Upload ⚙️run_klein_edit_9b_colab.ipynb
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colab_notebooks/⚙️run_klein_edit_9b_colab.ipynb
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"\n",
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"# Additional config values\n",
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"MODEL_ID = \"codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic\" #@param ['codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic' ]\n",
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"edit_prompt = \"as a real photo
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"resolution = \"1024 x 1024 (Square)\" #@param {type:\"string\"}\n",
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"\n",
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"# LORA Settings\n",
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"torch.cuda.empty_cache()"
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],
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"metadata": {
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"id": "QYWVhRcpmGRR"
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "f3dd50c5-8988-4248-d162-07a256145cf2"
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"🔑 Reading config files...\n",
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"✅ LoRA: Using better_skin_real1_klein_4b.safetensors at strength 0.8 from repo codeShare/flux-klein-4B-loras\n",
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"✅ Model: codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic\n",
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" Resolution: 1024 x 1024 (Square)\n",
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" Foregrounds: /content/drive/MyDrive/my_flux_dataset/foregrounds.zip\n",
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" Backgrounds: /content/drive/MyDrive/my_flux_dataset/backgrounds.zip\n"
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]
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}
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "glYFduHKLYdS"
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"execution_count": null,
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"outputs": []
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" print(f'\\n✅ LoRa {lora_name_loaded} loaded into pipe at strength {lora_strength_loaded}')\n",
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" #-----#\n",
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"\n",
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"# pipe = apply_lora_options_to_pipe(\n",
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"# pipe ,\n",
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"# lora_name = lora_name_loaded,\n",
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"# lora_repo_id = lora_repo_id_loaded ,\n",
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"# lora_strength = lora_strength_loaded).to(f\"cuda:{gpu_id}\")\n",
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"\n",
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" #---Collect and clear VRAM at end of cell---#\n",
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" gc.collect()\n",
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"torch.cuda.empty_cache()"
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],
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"metadata": {
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"id": "0XLZ_DXrmNE2"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 990,
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"outputId": "326e9a20-48d7-46e4-f055-79601ee0686d"
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"text": [
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"🚀 Detected 1 GPU(s)\n",
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"🔥 Klein 4B → 2 pipes per GPU\n",
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"Total Pipes: 2\n",
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"\n",
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"\n",
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"============================================================\n",
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"Loading Pipe 1/2 on GPU 0 (Pipe 0)\n",
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"============================================================\n",
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" [GPU 0 - Pipe 0] Loading...\n"
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"output_type": "display_data",
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"text": [
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"LoRa loaded from /content/better_skin_real1_klein_4b.safetensors\n",
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"Converting lora better_skin_real1_klein_4b.safetensors to diffusers format...\n",
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"✅ Kohya → Diffusers conversion completed:\n",
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" 200 original keys → 200 converted keys\n",
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" 200 keys modified\n",
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"\n",
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| 1198 |
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" lora_unet_single_blocks_0_attn_to_out.lora_down.weight → shape: torch.Size([48, 12288])\n",
