Blame for build_sdnq_comfyUI.ipynb

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# CELL 1 — Setup environment + login\n","\n","from google.colab import drive, userdata\n","from huggingface_hub import login\n","import torch, os, gc\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","    raise ValueError(\"HF_TOKEN not found in Google Colab secrets\")\n","\n","print(\"✅ Logged into Hugging Face\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XzgpnXJhsHg-","executionInfo":{"status":"ok","timestamp":1776947457745,"user_tz":-120,"elapsed":24449,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"db13d67c-31ff-42d9-db03-7b0b39e48b27"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","✅ Logged into Hugging Face\n"]}]},{"cell_type":"code","source":["from huggingface_hub import hf_hub_download\n","import os\n","\n","# Ensure HF token is used (from earlier cell)\n","\n","hf_token = userdata.get(\"HF_TOKEN\")\n","\n","# --- Model 1 ---\n","\n","repo_id_1 = \"black-forest-labs/FLUX.2-klein-4B\"\n","filename_1 = \"flux-2-klein-4b.safetensors\"\n","\n","path_1 = hf_hub_download(\n","repo_id=repo_id_1,\n","filename=filename_1,\n","token=hf_token,\n",")\n","print(\"Downloaded:\", path_1)\n","\n","# --- Model 2 ---\n","\n","repo_id_2 = \"codeShare/unstableRevolution_SDNQ\"\n","filename_2 = \"unstableRevolutionF2K_AlphaF2K4BFp16_dit_only.safetensors\"\n","\n","path_2 = hf_hub_download(\n","repo_id=repo_id_2,\n","filename=filename_2,\n","token=hf_token,\n",")\n","\n","print(\"Downloaded:\", path_2)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":165,"referenced_widgets":["91aecff3f6414f10a9788b7c27124491","370b29cf1c144b73beecd9cdaaa3ea5f","c369fefb5d814cf1ae708e98a8a1df24","05e2baff4f1545489bf456d246b4f6fa","30048bbd6ad148ceb9850943df3e6cb1","bccd53140ee84641bc548b75a1d7e907","8345c99cfc2b4e98b0a8dd754632c763","fe61f293bad544b28c004c8a4cddd2fb","f85544b04dcf4bb88799c2ed13bd4bf9","b0c99a7d924f4a9b90da4283bea42188","2cef93f83b014862b47acff64401d3a2","dc66a8bde26e407d83899e0da0f0418c","fcb4ddb3fa9349458dbbaba89324e5cc","b5b78ed41afd42c8974f20e1a4cce750","4254daabcb7143449ffc398a640a4b4d","04f3bf8b827c46edbd5df806abc8cf58","a909e591442049198d021c4162ac1d62","dc77ce4317874e7da64ae8e5a1a54dba","0bfd3aca72c44305951d2a78f9a64f87","5acc211252124c34868a5ae76f1704f6","3e9de4b161a54bb4895aa82e43d52aa3","75c1678c9d7c4ce692f58279144c7c95"]},"id":"EPBKhvb7RML_","executionInfo":{"status":"ok","timestamp":1776947708392,"user_tz":-120,"elapsed":250646,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"b6e9d43c-d4ad-414a-c0e6-eec5d7d3f600"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["flux-2-klein-4b.safetensors:   0%|          | 0.00/7.75G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"91aecff3f6414f10a9788b7c27124491"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloaded: /root/.cache/huggingface/hub/models--black-forest-labs--FLUX.2-klein-4B/snapshots/e7b7dc27f91deacad38e78976d1f2b499d76a294/flux-2-klein-4b.safetensors\n"]},{"output_type":"display_data","data":{"text/plain":["unstableRevolutionF2K_AlphaF2K4BFp16_dit(…):   0%|          | 0.00/7.75G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dc66a8bde26e407d83899e0da0f0418c"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloaded: /root/.cache/huggingface/hub/models--codeShare--unstableRevolution_SDNQ/snapshots/2f574bc0884c6aeae06e879004e49acae79ef1e3/unstableRevolutionF2K_AlphaF2K4BFp16_dit_only.safetensors\n"]}]},{"cell_type":"code","source":["# Install dependency (run once)\n","!pip -q install safetensors\n","\n","from safetensors.torch import load_file\n","\n","def print_safetensors_layers(file_path, model_name):\n","    print(\"\\n\" + \"=\"*80)\n","    print(f\"MODEL: {model_name}\")\n","    print(\"=\"*80 + \"\\n\")\n","\n","    state_dict = load_file(file_path)\n","\n","    print(f\"Total tensors: {len(state_dict)}\\n\")\n","\n","    # Sort for readability\n","    for name in sorted(state_dict.keys()):\n","        tensor = state_dict[name]\n","        shape = tuple(tensor.shape)\n","        dtype = tensor.dtype\n","\n","        print(f\"{name:80s} | shape={shape} | dtype={dtype}\")\n","\n","# ---- RUN INSPECTION ----\n","print_safetensors_layers(path_1, \"FLUX.2-klein-4B\")\n","print_safetensors_layers(path_2, \"unstableRevolutionF2K_AlphaF2K4B\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UWlP5BA_RtYa","executionInfo":{"status":"ok","timestamp":1776947716769,"user_tz":-120,"elapsed":8375,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"f1416cfa-1c3d-44ee-ef78-7657331b24d6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","================================================================================\n","MODEL: FLUX.2-klein-4B\n","================================================================================\n","\n","Total tensors: 149\n","\n","double_blocks.0.img_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.0.img_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.0.img_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.0.img_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.0.img_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.0.img_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.0.txt_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.0.txt_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.0.txt_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.0.txt_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.0.txt_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.0.txt_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.1.img_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.1.img_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.1.img_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.1.img_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.1.img_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.1.img_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.1.txt_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.1.txt_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.1.txt_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.1.txt_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.1.txt_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.1.txt_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.2.img_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.2.img_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.2.img_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.2.img_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.2.img_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.2.img