{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "44e828d7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/ubuntu/Qwen-Image-Edit-Angles\n" ] } ], "source": [ "%cd /home/ubuntu/Qwen-Image-Edit-Angles" ] }, { "cell_type": "code", "execution_count": 2, "id": "6f531872", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Skipping import of cpp extensions due to incompatible torch version 2.9.1+cu128 for torchao version 0.14.1 Please see https://github.com/pytorch/ao/issues/2919 for more info\n", "TMA benchmarks will be running without grid constant TMA descriptor.\n", "WARNING:bitsandbytes.cextension:Could not find the bitsandbytes CUDA binary at PosixPath('/usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda128.so')\n", "ERROR:bitsandbytes.cextension:Could not load bitsandbytes native library: /lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.32' not found (required by /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cpu.so)\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.10/dist-packages/bitsandbytes/cextension.py\", line 85, in \n", " lib = get_native_library()\n", " File \"/usr/local/lib/python3.10/dist-packages/bitsandbytes/cextension.py\", line 72, in get_native_library\n", " dll = ct.cdll.LoadLibrary(str(binary_path))\n", " File \"/usr/lib/python3.10/ctypes/__init__.py\", line 452, in LoadLibrary\n", " return self._dlltype(name)\n", " File \"/usr/lib/python3.10/ctypes/__init__.py\", line 374, in __init__\n", " self._handle = _dlopen(self._name, mode)\n", "OSError: /lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.32' not found (required by /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cpu.so)\n", "WARNING:bitsandbytes.cextension:\n", "CUDA Setup failed despite CUDA being available. Please run the following command to get more information:\n", "\n", "python -m bitsandbytes\n", "\n", "Inspect the output of the command and see if you can locate CUDA libraries. You might need to add them\n", "to your LD_LIBRARY_PATH. If you suspect a bug, please take the information from python -m bitsandbytes\n", "and open an issue at: https://github.com/bitsandbytes-foundation/bitsandbytes/issues\n", "\n", "2025-11-23 07:32:06.358872: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "2025-11-23 07:32:06.373313: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1763883126.391228 2355409 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1763883126.396766 2355409 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "W0000 00:00:1763883126.409466 2355409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1763883126.409483 2355409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1763883126.409485 2355409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1763883126.409487 2355409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "2025-11-23 07:32:06.413587: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/.local/lib/python3.10/site-packages/google/api_core/_python_version_support.py:266: FutureWarning: You are using a Python version (3.10.12) which Google will stop supporting in new releases of google.api_core once it reaches its end of life (2026-10-04). Please upgrade to the latest Python version, or at least Python 3.11, to continue receiving updates for google.api_core past that date.\n", " warnings.warn(message, FutureWarning)\n" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/sklearn/utils/fixes.py:25: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", " from pkg_resources import parse_version # type: ignore\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8f6bfafbeb9f4769a0e5e143fe7a4d0d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 7 files: 0%| | 0/7 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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experimentlpips_meanlpips_stdtime_meantime_std
0qwen_base0.0000000.0000000.0983810.036231
1qwen_te_int8wo0.1885740.0958650.1322100.029275
2qwen_te_fp8row0.2121020.1138120.1694020.026132
3qwen_te_int4wo_qkv0.2311040.1119310.1290370.028168
4qwen_te_int4wo_linear0.2295360.1078020.1228480.027134
5qwen_te_int4wo_linear_nofirstlast0.2271040.1192670.1270670.030136
\n", "
" ], "text/plain": [ " experiment lpips_mean lpips_std time_mean \\\n", "0 qwen_base 0.000000 0.000000 0.098381 \n", "1 qwen_te_int8wo 0.188574 0.095865 0.132210 \n", "2 qwen_te_fp8row 0.212102 0.113812 0.169402 \n", "3 qwen_te_int4wo_qkv 0.231104 0.111931 0.129037 \n", "4 qwen_te_int4wo_linear 0.229536 0.107802 0.122848 \n", "5 qwen_te_int4wo_linear_nofirstlast 0.227104 0.119267 0.127067 \n", "\n", " time_std \n", "0 0.036231 \n", "1 0.029275 \n", "2 0.026132 \n", "3 0.028168 \n", "4 0.027134 \n", "5 0.030136 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "compare_sets_with_timing(\n", " ExperimentSet.create(\n", " \"qwen_base\",\n", " \"qwen_te_int8wo\",\n", " # \"qwen_te_int4wo\",\n", " \"qwen_te_fp8row\",\n", " \"qwen_te_int4wo_qkv\",\n", " \"qwen_te_int4wo_linear\",\n", " \"qwen_te_int4wo_linear_nofirstlast\",\n", " ),\n", " profile_target=\"Encode Prompt\",\n", " sort_by=None,\n", ")\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "id": "4e730071", "metadata": {}, "outputs": [], "source": [ "import os\n", "import subprocess\n", "from pathlib import Path\n", "import argparse\n", "\n", "import yaml\n", "import diffusers\n", "\n", "\n", "from wandml.trainers.experiment_trainer import ExperimentTrainer\n", "from wandml import WandDataPipe\n", "import wandml\n", "from