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" lora_unet_single_blocks_0_attn_to_out.lora_up.weight → shape: torch.Size([3072, 48])\n",
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" lora_unet_single_blocks_0_attn_to_qkv_mlp_proj.lora_down.weight → shape: torch.Size([54, 3072])\n",
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" lora_unet_single_blocks_0_attn_to_qkv_mlp_proj.lora_up.weight → shape: torch.Size([27648, 54])\n",
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" lora_unet_single_blocks_1_attn_to_out.lora_down.weight → shape: torch.Size([44, 12288])\n",
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" lora_unet_single_blocks_1_attn_to_out.lora_up.weight → shape: torch.Size([3072, 44])\n",
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" lora_unet_single_blocks_1_attn_to_qkv_mlp_proj.lora_down.weight → shape: torch.Size([51, 3072])\n",
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" lora_unet_single_blocks_1_attn_to_qkv_mlp_proj.lora_up.weight → shape: torch.Size([27648, 51])\n",
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" lora_unet_single_blocks_10_attn_to_out.lora_down.weight → shape: torch.Size([44, 12288])\n",
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" lora_unet_single_blocks_10_attn_to_out.lora_up.weight → shape: torch.Size([3072, 44])\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"Unsupported keys for Kohya Flux2 LoRA conversion: ['lora_unet_double_blocks_0_attn_add_k_proj.lora_down.weight', 'lora_unet_double_blocks_0_attn_add_k_proj.lora_up.weight', 'lora_unet_double_blocks_0_attn_add_q_proj.lora_down.weight', 'lora_unet_double_blocks_0_attn_add_q_proj.lora_up.weight', 'lora_unet_double_blocks_0_attn_add_v_proj.lora_down.weight', 'lora_unet_double_blocks_0_attn_add_v_proj.lora_up.weight', 'lora_unet_double_blocks_0_attn_to_add_out.lora_down.weight', 'lora_unet_double_blocks_0_attn_to_add_out.lora_up.weight', 'lora_unet_double_blocks_0_attn_to_k.lora_down.weight', 'lora_unet_double_blocks_0_attn_to_k.lora_up.weight', 'lora_unet_double_blocks_0_attn_to_out_0.lora_down.weight', 'lora_unet_double_blocks_0_attn_to_out_0.lora_up.weight', 'lora_unet_double_blocks_0_attn_to_q.lora_down.weight', 'lora_unet_double_blocks_0_attn_to_q.lora_up.weight', 'lora_unet_double_blocks_0_attn_to_v.lora_down.weight', 'lora_unet_double_blocks_0_attn_to_v.lora_up.weight', 'lora_unet_double_blocks_0_ff_context_linear_in.lora_down.weight', 'lora_unet_double_blocks_0_ff_context_linear_in.lora_up.weight', 'lora_unet_double_blocks_0_ff_context_linear_out.lora_down.weight', 'lora_unet_double_blocks_0_ff_context_linear_out.lora_up.weight', 'lora_unet_double_blocks_0_ff_linear_in.lora_down.weight', 'lora_unet_double_blocks_0_ff_linear_in.lora_up.weight', 'lora_unet_double_blocks_0_ff_linear_out.lora_down.weight', 'lora_unet_double_blocks_0_ff_linear_out.lora_up.weight', 'lora_unet_double_blocks_1_attn_add_k_proj.lora_down.weight', 'lora_unet_double_blocks_1_attn_add_k_proj.lora_up.weight', 'lora_unet_double_blocks_1_attn_add_q_proj.lora_down.weight', 'lora_unet_double_blocks_1_attn_add_q_proj.lora_up.weight', 'lora_unet_double_blocks_1_attn_add_v_proj.lora_down.weight', 'lora_unet_double_blocks_1_attn_add_v_proj.lora_up.weight', 'lora_unet_double_blocks_1_attn_to_add_out.lora_down.weight', 'lora_unet_double_blocks_1_attn_to_add_out.lora_up.weight', 'lora_unet_double_blocks_1_attn_to_k.lora_down.weight', 'lora_unet_double_blocks_1_attn_to_k.lora_up.weight', 'lora_unet_double_blocks_1_attn_to_out_0.lora_down.weight', 'lora_unet_double_blocks_1_attn_to_out_0.lora_up.weight', 'lora_unet_double_blocks_1_attn_to_q.lora_down.weight', 'lora_unet_double_blocks_1_attn_to_q.lora_up.weight', 'lora_unet_double_blocks_1_attn_to_v.lora_down.weight', 'lora_unet_double_blocks_1_attn_to_v.lora_up.weight', 'lora_unet_double_blocks_1_ff_context_linear_in.lora_down.weight', 'lora_unet_double_blocks_1_ff_context_linear_in.lora_up.weight', 'lora_unet_double_blocks_1_ff_context_linear_out.lora_down.weight', 'lora_unet_double_blocks_1_ff_context_linear_out.lora_up.weight', 'lora_unet_double_blocks_1_ff_linear_in.lora_down.weight', 'lora_unet_double_blocks_1_ff_linear_in.lora_up.weight', 'lora_unet_double_blocks_1_ff_linear_out.lora_down.weight', 'lora_unet_double_blocks_1_ff_linear_out.lora_up.weight', 'lora_unet_double_blocks_2_attn_add_k_proj.lora_down.weight', 'lora_unet_double_blocks_2_attn_add_k_proj.lora_up.weight', 'lora_unet_double_blocks_2_attn_add_q_proj.lora_down.weight', 'lora_unet_double_blocks_2_attn_add_q_proj.lora_up.weight', 'lora_unet_double_blocks_2_attn_add_v_proj.lora_down.weight', 'lora_unet_double_blocks_2_attn_add_v_proj.lora_up.weight', 'lora_unet_double_blocks_2_attn_to_add_out.lora_down.weight', 'lora_unet_double_blocks_2_attn_to_add_out.lora_up.weight', 'lora_unet_double_blocks_2_attn_to_k.lora_down.weight', 'lora_unet_double_blocks_2_attn_to_k.lora_up.weight', 'lora_unet_double_blocks_2_attn_to_out_0.lora_down.weight', 'lora_unet_double_blocks_2_attn_to_out_0.lora_up.weight', 'lora_unet_double_blocks_2_attn_to_q.lora_down.weight', 'lora_unet_double_blocks_2_attn_to_q.lora_up.weight', 'lora_unet_double_blocks_2_attn_to_v.lora_down.weight', 'lora_unet_double_blocks_2_attn_to_v.lora_up.weight', 'lora_unet_double_blocks_2_ff_context_linear_in.lora_down.weight', 'lora_unet_double_blocks_2_ff_context_linear_in.lora_up.weight', 'lora_unet_double_blocks_2_ff_context_linear_out.lora_down.weight', 'lora_unet_double_blocks_2_ff_context_linear_out.lora_up.weight', 