chore: clean diffusion dead code (#29789)

python/sglang/multimodal_gen/apps/ComfyUI_SGLDiffusion/core/generator.py

@@ -164,7 +164,6 @@ class SGLDiffusionGenerator:
                 for k in diffusers_keys:
                     if k in sd:
                         new_sd[diffusers_keys[k]] = sd.pop(k)
-        offload_device = model_management.unet_offload_device()
         if dtype is None:
             unet_dtype = model_management.unet_dtype(
                 model_params=parameters,

python/sglang/multimodal_gen/apps/ComfyUI_SGLDiffusion/executors/qwen_image.py

@@ -26,8 +26,6 @@ class QwenImageExecutor(SGLDiffusionExecutor):
 
     def _pack_latents(self, x):
         """Process hidden states for QwenImage model."""
-        bs, c, t, h, w = x.shape
-        patch_size = self.patch_size
         latents = comfy.ldm.common_dit.pad_to_patch_size(
             x, (1, self.patch_size, self.patch_size)
         )

python/sglang/multimodal_gen/apps/ComfyUI_SGLDiffusion/nodes.py

@@ -641,7 +641,7 @@ class SGLDiffusionServerSetLora:
 
         # Call API
         try:
-            response = sgld_client.set_lora(**request_params)
+            sgld_client.set_lora(**request_params)
             return (sgld_client,)
         except Exception as e:
             raise RuntimeError(f"Failed to set LoRA adapter: {str(e)}")
@@ -685,7 +685,7 @@ class SGLDiffusionServerUnsetLora:
     ):
         """Unset LoRA adapter using SGLang Diffusion API."""
         try:
-            response = sgld_client.unset_lora(target=target)
+            sgld_client.unset_lora(target=target)
             return (sgld_client,)
         except Exception as e:
             raise RuntimeError(f"Failed to unset LoRA adapter: {str(e)}")

python/sglang/multimodal_gen/apps/ComfyUI_SGLDiffusion/test/test_flux_pipeline.py

@@ -82,13 +82,6 @@ def test_comfyui_flux_pipeline_direct() -> None:
     req.raw_latent_shape = torch.tensor(hidden_states.shape, dtype=torch.long)
 
     clip_dim = 768
-    dummy_clip_embedding = torch.zeros(
-        batch_size,
-        77,
-        clip_dim,
-        device="cuda",
-        dtype=torch.bfloat16,
-    )
     req.prompt_embeds = [pooled_projections, encoder_hidden_states]
 
     if req.guidance_scale > 1.0:
@@ -149,7 +142,7 @@ def test_comfyui_flux_pipeline_direct() -> None:
         noise_pred.dtype == torch.bfloat16
     ), f"noise_pred should be bfloat16, got {noise_pred.dtype}"
 
-    print(f"✓ Successfully retrieved noise_pred from OutputBatch!")
+    print("✓ Successfully retrieved noise_pred from OutputBatch!")
     print(f"  noise_pred shape: {noise_pred.shape}")
     print(f"  noise_pred dtype: {noise_pred.dtype}")
     print(f"  noise_pred device: {noise_pred.device}")

python/sglang/multimodal_gen/apps/ComfyUI_SGLDiffusion/test/test_qwen_image_edit_pipeline.py

@@ -123,7 +123,7 @@ def test_comfyui_qwen_image_edit_pipeline_direct() -> None:
         noise_pred.dtype == torch.bfloat16
     ), f"noise_pred should be bfloat16, got {noise_pred.dtype}"
 
-    print(f"✓ Successfully retrieved noise_pred from OutputBatch (Edit Mode)!")
+    print("✓ Successfully retrieved noise_pred from OutputBatch (Edit Mode)!")
     print(f"  noise_pred shape: {noise_pred.shape}")
     print(f"  noise_pred dtype: {noise_pred.dtype}")
     print(f"  noise_pred device: {noise_pred.device}")

python/sglang/multimodal_gen/apps/ComfyUI_SGLDiffusion/test/test_qwen_image_pipeline.py