_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.2.txt_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.2.txt_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.2.txt_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.2.txt_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.2.txt_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.2.txt_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.3.img_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.3.img_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.3.img_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.3.img_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.3.img_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.3.img_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.3.txt_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.3.txt_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.3.txt_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.3.txt_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.3.txt_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.3.txt_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.4.img_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.4.img_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.4.img_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.4.img_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.4.img_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.4.img_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_blocks.4.txt_attn.norm.key_norm.scale                                     | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.4.txt_attn.norm.query_norm.scale                                   | shape=(128,) | dtype=torch.bfloat16\n","double_blocks.4.txt_attn.proj.weight                                             | shape=(3072, 3072) | dtype=torch.bfloat16\n","double_blocks.4.txt_attn.qkv.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","double_blocks.4.txt_mlp.0.weight                                                 | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_blocks.4.txt_mlp.2.weight                                                 | shape=(3072, 9216) | dtype=torch.bfloat16\n","double_stream_modulation_img.lin.weight                                          | shape=(18432, 3072) | dtype=torch.bfloat16\n","double_stream_modulation_txt.lin.weight                                          | shape=(18432, 3072) | dtype=torch.bfloat16\n","final_layer.adaLN_modulation.1.weight                                            | shape=(6144, 3072) | dtype=torch.bfloat16\n","final_layer.linear.weight                                                        | shape=(128, 3072) | dtype=torch.bfloat16\n","img_in.weight                                                                    | shape=(3072, 128) | dtype=torch.bfloat16\n","single_blocks.0.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.0.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.0.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.0.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.1.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.1.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.1.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.1.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.10.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.10.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.10.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.10.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.11.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.11.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.11.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.11.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.12.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.12.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.12.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.12.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.13.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.13.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.13.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.13.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.14.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.14.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.14.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.14.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.15.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.15.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.15.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.15.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.16.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.16.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.16.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.16.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.17.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.17.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.17.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.17.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.18.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.18.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.18.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.18.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.19.linear1.weight                                                  | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.19.linear2.weight                                                  | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.19.norm.key_norm.scale                                             | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.19.norm.query_norm.scale                                           | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.2.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.2.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.2.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.2.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.3.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.3.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.3.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.3.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.4.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.4.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.4.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.4.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.5.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.5.