Updates QwenImage performance documentation

Removes detailed attention backend benchmarks and simplifies torch.compile performance description

Focuses on key performance improvement with torch.compile, highlighting the specific speedup from 4.70s to 1.93s on an A100 GPU

Streamlines the documentation to provide more concise and actionable performance insights

Author: cdutr

docs/source/en/api/pipelines/qwenimage.md

@@ -116,31 +116,9 @@ image = pipe(
 
 ## Performance
 
-### Attention Backends
-
-QwenImage supports multiple attention backends. Benchmarks on A100 80GB:
-
-**Single Image (30 steps, 512x512):**
-
-| Backend | Time (s) |
-|---------|----------|
-| flash_hub | 2.34 |
-| native | 2.38 |
-| xformers | 2.58 |
-| flash_varlen | 2.78 |
-
-**Batch (2 images, 25 steps, 512x512):**
-
-| Backend | Time (s) |
-|---------|----------|
-| flash_hub | 2.85 |
-| native | 3.16 |
-| flash_varlen | 3.29 |
-| xformers | 3.52 |
-
 ### torch.compile
 
-Using `torch.compile` provides significant speedups with a one-time compilation overhead:
+Using `torch.compile` on the transformer provides ~2.4x speedup (A100 80GB: 4.70s → 1.93s):
 
 ```python
 import torch
@@ -149,8 +127,8 @@ from diffusers import QwenImagePipeline
 pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16).to("cuda")
 pipe.transformer = torch.compile(pipe.transformer)
 
-# First call triggers compilation (~7s overhead on A100)
-# Subsequent calls see ~2.4x speedup
+# First call triggers compilation (~7s overhead)
+# Subsequent calls run at ~2.4x faster
 image = pipe("a cat", num_inference_steps=50).images[0]
 ```