@@ -107,7 +107,7 @@ def test_comfyui_qwen_image_pipeline_direct() -> None:
         noise_pred.dtype == torch.bfloat16
     ), f"noise_pred should be bfloat16, got {noise_pred.dtype}"
 
-    print(f"✓ Successfully retrieved noise_pred from OutputBatch!")
+    print("✓ Successfully retrieved noise_pred from OutputBatch!")
     print(f"  noise_pred shape: {noise_pred.shape}")
     print(f"  noise_pred dtype: {noise_pred.dtype}")
     print(f"  noise_pred device: {noise_pred.device}")

python/sglang/multimodal_gen/apps/ComfyUI_SGLDiffusion/test/test_zimage_pipeline.py

@@ -109,7 +109,7 @@ def test_comfyui_zimage_pipeline_direct() -> None:
         noise_pred.dtype == torch.bfloat16
     ), f"noise_pred should be bfloat16, got {noise_pred.dtype}"
 
-    print(f"✓ Successfully retrieved noise_pred from OutputBatch!")
+    print("✓ Successfully retrieved noise_pred from OutputBatch!")
     print(f"  noise_pred shape: {noise_pred.shape}")
     print(f"  noise_pred dtype: {noise_pred.dtype}")
     print(f"  noise_pred device: {noise_pred.device}")

python/sglang/multimodal_gen/apps/webui/main.py

@@ -174,8 +174,8 @@ def run_sgl_diffusion_webui(server_args: ServerArgs):
     with gr.Blocks() as demo:
         gr.Markdown("# 🚀 SGLang Diffusion Application")
         with gr.Row():
-            launched_model_box = gr.Textbox(label="Model", value=server_args.model_path)
-            task_name_box = gr.Textbox(label="Task name", value=task_name)
+            gr.Textbox(label="Model", value=server_args.model_path)
+            gr.Textbox(label="Task name", value=task_name)
 
         with gr.Row():
             with gr.Column(scale=4):

python/sglang/multimodal_gen/benchmarks/bench_serving.py

@@ -567,7 +567,7 @@ async def benchmark(args):
     else:
         raise ValueError(f"Unknown dataset: {args.dataset}")
 
-    logger.info(f"Loading requests...")
+    logger.info("Loading requests...")
     requests_list = dataset.get_requests()
     logger.info(f"Prepared {len(requests_list)} requests from {args.dataset} dataset.")
 

python/sglang/multimodal_gen/configs/pipeline_configs/joy_image.py

@@ -93,7 +93,6 @@ class JoyImageEditPipelineConfig(ImagePipelineConfig):
 
         while height >= step_height:
             if max(height, width) / min(height, width) <= max_ratio:
-                ratio = height / width
                 buckets.append((1, 1, 1, height, width))
             # Try to increase width or decrease height
             if height * (width + step_width) <= target_pixels:

python/sglang/multimodal_gen/csrc/attn/vmoba_attn/vmoba/__init__.py

@@ -1,2 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
-from .vmoba import moba_attn_varlen, process_moba_input, process_moba_output
+from .vmoba import (
+    moba_attn_varlen as moba_attn_varlen,
+    process_moba_input as process_moba_input,
+    process_moba_output as process_moba_output,
+)
+
+__all__ = ["moba_attn_varlen", "process_moba_input", "process_moba_output"]

python/sglang/multimodal_gen/csrc/attn/vmoba_attn/vmoba/vmoba.py

@@ -239,7 +239,6 @@ def _select_threshold_overall(
     Normalization and sorting happen across all valid entries.
     """
     C, H, S = gate.shape
-    CHS = C * H * S
     eps = 1e-6
 
