From a73fb2d8550ee9e53013742f4d2552167c1a623b Mon Sep 17 00:00:00 2001
From: Ylean <61880605+Woolgathererer@users.noreply.github.com>
Date: Sun, 13 Sep 2026 10:12:35 +0800
Subject: [PATCH] [Bugfix][Gemma4] Keep image kwargs out of video preprocessing

Clear image-only overrides when processing video frames so image token
budgets do not conflict with the fixed video budget and video pixels stay
independent of options excluded from the video cache key.

Cover configured and request overrides, cache reuse, and video UUIDs.

Co-authored-by: Codex <noreply@openai.com>
Signed-off-by: Ylean <61880605+Woolgathererer@users.noreply.github.com>
---
 .../multimodal/processing/test_gemma4.py      | 68 +++++++++++++++++++
 vllm/model_executor/models/gemma4_mm.py       |  3 +
 2 files changed, 71 insertions(+)

diff --git a/tests/models/multimodal/processing/test_gemma4.py b/tests/models/multimodal/processing/test_gemma4.py
index f30afe47dde2..a28cc50be5fb 100644
--- a/tests/models/multimodal/processing/test_gemma4.py
+++ b/tests/models/multimodal/processing/test_gemma4.py
@@ -3,6 +3,7 @@
 
 from collections.abc import Mapping
 
+import numpy as np
 import pytest
 import torch
 from PIL import Image as PILImage
@@ -13,6 +14,7 @@
     Gemma4ImagePixelInputs,
 )
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.cache import MultiModalProcessorOnlyCache
 from vllm.multimodal.inputs import MultiModalFieldConfig
 from vllm.utils.mem_constants import GiB_bytes
 
@@ -199,6 +201,72 @@ def test_get_prompt_updates_respects_nested_max_soft_tokens(model_id: str):
     assert replacement == expected
 
 
+@pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
+@pytest.mark.parametrize("kwargs_on_init", [False, True])
+@pytest.mark.parametrize(
+    "image_kwargs", [{"rescale_factor": 1 / 127.5}, {"max_soft_tokens": 560}]
+)
+@pytest.mark.parametrize("video_uuid", [None, "same-video"])
+def test_video_cache_is_independent_of_image_kwargs(
+    model_id: str,
+    kwargs_on_init: bool,
+    image_kwargs: dict[str, object],
+    video_uuid: str | None,
+):
+    """Image overrides must not change video frames or depend on cache warmth."""
+    kwargs = {"images_kwargs": image_kwargs}
+    ctx = build_model_context(
+        model_id,
+        limit_mm_per_prompt={"image": 1, "video": 1},
+        mm_processor_cache_gb=1,
+    )
+    cache = MultiModalProcessorOnlyCache(ctx.model_config)
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config, cache=cache)
+    hf_processor = processor.info.get_hf_processor()
+    image = PILImage.new("RGB", (48, 48), color=(128, 128, 128))
+    frames = np.stack([np.asarray(image)] * 2)
+    metadata = {"fps": 2.0, "frames_indices": [0, 1]}
+    mm_items = processor.info.parse_mm_data(
+        {"image": image, "video": [(frames, metadata)]}
+    )
+
+    def process(mm_kwargs):
+        return processor(
+            hf_processor.image_token + hf_processor.video_token,
+            mm_items,
+            mm_uuid_items={"video": [video_uuid]},
+            hf_processor_mm_kwargs=mm_kwargs,
+        )
+
+    baseline = process({})
+    if kwargs_on_init:
+        ctx = build_model_context(
+            model_id,
+            mm_processor_kwargs=kwargs,
+            limit_mm_per_prompt={"image": 1, "video": 1},
+            mm_processor_cache_gb=1,
+        )
+        cache = MultiModalProcessorOnlyCache(ctx.model_config)
+        processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config, cache=cache)
+    request_kwargs = {} if kwargs_on_init else kwargs
+    process(request_kwargs)
+    cached = process(request_kwargs)
+    cache.clear_cache()
+    fresh = process(request_kwargs)
+
+    def pixels(result, modality, field):
+        return result["mm_kwargs"][modality][0][field].data
+
+    video_pixels = pixels(fresh, "video", "pixel_values_videos")
+    assert torch.equal(pixels(cached, "video", "pixel_values_videos"), video_pixels)
+    assert torch.equal(pixels(baseline, "video", "pixel_values_videos"), video_pixels)
+    assert baseline["mm_hashes"]["video"] == fresh["mm_hashes"]["video"]
+    assert not torch.equal(
+        pixels(baseline, "image", "pixel_values"),
+        pixels(fresh, "image", "pixel_values"),
+    )
+
+
 @pytest.mark.parametrize("model_id", [GEMMA4_MODEL_ID])
 def test_limit_mm_per_prompt(
     image_assets: ImageTestAssets,
diff --git a/vllm/model_executor/models/gemma4_mm.py b/vllm/model_executor/models/gemma4_mm.py
index 326d179cc237..2dddc72b218d 100644
--- a/vllm/model_executor/models/gemma4_mm.py
+++ b/vllm/model_executor/models/gemma4_mm.py
@@ -629,6 +629,9 @@ def _apply_hf_processor_main(
 
                 # Process frames as images with max_soft_tokens=70
                 video_mm_kwargs = dict(hf_processor_mm_kwargs)
+                # Override configured image options too: these inputs are video
+                # frames, whose cache keys exclude images_kwargs.
+                video_mm_kwargs["images_kwargs"] = {}
                 video_mm_kwargs["max_soft_tokens"] = _VIDEO_MAX_SOFT_TOKENS
 
                 dummy_prompt = ("\t" + processor.image_token) * len(frames)