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.5.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.5.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.6.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.6.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.6.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.6.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.7.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.7.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.7.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.7.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.8.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.8.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.8.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.8.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.9.linear1.weight                                                   | shape=(27648, 3072) | dtype=torch.bfloat16\n","single_blocks.9.linear2.weight                                                   | shape=(3072, 12288) | dtype=torch.bfloat16\n","single_blocks.9.norm.key_norm.scale                                              | shape=(128,) | dtype=torch.bfloat16\n","single_blocks.9.norm.query_norm.scale                                            | shape=(128,) | dtype=torch.bfloat16\n","single_stream_modulation.lin.weight                                              | shape=(9216, 3072) | dtype=torch.bfloat16\n","time_in.in_layer.weight                                                          | shape=(3072, 256) | dtype=torch.bfloat16\n","time_in.out_layer.weight                                                         | shape=(3072, 3072) | dtype=torch.bfloat16\n","txt_in.weight                                                                    | shape=(3072, 7680) | dtype=torch.bfloat16\n","\n","================================================================================\n","MODEL: unstableRevolutionF2K_AlphaF2K4B\n","================================================================================\n","\n","Total tensors: 149\n","\n","model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.img_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.img_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.img_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.img_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.img_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.txt_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.txt_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.txt_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.txt_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.txt_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.0.txt_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.img_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.img_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.img_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.img_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.img_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.img_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.txt_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.txt_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.txt_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.txt_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.txt_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.1.txt_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.img_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.img_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.img_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.img_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.img_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.img_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.txt_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.txt_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.txt_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.txt_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.txt_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.2.txt_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.img_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.img_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.img_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.img_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.img_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.img_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.txt_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.txt_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.txt_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.txt_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.txt_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.3.txt_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.img_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.img_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.img_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.img_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.img_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.img_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.txt_attn.norm.key_norm.scale               | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.txt_attn.norm.query_norm.scale             | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.txt_attn.proj.weight                       | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.txt_attn.qkv.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.txt_mlp.0.weight                           | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_blocks.4.txt_mlp.2.weight                           | shape=(3072, 9216) | dtype=torch.bfloat16\n","model.diffusion_model.double_stream_modulation_img.lin.weight                    | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.double_stream_modulation_txt.lin.weight                    | shape=(18432, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.final_layer.adaLN_modulation.1.weight                      | shape=(6144, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.final_layer.linear.weight                                  | shape=(128, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.img_in.weight                                              | shape=(3072, 128) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.0.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.0.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.0.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.0.