wandml import WandAuth\n", "from wandml import utils as wandml_utils\n", "from wandml.trainers.datamodels import ExperimentTrainerParameters\n", "from wandml.trainers.experiment_trainer import ExperimentTrainer\n", "\n", "\n", "from qwenimage.finetuner import QwenLoraFinetuner\n", "from qwenimage.datasets import StyleSourceWithRandomRef\n", "from qwenimage.task import TextToImageWithRefTask\n", "from qwenimage.datamodels import QwenConfig\n", "from qwenimage.foundation import QwenImageFoundation\n", "from qwenimage.experiments.experiments_qwen import PipeInputs\n", "from qwenimage.models.encode_prompt import encode_prompt\n", "from qwenimage.optimization import simple_quantize_model\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "2548215a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "self.device='cpu'\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "da7f2dad19974600a61edcd39ab73d12", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 5 files: 0%| | 0/5 [00:00', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>']}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n", "\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151646: AddedToken(\"<|object_ref_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151647: AddedToken(\"<|object_ref_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151648: AddedToken(\"<|box_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151649: AddedToken(\"<|box_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151650: AddedToken(\"<|quad_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151651: AddedToken(\"<|quad_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151652: AddedToken(\"<|vision_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151653: AddedToken(\"<|vision_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151654: AddedToken(\"<|vision_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151655: AddedToken(\"<|image_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151656: AddedToken(\"<|video_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t151657: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151658: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151659: AddedToken(\"<|fim_prefix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151660: AddedToken(\"<|fim_middle|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151661: AddedToken(\"<|fim_suffix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151662: AddedToken(\"<|fim_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151663: AddedToken(\"<|repo_name|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "\t151664: AddedToken(\"<|file_sep|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", "}\n", ")" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "foundation.pipe.tokenizer" ] }, { "cell_type": "code", "execution_count": 19, "id": "4872b9f3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['SPECIAL_TOKENS_ATTRIBUTES',\n", " 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'byte_encoder',\n", " 'cache',\n", " 'chat_template',\n", " 'clean_up_tokenization',\n", " 'clean_up_tokenization_spaces',\n", " 'convert_added_tokens',\n", " 'convert_ids_to_tokens',\n", " 'convert_tokens_to_ids',\n", " 'convert_tokens_to_string',\n", " 'create_token_type_ids_from_sequences',\n", " 'decode',\n", " 'decoder',\n", " 'deprecation_warnings',\n", " 'encode',\n", " 'encode_message_with_chat_template',\n", " 'encode_plus',\n", " 'encoder',\n", " 'errors',\n", " 'extra_special_tokens',\n", " 'from_pretrained',\n", " 'get_added_vocab',\n", " 'get_chat_template',\n", " 'get_special_tokens_mask',\n", " 'get_vocab',\n", " 'init_inputs',\n", " 'init_kwargs',\n", " 'is_fast',\n", " 'max_len_sentences_pair',\n", " 'max_len_single_sentence',\n", " 'model_input_names',\n", " 'model_max_length',\n", " 'name_or_path',\n", " 'num_special_tokens_to_add',\n", " 'pad',\n", " 'pad_token_type_id',\n", " 'padding_side',\n", " 'pat',\n", " 'prepare_for_model',\n", " 'prepare_for_tokenization',\n", " 'prepare_seq2seq_batch',\n", " 'pretrained_vocab_files_map',\n", " 'push_to_hub',\n", " 'register_for_auto_class',\n", " 'sanitize_special_tokens',\n", " 'save_chat_templates',\n", " 'save_pretrained',\n", " 'save_vocabulary',\n", " 'slow_tokenizer_class',\n", " 'special_tokens_map',\n", " 'special_tokens_map_extended',\n", " 'split_special_tokens',\n", " 'tokenize',\n", " 'tokens_trie',\n", " 'total_vocab_size',\n", " 'truncate_sequences',\n", " 'truncation_side',\n", " 'verbose',\n", " 'vocab_files_names',\n", " 'vocab_size']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(foundation.pipe.tokenizer)" ] }, { "cell_type": "code", "execution_count": 6, "id": "5c65ac03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_get_qwen_prompt_embeds, image\n", "Shape: (3, 683, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 63.53046417236328\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.7777, -1.7632, -1.7631, ..., -0.5286, -0.6148, -0.7006],\n", " [-1.7381, -1.7182, -1.7566, ..., -0.9265, -0.9171, -0.9117],\n", " [-1.7631, -1.7485, -1.7555, ..., -0.4396, -0.4960, -0.5632],\n", " ...,\n", " [-1.6908, -1.7087, -1.7196, ..., -0.5421, -0.5706, -0.5549],\n", " [-1.7173, -1.7147, -1.6992, ..., -0.7265, -0.7266, -0.7266],\n", " [-1.7319, -1.7304, -1.7315, ..., -0.6268, -0.6268, -0.5985]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 48, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3552, 1176)\n", "Min: -1.8659758567810059, Max: 2.1775243282318115, Mean: -0.7494064569473267\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 954, 3584)\n", "Min: -188.0, Max: 127.5, Mean: -0.11572265625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.23725280002690852 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 768, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 107.94649505615234\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[ 0.5428, 0.5998, 0.6863, ..., 0.8736, 0.9324, 0.9669],\n", " [ 0.7459, 0.6964, 0.6597, ..., 0.7373, 0.6676, 0.6491],\n", " [-0.1431, -0.0794, -0.0050, ..., 1.0617, 1.0885, 1.0392],\n", " ...,\n", " [-1.1572, -1.0803, -0.8879, ..., -0.9471, -1.0033, -1.0551],\n", " [-0.3540, -0.3737, -0.3771, ..., 0.5403, 0.6746, 0.5794],\n", " [-0.5961, -0.6764, -0.7813, ..., 0.2935, 0.2378, 0.1684]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 54, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3996, 1176)\n", "Min: -1.7068719863891602, Max: 2.0894978046417236, Mean: -0.09713771194219589\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "encode_prompt, prompt_embeds\n", "Shape: (1, 1047, 3584)\n", "Min: -179.0, Max: 126.0, Mean: -0.099609375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2631705839885399 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 768, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 107.94649505615234\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[ 0.5428, 0.5998, 0.6863, ..., 0.8736, 0.9324, 0.9669],\n", " [ 0.7459, 0.6964, 0.6597, ..., 0.7373, 0.6676, 0.6491],\n", " [-0.1431, -0.0794, -0.0050, ..., 1.0617, 1.0885, 1.0392],\n", " ...,\n", " [-1.1572, -1.0803, -0.8879, ..., -0.9471, -1.0033, -1.0551],\n", " [-0.3540, -0.3737, -0.3771, ..., 0.5403, 0.6746, 0.5794],\n", " [-0.5961, -0.6764, -0.7813, ..., 0.2935, 0.2378, 0.1684]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 54, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3996, 1176)\n", "Min: -1.7068719863891602, Max: 2.0894978046417236, Mean: -0.09713771194219589\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1064, 3584)\n", "Min: -179.0, Max: 128.0, Mean: -0.099609375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2707125770393759 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 575, 1024)\n", "Min: 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-1.2751],\n", " [-1.5183, -1.4832, -1.5793, ..., -1.3038, -1.3043, -1.3074],\n", " [-1.3133, -1.3185, -1.2903, ..., -1.3487, -1.3452, -1.3598],\n", " ...,\n", " [-1.6626, -1.6774, -1.6764, ..., -1.3340, -1.3299, -1.3187],\n", " [-1.6502, -1.6630, -1.6645, ..., -1.3067, -1.2753, -1.2526],\n", " [-1.6754, -1.6748, -1.6749, ..., -1.3378, -1.3236, -1.3238]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 42, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3108, 1176)\n", "Min: -1.8282335996627808, Max: 2.168954372406006, Mean: -0.7451295256614685\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 860, 3584)\n", "Min: -181.0, Max: 128.0, Mean: -0.130859375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.23752214293926954 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 512, 896)\n", "Min: 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151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151653, 44063,\n", " 105995, 106315, 101148, 14561, 279, 6249, 4637, 13, 220,\n", " 58230, 228, 105995, 46670, 17714, 80942, 63836, 105995, 11999,\n", " 279, 6249, 311, 264, 6884, 34381, 18342, 13, 151645,\n", " 198, 151644, 77091, 198]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[1.0544, 1.0398, 0.9960, ..., 1.4028, 1.4420, 1.4439],\n", " [1.0690, 1.0690, 1.0690, ..., 1.4382, 1.4361, 1.4336],\n", " [1.0948, 1.1056, 1.1284, ..., 1.4509, 1.4499, 1.4431],\n", " ...,\n", " [0.5789, 0.5943, 0.6037, ..., 0.7682, 0.7563, 0.7712],\n", " [0.3476, 0.3538, 0.4004, ..., 0.5106, 0.5245, 0.4960],\n", " [0.4727, 0.4748, 0.4720, ..., 0.6245, 0.6248, 0.6248]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 36, 64]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (2304, 1176)\n", "Min: -1.8204282522201538, Max: 2.206862211227417, Mean: 0.18789313733577728\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 615, 3584)\n", "Min: -189.0, Max: 126.0, Mean: -0.1318359375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.1779305540258065 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 1024, 812)\n", "Min: 0.0, Max: 255.0, Mean: 159.07200622558594\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-0.1280, -0.1572, -0.1718, ..., 0.0113, 0.0130, 0.0130],\n", " [-0.1280, -0.1280, -0.0842, ..., 0.0413, 0.0413, 0.0413],\n", " [-0.1426, -0.1572, -0.1718, ..., 0.0516, 0.0560, 0.0516],\n", " ...,\n", " [ 1.5946, 1.5946, 1.5946, ..., 1.8331, 1.8331, 1.8188],\n", " [ 1.6100, 1.6100, 1.6100, ..., 1.1334, 1.1334, 1.1334],\n", " [ 1.6100, 1.6100, 1.6092, ..., 1.1050, 1.1050, 1.1050]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 74, 58]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7411859035491943, Max: 2.1950085163116455, Mean: 0.6495240926742554\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1122, 3584)\n", "Min: -176.0, Max: 134.0, Mean: -0.142578125\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2897540549747646 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 711, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 