n","# LOAD TRANSFORMER (SDNQ 4-bit)\n","# -------------------------\n","transformer = Flux2Transformer2DModel.from_pretrained(\n"," flux_path,\n"," subfolder=\"transformer\",\n"," quantization_config=sdnq_config,\n"," torch_dtype=torch.float16,\n"," low_cpu_mem_usage=True,\n"," device_map=\"cpu\", # CRITICAL: keep off GPU initially\n",")\n","\n","print(\"✅ Transformer loaded on CPU (quantized)\")"],"metadata":{"id":"esMA4q0GwKXY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from diffusers import Flux2KleinPipeline\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","pipe = Flux2KleinPipeline.from_pretrained(\n"," flux_path,\n"," transformer=transformer,\n"," text_encoder=text_encoder,\n"," tokenizer=tokenizer,\n"," torch_dtype=torch.float16,\n"," low_cpu_mem_usage=True,\n",")\n","\n","print(\"✅ Pipeline assembled safely (no VRAM spike)\")"],"metadata":{"id":"x9mSYR57wTkV"},"execution_count":null,"outputs":[]}]}
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