[modular] add tests for qwen modular

src/diffusers/modular_pipelines/qwenimage/before_denoise.py

@@ -132,6 +132,7 @@ def expected_components(self) -> List[ComponentSpec]:
     @property
     def inputs(self) -> List[InputParam]:
         return [
+            InputParam("latents"),
             InputParam(name="height"),
             InputParam(name="width"),
             InputParam(name="num_images_per_prompt", default=1),
@@ -196,11 +197,11 @@ def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -
                 f"You have passed a list of generators of length {len(block_state.generator)}, but requested an effective batch"
                 f" size of {batch_size}. Make sure the batch size matches the length of the generators."
             )
-
-        block_state.latents = randn_tensor(
-            shape, generator=block_state.generator, device=device, dtype=block_state.dtype
-        )
-        block_state.latents = components.pachifier.pack_latents(block_state.latents)
+        if block_state.latents is None:
+            block_state.latents = randn_tensor(
+                shape, generator=block_state.generator, device=device, dtype=block_state.dtype
+            )
+            block_state.latents = components.pachifier.pack_latents(block_state.latents)
 
         self.set_block_state(state, block_state)
         return components, state
@@ -549,7 +550,7 @@ def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -
                     block_state.width // components.vae_scale_factor // 2,
                 )
             ]
-            * block_state.batch_size
+            for _ in range(block_state.batch_size)
         ]
         block_state.txt_seq_lens = (
             block_state.prompt_embeds_mask.sum(dim=1).tolist() if block_state.prompt_embeds_mask is not None else None

src/diffusers/modular_pipelines/qwenimage/decoders.py

@@ -74,8 +74,9 @@ def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -
         block_state = self.get_block_state(state)
 
         # YiYi Notes: remove support for output_type = "latents', we can just skip decode/encode step in modular
+        vae_scale_factor = 2 ** len(components.vae.temperal_downsample)
         block_state.latents = components.pachifier.unpack_latents(
-            block_state.latents, block_state.height, block_state.width
+            block_state.latents, block_state.height, block_state.width, vae_scale_factor=vae_scale_factor
         )
         block_state.latents = block_state.latents.to(components.vae.dtype)
 

src/diffusers/modular_pipelines/qwenimage/encoders.py

@@ -503,6 +503,8 @@ def __call__(self, components: QwenImageModularPipeline, state: PipelineState):
         block_state.prompt_embeds = block_state.prompt_embeds[:, : block_state.max_sequence_length]
         block_state.prompt_embeds_mask = block_state.prompt_embeds_mask[:, : block_state.max_sequence_length]
 
+        block_state.negative_prompt_embeds = None
+        block_state.negative_prompt_embeds_mask = None
         if components.requires_unconditional_embeds:
             negative_prompt = block_state.negative_prompt or ""
             block_state.negative_prompt_embeds, block_state.negative_prompt_embeds_mask = get_qwen_prompt_embeds(
@@ -627,6 +629,8 @@ def __call__(self, components: QwenImageModularPipeline, state: PipelineState):
             device=device,
         )
 
+        block_state.negative_prompt_embeds = None
+        block_state.negative_prompt_embeds_mask = None
         if components.requires_unconditional_embeds:
             negative_prompt = block_state.negative_prompt or " "
             block_state.negative_prompt_embeds, block_state.negative_prompt_embeds_mask = get_qwen_prompt_embeds_edit(
@@ -679,6 +683,8 @@ def __call__(self, components: QwenImageModularPipeline, state: PipelineState):
             device=device,
         )
 
+        block_state.negative_prompt_embeds = None
+        block_state.negative_prompt_embeds_mask = None
         if components.requires_unconditional_embeds:
             negative_prompt = block_state.negative_prompt or " "
             block_state.negative_prompt_embeds, block_state.negative_prompt_embeds_mask = (

src/diffusers/modular_pipelines/qwenimage/modular_pipeline.py

@@ -26,10 +26,7 @@ class QwenImagePachifier(ConfigMixin):
     config_name = "config.json"
 