'lora_unet_double_blocks_2_ff_linear_in.lora_down.weight', 'lora_unet_double_blocks_2_ff_linear_in.lora_up.weight', 'lora_unet_double_blocks_2_ff_linear_out.lora_down.weight', 'lora_unet_double_blocks_2_ff_linear_out.lora_up.weight', 'lora_unet_double_blocks_3_attn_add_k_proj.lora_down.weight', 'lora_unet_double_blocks_3_attn_add_k_proj.lora_up.weight', 'lora_unet_double_blocks_3_attn_add_q_proj.lora_down.weight', 'lora_unet_double_blocks_3_attn_add_q_proj.lora_up.weight', 'lora_unet_double_blocks_3_attn_add_v_proj.lora_down.weight', 'lora_unet_double_blocks_3_attn_add_v_proj.lora_up.weight', 'lora_unet_double_blocks_3_attn_to_add_out.lora_down.weight', 'lora_unet_double_blocks_3_attn_to_add_out.lora_up.weight', 'lora_unet_double_blocks_3_attn_to_k.lora_down.weight', 'lora_unet_double_blocks_3_attn_to_k.lora_up.weight', 'lora_unet_double_blocks_3_attn_to_out_0.lora_down.weight', 'lora_unet_double_blocks_3_attn_to_out_0.lora_up.weight', 'lora_unet_double_blocks_3_attn_to_q.lora_down.weight', 'lora_unet_double_blocks_3_attn_to_q.lora_up.weight', 'lora_unet_double_blocks_3_attn_to_v.lora_down.weight', 'lora_unet_double_blocks_3_attn_to_v.lora_up.weight', 'lora_unet_double_blocks_3_ff_context_linear_in.lora_down.weight', 'lora_unet_double_blocks_3_ff_context_linear_in.lora_up.weight', 'lora_unet_double_blocks_3_ff_context_linear_out.lora_down.weight', 'lora_unet_double_blocks_3_ff_context_linear_out.lora_up.weight', 'lora_unet_double_blocks_3_ff_linear_in.lora_down.weight', 'lora_unet_double_blocks_3_ff_linear_in.lora_up.weight', 'lora_unet_double_blocks_3_ff_linear_out.lora_down.weight', 'lora_unet_double_blocks_3_ff_linear_out.lora_up.weight', 'lora_unet_double_blocks_4_attn_add_k_proj.lora_down.weight', 'lora_unet_double_blocks_4_attn_add_k_proj.lora_up.weight', 'lora_unet_double_blocks_4_attn_add_q_proj.lora_down.weight', 'lora_unet_double_blocks_4_attn_add_q_proj.lora_up.weight', 'lora_unet_double_blocks_4_attn_add_v_proj.lora_down.weight', 'lora_unet_double_blocks_4_attn_add_v_proj.lora_up.weight', 'lora_unet_double_blocks_4_attn_to_add_out.lora_down.weight', 'lora_unet_double_blocks_4_attn_to_add_out.lora_up.weight', 'lora_unet_double_blocks_4_attn_to_k.lora_down.weight', 'lora_unet_double_blocks_4_attn_to_k.lora_up.weight', 'lora_unet_double_blocks_4_attn_to_out_0.lora_down.weight', 'lora_unet_double_blocks_4_attn_to_out_0.lora_up.weight', 'lora_unet_double_blocks_4_attn_to_q.lora_down.weight', 'lora_unet_double_blocks_4_attn_to_q.lora_up.weight', 'lora_unet_double_blocks_4_attn_to_v.lora_down.weight', 'lora_unet_double_blocks_4_attn_to_v.lora_up.weight', 'lora_unet_double_blocks_4_ff_context_linear_in.lora_down.weight', 'lora_unet_double_blocks_4_ff_context_linear_in.lora_up.weight', 'lora_unet_double_blocks_4_ff_context_linear_out.lora_down.weight', 'lora_unet_double_blocks_4_ff_context_linear_out.lora_up.weight', 'lora_unet_double_blocks_4_ff_linear_in.lora_down.weight', 'lora_unet_double_blocks_4_ff_linear_in.lora_up.weight', 'lora_unet_double_blocks_4_ff_linear_out.lora_down.weight', 'lora_unet_double_blocks_4_ff_linear_out.lora_up.weight', 'lora_unet_single_blocks_0_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_0_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_0_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_0_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_10_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_10_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_10_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_10_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_11_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_11_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_11_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_11_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_12_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_12_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_12_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_12_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_13_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_13_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_13_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_13_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_14_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_14_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_14_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_14_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_15_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_15_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_15_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_15_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_16_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_16_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_16_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_16_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_17_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_17_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_17_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_17_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_18_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_18_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_18_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_18_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_19_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_19_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_19