113.62726593017578\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.2667, -1.2667, -1.2667, ..., -0.8217, -0.8335, -0.8606],\n", " [-1.3121, -1.3076, -1.3473, ..., -1.1973, -1.1864, -1.1730],\n", " [-1.1496, -1.1381, -1.1240, ..., -0.6462, -0.6098, -0.6079],\n", " ...,\n", " [ 1.0859, -0.1256, -0.2868, ..., 0.6197, 0.4291, 0.0579],\n", " [-1.1702, -1.0602, -0.5905, ..., 0.6149, -0.1937, -1.1534],\n", " [-1.6150, -0.8690, 0.2011, ..., 0.5735, 0.3593, 0.0601]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 50, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3700, 1176)\n", "Min: -1.8720948696136475, Max: 2.249417543411255, Mean: -0.012816129252314568\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1007, 3584)\n", "Min: -186.0, Max: 135.0, Mean: -0.1396484375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.25078090804163367 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 711, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 113.62726593017578\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.2667, -1.2667, -1.2667, ..., -0.8217, -0.8335, -0.8606],\n", " [-1.3121, -1.3076, -1.3473, ..., -1.1973, -1.1864, -1.1730],\n", " [-1.1496, -1.1381, -1.1240, ..., -0.6462, -0.6098, -0.6079],\n", " ...,\n", " [ 1.0859, -0.1256, -0.2868, ..., 0.6197, 0.4291, 0.0579],\n", " [-1.1702, -1.0602, -0.5905, ..., 0.6149, -0.1937, -1.1534],\n", " [-1.6150, -0.8690, 0.2011, ..., 0.5735, 0.3593, 0.0601]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 50, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3700, 1176)\n", "Min: -1.8720948696136475, Max: 2.249417543411255, Mean: -0.012816129252314568\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1001, 3584)\n", "Min: -186.0, Max: 135.0, Mean: -0.1396484375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: 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Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 625, 3584)\n", "Min: -189.0, Max: 127.5, Mean: -0.1328125\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.1791253340197727 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 575, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 63.50874710083008\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, 74785, 279, 1376, 4419, 315, 279,\n", " 1946, 2168, 320, 3423, 11, 6083, 11, 1379, 11,\n", " 10434, 11, 6171, 11, 4004, 701, 1221, 10339, 1246,\n", " 279, 1196, 594, 1467, 7600, 1265, 11596, 476, 5602,\n", " 279, 2168, 13, 19813, 264, 501, 2168, 429, 20027,\n", " 279, 1196, 594, 8502, 1393, 20337, 28137, 448, 279,\n", " 4024, 1946, 1380, 8311, 13, 151645, 198, 151644, 872,\n", " 198, 24669, 220, 16, 25, 220, 151652, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 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-1.3964, ..., -1.2787, -1.2597, -1.2751],\n", " [-1.5183, -1.4832, -1.5793, ..., -1.3038, -1.3043, -1.3074],\n", " [-1.3133, -1.3185, -1.2903, ..., -1.3487, -1.3452, -1.3598],\n", " ...,\n", " [-1.6626, -1.6774, -1.6764, ..., -1.3340, -1.3299, -1.3187],\n", " [-1.6502, -1.6630, -1.6645, ..., -1.3067, -1.2753, -1.2526],\n", " [-1.6754, -1.6748, -1.6749, ..., -1.3378, -1.3236, -1.3238]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 42, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3108, 1176)\n", "Min: -1.8282335996627808, Max: 2.168954372406006, Mean: -0.7451295256614685\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 824, 3584)\n", "Min: -181.0, Max: 131.0, Mean: -0.130859375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.23407166998367757 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 683, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 63.53046417236328\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.7777, -1.7632, -1.7631, ..., -0.5286, -0.6148, -0.7006],\n", " [-1.7381, -1.7182, -1.7566, ..., -0.9265, -0.9171, -0.9117],\n", " [-1.7631, -1.7485, -1.7555, ..., -0.4396, -0.4960, -0.5632],\n", " ...,\n", " [-1.6908, -1.7087, -1.7196, ..., -0.5421, -0.5706, -0.5549],\n", " [-1.7173, -1.7147, -1.6992, ..., -0.7265, -0.7266, -0.7266],\n", " [-1.7319, -1.7304, -1.7315, ..., -0.6268, -0.6268, -0.5985]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 48, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3552, 1176)\n", "Min: -1.8659758567810059, Max: 2.1775243282318115, Mean: -0.7494064569473267\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 971, 3584)\n", "Min: -188.0, Max: 128.0, Mean: -0.11572265625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.23473224299959838 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 768, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 107.94649505615234\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[ 0.5428, 0.5998, 0.6863, ..., 0.8736, 0.9324, 0.9669],\n", " [ 0.7459, 0.6964, 0.6597, ..., 0.7373, 0.6676, 0.6491],\n", " [-0.1431, -0.0794, -0.0050, ..., 1.0617, 1.0885, 1.0392],\n", " ...,\n", " [-1.1572, -1.0803, -0.8879, ..., -0.9471, -1.0033, -1.0551],\n", " [-0.3540, -0.3737, -0.3771, ..., 0.5403, 0.6746, 0.5794],\n", " [-0.5961, -0.6764, -0.7813, ..., 0.2935, 0.2378, 0.1684]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 54, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3996, 1176)\n", "Min: -1.7068719863891602, Max: 2.0894978046417236, Mean: -0.09713771194219589\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1066, 3584)\n", "Min: -179.0, Max: 126.5, Mean: -0.099609375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.26784510200377554 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 1024, 812)\n", "Min: 0.0, Max: 255.0, Mean: 