     @register_to_config
-    def __init__(
-        self,
-        patch_size: int = 2,
-    ):
+    def __init__(self, patch_size: int = 2):
         super().__init__()
 
     def pack_latents(self, latents):

tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py

@@ -0,0 +1,141 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import numpy as np
+import PIL
+import pytest
+
+from diffusers import ClassifierFreeGuidance
+from diffusers.modular_pipelines import (
+    QwenImageAutoBlocks,
+    QwenImageEditAutoBlocks,
+    QwenImageEditModularPipeline,
+    QwenImageEditPlusAutoBlocks,
+    QwenImageEditPlusModularPipeline,
+    QwenImageModularPipeline,
+)
+
+from ...testing_utils import torch_device
+from ..test_modular_pipelines_common import ModularPipelineTesterMixin
+
+
+class QwenImageModularGuiderMixin:
+    def test_guider_cfg(self, tol=1e-2):
+        pipe = self.get_pipeline()
+        pipe = pipe.to(torch_device)
+
+        guider = ClassifierFreeGuidance(guidance_scale=1.0)
+        pipe.update_components(guider=guider)
+
+        inputs = self.get_dummy_inputs()
+        out_no_cfg = pipe(**inputs, output="images")
+
+        guider = ClassifierFreeGuidance(guidance_scale=7.5)
+        pipe.update_components(guider=guider)
+        inputs = self.get_dummy_inputs()
+        out_cfg = pipe(**inputs, output="images")
+
+        assert out_cfg.shape == out_no_cfg.shape
+        max_diff = np.abs(out_cfg - out_no_cfg).max()
+        assert max_diff > tol, "Output with CFG must be different from normal inference"
+
+
+class TestQwenImageModularPipelineFast(ModularPipelineTesterMixin, QwenImageModularGuiderMixin):
+    pipeline_class = QwenImageModularPipeline
+    pipeline_blocks_class = QwenImageAutoBlocks
+    repo = "hf-internal-testing/tiny-qwenimage-modular"
+
+    params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image", "mask_image"])
+    batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
+
+    def get_dummy_inputs(self):
+        generator = self.get_generator()
+        inputs = {
+            "prompt": "dance monkey",
+            "negative_prompt": "bad quality",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "height": 32,
+            "width": 32,
+            "max_sequence_length": 16,
+            "output_type": "pt",
+        }
+        return inputs
+
+
+class TestQwenImageEditModularPipelineFast(ModularPipelineTesterMixin, QwenImageModularGuiderMixin):
+    pipeline_class = QwenImageEditModularPipeline
+    pipeline_blocks_class = QwenImageEditAutoBlocks
+    repo = "hf-internal-testing/tiny-qwenimage-edit-modular"
+
+    params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image", "mask_image"])
+    batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
+
+    def get_dummy_inputs(self):
+        generator = self.get_generator()
+        inputs = {
+            "prompt": "dance monkey",
+            "negative_prompt": "bad quality",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "height": 32,
+            "width": 32,
+            "output_type": "pt",
+        }
+        inputs["image"] = PIL.Image.new("RGB", (32, 32), 0)
+        return inputs
+
+    def test_guider_cfg(self):
+        super().test_guider_cfg(7e-5)
+
+
+class TestQwenImageEditPlusModularPipelineFast(ModularPipelineTesterMixin, QwenImageModularGuiderMixin):
+    pipeline_class = QwenImageEditPlusModularPipeline
+    pipeline_blocks_class = QwenImageEditPlusAutoBlocks
+    repo = "hf-internal-testing/tiny-qwenimage-edit-plus-modular"
+
+    # No `mask_image` yet.
+    params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image"])
+    batch_params = frozenset(["prompt", "negative_prompt", "image"])
+
+    def get_dummy_inputs(self):
+        generator = self.get_generator()
+        inputs = {
+            "prompt": "dance monkey",
+            "negative_prompt": "bad quality",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "height": 32,
+            "width": 32,
+            "output_type": "pt",
+        }
+        inputs["image"] = PIL.Image.new("RGB", (32, 32), 0)
+        return inputs
+
+    @pytest.mark.xfail(condition=True, reason="Batch of multiple images needs to be revisited", strict=True)
+    def test_num_images_per_prompt(self):
+        super().test_num_images_per_prompt()
+
+    @pytest.mark.xfail(condition=True, reason="Batch of multiple images needs to be revisited", strict=True)
+    def test_inference_batch_consistent():
+        super().test_inference_batch_consistent()
+
+    @pytest.mark.xfail(condition=True, reason="Batch of multiple images needs to be revisited", strict=True)
+    def test_inference_batch_single_identical():
+        super().test_inference_batch_single_identical()
+
+    def test_guider_cfg(self):
+        super().test_guider_cfg(1e-3)