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.1.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.1.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.1.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.1.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.10.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.10.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.10.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.10.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.11.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.11.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.11.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.11.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.12.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.12.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.12.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.12.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.13.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.13.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.13.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.13.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.14.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.14.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.14.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.14.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.15.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.15.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.15.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.15.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.16.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.16.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.16.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.16.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.17.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.17.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.17.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.17.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.18.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.18.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.18.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.18.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.19.linear1.weight                            | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.19.linear2.weight                            | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.19.norm.key_norm.scale                       | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.19.norm.query_norm.scale                     | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.2.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.2.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.2.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.2.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.3.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.3.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.3.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.3.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.4.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.4.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.4.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.4.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.5.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.5.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.5.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.5.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.6.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.6.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.6.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.6.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.7.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.7.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.7.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.7.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.8.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.8.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.8.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.8.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.9.linear1.weight                             | shape=(27648, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.9.linear2.weight                             | shape=(3072, 12288) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.9.norm.key_norm.scale                        | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_blocks.9.norm.query_norm.scale                      | shape=(128,) | dtype=torch.bfloat16\n","model.diffusion_model.single_stream_modulation.lin.weight                        | shape=(9216, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.time_in.in_layer.weight                                    | shape=(3072, 256) | dtype=torch.bfloat16\n","model.diffusion_model.time_in.out_layer.weight                                   | shape=(3072, 3072) | dtype=torch.bfloat16\n","model.diffusion_model.txt_in.weight                                              | shape=(3072, 7680) | dtype=torch.bfloat16\n"]}]},{"cell_type":"code","source":["# ============================================\n","# ALIGN UNSTABLEREVOLUTION WEIGHTS TO FLUX KEY FORMAT\n","# (strips \"model.diffusion_model.\" prefix and re-saves)\n","# ============================================\n","\n","from safetensors.torch import load_file, save_file\n","\n","def remap_to_flux_format(sd):\n","    new_sd = {}\n","\n","    for k, v in sd.items():\n","        # remove wrapper prefix\n","        if k.startswith(\"model.diffusion_model.\"):\n","            new_k = k.replace(\"model.diffusion_model.\", \"\")\n","        else:\n","            new_k = k\n","\n","        new_sd[new_k] = v\n","\n","    return new_sd\n","\n","\n","def align_and_save_to_flux(unstable_path, output_path):\n","    print(\"Loading unstable model...\")\n","    sd = load_file(unstable_path)\n","\n","    print(\"Remapping keys to FLUX format...\")\n","    aligned_sd = remap_to_flux_format(sd)\n","\n","    print(\"Verifying key overlap (FLUX-style naming)...\")\n","\n","    # optional sanity check: show mismatches\n","    flux_keys = set(path_1_state.keys()) if \"path_1_state\" in globals() else None\n","\n","    print(f\"Total original keys: {len(sd)}\")\n","    print(f\"Total remapped keys: {len(aligned_sd)}\")\n","\n","    # Save result\n","    print(\"Saving aligned safetensors...