     # LSE‐style normalization globally across all valid entries
@@ -688,7 +687,6 @@ def moba_attn_varlen(
         .expand(-1, num_head, -1)
     )
     amplification_factor = 1e9  # Example factor; adjust as needed.
-    origin_gate = gate.clone()
     gate = gate.clone()
     if select_mode == "topk":
         gate[gate_self_chunk_mask] += amplification_factor
@@ -963,7 +961,6 @@ def generate_data(batch_size, seqlen, num_head, head_dim, dtype):
     random.seed(0)
     torch.manual_seed(0)
     torch.cuda.manual_seed(0)
-    device = torch.cuda.current_device()
 
     q = torch.randn((batch_size, seqlen, num_head, head_dim), requires_grad=True).to(
         dtype=dtype, device="cuda"

python/sglang/multimodal_gen/csrc/render/hunyuan3d_rasterizer/__init__.py

@@ -9,7 +9,7 @@ Adapted from Hunyuan3D-2: https://github.com/Tencent/Hunyuan3D-2
 from __future__ import annotations
 
 import os
-from typing import List, Tuple
+from typing import Tuple
 
 import torch
 from sglang.multimodal_gen.csrc.render import load_extension_with_recovery

python/sglang/multimodal_gen/runtime/distributed/group_coordinator.py

@@ -1262,11 +1262,11 @@ class SequenceParallelGroupCoordinator(GroupCoordinator):
         ring_group = kwargs.get("ring_group", None)
         if ulysses_group is None:
             raise RuntimeError(
-                f"Please pass argument 'ulysses_group' when calling init func of SequenceParallelGroupCoordinator"
+                "Please pass argument 'ulysses_group' when calling init func of SequenceParallelGroupCoordinator"
             )
         if ring_group is None:
             raise RuntimeError(
-                f"Please pass argument 'ring_group' when calling init func of SequenceParallelGroupCoordinator"
+                "Please pass argument 'ring_group' when calling init func of SequenceParallelGroupCoordinator"
             )
         self.ulysses_group = ulysses_group
         self.ring_group = ring_group

python/sglang/multimodal_gen/runtime/entrypoints/diffusion_generator.py

@@ -54,7 +54,6 @@ from sglang.multimodal_gen.runtime.utils.trace_wrapper import (
 
 logger = init_logger(__name__)
 
-# TODO: move to somewhere appropriate
 try:
     # Set the start method to 'spawn' to avoid CUDA errors in forked processes.
     # This must be done at the top level of the module, before any CUDA context

python/sglang/multimodal_gen/runtime/entrypoints/utils.py

@@ -582,7 +582,7 @@ def save_materialized_output(
     if not save_output:
         return
     if not save_file_path:
-        logger.info(f"No output path provided, output not saved")
+        logger.info("No output path provided, output not saved")
         return
 
     os.makedirs(os.path.dirname(save_file_path), exist_ok=True)

python/sglang/multimodal_gen/runtime/launch_server.py

@@ -200,7 +200,7 @@ def launch_server(server_args: ServerArgs, launch_http_server: bool = True):
             http_server_process = mp.Process(
                 target=launch_http_server_only,
                 args=(server_args,),
-                name=f"sglang-diffusion-webui",
+                name="sglang-diffusion-webui",
                 daemon=True,
             )
             http_server_process.start()

python/sglang/multimodal_gen/runtime/layers/quantization/fp8.py

@@ -100,7 +100,7 @@ class Fp8Config(QuantizationConfig):
         if weight_block_size is not None:
             if not is_checkpoint_fp8_serialized:
                 raise ValueError(
-                    f"The block-wise quantization only supports fp8-serialized checkpoint for now."
+                    "The block-wise quantization only supports fp8-serialized checkpoint for now."
                 )
             if len(weight_block_size) != 2:
                 raise ValueError(

python/sglang/multimodal_gen/runtime/models/bridges/mova_dual_tower.py

@@ -536,7 +536,6 @@ class DualTowerConditionalBridge(
             A tuple of ((cos_v, sin_v), (cos_a, sin_a)).
         """
         f_v, h, w = grid_size
-        L_v = f_v * h * w
         L_a = int(audio_steps)
 
         device = device or next(self.parameters()).device

python/sglang/multimodal_gen/runtime/models/dits/ernie_image.py

@@ -297,8 +297,6 @@ class ErnieImageTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin)
         self.out_channels = arch.out_channels
         self.inner_dim = self.hidden_size
 