_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_19_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_1_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_1_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_1_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_1_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_2_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_2_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_2_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_2_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_3_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_3_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_3_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_3_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_4_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_4_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_4_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_4_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_5_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_5_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_5_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_5_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_6_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_6_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_6_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_6_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_7_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_7_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_7_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_7_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_8_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_8_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_8_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_8_attn_to_qkv_mlp_proj.lora_up.weight', 'lora_unet_single_blocks_9_attn_to_out.lora_down.weight', 'lora_unet_single_blocks_9_attn_to_out.lora_up.weight', 'lora_unet_single_blocks_9_attn_to_qkv_mlp_proj.lora_down.weight', 'lora_unet_single_blocks_9_attn_to_qkv_mlp_proj.lora_up.weight']\n",
|
| 1215 |
-
"No LoRA keys associated to Flux2Transformer2DModel found with the prefix='transformer'. This is safe to ignore if LoRA state dict didn't originally have any Flux2Transformer2DModel related params. You can also try specifying `prefix=None` to resolve the warning. Otherwise, open an issue if you think it's unexpected: https://github.com/huggingface/diffusers/issues/new\n"
|
| 1216 |
-
]
|
| 1217 |
-
},
|
| 1218 |
-
{
|
| 1219 |
-
"output_type": "stream",
|
| 1220 |
-
"name": "stdout",
|
| 1221 |
-
"text": [
|
| 1222 |
-
"Done! Converted LoRa saved at /content/converted_lora_weights.safetensors\n"
|
| 1223 |
-
]
|
| 1224 |
-
},
|
| 1225 |
-
{
|
| 1226 |
-
"output_type": "error",
|
| 1227 |
-
"ename": "ValueError",
|
| 1228 |
-
"evalue": "Adapter name(s) {'default_lora'} not in the list of present adapters: set().",
|
| 1229 |
-
"traceback": [
|
| 1230 |
-
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 1231 |
-
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
| 1232 |
-
"\u001b[0;32m/tmp/ipykernel_183/3407728754.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_peak_memory_stats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m \u001b[0mpipe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_klein_pipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# your existing function (with small tweaks below)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mpipes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1233 |
-
"\u001b[0;32m/tmp/ipykernel_183/3407728754.py\u001b[0m in \u001b[0;36mload_klein_pipe\u001b[0;34m(gpu_id, pipe_id)\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0;31m#-----#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_lora_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlora_loaded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlora_name_loaded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"default_lora\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m \u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_adapters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"default_lora\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlora_strength_loaded\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 208\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'\\n✅ LoRa {lora_name_loaded} loaded into pipe at strength {lora_strength_loaded}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0;31m#-----#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1234 |
-
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/diffusers/loaders/lora_base.py\u001b[0m in \u001b[0;36mset_adapters\u001b[0;34m(self, adapter_names, adapter_weights)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[0mmissing_adapters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madapter_names\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mall_adapters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 736\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_adapters\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 738\u001b[0m \u001b[0;34mf\"Adapter name(s) {missing_adapters} not in the list of present adapters: {all_adapters}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 739\u001b[0m )\n",
|
| 1235 |
-
"\u001b[0;31mValueError\u001b[0m: Adapter name(s) {'default_lora'} not in the list of present adapters: set()."