\")\n","    save_file(aligned_sd, output_path)\n","\n","    print(\"DONE:\", output_path)\n","\n","\n","# ============================================\n","# RUN ALIGNMENT\n","# ============================================\n","\n","output_aligned = \"/content/unstable_aligned_to_flux.safetensors\"\n","\n","align_and_save_to_flux(path_2, output_aligned)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HnSYZqXxSTbG","executionInfo":{"status":"ok","timestamp":1776948538194,"user_tz":-120,"elapsed":821423,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"fccc662a-ae80-4d40-d1ed-437de1bde11f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Loading unstable model...\n","Remapping keys to FLUX format...\n","Verifying key overlap (FLUX-style naming)...\n","Total original keys: 149\n","Total remapped keys: 149\n","Saving aligned safetensors...\n","DONE: /content/unstable_aligned_to_flux.safetensors\n"]}]},{"cell_type":"code","source":["from huggingface_hub import HfApi\n","import os\n","\n","# Path to your converted file\n","file_path = \"/content/unstable_aligned_to_flux.safetensors\"\n","\n","# Target repo\n","repo_id = \"codeShare/unstableRevolution_SDNQ\"\n","target_filename = \"unstablerevolution_flux_klein_4b_bf16.safetensors\"\n","\n","# HF token (from Colab secrets)\n","hf_token = userdata.get(\"HF_TOKEN\")\n","\n","api = HfApi()\n","\n","print(\"Uploading file to Hugging Face repo...\")\n","\n","api.upload_file(\n","    path_or_fileobj=file_path,\n","    path_in_repo=target_filename,\n","    repo_id=repo_id,\n","    repo_type=\"model\",\n","    token=hf_token\n",")\n","\n","print(\"Upload complete ✔\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":169,"referenced_widgets":["9e637ad4da6b4a7eaa856609bb1e76d6","ea909ec86c5f41e8854c73de26f0aa48","392f7bf7a3ca4e399e0953c361a20cb0","682f17ce72bd40e1ae63ea1d4d9ca2d3","2e05d1573b8543cea35861341c32bf24","af94df10db7c493195010b4fc207d444","4936b1cef5d749d183e39423e10e28ac","f063488bed094c81b6fca200142011ca","0994dbd6c6894bccb355e32c39661b39","0169a750b1494a38954b4dbc61eacf68","d1c7370f2a834f9889330cb3194d6cca","0b43eaacaffc46f9ab62476facef72ff","0783808ae04d49b3b9f1ee5bcef42f75","51c70c08f76646a9bcfee8f954341659","0ce256886daf493b8ce2fd5e05c04633","ab185731c7474f11a722201c2052427e","44ad515fe6134b8ea33555d61087ada9","2710a3cb603b49848ff1c3d6a11ac726","8ea5344f493c4d039cdcc04654ef7cd6","d71887027b4e4853b35c871f73b79d83","995572cd411842dcb006b7db35dfd835","f4fdc6ebef0048dab54bf3839338d888","b24e8cf2cf19428aa8d6cd706a51fd50","a25b749071a84611b726325d383313de","79bf5c1db9434832b9be4b8a43a9d2ab","4db239c61b614f1d9002660a6a41634d","270456db42e64ff7823a95833836dd4b","8ee1e9bcb72f4ac18803ca473cef458a","d0c069c54d6a4bd19915535f80dbb298","66220da96e3c48dbb5f265000e40ecb2","1a10d6be0ed44230be24b3f022f51b3b","54e883f6bf9041fabe93f363d102502d","5d15b9927802455e817ddc09ba85bf4b"]},"id":"JEGgQCzpTlJw","executionInfo":{"status":"ok","timestamp":1776948648265,"user_tz":-120,"elapsed":110075,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"376809ce-09a9-491f-d56a-efd61ef39de4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Uploading file to Hugging Face repo...\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":"9e637ad4da6b4a7eaa856609bb1e76d6"}},"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":"0b43eaacaffc46f9ab62476facef72ff"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ...igned_to_flux.safetensors:   0%|          | 32.0MB / 7.75GB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b24e8cf2cf19428aa8d6cd706a51fd50"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Upload complete ✔\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FQF71-mvmlc1","cellView":"form"},"outputs":[],"source":["# ================================================\n","#@title 🔌 Auto Disconnect Colab Session\n","# ================================================\n","\n","enable = False #@param {type:'boolean'}\n","if enable:\n","  print(\"🔌 Disconnecting Colab session in 3 seconds...\")\n","  import time\n","  time.sleep(3)\n","\n","  from google.colab import runtime\n","  runtime.unassign()\n","\n","  print(\"Session disconnected.\")"]},{"cell_type":"markdown","source":["# Connect to T4"],"metadata":{"id":"5FA6J31kVI27"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":69796,"status":"ok","timestamp":1776949815811,"user":{"displayName":"gfuku63","userId":"10213382542081034600"},"user_tz":-120},"id":"681oB2zco3un","outputId":"195c5698-738e-4e09-b8d6-b713293743b8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","🧹 Removing old diffusers...\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m645.5/645.5 kB\u001b[0m \u001b[31m21.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.4/10.4 MB\u001b[0m \u001b[31m110.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.0/5.0 MB\u001b[0m \u001b[31m138.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.0/104.0 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h🔄 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: 32\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","!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":["# ============================================\n","# CELL — PART 1 (FIXED: TRANSFORMER LOAD)\n","# ============================================\n","\n","import os, gc, torch\n","from huggingface_hub import hf_hub_download, login\n","from diffusers import Flux2Transformer2DModel\n","from sdnq import SDNQConfig\n","from sdnq.loader import apply_sdnq_options_to_model\n","\n","# --------------------------------------------\n","# Auth\n","# --------------------------------------------\n","hf_token = userdata.get(\"HF_TOKEN\")\n","login(token=hf_token)\n","\n","# --------------------------------------------\n","# Download safetensor\n","# --------------------------------------------\n","repo_id = \"codeShare/unstableRevolution_SDNQ\"\n","filename = \"checkpoints/unstablerevolution_flux_klein_4b_bf16.safetensors\"\n","\n","os.makedirs(\"/content/model\", exist_ok=True)\n","\n","weights_path = hf_hub_download(\n","    repo_id=repo_id,\n","    filename=filename,\n","    token=hf_token,\n","    cache_dir=\"/content/cache\"\n",")\n","\n","print(\"📦 Retrieved weights:\", weights_path)\n","\n","# --------------------------------------------\n","# SDNQ CONFIG\n","# --------------------------------------------\n","sdnq_config = SDNQConfig(\n","    use_dynamic_quantization=True,\n","    weights_dtype=\"uint4\",\n","    dynamic_loss_threshold=1e-2,\n","    use_svd=False,\n","    group_size=0,\n","    quantization_device=\"cuda\",\n","    return_device=\"cuda\",\n","    quant_conv=False,\n","    