159.07200622558594\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-0.1280, -0.1572, -0.1718, ..., 0.0113, 0.0130, 0.0130],\n", " [-0.1280, -0.1280, -0.0842, ..., 0.0413, 0.0413, 0.0413],\n", " [-0.1426, -0.1572, -0.1718, ..., 0.0516, 0.0560, 0.0516],\n", " ...,\n", " [ 1.5946, 1.5946, 1.5946, ..., 1.8331, 1.8331, 1.8188],\n", " [ 1.6100, 1.6100, 1.6100, ..., 1.1334, 1.1334, 1.1334],\n", " [ 1.6100, 1.6100, 1.6092, ..., 1.1050, 1.1050, 1.1050]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 74, 58]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7411859035491943, Max: 2.1950085163116455, Mean: 0.6495240926742554\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1114, 3584)\n", "Min: -176.0, Max: 129.0, Mean: -0.142578125\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.29315092496108264 seconds\n", 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'pixel_values': tensor([[-1.2809, -1.3385, -1.3964, ..., -1.2787, -1.2597, -1.2751],\n", " [-1.5183, -1.4832, -1.5793, ..., -1.3038, -1.3043, -1.3074],\n", " [-1.3133, -1.3185, -1.2903, ..., -1.3487, -1.3452, -1.3598],\n", " ...,\n", " [-1.6626, -1.6774, -1.6764, ..., -1.3340, -1.3299, -1.3187],\n", " [-1.6502, -1.6630, -1.6645, ..., -1.3067, -1.2753, -1.2526],\n", " [-1.6754, -1.6748, -1.6749, ..., -1.3378, -1.3236, -1.3238]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 42, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3108, 1176)\n", "Min: -1.8282335996627808, Max: 2.168954372406006, Mean: -0.7451295256614685\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 859, 3584)\n", "Min: -181.0, Max: 126.5, Mean: -0.130859375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2358077869284898 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 819, 1024)\n", "Min: 5.0, Max: 245.00001525878906, Mean: 73.38787078857422\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.5443, -1.5443, -1.5443, ..., -1.2811, -1.2811, -1.2811],\n", " [-1.5304, -1.5153, -1.5399, ..., -1.2807, -1.2811, -1.2811],\n", " [-1.5879, -1.5879, -1.5879, ..., -1.2811, -1.2811, -1.2811],\n", " ...,\n", " [-1.5606, -1.5608, -1.5594, ..., -1.2243, -1.2243, -1.2243],\n", " [-1.5721, -1.5686, -1.5575, ..., -1.3348, -1.3080, -1.3080],\n", " [-1.5571, -1.5433, -1.5442, ..., -1.2525, -1.2382, -1.2242]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 58, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7186729907989502, Max: 2.005134344100952, Mean: -0.602754533290863\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1138, 3584)\n", "Min: -166.0, Max: 132.0, Mean: -0.1259765625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.29242081195116043 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 575, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 63.50874710083008\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, 74785, 279, 1376, 4419, 315, 279,\n", " 1946, 2168, 320, 3423, 11, 6083, 11, 1379, 11,\n", " 10434, 11, 6171, 11, 4004, 701, 1221, 10339, 1246,\n", " 279, 1196, 594, 1467, 7600, 1265, 11596, 476, 5602,\n", " 279, 2168, 13, 19813, 264, 501, 2168, 429, 20027,\n", " 279, 1196, 594, 8502, 1393, 20337, 28137, 448, 279,\n", " 4024, 1946, 1380, 8311, 13, 151645, 198, 151644, 872,\n", " 198, 24669, 220, 16, 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5239,\n", " 13, 58230, 228, 107620, 110697, 26939, 102690, 57452, 107372,\n", " 11999, 279, 6249, 311, 264, 34211, 594, 46697, 1651,\n", " 13, 220, 58230, 228, 105995, 46670, 17714, 80942, 63836,\n", " 105995, 11999, 279, 6249, 311, 264, 6884, 34381, 18342,\n", " 13, 151645, 198, 151644, 77091, 198]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.2809, -1.3385, -1.3964, ..., -1.2787, -1.2597, -1.2751],\n", " [-1.5183, -1.4832, -1.5793, ..., -1.3038, -1.3043, -1.3074],\n", " [-1.3133, -1.3185, -1.2903, ..., -1.3487, -1.3452, -1.3598],\n", " ...,\n", " [-1.6626, -1.6774, -1.6764, ..., -1.3340, -1.3299, -1.3187],\n", " [-1.6502, -1.6630, -1.6645, ..., -1.3067, -1.2753, -1.2526],\n", " [-1.6754, -1.6748, -1.6749, ..., -1.3378, -1.3236, -1.3238]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 42, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3108, 1176)\n", "Min: -1.8282335996627808, Max: 2.168954372406006, Mean: -0.7451295256614685\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 860, 3584)\n", "Min: -181.0, Max: 128.0, Mean: -0.130859375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.23583469400182366 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 711, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 113.62726593017578\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.2667, -1.2667, -1.2667, ..., -0.8217, -0.8335, -0.8606],\n", " [-1.3121, -1.3076, -1.3473, ..., -1.1973, -1.1864, -1.1730],\n", " [-1.1496, -1.1381, -1.1240, ..., -0.6462, -0.6098, -0.6079],\n", " ...,\n", " [ 1.0859, -0.1256, -0.2868, ..., 0.6197, 0.4291, 0.0579],\n", " [-1.1702, -1.0602, -0.5905, ..., 0.6149, -0.1937, -1.1534],\n", " [-1.6150, -0.8690, 0.2011, ..., 0.5735, 0.3593, 0.0601]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 50, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3700, 1176)\n", "Min: -1.8720948696136475, Max: 2.249417543411255, Mean: -0.012816129252314568\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 992, 3584)\n", "Min: -186.0, Max: 135.0, Mean: -0.1396484375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2506421610014513 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 711, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 