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -2,22 +2,16 @@
 import tempfile
 from typing import Callable, Union
 
+import pytest
 import torch
 
 import diffusers
 from diffusers import ComponentsManager, ModularPipeline, ModularPipelineBlocks
 from diffusers.utils import logging
 
-from ..testing_utils import (
-    backend_empty_cache,
-    numpy_cosine_similarity_distance,
-    require_accelerator,
-    require_torch,
-    torch_device,
-)
+from ..testing_utils import backend_empty_cache, numpy_cosine_similarity_distance, torch_device
 
 
-@require_torch
 class ModularPipelineTesterMixin:
     """
     It provides a set of common tests for each modular pipeline,
@@ -32,20 +26,9 @@ class ModularPipelineTesterMixin:
     # Canonical parameters that are passed to `__call__` regardless
     # of the type of pipeline. They are always optional and have common
     # sense default values.
-    optional_params = frozenset(
-        [
-            "num_inference_steps",
-            "num_images_per_prompt",
-            "latents",
-            "output_type",
-        ]
-    )
+    optional_params = frozenset(["num_inference_steps", "num_images_per_prompt", "latents", "output_type"])
     # this is modular specific: generator needs to be a intermediate input because it's mutable
-    intermediate_params = frozenset(
-        [
-            "generator",
-        ]
-    )
+    intermediate_params = frozenset(["generator"])
 
     def get_generator(self, seed=0):
         generator = torch.Generator("cpu").manual_seed(seed)
@@ -215,7 +198,7 @@ def test_inference_batch_single_identical(
         max_diff = torch.abs(output_batch[0] - output[0]).max()
         assert max_diff < expected_max_diff, "Batch inference results different from single inference results"
 
-    @require_accelerator
+    @pytest.mark.skipif(torch_device == "cpu", reason="Test needs an accelerator.")
     def test_float16_inference(self, expected_max_diff=5e-2):
         pipe = self.get_pipeline()
         pipe.to(torch_device, torch.float32)
@@ -244,7 +227,7 @@ def test_float16_inference(self, expected_max_diff=5e-2):
         max_diff = numpy_cosine_similarity_distance(output.flatten(), output_fp16.flatten())
         assert max_diff < expected_max_diff, "FP16 inference is different from FP32 inference"
 
-    @require_accelerator
+    @pytest.mark.skipif(torch_device == "cpu", reason="Test needs an accelerator.")
     def test_to_device(self):
         pipe = self.get_pipeline()
         pipe.set_progress_bar_config(disable=None)
@@ -271,7 +254,7 @@ def test_inference_is_not_nan_cpu(self):
         output = pipe(**self.get_dummy_inputs(), output="images")
         assert torch.isnan(output).sum() == 0, "CPU Inference returns NaN"
 
-    @require_accelerator
+    @pytest.mark.skipif(torch_device == "cpu", reason="Test needs an accelerator.")
     def test_inference_is_not_nan(self):
         pipe = self.get_pipeline()
         pipe.set_progress_bar_config(disable=None)
@@ -304,7 +287,7 @@ def test_num_images_per_prompt(self):
 
                 assert images.shape[0] == batch_size * num_images_per_prompt
 
-    @require_accelerator
+    @pytest.mark.skipif(torch_device == "cpu", reason="Test needs an accelerator.")
     def test_components_auto_cpu_offload_inference_consistent(self):
         base_pipe = self.get_pipeline().to(torch_device)