-        tp_size = get_tp_world_size()
-
         self.x_embedder = nn.ModuleDict(
             {
                 "proj": nn.Conv2d(

python/sglang/multimodal_gen/runtime/models/dits/glm_image.py

@@ -901,7 +901,7 @@ class GlmImageTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
             ]
         ] = None,
         ###
-        guidance: torch.Tensor = None,  # TODO: this should probably be removed
+        guidance: torch.Tensor = None,
     ) -> Tuple[torch.Tensor]:
         if kv_caches is not None:
             kv_caches.set_mode(kv_caches_mode)

python/sglang/multimodal_gen/runtime/models/dits/helios.py

@@ -655,7 +655,6 @@ class HeliosTransformer3DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
         latents_history_long=None,
         **kwargs,
     ) -> torch.Tensor:
-        orig_dtype = hidden_states.dtype
         if not isinstance(encoder_hidden_states, torch.Tensor):
             encoder_hidden_states = encoder_hidden_states[0]
 

python/sglang/multimodal_gen/runtime/models/dits/joy_image.py

@@ -421,7 +421,7 @@ class JoyTransformer3DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
             self.hidden_size,
             self.out_channels * math.prod(self.patch_size),
             quant_config=quant_config,
-            prefix=f"proj_out",
+            prefix="proj_out",
         )
         self.__post_init__()
 

python/sglang/multimodal_gen/runtime/models/dits/qwen_image.py

@@ -1048,7 +1048,6 @@ class QwenImageTransformerBlock(nn.Module):
         # Apply attention gates and add residual (like in Megatron)
         #   - residual_out = gate_x * x + residual_x
         # - x = norm(residual_out) * (1 + scale) + shift
-        # TODO: clean code here
         is_scale_residual = isinstance(norm_module, ScaleResidualLayerNormScaleShift)
 
         shift, scale, gate = mod_params.chunk(3, dim=-1)
@@ -1389,7 +1388,7 @@ class QwenImageTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
         txt_seq_lens: Optional[List[int]] = None,
         freqs_cis: tuple[torch.Tensor, torch.Tensor] = None,
         additional_t_cond: Optional[torch.Tensor] = None,
-        guidance: torch.Tensor = None,  # TODO: this should probably be removed
+        guidance: torch.Tensor = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         controlnet_block_samples=None,
         return_dict: bool = True,

python/sglang/multimodal_gen/runtime/models/dits/sana_wm_components.py

@@ -2839,11 +2839,9 @@ class BidirectionalGDNUCPESinglePathLiteLA(nn.Module):
         kv_proj = apply_kv(torch.cat([k_bhnd, v_bhnd], dim=1))
         k_proj, v_proj = torch.chunk(kv_proj, chunks=2, dim=1)
 
-        q_pre_dn = q_bhnd.permute(0, 1, 3, 2)
         q_dn = q_proj.permute(0, 1, 3, 2)
         k_pre_dn = k_bhnd.permute(0, 1, 3, 2)
         k_dn = k_proj.permute(0, 1, 3, 2)
-        v_pre_dn = v_bhnd.permute(0, 1, 3, 2)
         v_dn = v_proj.permute(0, 1, 3, 2)
 