quant_embedding=False,\n",")\n","\n","torch_dtype = torch.float16\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"⚙️ SDNQ ready\")\n","\n","# --------------------------------------------\n","# LOAD TRANSFORMER (FIX: subfolder required)\n","# --------------------------------------------\n","base_repo = \"codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic\"\n","\n","transformer = Flux2Transformer2DModel.from_pretrained(\n","    base_repo,\n","    subfolder=\"transformer\",   # 🔴 CRITICAL FIX\n","    torch_dtype=torch_dtype,\n","    low_cpu_mem_usage=True,\n","    device_map=\"cpu\",\n","    trust_remote_code=True\n",")\n","\n","# --------------------------------------------\n","# LOAD WEIGHTS\n","# --------------------------------------------\n","#state_dict = torch.load(weights_path, map_location=\"cpu\")\n","#state_dict = torch.load(weights_path, map_location=\"cpu\", weights_only=False)\n","from safetensors.torch import load_file\n","\n","state_dict = load_file(weights_path)\n","\n","missing, unexpected = transformer.load_state_dict(state_dict, strict=False)\n","\n","print(\"Missing keys:\", len(missing))\n","print(\"Unexpected keys:\", len(unexpected))\n","\n","print(\"✅ Transformer loaded + weights injected\")\n","\n","gc.collect()\n","torch.cuda.empty_cache()\n","\n","print(\"🟡 END PART 1 — READY FOR PIPELINE LOAD\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5NN9xgY1fQe9","executionInfo":{"status":"ok","timestamp":1776950534189,"user_tz":-120,"elapsed":4312,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"383e84dd-8a6a-4b2b-bc79-0d3823c99b88"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["The config attributes {'auto_map': {'AutoModel': 'modeling_flux2.Flux2Transformer2DModel'}} were passed to Flux2Transformer2DModel, but are not expected and will be ignored. Please verify your config.json configuration file.\n"]},{"output_type":"stream","name":"stdout","text":["📦 Retrieved weights: /content/cache/models--codeShare--unstableRevolution_SDNQ/snapshots/d5d78d858619dc4117dee9776f8e18c654fc6e57/checkpoints/unstablerevolution_flux_klein_4b_bf16.safetensors\n","⚙️ SDNQ ready\n","Missing keys: 367\n","Unexpected keys: 149\n","✅ Transformer loaded + weights injected\n","🟡 END PART 1 — READY FOR PIPELINE LOAD\n"]}]},{"cell_type":"code","source":["# ============================================\n","# CELL — PART 2 (FIXED: PIPELINE + SDNQ)\n","# ============================================\n","\n","import torch\n","from diffusers import Flux2KleinPipeline\n","from sdnq.loader import apply_sdnq_options_to_model\n","\n","# --------------------------------------------\n","# PIPELINE LOAD (FIXED)\n","# --------------------------------------------\n","pipe = Flux2KleinPipeline.from_pretrained(\n","    base_repo,\n","    torch_dtype=torch.float16,\n","    low_cpu_mem_usage=True,\n","    device_map=\"cpu\",\n","    trust_remote_code=True\n",")\n","\n","# --------------------------------------------\n","# REPLACE TRANSFORMER BEFORE OFFLOAD\n","# --------------------------------------------\n","pipe.transformer = transformer\n","\n","# --------------------------------------------\n","# APPLY SDNQ (BEFORE OFFLOAD)\n","# --------------------------------------------\n","pipe.transformer = apply_sdnq_options_to_model(\n","    pipe.transformer,\n","    use_quantized_matmul=True\n",")\n","\n","# --------------------------------------------\n","# DEVICE MANAGEMENT (ONLY ONCE)\n","# --------------------------------------------\n","pipe.enable_model_cpu_offload()\n","\n","pipe.vae.enable_slicing()\n","pipe.vae.enable_tiling()\n","\n","print(\"🚀 Pipeline ready with SDNQ + custom transformer\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":233,"referenced_widgets":["af4d2d009dc14e788bcccb715ceb0f0d","cbb63fd1282e41e39ed3008c7e267b30","af11219a7d48463298c360181ceecc4e","c3c4ac29ccb942ada92ded037038652d","20c9722dc6cb4904a49ff261703868a6","14fa7347150848b3b9a41a73ce7a7fda","eb59466988294cdea7a0a7553853d1c3","6fd2fb969ab64108a914e89cde1810ff","c69dd2270e374ab4b1e8c1935c3f7d8b","88055896d52b440fbf0cbfa06bd5d8ac","1c6ef6e5aa8e49ae96d1b60b6422906f","6f6da03678f4487b83a918aeb1daad3d","2ed60a5859ab46c1b704df75a4aaa834","a2942e5b9c1e4faea668d63e45ecc6a5","bbede3ef819d4f9d90c52979e453c447","befeab61a4394c8babe01e03e9ebe152","7d5186490cab4fd6a6c4b853cafe541d","5d8253eee22046c8a31a2f5beec50d3c","5b373a24f46c47ed8f638c8b65d0c65f","5a18149034f649d6bf3b6fd3a525422a","67b868a3555749c189050eb190cea121","10d2279ae97344d3ae9aa4e9a583829b","337fdc38fc3f4bc0917a15c05be7adb9","1cc6f6a6080e4ab8b4869d1a63a2d438","3bdaf700215340dda63d358819ca478e","24f965e8dbed469fa887b2bec8d0c004","9cb26dd621ab4052aa4fafd33b4b8920","cef15ba5ed494e64bec7415a7bb6bcf3","a6ba55a47fc74d4aa122f7f55611fcf2","936629559aff4a5e8a6a5cf78c888ad4","9d1c5f48ccca4a5bbce60e07f9fb0c40","f900851992f442879fef4bdc988455d4","d91c98a5f9f945c08683869d4f611b7b","0aad33da1fa9493780fa15cc6af0f28c","0ea3269c9cbf4ff6a6eed94ce155d6ae","be251d7d99a747eabd2c028b1c9a33c9","9f87f4c7346b419fa867e219b2314091","04b4e7d9b9c64e6081b0c4395b9dbf0c","e70c7c89b9dd4e7eb860b2b1afd3b291","32231c14f2c64a39846a7835659a9d3e","234ba431f29c4d08886d4d0e5a36d725","531a696cc3db4a96868bf9a03340b47b","c44f02c3031c424fb50e08757bd24e1d","ce25a72104e1448d89f9c4b4fbe639b5","bdf011f7b2264a189f0e8778c3218dd1","01f4f941e3264e929fcb0dcadbb83d4f","53eb8f5ca54c438e9f7505b89c334718","fedc5f4f56c14170b6619df60336c648","86399cd5c5d743f6899d4ea5383e0553","a2424b2b87334261b7f2c36ef5038992","a0b0cf2b39224839a5f5f0bdeedfced4","dd0763187c684f5d90db7f372e40eedc","b46f912e4f7140be9d1cb54c0860b920","0893db4122134235b337c5fbaf5e02ff","d9cd14f13edb4fa78b0a39b00c44e1d7"]},"id":"LArlwr-rfT8r","executionInfo":{"status":"ok","timestamp":1776950609087,"user_tz":-120,"elapsed":55210,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"79bac65f-9192-4412-eef8-ca2df3a6bb32"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["model_index.json:   0%|          | 0.00/509 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"af4d2d009dc14e788bcccb715ceb0f0d"}},"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":"6f6da03678f4487b83a918aeb1daad3d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Fetching 11 files:   0%|          | 0/11 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"337fdc38fc3f4bc0917a15c05be7adb9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0aad33da1fa9493780fa15cc6af0f28c"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The config attributes {'auto_map': {'AutoModel': 'modeling_flux2.Flux2Transformer2DModel'}} were passed to Flux2Transformer2DModel, but are not expected and will be ignored. Please verify your config.json configuration file.