|
| 1236 |
-
]
|
| 1237 |
-
}
|
| 1238 |
-
]
|
| 1239 |
},
|
| 1240 |
{
|
| 1241 |
"cell_type": "code",
|
|
@@ -1373,27 +1168,10 @@
|
|
| 1373 |
" return pipe"
|
| 1374 |
],
|
| 1375 |
"metadata": {
|
| 1376 |
-
"
|
| 1377 |
-
"base_uri": "https://localhost:8080/",
|
| 1378 |
-
"height": 141
|
| 1379 |
-
},
|
| 1380 |
-
"id": "ibvDcHtJFtr8",
|
| 1381 |
-
"outputId": "dab5f14e-dbca-4f43-85ab-5882c8458679"
|
| 1382 |
},
|
| 1383 |
-
"execution_count":
|
| 1384 |
-
"outputs": [
|
| 1385 |
-
{
|
| 1386 |
-
"output_type": "error",
|
| 1387 |
-
"ename": "NameError",
|
| 1388 |
-
"evalue": "name 'pipe' is not defined",
|
| 1389 |
-
"traceback": [
|
| 1390 |
-
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 1391 |
-
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
| 1392 |
-
"\u001b[0;32m/tmp/ipykernel_907/2122711465.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
| 1393 |
-
"\u001b[0;31mNameError\u001b[0m: name 'pipe' is not defined"
|
| 1394 |
-
]
|
| 1395 |
-
}
|
| 1396 |
-
]
|
| 1397 |
},
|
| 1398 |
{
|
| 1399 |
"cell_type": "code",
|
|
@@ -1602,26 +1380,10 @@
|
|
| 1602 |
"print(\"You can download it from the file browser.\")"
|
| 1603 |
],
|
| 1604 |
"metadata": {
|
| 1605 |
-
"id": "kwF4CB3GmTwB"
|
| 1606 |
-
"colab": {
|
| 1607 |
-
"base_uri": "https://localhost:8080/"
|
| 1608 |
-
},
|
| 1609 |
-
"outputId": "591be8ed-5d94-41df-f612-faf145bc1c57"
|
| 1610 |
},
|
| 1611 |
-
"execution_count":
|
| 1612 |
-
"outputs": [
|
| 1613 |
-
{
|
| 1614 |
-
"output_type": "stream",
|
| 1615 |
-
"name": "stdout",
|
| 1616 |
-
"text": [
|
| 1617 |
-
"📦 Creating final zip...\n",
|
| 1618 |
-
"Collected 10 encrypted files\n",
|
| 1619 |
-
"\n",
|
| 1620 |
-
"✅ SUCCESS! Final file: /content/final_encrypted_outputs.zip\n",
|
| 1621 |
-
"You can download it from the file browser.\n"
|
| 1622 |
-
]
|
| 1623 |
-
}
|
| 1624 |
-
]
|
| 1625 |
},
|
| 1626 |
{
|
| 1627 |
"cell_type": "code",
|
|
@@ -1744,114 +1506,10 @@
|
|
| 1744 |
" print(\"📥 Download started...\")"
|
| 1745 |
],
|
| 1746 |
"metadata": {
|
| 1747 |
-
"
|
| 1748 |
-
"base_uri": "https://localhost:8080/",
|
| 1749 |
-
"height": 399
|
| 1750 |
-
},
|
| 1751 |
-
"id": "GvLYqh4iMBf_",
|
| 1752 |
-
"outputId": "5f23f843-3b17-4d63-9b9b-b76a1b23e9fc"
|
| 1753 |
},
|
| 1754 |
-
"execution_count":
|
| 1755 |
-
"outputs": [
|
| 1756 |
-
{
|
| 1757 |
-
"output_type": "stream",
|
| 1758 |
-
"name": "stdout",
|
| 1759 |
-
"text": [
|
| 1760 |
-
"Requirement already satisfied: pynacl in /usr/local/lib/python3.12/dist-packages (1.6.2)\n",
|
| 1761 |
-
"Requirement already satisfied: cffi>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from pynacl) (2.0.0)\n",
|
| 1762 |
-
"Requirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi>=2.0.0->pynacl) (3.0)\n",
|
| 1763 |
-
"🔓 Decryption Notebook Ready\n",
|
| 1764 |
-
"✅ Using manual filepath: /content/final_encrypted_outputs.zip\n",
|
| 1765 |
-
"\n",
|
| 1766 |
-
"📂 Extracting and decrypting to: /content/decrypted_images\n",
|
| 1767 |
-
"🔍 Found 10 encrypted files\n",
|
| 1768 |
-
"✅ Decrypted: edited_000008.enc → edited_000008.jpg\n",
|
| 1769 |
-
"✅ Decrypted: edited_000005.enc → edited_000005.jpg\n",
|
| 1770 |
-
"✅ Decrypted: edited_000007.enc → edited_000007.jpg\n",
|
| 1771 |
-
"✅ Decrypted: edited_000001.enc → edited_000001.jpg\n",
|
| 1772 |
-
"✅ Decrypted: edited_000004.enc → edited_000004.jpg\n",
|
| 1773 |
-
"✅ Decrypted: edited_000006.enc → edited_000006.jpg\n",