113.62726593017578\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.2667, -1.2667, -1.2667, ..., -0.8217, -0.8335, -0.8606],\n", " [-1.3121, -1.3076, -1.3473, ..., -1.1973, -1.1864, -1.1730],\n", " [-1.1496, -1.1381, -1.1240, ..., -0.6462, -0.6098, -0.6079],\n", " ...,\n", " [ 1.0859, -0.1256, -0.2868, ..., 0.6197, 0.4291, 0.0579],\n", " [-1.1702, -1.0602, -0.5905, ..., 0.6149, -0.1937, -1.1534],\n", " [-1.6150, -0.8690, 0.2011, ..., 0.5735, 0.3593, 0.0601]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 50, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3700, 1176)\n", "Min: -1.8720948696136475, Max: 2.249417543411255, Mean: -0.012816129252314568\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 969, 3584)\n", "Min: -186.0, Max: 135.0, Mean: -0.1396484375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.24892258399631828 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 819, 1024)\n", "Min: 5.0, Max: 245.00001525878906, Mean: 73.38787078857422\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.5443, -1.5443, -1.5443, ..., -1.2811, -1.2811, -1.2811],\n", " [-1.5304, -1.5153, -1.5399, ..., -1.2807, -1.2811, -1.2811],\n", " [-1.5879, -1.5879, -1.5879, ..., -1.2811, -1.2811, -1.2811],\n", " ...,\n", " [-1.5606, -1.5608, -1.5594, ..., -1.2243, -1.2243, -1.2243],\n", " [-1.5721, -1.5686, -1.5575, ..., -1.3348, -1.3080, -1.3080],\n", " [-1.5571, -1.5433, -1.5442, ..., -1.2525, -1.2382, -1.2242]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 58, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7186729907989502, Max: 2.005134344100952, Mean: -0.602754533290863\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1132, 3584)\n", "Min: -166.0, Max: 126.5, Mean: -0.1259765625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2945258499821648 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 711, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 113.62726593017578\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.2667, -1.2667, -1.2667, ..., -0.8217, -0.8335, -0.8606],\n", " [-1.3121, -1.3076, -1.3473, ..., -1.1973, -1.1864, -1.1730],\n", " [-1.1496, -1.1381, -1.1240, ..., -0.6462, -0.6098, -0.6079],\n", " ...,\n", " [ 1.0859, -0.1256, -0.2868, ..., 0.6197, 0.4291, 0.0579],\n", " [-1.1702, -1.0602, -0.5905, ..., 0.6149, -0.1937, -1.1534],\n", " [-1.6150, -0.8690, 0.2011, ..., 0.5735, 0.3593, 0.0601]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 50, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3700, 1176)\n", "Min: -1.8720948696136475, Max: 2.249417543411255, Mean: -0.012816129252314568\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 972, 3584)\n", "Min: -186.0, Max: 135.0, Mean: -0.140625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.25253281102050096 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 575, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 63.50874710083008\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, 74785, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1]], device='cuda:0'), 'pixel_values': tensor([[-1.2809, -1.3385, -1.3964, ..., -1.2787, -1.2597, -1.2751],\n", " [-1.5183, -1.4832, -1.5793, ..., -1.3038, -1.3043, -1.3074],\n", " [-1.3133, -1.3185, -1.2903, ..., -1.3487, -1.3452, -1.3598],\n", " ...,\n", " [-1.6626, -1.6774, -1.6764, ..., -1.3340, -1.3299, -1.3187],\n", " [-1.6502, -1.6630, -1.6645, ..., -1.3067, -1.2753, -1.2526],\n", " [-1.6754, -1.6748, -1.6749, ..., -1.3378, -1.3236, -1.3238]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 42, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3108, 1176)\n", "Min: -1.8282335996627808, Max: 2.168954372406006, Mean: -0.7451295256614685\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 825, 3584)\n", "Min: -181.0, Max: 128.0, Mean: -0.130859375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.23513541498687118 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 1024, 812)\n", "Min: 0.0, Max: 255.0, Mean: 159.07200622558594\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-0.1280, -0.1572, -0.1718, ..., 0.0113, 0.0130, 0.0130],\n", " [-0.1280, -0.1280, -0.0842, ..., 0.0413, 0.0413, 0.0413],\n", " [-0.1426, -0.1572, -0.1718, ..., 0.0516, 0.0560, 0.0516],\n", " ...,\n", " [ 1.5946, 1.5946, 1.5946, ..., 1.8331, 1.8331, 1.8188],\n", " [ 1.6100, 1.6100, 1.6100, ..., 1.1334, 1.1334, 1.1334],\n", " [ 1.6100, 1.6100, 1.6092, ..., 1.1050, 1.1050, 1.1050]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 74, 58]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7411859035491943, Max: 2.1950085163116455, Mean: 0.6495240926742554\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1150, 3584)\n", "Min: -176.0, Max: 128.0, Mean: -0.1416015625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.291092328960076 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 1024, 812)\n", "Min: 0.0, Max: 255.0, Mean: 159.07200622558594\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-0.1280, -0.1572, -0.1718, ..., 0.0113, 0.0130, 0.0130],\n", " [-0.1280, -0.1280, -0.0842, ..., 0.0413, 0.0413, 0.0413],\n", " [-0.1426, -0.1572, -0.1718, ..., 0.0516, 0.0560, 0.0516],\n", " ...,\n", " [ 1.5946, 1.5946, 1.5946, ..., 1.8331, 1.8331, 1.8188],\n", " [ 1.6100, 1.6100, 1.6100, ..., 1.1334, 1.1334, 1.1334],\n", " [ 1.6100, 1.6100, 1.6092, ..., 1.1050, 1.1050, 1.1050]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 74, 58]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7411859035491943, Max: 