         # No RMS downscale here: full post-UCPE q/k/v feed the scan; inflation

python/sglang/multimodal_gen/runtime/models/dits/wanvideo.py

@@ -939,7 +939,7 @@ class WanTransformer3DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
             config.out_channels * math.prod(config.patch_size),
             bias=True,
             gather_output=True,
-            prefix=f"proj_out",
+            prefix="proj_out",
             quant_config=quant_config,
         )
         self.scale_shift_table = nn.Parameter(

python/sglang/multimodal_gen/runtime/models/dits/zimage.py

@@ -954,7 +954,6 @@ class ZImageTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
         cap_feats = self._as_caption_list(encoder_hidden_states)
         timestep = 1000.0 - timestep
         t = timestep
-        device = x[0].device
         t = self.t_embedder(t)
         adaln_input = t.to(dtype=x[0].dtype)
         (

python/sglang/multimodal_gen/runtime/models/encoders/qwen2_5vl.py

@@ -57,7 +57,7 @@ from sglang.multimodal_gen.runtime.utils.common import add_prefix
 # limitations under the License.
 """Inference-only Qwen2-VL model compatible with HuggingFace weights."""
 import logging
-from typing import Callable, Iterable, Optional, Tuple, Union
+from typing import Iterable, Optional, Tuple, Union
 
 try:
     from typing import Unpack  # type: ignore[attr-defined]
@@ -76,7 +76,6 @@ from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
     Qwen2_5_VLModelOutputWithPast,
     Qwen2_5_VLRotaryEmbedding,
     apply_multimodal_rotary_pos_emb,
-    eager_attention_forward,
 )
 
 logger = logging.getLogger(__name__)
@@ -260,26 +259,10 @@ class Qwen2_5_VLAttention(nn.Module):
                 key_states, value_states, self.layer_idx, cache_kwargs
             )
 
-        attention_interface: Callable = eager_attention_forward
-        # if self.config._attn_implementation != "eager":
-        # attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"]
         query_states = query_states.transpose(1, 2)
         key_states = key_states.transpose(1, 2)
         value_states = value_states.transpose(1, 2)
         attn_output = self.attn(query_states, key_states, value_states)
-        #
-        # attn_output, attn_weights = attention_interface(
-        #     self,
-        #     query_states,
-        #     key_states,
-        #     value_states,
-        #     attention_mask,
-        #     dropout=0.0 if not self.training else self.attention_dropout,
-        #     scaling=self.scaling,
-        #     sliding_window=self.sliding_window,
-        #     position_ids=position_ids,  # pass positions for FA2
-        #     **kwargs,
-        # )
 
         attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
         attn_output = _linear_output(self.o_proj, attn_output)

python/sglang/multimodal_gen/runtime/models/vaes/autoencoder.py

@@ -455,13 +455,6 @@ class AutoencoderKL(nn.Module, LayerwiseOffloadableModuleMixin):
                 If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
                 `tuple` is returned.
         """
-        deprecation_message = (
-            "The tiled_encode implementation supporting the `return_dict` parameter is deprecated. In the future, the "
-            "implementation of this method will be replaced with that of `_tiled_encode` and you will no longer be able "
-            "to pass `return_dict`. You will also have to create a `DiagonalGaussianDistribution()` from the returned value."
-        )
-        # deprecate("tiled_encode", "1.0.0", deprecation_message, standard_warn=False)
-
         overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
         blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
         row_limit = self.tile_latent_min_size - blend_extent

python/sglang/multimodal_gen/runtime/models/vaes/autoencoder_kl_flux2.py

@@ -64,7 +64,6 @@ class AutoencoderKLFlux2(ParallelTiledVAE):
         latent_channels: int = arch_config.latent_channels
         norm_num_groups: int = arch_config.norm_num_groups
         sample_size: int = arch_config.sample_size
-        force_upcast: bool = arch_config.force_upcast
         use_quant_conv: bool = arch_config.use_quant_conv
         use_post_quant_conv: bool = arch_config.use_post_quant_conv
         mid_block_add_attention: bool = arch_config.mid_block_add_attention
@@ -411,12 +410,6 @@ class AutoencoderKLFlux2(ParallelTiledVAE):
                 If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
                 `tuple` is returned.
         """
-        deprecation_message = (
-            "The tiled_encode implementation supporting the `return_dict` parameter is deprecated. In the future, the "
-            "implementation of this method will be replaced with that of `_tiled_encode` and you will no longer be able "
-            "to pass `return_dict`. You will also have to create a `DiagonalGaussianDistribution()` from the returned value."
-        )
-
         overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
         blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
         row_limit = self.tile_latent_min_size - blend_extent