|
| 1774 |
-
"✅ Decrypted: edited_000002.enc → edited_000002.jpg\n",
|
| 1775 |
-
"✅ Decrypted: edited_000009.enc → edited_000009.jpg\n",
|
| 1776 |
-
"✅ Decrypted: edited_000000.enc → edited_000000.jpg\n",
|
| 1777 |
-
"✅ Decrypted: edited_000003.enc → edited_000003.jpg\n",
|
| 1778 |
-
"\n",
|
| 1779 |
-
"🎉 Decryption complete! 10/10 files decrypted successfully.\n",
|
| 1780 |
-
"📁 Decrypted images saved to: /content/decrypted_images\n"
|
| 1781 |
-
]
|
| 1782 |
-
},
|
| 1783 |
-
{
|
| 1784 |
-
"output_type": "display_data",
|
| 1785 |
-
"data": {
|
| 1786 |
-
"text/plain": [
|
| 1787 |
-
"<IPython.core.display.Javascript object>"
|
| 1788 |
-
],
|
| 1789 |
-
"application/javascript": [
|
| 1790 |
-
"\n",
|
| 1791 |
-
" async function download(id, filename, size) {\n",
|
| 1792 |
-
" if (!google.colab.kernel.accessAllowed) {\n",
|
| 1793 |
-
" return;\n",
|
| 1794 |
-
" }\n",
|
| 1795 |
-
" const div = document.createElement('div');\n",
|
| 1796 |
-
" const label = document.createElement('label');\n",
|
| 1797 |
-
" label.textContent = `Downloading \"${filename}\": `;\n",
|
| 1798 |
-
" div.appendChild(label);\n",
|
| 1799 |
-
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|
| 1800 |
-
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|
| 1801 |
-
" div.appendChild(progress);\n",
|
| 1802 |
-
" document.body.appendChild(div);\n",
|
| 1803 |
-
"\n",
|
| 1804 |
-
" const buffers = [];\n",
|
| 1805 |
-
" let downloaded = 0;\n",
|
| 1806 |
-
"\n",
|
| 1807 |
-
" const channel = await google.colab.kernel.comms.open(id);\n",
|
| 1808 |
-
" // Send a message to notify the kernel that we're ready.\n",
|
| 1809 |
-
" channel.send({})\n",
|
| 1810 |
-
"\n",
|
| 1811 |
-
" for await (const message of channel.messages) {\n",
|
| 1812 |
-
" // Send a message to notify the kernel that we're ready.\n",
|
| 1813 |
-
" channel.send({})\n",
|
| 1814 |
-
" if (message.buffers) {\n",
|
| 1815 |
-
" for (const buffer of message.buffers) {\n",
|
| 1816 |
-
" buffers.push(buffer);\n",
|
| 1817 |
-
" downloaded += buffer.byteLength;\n",
|
| 1818 |
-
" progress.value = downloaded;\n",
|
| 1819 |
-
" }\n",
|
| 1820 |
-
" }\n",
|
| 1821 |
-
" }\n",
|
| 1822 |
-
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
|
| 1823 |
-
" const a = document.createElement('a');\n",
|
| 1824 |
-
" a.href = window.URL.createObjectURL(blob);\n",
|
| 1825 |
-
" a.download = filename;\n",
|
| 1826 |
-
" div.appendChild(a);\n",
|
| 1827 |
-
" a.click();\n",
|
| 1828 |
-
" div.remove();\n",
|
| 1829 |
-
" }\n",
|
| 1830 |
-
" "
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| 1831 |
-
]
|
| 1832 |
-
},
|
| 1833 |
-
"metadata": {}
|
| 1834 |
-
},
|
| 1835 |
-
{
|
| 1836 |
-
"output_type": "display_data",
|
| 1837 |
-
"data": {
|
| 1838 |
-
"text/plain": [
|
| 1839 |
-
"<IPython.core.display.Javascript object>"
|
| 1840 |
-
],
|
| 1841 |
-
"application/javascript": [
|
| 1842 |
-
"download(\"download_aafade43-8857-4ab0-af6a-92f04dc5f600\", \"decrypted_images.zip\", 3496477)"
|
| 1843 |
-
]
|
| 1844 |
-
},
|
| 1845 |
-
"metadata": {}
|
| 1846 |
-
},
|
| 1847 |
-
{
|
| 1848 |
-
"output_type": "stream",
|
| 1849 |
-
"name": "stdout",
|
| 1850 |
-
"text": [
|
| 1851 |
-
"📥 Download started...\n"
|
| 1852 |
-
]
|
| 1853 |
-
}
|
| 1854 |
-
]
|
| 1855 |
}
|
| 1856 |
],
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| 1857 |
"metadata": {
|
|
@@ -1866,1379 +1524,7 @@
|
|
| 1866 |
"language_info": {