2.1950085163116455, Mean: 0.6495240926742554\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1136, 3584)\n", "Min: -176.0, Max: 131.0, Mean: -0.1416015625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.29379134089685977 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 1024, 812)\n", "Min: 0.0, Max: 255.0, Mean: 159.07200622558594\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-0.1280, -0.1572, -0.1718, ..., 0.0113, 0.0130, 0.0130],\n", " [-0.1280, -0.1280, -0.0842, ..., 0.0413, 0.0413, 0.0413],\n", " [-0.1426, -0.1572, -0.1718, ..., 0.0516, 0.0560, 0.0516],\n", " ...,\n", " [ 1.5946, 1.5946, 1.5946, ..., 1.8331, 1.8331, 1.8188],\n", " [ 1.6100, 1.6100, 1.6100, ..., 1.1334, 1.1334, 1.1334],\n", " [ 1.6100, 1.6100, 1.6092, ..., 1.1050, 1.1050, 1.1050]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 74, 58]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7411859035491943, Max: 2.1950085163116455, Mean: 0.6495240926742554\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1104, 3584)\n", "Min: -176.0, Max: 124.0, Mean: -0.142578125\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2909910239977762 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 711, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 113.62726593017578\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.2667, -1.2667, -1.2667, ..., -0.8217, -0.8335, -0.8606],\n", " [-1.3121, -1.3076, -1.3473, ..., -1.1973, -1.1864, -1.1730],\n", " [-1.1496, -1.1381, -1.1240, ..., -0.6462, -0.6098, -0.6079],\n", " ...,\n", " [ 1.0859, -0.1256, -0.2868, ..., 0.6197, 0.4291, 0.0579],\n", " [-1.1702, -1.0602, -0.5905, ..., 0.6149, -0.1937, -1.1534],\n", " [-1.6150, -0.8690, 0.2011, ..., 0.5735, 0.3593, 0.0601]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 50, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3700, 1176)\n", "Min: -1.8720948696136475, Max: 2.249417543411255, Mean: -0.012816129252314568\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 952, 3584)\n", "Min: -186.0, Max: 135.0, Mean: -0.140625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2506160920020193 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 1024, 812)\n", "Min: 0.0, Max: 255.0, Mean: 159.07200622558594\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-0.1280, -0.1572, -0.1718, ..., 0.0113, 0.0130, 0.0130],\n", " [-0.1280, -0.1280, -0.0842, ..., 0.0413, 0.0413, 0.0413],\n", " [-0.1426, -0.1572, -0.1718, ..., 0.0516, 0.0560, 0.0516],\n", " ...,\n", " [ 1.5946, 1.5946, 1.5946, ..., 1.8331, 1.8331, 1.8188],\n", " [ 1.6100, 1.6100, 1.6100, ..., 1.1334, 1.1334, 1.1334],\n", " [ 1.6100, 1.6100, 1.6092, ..., 1.1050, 1.1050, 1.1050]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 74, 58]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7411859035491943, Max: 2.1950085163116455, Mean: 0.6495240926742554\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1118, 3584)\n", "Min: -176.0, Max: 126.5, Mean: -0.1416015625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.29039270197972655 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 819, 1024)\n", "Min: 5.0, Max: 245.00001525878906, Mean: 73.38787078857422\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.5443, -1.5443, -1.5443, ..., -1.2811, -1.2811, -1.2811],\n", " [-1.5304, -1.5153, -1.5399, ..., -1.2807, -1.2811, -1.2811],\n", " [-1.5879, -1.5879, -1.5879, ..., -1.2811, -1.2811, -1.2811],\n", " ...,\n", " [-1.5606, -1.5608, -1.5594, ..., -1.2243, -1.2243, -1.2243],\n", " [-1.5721, -1.5686, -1.5575, ..., -1.3348, -1.3080, -1.3080],\n", " [-1.5571, -1.5433, -1.5442, ..., -1.2525, -1.2382, -1.2242]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 58, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7186729907989502, Max: 2.005134344100952, Mean: -0.602754533290863\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1131, 3584)\n", "Min: -166.0, Max: 127.5, Mean: -0.1259765625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2878430059645325 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 1024, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 169.12490844726562\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[0.1639, 0.1638, 0.1783, ..., 0.4110, 0.4110, 0.4110],\n", " [0.1639, 0.1639, 0.1639, ..., 0.4090, 0.4319, 0.4405],\n", " [0.1769, 0.1768, 0.1768, ..., 0.4213, 0.4213, 0.4213],\n", " ...,\n", " [1.3755, 1.3754, 1.3810, ..., 1.6029, 1.6011, 1.5879],\n", " [1.3752, 1.3795, 1.3913, ..., 1.7336, 1.7336, 1.7336],\n", " [1.3904, 1.3903, 1.3903, ..., 1.7336, 1.7336, 1.7336]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 74, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (5476, 1176)\n", "Min: -1.7604045867919922, Max: 2.2139892578125, Mean: 0.7960162162780762\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1390, 3584)\n", "Min: -190.0, Max: 122.5, Mean: -0.162109375\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.3776645170291886 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 819, 1024)\n", "Min: 5.0, Max: 245.00001525878906, Mean: 73.38787078857422\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.5443, -1.5443, -1.5443, ..., -1.2811, -1.2811, -1.2811],\n", " [-1.5304, -1.5153, -1.5399, ..., -1.2807, -1.2811, -1.2811],\n", " [-1.5879, -1.5879, -1.5879, ..., -1.2811, -1.2811, -1.2811],\n", " ...,\n", " [-1.5606, -1.5608, -1.5594, ..., -1.2243, -1.2243, -1.2243],\n", " [-1.5721, -1.5686, -1.5575, ..., -1.3348, -1.3080, -1.3080],\n", " [-1.5571, -1.5433, -1.5442, ..., -1.2525, -1.2382, -1.2242]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 58, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7186729907989502, Max: 2.005134344100952, Mean: -0.602754533290863\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1149, 3584)\n", "Min: -166.0, Max: 127.0, Mean: -0.1259765625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2933834990253672 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 512, 896)\n", "Min: 0.0, Max: 255.0, Mean: 127.4706802368164\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, 74785, 279, 1376, 4419, 315, 279,\n", " 1946, 2168, 320, 3423, 11, 6083, 11, 1379, 11,\n", " 10434, 11, 6171, 11, 4004, 701, 1221, 10339, 1246,\n", " 279, 1196, 594, 1467, 7600, 1265, 11596, 476, 5602,\n", " 279, 2168, 13, 19813, 264, 501, 2168, 429, 20027,\n", " 279, 1196, 594, 8502, 1393, 20337, 28137, 448, 279,\n", " 4024, 1946, 1380, 8311, 13, 151645, 198, 151644, 872,\n", " 198, 24669, 220, 16, 25, 220, 151652, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,\n", " 151655, 151655, 151655, 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6249, 311, 264, 34211, 594, 46697, 1651, 13,\n", " 151645, 198, 151644, 77091, 198]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[1.0544, 1.0398, 0.9960, ..., 1.4028, 1.4420, 1.4439],\n", " [1.0690, 1.0690, 1.0690, ..., 1.4382, 1.4361, 1.4336],\n", " [1.0948, 1.1056, 1.1284, ..., 1.4509, 1.4499, 1.4431],\n", " ...,\n", " [0.5789, 0.5943, 0.6037, ..., 0.7682, 0.7563, 0.7712],\n", " [0.3476, 0.3538, 0.4004, ..., 0.5106, 0.5245, 0.4960],\n", " [0.4727, 0.4748, 0.4720, ..., 0.6245, 0.6248, 0.6248]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 36, 64]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (2304, 1176)\n", "Min: -1.8204282522201538, Max: 2.206862211227417, Mean: 0.18789313733577728\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 625, 3584)\n", "Min: -189.0, Max: 126.5, Mean: -0.1328125\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.17909824196249247 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 819, 1024)\n", "Min: 5.0, Max: 245.00001525878906, Mean: 73.38787078857422\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, ..., 151644, 77091, 198]],\n", " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), 'pixel_values': tensor([[-1.5443, -1.5443, -1.5443, ..., -1.2811, -1.2811, -1.2811],\n", " [-1.5304, -1.5153, -1.5399, ..., -1.2807, -1.2811, -1.2811],\n", " [-1.5879, -1.5879, -1.5879, ..., -1.2811, -1.2811, -1.2811],\n", " ...,\n", " [-1.5606, -1.5608, -1.5594, ..., -1.2243, -1.2243, -1.2243],\n", " [-1.5721, -1.5686, -1.5575, ..., -1.3348, -1.3080, -1.3080],\n", " [-1.5571, -1.5433, -1.5442, ..., -1.2525, -1.2382, -1.2242]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 58, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (4292, 1176)\n", "Min: -1.7186729907989502, Max: 2.005134344100952, Mean: -0.602754533290863\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 1136, 3584)\n", "Min: -166.0, Max: 132.0, Mean: -0.125\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.290938989026472 seconds\n", "_get_qwen_prompt_embeds, image\n", "Shape: (3, 683, 1024)\n", "Min: 0.0, Max: 255.0, Mean: 63.53046417236328\n", "Device: cpu, Dtype: torch.float32, Requires Grad: False\n", "{'input_ids': tensor([[151644, 8948, 198, 74785, 279, 1376, 4419, 315, 279,\n", " 1946, 2168, 320, 3423, 11, 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..., -0.7265, -0.7266, -0.7266],\n", " [-1.7319, -1.7304, -1.7315, ..., -0.6268, -0.6268, -0.5985]],\n", " device='cuda:0'), 'image_grid_thw': tensor([[ 1, 48, 74]], device='cuda:0')}\n", "_get_qwen_prompt_embeds, model_inputs.pixel_values\n", "Shape: (3552, 1176)\n", "Min: -1.8659758567810059, Max: 2.1775243282318115, Mean: -0.7494064569473267\n", "Device: cuda:0, Dtype: torch.float32, Requires Grad: False\n", "encode_prompt, prompt_embeds\n", "Shape: (1, 936, 3584)\n", "Min: -188.0, Max: 128.0, Mean: -0.11572265625\n", "Device: cuda:0, Dtype: torch.bfloat16, Requires Grad: False\n", "Time taken by QwenImageEditPlusPipeline.encode_prompt: 0.2587369130924344 seconds\n", "torch.Size([31844, 3584])\n" ] } ], "source": [ "import torch\n", "\n", "import torch.nn.functional as F\n", "import torchvision.transforms.v2 as T\n", "import torch\n", "\n", "_transforms = T.Compose([\n", " T.ToImage(),\n", " T.RGB(),\n", " T.ToDtype(torch.float32, scale=True), # [0,1]\n", "])\n", "\n", "\n", "iterations = 32\n", "quant_type = None # \"int8wo\" \"int4wo\" \"fp8row\"\n", "if quant_type:\n", " simple_quantize_model(text_encoder, quant_type)\n", "\n", "all_embeds = []\n", "for i in range(iterations):\n", " prompt_embeds, prompt_embeds_mask = foundation.pipe.encode_prompt(\n", " inps[i][\"prompt\"],\n", " _transforms(inps[i][\"image\"][0]).mul(255),\n", " device=\"cuda\",\n", " # dtype=foundation.dtype,\n", " max_sequence_length = foundation.config.train_max_sequence_length,\n", " )\n", " all_embeds.append(prompt_embeds.squeeze(0))\n", "stacked_embd = torch.cat(all_embeds, dim=0)\n", "print(f\"{stacked_embd.shape}\")\n", "torch.save(stacked_embd, f\"{quant_type or 'base'}_stacked_embd_{iterations}.pt\")\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "e37beb19", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Image([[[0.0039, 0.0078, 0.0078, ..., 0.0118, 0.0118, 0.0118],\n", " [0.0039, 0.0078, 0.0078, ..., 0.0118, 0.0118, 0.0118],\n", " [0.0039, 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