python/sglang/multimodal_gen/runtime/models/vaes/hunyuan3d_vae.py

@@ -556,8 +556,6 @@ def generate_dense_grid_points(
 
 def extract_near_surface_volume_fn(input_tensor: torch.Tensor, alpha: float):
     """Extract near-surface voxels for hierarchical decoding."""
-    device = input_tensor.device
-
     val = input_tensor + alpha
     valid_mask = val > -9000
 
@@ -833,7 +831,6 @@ class FlashVDMVolumeDecoding:
         dtype = latents.dtype
 
         resolutions = []
-        orig_resolution = octree_resolution
         if octree_resolution < min_resolution:
             resolutions.append(octree_resolution)
         while octree_resolution >= min_resolution:

python/sglang/multimodal_gen/runtime/pipelines_core/__init__.py

@@ -17,8 +17,10 @@ from sglang.multimodal_gen.runtime.pipelines_core.lora_pipeline import LoRAPipel
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
 from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (
-    maybe_download_model,
-    verify_model_config_and_directory,
+    maybe_download_model as maybe_download_model,
+)
+from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (
+    verify_model_config_and_directory as verify_model_config_and_directory,
 )
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/mova.py

@@ -888,7 +888,6 @@ class MOVADenoisingStage(PipelineStage):
         """
         min_layers = min(len(visual_dit.blocks), len(self.audio_dit.blocks))
         visual_layers = len(visual_dit.blocks)
-        sp_size = get_sp_world_size()
 
         # Build RoPE frequencies for cross-attention if needed (only used when SP == 1)
         # When SP > 1, we rebuild freqs inside the loop after gathering full sequences

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/qwen_image_layered.py

@@ -579,7 +579,6 @@ the image\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>as
         # 5. Prepare timesteps
         scheduler = self.scheduler
         sigmas = np.linspace(1.0, 0, num_inference_steps + 1)[:-1]
-        image_seq_len = latents.shape[1]
         base_seqlen = 256 * 256 / 16 / 16
         mu = (image_latents.shape[1] / base_seqlen) ** 0.5
         timesteps, num_inference_steps = retrieve_timesteps(
@@ -594,8 +593,6 @@ the image\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>as
         negative_txt_seq_lens = _seq_lens_from_optional_mask(
             negative_prompt_embeds, negative_prompt_embeds_mask
         )
-        is_rgb = torch.tensor([0]).to(device=device, dtype=torch.long)
-
         batch.prompt_embeds = [prompt_embeds]
         batch.prompt_embeds_mask = [prompt_embeds_mask]
         batch.prompt_seq_lens = [txt_seq_lens]

python/sglang/multimodal_gen/runtime/utils/quantization_utils.py

@@ -194,7 +194,6 @@ def get_quant_config(
         return quant_cls.from_config(hf_quant_config)
 
     model_name_or_path = model_config["model_path"]
-    is_local = os.path.isdir(model_name_or_path)
     hf_folder = model_name_or_path
 
     possible_config_filenames = quant_cls.get_config_filenames()

python/sglang/multimodal_gen/test/scripts/gen_diffusion_ci_outputs.py

@@ -105,7 +105,7 @@ def main():
         "1"  # Skip consistency checks in GT gen mode
     )
 
-    logger.info(f"GT generation mode enabled")
+    logger.info("GT generation mode enabled")
     logger.info(f"Output directory: {out_dir}")
 