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| 1867 |
"name": "python"
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| 1868 |
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"left": null,
|
| 3139 |
-
"margin": null,
|
| 3140 |
-
"max_height": null,
|
| 3141 |
-
"max_width": null,
|
| 3142 |
-
"min_height": null,
|
| 3143 |
-
"min_width": null,
|
| 3144 |
-
"object_fit": null,
|
| 3145 |
-
"object_position": null,
|
| 3146 |
-
"order": null,
|
| 3147 |
-
"overflow": null,
|
| 3148 |
-
"overflow_x": null,
|
| 3149 |
-
"overflow_y": null,
|
| 3150 |
-
"padding": null,
|
| 3151 |
-
"right": null,
|
| 3152 |
-
"top": null,
|
| 3153 |
-
"visibility": null,
|
| 3154 |
-
"width": null
|
| 3155 |
-
}
|
| 3156 |
-
},
|
| 3157 |
-
"bde47ff0a15e4d058340c368aa62a9b8": {
|
| 3158 |
-
"model_module": "@jupyter-widgets/controls",
|
| 3159 |
-
"model_name": "ProgressStyleModel",
|
| 3160 |
-
"model_module_version": "1.5.0",
|
| 3161 |
-
"state": {
|
| 3162 |
-
"_model_module": "@jupyter-widgets/controls",
|
| 3163 |
-
"_model_module_version": "1.5.0",
|
| 3164 |
-
"_model_name": "ProgressStyleModel",
|
| 3165 |
-
"_view_count": null,
|
| 3166 |
-
"_view_module": "@jupyter-widgets/base",
|
| 3167 |
-
"_view_module_version": "1.2.0",
|
| 3168 |
-
"_view_name": "StyleView",
|
| 3169 |
-
"bar_color": null,
|
| 3170 |
-
"description_width": ""
|
| 3171 |
-
}
|
| 3172 |
-
},
|
| 3173 |
-
"cd1278fb1b3a4a24974c392e509b4057": {
|
| 3174 |
-
"model_module": "@jupyter-widgets/base",
|
| 3175 |
-
"model_name": "LayoutModel",
|
| 3176 |
-
"model_module_version": "1.2.0",
|
| 3177 |
-
"state": {
|
| 3178 |
-
"_model_module": "@jupyter-widgets/base",
|
| 3179 |
-
"_model_module_version": "1.2.0",
|
| 3180 |
-
"_model_name": "LayoutModel",
|
| 3181 |
-
"_view_count": null,
|
| 3182 |
-
"_view_module": "@jupyter-widgets/base",
|
| 3183 |
-
"_view_module_version": "1.2.0",
|
| 3184 |
-
"_view_name": "LayoutView",
|
| 3185 |
-
"align_content": null,
|
| 3186 |
-
"align_items": null,
|
| 3187 |
-
"align_self": null,
|
| 3188 |
-
"border": null,
|
| 3189 |
-
"bottom": null,
|
| 3190 |
-
"display": null,
|
| 3191 |
-
"flex": null,
|
| 3192 |
-
"flex_flow": null,
|
| 3193 |
-
"grid_area": null,
|
| 3194 |
-
"grid_auto_columns": null,
|
| 3195 |
-
"grid_auto_flow": null,
|
| 3196 |
-
"grid_auto_rows": null,
|
| 3197 |
-
"grid_column": null,
|
| 3198 |
-
"grid_gap": null,
|
| 3199 |
-
"grid_row": null,
|
| 3200 |
-
"grid_template_areas": null,
|
| 3201 |
-
"grid_template_columns": null,
|
| 3202 |
-
"grid_template_rows": null,
|
| 3203 |
-
"height": null,
|
| 3204 |
-
"justify_content": null,
|
| 3205 |
-
"justify_items": null,
|
| 3206 |
-
"left": null,
|
| 3207 |
-
"margin": null,
|
| 3208 |
-
"max_height": null,
|
| 3209 |
-
"max_width": null,
|
| 3210 |
-
"min_height": null,
|
| 3211 |
-
"min_width": null,
|
| 3212 |
-
"object_fit": null,
|
| 3213 |
-
"object_position": null,
|
| 3214 |
-
"order": null,
|
| 3215 |
-
"overflow": null,
|
| 3216 |
-
"overflow_x": null,
|
| 3217 |
-
"overflow_y": null,
|
| 3218 |
-
"padding": null,
|
| 3219 |
-
"right": null,
|
| 3220 |
-
"top": null,
|
| 3221 |
-
"visibility": null,
|
| 3222 |
-
"width": null
|
| 3223 |
-
}
|
| 3224 |
-
},
|
| 3225 |
-
"6b7ded2d32a74bca98a82db9fa112996": {
|
| 3226 |
-
"model_module": "@jupyter-widgets/controls",
|
| 3227 |
-
"model_name": "DescriptionStyleModel",
|
| 3228 |
-
"model_module_version": "1.5.0",
|
| 3229 |
-
"state": {
