Fix flash_attn3 in dispatch attn backend by _flash_attn_forward, replace its origin implement; Add DocString in pipeline for that.

src/diffusers/models/attention_dispatch.py

@@ -79,9 +79,11 @@ else:
 if _CAN_USE_FLASH_ATTN_3:
     from flash_attn_interface import flash_attn_func as flash_attn_3_func
     from flash_attn_interface import flash_attn_varlen_func as flash_attn_3_varlen_func
+    from flash_attn_interface import _flash_attn_forward as flash_attn_3_forward
 else:
     flash_attn_3_func = None
     flash_attn_3_varlen_func = None
+    flash_attn_3_forward = None
 
 if _CAN_USE_AITER_ATTN:
     from aiter import flash_attn_func as aiter_flash_attn_func
@@ -621,22 +623,42 @@ def _wrapped_flash_attn_3(
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     # Hardcoded for now because pytorch does not support tuple/int type hints
     window_size = (-1, -1)
-    out, lse, *_ = flash_attn_3_func(
+    max_seqlen_q = q.shape[2]
+    max_seqlen_k = k.shape[2]
+    
+    out, lse, *_ = flash_attn_3_forward(
         q=q,
         k=k,
         v=v,
-        softmax_scale=softmax_scale,
-        causal=causal,
+        k_new=None,
+        v_new=None,
         qv=qv,
+        out=None,
+        cu_seqlens_q=None,
+        cu_seqlens_k=None,
+        cu_seqlens_k_new=None,
+        seqused_q=None,
+        seqused_k=None,
+        max_seqlen_q=max_seqlen_q,
+        max_seqlen_k=max_seqlen_k,
+        page_table=None,
+        kv_batch_idx=None,
+        leftpad_k=None,
+        rotary_cos=None,
+        rotary_sin=None,
+        seqlens_rotary=None,
         q_descale=q_descale,
         k_descale=k_descale,
         v_descale=v_descale,
+        softmax_scale=softmax_scale,
+        causal=causal,
         window_size=window_size,
         attention_chunk=attention_chunk,
         softcap=softcap,
+        rotary_interleaved=True,
+        scheduler_metadata=None,
         num_splits=num_splits,
         pack_gqa=pack_gqa,
-        deterministic=deterministic,
         sm_margin=sm_margin,
     )
     lse = lse.permute(0, 2, 1)

src/diffusers/pipelines/z_image/pipeline_z_image.py

@@ -39,9 +39,14 @@ EXAMPLE_DOC_STRING = """
 
         >>> pipe = ZImagePipeline.from_pretrained("Z-a-o/Z-Image-Turbo", torch_dtype=torch.bfloat16)
         >>> pipe.to("cuda")
+
+        >>> # Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.
+        >>> # (1) Use flash attention 2
+        >>> # pipe.transformer.set_attention_backend("flash")
+        >>> # (2) Use flash attention 3
+        >>> # pipe.transformer.set_attention_backend("_flash_3")
+        
         >>> prompt = "一幅为名为“造相「Z-IMAGE-TURBO」”的项目设计的创意海报。画面巧妙地将文字概念视觉化：一辆复古蒸汽小火车化身为巨大的拉链头，正拉开厚厚的冬日积雪，展露出一个生机盎然的春天。"
-        >>> # Depending on the variant being used, the pipeline call will slightly vary.
-        >>> # Refer to the pipeline documentation for more details.
         >>> image = pipe(
         ...     prompt,
         ...     height=1024,