     # Resolve test files path (same as run_suite.py)

python/sglang/multimodal_gen/test/scripts/gen_perf_baselines.py

@@ -112,7 +112,6 @@ def _run_case(case: DiffusionTestCase) -> dict:
     ctx = mgr.start()
     try:
         sp = case.sampling_params
-        output_size = os.environ.get("SGLANG_TEST_OUTPUT_SIZE", sp.output_size)
         client = _openai_client(ctx.port)
         gen = get_generate_fn(
             model_path=case.server_args.model_path,

python/sglang/multimodal_gen/test/server/common/slack.py

@@ -110,11 +110,11 @@ def upload_file_to_slack(
 
         token = os.environ.get("SGLANG_DIFFUSION_SLACK_TOKEN")
         if not token:
-            logger.info(f"Slack upload failed: no token")
+            logger.info("Slack upload failed: no token")
             return False
 
         if not file_path or not os.path.exists(file_path):
-            logger.info(f"Slack upload failed: no file path")
+            logger.info("Slack upload failed: no file path")
             return False
 
         origin_paths = []

python/sglang/multimodal_gen/test/server/test_server_common.py

@@ -101,7 +101,6 @@ def diffusion_server(case: DiffusionTestCase) -> ServerContext:
 
     default_port = get_dynamic_server_port()
     port = int(os.environ.get("SGLANG_TEST_SERVER_PORT", default_port))
-    sampling_params = case.sampling_params
     extra_args = os.environ.get("SGLANG_TEST_SERVE_ARGS", "")
     extra_args = f"--model-type diffusion {extra_args}".strip()
 
@@ -114,7 +113,7 @@ def diffusion_server(case: DiffusionTestCase) -> ServerContext:
         extra_args += f" --ulysses-degree {server_args.ulysses_degree}"
 
     if server_args.dit_layerwise_offload:
-        extra_args += f" --dit-layerwise-offload true"
+        extra_args += " --dit-layerwise-offload true"
 
     if server_args.dit_offload_prefetch_size:
         extra_args += (
@@ -122,7 +121,7 @@ def diffusion_server(case: DiffusionTestCase) -> ServerContext:
         )
 
     if server_args.text_encoder_cpu_offload:
-        extra_args += f" --text-encoder-cpu-offload"
+        extra_args += " --text-encoder-cpu-offload"
 
     if server_args.ring_degree is not None:
         extra_args += f" --ring-degree {server_args.ring_degree}"
@@ -204,17 +203,6 @@ def diffusion_server(case: DiffusionTestCase) -> ServerContext:
             )
         raise
 
-    try:
-        # Reconstruct output size for OpenAI API
-        # Allow override via environment variable (useful for AMD where large resolutions can cause GPU hang)
-        output_size = os.environ.get(
-            "SGLANG_TEST_OUTPUT_SIZE", sampling_params.output_size
-        )
-    except Exception as exc:
-        logger.error("Warm-up failed for %s: %s", case.id, exc)
-        ctx.cleanup()
-        raise
-
     try:
         yield ctx
     finally:
@@ -425,14 +413,11 @@ class DiffusionServerBase:
             if not is_baseline_generation_mode:
                 missing_scenario = True
 
-        # Check for missing estimated_full_test_time_s
-        missing_estimated_time = False
         if (
             not missing_scenario
             and not is_baseline_generation_mode
             and scenario.estimated_full_test_time_s is None
         ):
-            missing_estimated_time = True
             _MISSING_ESTIMATED_TIME_CASES.add(case.id)
 
         validator_name = case.server_args.custom_validator or "default"

python/sglang/multimodal_gen/test/unit/test_ideogram4.py

@@ -1065,7 +1065,7 @@ class TestIdeogram4(unittest.TestCase):
         self.assertTrue(layer.mlp.down_proj.input_is_parallel)
         self.assertTrue(layer.mlp.down_proj.reduce_results)
 
-    def test_denoise_and_decode_shape_smoke(self):
+    def test_denoise_and_decode_shape_check(self):
         import sglang.multimodal_gen.runtime.server_args as server_args_module
 
         cfg = Ideogram4PipelineConfig()