|
| 3230 |
-
"_model_module": "@jupyter-widgets/controls",
|
| 3231 |
-
"_model_module_version": "1.5.0",
|
| 3232 |
-
"_model_name": "DescriptionStyleModel",
|
| 3233 |
-
"_view_count": null,
|
| 3234 |
-
"_view_module": "@jupyter-widgets/base",
|
| 3235 |
-
"_view_module_version": "1.2.0",
|
| 3236 |
-
"_view_name": "StyleView",
|
| 3237 |
-
"description_width": ""
|
| 3238 |
-
}
|
| 3239 |
-
}
|
| 3240 |
-
}
|
| 3241 |
-
}
|
| 3242 |
},
|
| 3243 |
"nbformat": 4,
|
| 3244 |
"nbformat_minor": 0
|
|
|
|
| 343 |
"\n",
|
| 344 |
"# Additional config values\n",
|
| 345 |
"MODEL_ID = \"codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic\" #@param ['codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic' ]\n",
|
| 346 |
+
"edit_prompt = \"remove the white background. as a real photo this girl has bare skin. put this character on the color gradient background. add diagonal border with dark gray background. the background has a stylish pattern.\" #@param {type:\"string\"}\n",
|
| 347 |
"resolution = \"1024 x 1024 (Square)\" #@param {type:\"string\"}\n",
|
| 348 |
"\n",
|
| 349 |
"# LORA Settings\n",
|
|
|
|
| 741 |
"torch.cuda.empty_cache()"
|
| 742 |
],
|
| 743 |
"metadata": {
|
| 744 |
+
"id": "QYWVhRcpmGRR"
|
|
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|
| 745 |
},
|
| 746 |
"execution_count": null,
|
| 747 |
"outputs": []
|
|
|
|
| 959 |
" print(f'\\n✅ LoRa {lora_name_loaded} loaded into pipe at strength {lora_strength_loaded}')\n",
|
| 960 |
" #-----#\n",
|
| 961 |
"\n",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 962 |
"\n",
|
| 963 |
" #---Collect and clear VRAM at end of cell---#\n",
|
| 964 |
" gc.collect()\n",
|
|
|
|
| 1027 |
"torch.cuda.empty_cache()"
|
| 1028 |
],
|
| 1029 |
"metadata": {
|
| 1030 |
+
"id": "0XLZ_DXrmNE2"
|
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|
| 1031 |
},
|
| 1032 |
+
"execution_count": null,
|
| 1033 |
+
"outputs": []
|
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|
| 1034 |
},
|
| 1035 |
{
|
| 1036 |
"cell_type": "code",
|
|
|
|
| 1168 |
" return pipe"
|
| 1169 |
],
|
| 1170 |
"metadata": {
|
| 1171 |
+
"id": "ibvDcHtJFtr8"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1172 |
},
|
| 1173 |
+
"execution_count": null,
|
| 1174 |
+
"outputs": []
|
|
|
|
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|
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|
| 1175 |
},
|
| 1176 |
{
|
| 1177 |
"cell_type": "code",
|
|
|
|
| 1380 |
"print(\"You can download it from the file browser.\")"
|
| 1381 |
],
|
| 1382 |
"metadata": {
|
| 1383 |
+
"id": "kwF4CB3GmTwB"
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1384 |
},
|
| 1385 |
+
"execution_count": null,
|
| 1386 |
+
"outputs": []
|
|
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|
|
| 1387 |
},
|
| 1388 |
{
|
| 1389 |
"cell_type": "code",
|
|
|
|
| 1506 |
" print(\"📥 Download started...\")"
|
| 1507 |
],
|
| 1508 |
"metadata": {
|
| 1509 |
+
"id": "GvLYqh4iMBf_"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1510 |
},
|
| 1511 |
+
"execution_count": null,
|
| 1512 |
+
"outputs": []
|
|
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|
| 1513 |
}
|
| 1514 |
],
|
| 1515 |
"metadata": {
|
|
|
|
| 1524 |
"language_info": {
|
| 1525 |
"name": "python"
|
| 1526 |
},
|
| 1527 |
+
"accelerator": "GPU"
|
|
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| 1528 |
},
|
| 1529 |
"nbformat": 4,
|
| 1530 |
"nbformat_minor": 0
|