How to run qwen-image in kaggle gpu T4 * 2 successfully?

```python3
!python3 -m pip install -U diffusers peft bitsandbytes
import diffusers, torch, math
qwen = diffusers.QwenImagePipeline.from_pretrained('Qwen/Qwen-Image', torch_dtype=torch.float16, low_cpu_mem_usage=True, quantization_config=diffusers.PipelineQuantizationConfig(quant_backend='bitsandbytes_4bit', quant_kwargs={'load_in_4bit':True, 'bnb_4bit_quant_type':'nf4', 'bnb_4bit_compute_dtype':torch.float16}, components_to_quantize=['transformer', 'text_encoder']))
qwen.scheduler = diffusers.FlowMatchEulerDiscreteScheduler.from_config({'base_image_seq_len':256, 'base_shift':math.log(3), 'invert_sigmas':False, 'max_image_seq_len':8192, 'max_shift':math.log(3), 'num_train_timesteps':1000, 'shift':1, 'shift_terminal':None, 'stochastic_sampling':False, 'time_shift_type':'exponential', 'use_beta_sigmas':False, 'use_dynamic_shifting':True, 'use_exponential_sigmas':False, 'use_karras_sigmas':False})
qwen.load_lora_weights('lightx2v/Qwen-Image-Lightning', weight_name='Qwen-Image-Lightning-4steps-V2.0.safetensors', adapter_name='lightning')
qwen.set_adapters('lightning', adapter_weights=1)
qwen.enable_sequential_cpu_offload()
qwen(prompt='a beautiful girl', height=1280, width=720, num_inference_steps=4, true_cfg_scale=1).images[0].save('a.png')
```
----> 3 qwen = diffusers.QwenImagePipeline.from_pretrained('Qwen/Qwen-Image', torch_dtype=torch.float16, low_cpu_mem_usage=True, quantization_config=diffusers.PipelineQuantizationConfig(quant_backend='bitsandbytes_4bit', quant_kwargs={'load_in_4bit':True, 'bnb_4bit_quant_type':'nf4', 'bnb_4bit_compute_dtype':torch.float16}, components_to_quantize=['transformer', 'text_encoder']))

OutOfMemoryError: CUDA out of memory. Tried to allocate 34.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 4.19 MiB is free. Process 8568 has 14.73 GiB memory in use. Of the allocated memory 14.50 GiB is allocated by PyTorch, and 129.00 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

How to get more cuda memory?

@yiyixuxu @DN6

Start with the provided Kaggle Python snippet and the CUDA out-of-memory traceback, focusing on QwenImagePipeline.from_pretrained and the listed quantization and CPU-offload settings. Compare the reported GPU usage with the documented memory requirements and determine what configuration allows the pipeline to load and generate the requested image successfully.

Written by the indexing model from the issue text.