\n"]},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/901 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bdf011f7b2264a189f0e8778c3218dd1"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🚀 Pipeline ready with SDNQ + custom transformer\n"]}]},{"cell_type":"markdown","source":["# Save"],"metadata":{"id":"fg8BzBTJPUKz"}},{"cell_type":"code","source":["!pip install -q safetensors huggingface_hub"],"metadata":{"id":"8ikY7CSEPXrQ","executionInfo":{"status":"ok","timestamp":1776950624196,"user_tz":-120,"elapsed":7592,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["pipe.to(\"cpu\")\n","pipe.transformer.to(\"cpu\")\n","pipe.text_encoder.to(\"cpu\")\n","pipe.vae.to(\"cpu\")"],"metadata":{"id":"lzpyAGZMPjNf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1776950626714,"user_tz":-120,"elapsed":151,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"df8b27de-3d2b-466e-eaa2-8425e830a82f"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["AutoencoderKLFlux2(\n","  (encoder): Encoder(\n","    (conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (down_blocks): ModuleList(\n","      (0): DownEncoderBlock2D(\n","        (resnets): ModuleList(\n","          (0-1): 2 x ResnetBlock2D(\n","            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n","            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","          )\n","        )\n","        (downsamplers): ModuleList(\n","          (0): Downsample2D(\n","            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))\n","          )\n","        )\n","      )\n","      (1): DownEncoderBlock2D(\n","        (resnets): ModuleList(\n","          (0): ResnetBlock2D(\n","            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n","            (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","            (conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n","          )\n","          (1): ResnetBlock2D(\n","            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n","            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","          )\n","        )\n","        (downsamplers): ModuleList(\n","          (0): Downsample2D(\n","            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))\n","          )\n","        )\n","      )\n","      (2): DownEncoderBlock2D(\n","        (resnets): ModuleList(\n","          (0): ResnetBlock2D(\n","            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n","            (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","            (conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n","          )\n","          (1): ResnetBlock2D(\n","            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n","            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","          )\n","        )\n","        (downsamplers): ModuleList(\n","          (0): Downsample2D(\n","            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))\n","          )\n","        )\n","      )\n","      (3): DownEncoderBlock2D(\n","        (resnets): ModuleList(\n","          (0-1): 2 x ResnetBlock2D(\n","            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n","            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","          )\n","        )\n","      )\n","    )\n","    (mid_block): UNetMidBlock2D(\n","      (attentions): ModuleList(\n","        (0): Attention(\n","          (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n","          (to_q): Linear(in_features=512, out_features=512, bias=True)\n","          (to_k): Linear(in_features=512, out_features=512, bias=True)\n","          (to_v): Linear(in_features=512, out_features=512, bias=True)\n","          (to_out): ModuleList(\n","            (0): Linear(in_features=512, out_features=512, bias=True)\n","            (1): Dropout(p=0.0, inplace=False)\n","          )\n","        )\n","      )\n","      (resnets): ModuleList(\n","        (0-1): 2 x ResnetBlock2D(\n","          (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n","          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","          (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n","          (dropout): Dropout(p=0.0, inplace=False)\n","          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","          (nonlinearity): SiLU()\n","        )\n","      )\n","    )\n","    (conv_norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)\n","    (conv_act): SiLU()\n","    (conv_out): Conv2d(512, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  )\n","  (decoder): Decoder(\n","    (conv_in): Conv2d(32, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (up_blocks): ModuleList(\n","      (0-1): 2 x UpDecoderBlock2D(\n","        (resnets): ModuleList(\n","          (0-2): 3 x ResnetBlock2D(\n","            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n","            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","          )\n","        )\n","        (upsamplers): ModuleList(\n","          (0): Upsample2D(\n","            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","          )\n","        )\n","      )\n","      (2): UpDecoderBlock2D(\n","        (resnets): ModuleList(\n","          (0): ResnetBlock2D(\n","            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n","            (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","            (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n","          )\n","          (1-2): 2 x ResnetBlock2D(\n","            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n","            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","          )\n","        )\n","        (upsamplers): ModuleList(\n","          (0): Upsample2D(\n","            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","          )\n","        )\n","      )\n","      (3): UpDecoderBlock2D(\n","        (resnets): ModuleList(\n","          (0): ResnetBlock2D(\n","            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n","            (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","            (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n","          )\n","          (1-2): 2 x ResnetBlock2D(\n","            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n","            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n","            (dropout): Dropout(p=0.0, inplace=False)\n","            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","            (nonlinearity): SiLU()\n","          )\n","        )\n","      )\n","    )\n","    (mid_block): UNetMidBlock2D(\n","      (attentions): ModuleList(\n","        (0): Attention(\n","          (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n","          (to_q): Linear(in_features=512, out_features=512, bias=True)\n","          (to_k): Linear(in_features=512, out_features=512, bias=True)\n","          (to_v): Linear(in_features=512, out_features=512, bias=True)\n","          (to_out): ModuleList(\n","            (0): Linear(in_features=512, out_features=512, bias=True)\n","            (1): Dropout(p=0.0, inplace=False)\n","          )\n","        )\n","      )\n","      (resnets): ModuleList(\n","        (0-1): 2 x ResnetBlock2D(\n","          (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n","          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","          (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n","          (dropout): Dropout(p=0.0, inplace=False)\n","          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","          (nonlinearity): SiLU()\n","        )\n","      )\n","    )\n","    (conv_norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)\n","    (conv_act): SiLU()\n","    (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  )\n","  (quant_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n","  (post_quant_conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n","  (bn): BatchNorm2d(128, eps=0.0001, momentum=0.1, affine=False, track_running_stats=True)\n",")"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["from huggingface_hub import login\n","\n","save_to_repo = 'codeShare/unstableRevolution_SDNQ' #@param {type:'string'}\n","login(token=hf_token)\n","\n","pipe.push_to_hub(\n","    repo_id=save_to_repo,\n","    safe_serialization=True,\n","    commit_message=\"Upload SDNQ Flux2 Klein fine-tuned transformer\"\n",")"],"metadata":{"id":"Y2IgPur0PmyJ","colab":{"base_uri":"https://localhost:8080/","height":477,"referenced_widgets":["a3d7781fbe5844d2a37440b3c9a8c264","a0ca1245bf5e4f64962d4cbc9cf4398f","fdf063bf913f49908a8cb1df97205a93","c1b0988983d74b80a508c4e5fc860d60","e099e5b0eb704614af851e2f0c8b3a28","e613158c47014d3187c17830af669f0d","7eabd2ee764146ef8272399ed3e29950","b0ee03ff962d4aae95315510e0ee8429","d10495f8bb824156bfe1f8a72893afa0","0d902d1525f24ebf9a74e8e2ce01e33b","71da0c5928a1482c9c70ad800bc45a36","62f680f5d931488785cf85ba7e12ea47","f05ba56dbb50436f9152120c61148560","468ae261b7db4d4eac8e18f928408e2a","45784ab5ebe54528b251a23a606f209b","9a746a34ccde4d7d9b4c11cba92ebb19","a2935c4e6dac40daa5486e6943307b21","d5131599a8c14d68b9deec156d9e09ff","2e3b886a17d045c7965e3b75278ea0ba","e52f0a3357fa469192b4ce6eb9304fc7","bd4163602e0d456d87ce3581be809ec6","f04bc739ccce466b901aa6bc507ae6de","6c5dad2f9d734ed5923887012b0a1e95","624e08e7367548f7b94cb31f37a65f19","06c8b4a921c54713b95c9a708fb4f52e","d25830d6d97c40eab7321ed07b4c30a1","9090c5225c244eee97056068ae732556","d53e37c20d7d4f1f834c0944ca1d0d46","d7457d71080840c0aeaa38732c34c300","5a19fdcf5a3b46f2abcf8318aad2bd71","31f7bd14ec9e4937afeb2f3dd6067059","887c1ecd9547401faedb3580722a04c8","dcdfc0b967c249d6ae4d68f15de28211","063d477fd6a5474faebb5868088d4664","f2520d0da90e47edae8697101d9705a3","36f836e7101143fdb43c39b52b7bb511","feb339c9beb14a068f35ab16f686e1aa","3c8600fae49d49d788e820c7cd2e4436","8e0d8e0cb8b14cfcb40578a4ef8e6a6f","b168a3af360f4a3d9de2e98ead37b407","49f8d6390825481f8c359afcd5dcb904","7e2c4804ace74d87b2445d25408771f3","a447452141fb483593e37e5f85d7db6e","69230f8995544a3081d56e6094d81785","b498cac2bb2c4490a42a305814dc50b5","1472fdfcd37046879cccfb7af021c909","8685af3fcfd64fb3b9c5db0ec1614416","7307753851a24ddbbb999a654ceee26e","bc6bb500b30a48a98b7b6104ecabb811","8f4d9b619d3945fba174c72e7e7715ac","32e62906988a4fa1bfd8e224c326ee27","da2352257a7d46b8b2c01bfcf3f1b1de","91611724fe70421c8a318cb7f63bf6b5","b8cfe640e78d4800970aadf4bbeb96db","c65c55c869fd459487d9f28f47cfa355","8ccda2073605457888222867ed49e9d8","a5ebc5d7960e4bedab487de24ba627b2","787878fc9f604c50b83825327f9b66b4","8d8c5115e0ce40afa10a1b7a05506901","5e018e4bb3bd45d7b773b68a988a331c","67adc97198a04ee1bdbccc204549ae8a","b3b87585b6ed42afb23846924ad49581","b49d180267d148c386350b36ee59fe88","828c2e1efbfb4964a5624e996a21c340","61a90a58d9a94243a7500531de98521e","a9a31119f08248eda9fa321a736ba0f3","ab010c5269324ffbadadf928a7500c95","55f415abbf7042d3abd0f0cb5881c82f","a517e3699a7643dc8c85ddeb7e9a4549","0aed84e27a074a0681b594c3141ec8b6","6a09c649654540d58f502f66957fe04e","f24249071106408ba4c01e9eb42adf05","b10c08d09e134d1eb7a4c66ce2cbb70d","879af79fa8614eb7b9b11cb3eade7104","70096ca7cf134a2d8479c2fb6d57175f","31be66ee08114e2fa306d92abca2fa1d","b802429de4ef48069566180bef0a141f"]},"executionInfo":{"status":"ok","timestamp":1776950818350,"user_tz":-120,"elapsed":166259,"user":{"displayName":"gfuku63","userId":"10213382542081034600"}},"outputId":"d9ef3a3e-8401-447f-e810-33d7e5df6069"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a3d7781fbe5844d2a37440b3c9a8c264"}},"metadata":{}},{"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":"62f680f5d931488785cf85ba7e12ea47"}},"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":"6c5dad2f9d734ed5923887012b0a1e95"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  .../tokenizer/tokenizer.json: 100%|##########| 11.4MB / 11.4MB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"063d477fd6a5474faebb5868088d4664"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ...encoder/model.safetensors:   1%|          | 24.0MB / 3.05GB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b498cac2bb2c4490a42a305814dc50b5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ...pytorch_model.safetensors:   9%|9         | 16.0MB /  168MB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8ccda2073605457888222867ed49e9d8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ...pytorch_model.safetensors:   0%|          | 9.26MB / 2.69GB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ab010c5269324ffbadadf928a7500c95"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/codeShare/unstableRevolution_SDNQ/commit/84b7af906423ab29aa9db221dc9012d4d5ec766c', commit_message='Upload SDNQ Flux2 Klein fine-tuned transformer', commit_description='', oid='84b7af906423ab29aa9db221dc9012d4d5ec766c', pr_url=None, repo_url=RepoUrl('https://huggingface.co/codeShare/unstableRevolution_SDNQ', endpoint='https://huggingface.co', repo_type='model', repo_id='codeShare/unstableRevolution_SDNQ'), pr_revision=None, pr_num=None)"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":10}]}]}