[tests] add tests for flux modular (t2i, i2i, kontext) (#12566)

* start flux modular tests.

* up

* add kontext

* up

* up

* up

* Update src/diffusers/modular_pipelines/flux/denoise.py

Co-authored-by: YiYi Xu <yixu310@gmail.com>

* up

* up

---------

Co-authored-by: YiYi Xu <yixu310@gmail.com>

Author: sayakpaul

src/diffusers/modular_pipelines/components_manager.py

@@ -164,7 +164,11 @@ def __call__(self, hooks, model_id, model, execution_device):
 
         device_type = execution_device.type
         device_module = getattr(torch, device_type, torch.cuda)
-        mem_on_device = device_module.mem_get_info(execution_device.index)[0]
+        try:
+            mem_on_device = device_module.mem_get_info(execution_device.index)[0]
+        except AttributeError:
+            raise AttributeError(f"Do not know how to obtain obtain memory info for {str(device_module)}.")
+
         mem_on_device = mem_on_device - self.memory_reserve_margin
         if current_module_size < mem_on_device:
             return []
@@ -699,6 +703,8 @@ def enable_auto_cpu_offload(self, device: Union[str, int, torch.device] = None,
         if not is_accelerate_available():
             raise ImportError("Make sure to install accelerate to use auto_cpu_offload")
 
+        # TODO: add a warning if mem_get_info isn't available on `device`.
+
         for name, component in self.components.items():
             if isinstance(component, torch.nn.Module) and hasattr(component, "_hf_hook"):
                 remove_hook_from_module(component, recurse=True)

src/diffusers/modular_pipelines/flux/before_denoise.py

@@ -598,7 +598,7 @@ def __call__(self, components: FluxModularPipeline, state: PipelineState) -> Pip
             and getattr(block_state, "image_width", None) is not None
         ):
             image_latent_height = 2 * (int(block_state.image_height) // (components.vae_scale_factor * 2))
-            image_latent_width = 2 * (int(block_state.width) // (components.vae_scale_factor * 2))
+            image_latent_width = 2 * (int(block_state.image_width) // (components.vae_scale_factor * 2))
             img_ids = FluxPipeline._prepare_latent_image_ids(
                 None, image_latent_height // 2, image_latent_width // 2, device, dtype
             )

src/diffusers/modular_pipelines/flux/denoise.py

@@ -59,7 +59,7 @@ def inputs(self) -> List[Tuple[str, Any]]:
             ),
             InputParam(
                 "guidance",
-                required=True,
+                required=False,
                 type_hint=torch.Tensor,
                 description="Guidance scale as a tensor",
             ),
@@ -141,7 +141,7 @@ def inputs(self) -> List[Tuple[str, Any]]:
             ),
             InputParam(
                 "guidance",
-                required=True,
+                required=False,
                 type_hint=torch.Tensor,
                 description="Guidance scale as a tensor",
             ),

src/diffusers/modular_pipelines/flux/encoders.py

@@ -95,7 +95,7 @@ def expected_components(self) -> List[ComponentSpec]:
             ComponentSpec(
                 "image_processor",
                 VaeImageProcessor,
-                config=FrozenDict({"vae_scale_factor": 16}),
+                config=FrozenDict({"vae_scale_factor": 16, "vae_latent_channels": 16}),
                 default_creation_method="from_config",
             ),
         ]
@@ -143,10 +143,6 @@ def __call__(self, components: FluxModularPipeline, state: PipelineState):
 class FluxKontextProcessImagesInputStep(ModularPipelineBlocks):
     model_name = "flux-kontext"
 
-    def __init__(self, _auto_resize=True):
-        self._auto_resize = _auto_resize
-        super().__init__()
-
     @property
     def description(self) -> str:
         return (
@@ -167,7 +163,7 @@ def expected_components(self) -> List[ComponentSpec]:
 
     @property
     def inputs(self) -> List[InputParam]:
-        return [InputParam("image")]
+        return [InputParam("image"), InputParam("_auto_resize", type_hint=bool, default=True)]
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
@@ -195,7 +191,8 @@ def __call__(self, components: FluxModularPipeline, state: PipelineState):
             img = images[0]
             image_height, image_width = components.image_processor.get_default_height_width(img)
             aspect_ratio = image_width / image_height
-            if self._auto_resize:
+            _auto_resize = block_state._auto_resize
+            if _auto_resize:
                 # Kontext is trained on specific resolutions, using one of them is recommended
                 _, image_width, image_height = min(
                     (abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_KONTEXT_RESOLUTIONS

src/diffusers/modular_pipelines/flux/inputs.py

@@ -112,6 +112,10 @@ def __call__(self, components: FluxModularPipeline, state: PipelineState) -> Pip
         block_state.prompt_embeds = block_state.prompt_embeds.view(
             block_state.batch_size * block_state.num_images_per_prompt, seq_len, -1
         )
+        pooled_prompt_embeds = block_state.pooled_prompt_embeds.repeat(1, block_state.num_images_per_prompt)
+        block_state.pooled_prompt_embeds = pooled_prompt_embeds.view(
+            block_state.batch_size * block_state.num_images_per_prompt, -1
+        )
         self.set_block_state(state, block_state)
 
         return components, state

tests/modular_pipelines/flux/__init__.py

@@ -0,0 +1,130 @@
+# 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 random
+import tempfile
+import unittest
+
+import numpy as np
+import PIL
+import torch
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.modular_pipelines import (
+    FluxAutoBlocks,
+    FluxKontextAutoBlocks,
+    FluxKontextModularPipeline,
+    FluxModularPipeline,
+    ModularPipeline,
+)
+
+from ...testing_utils import floats_tensor, torch_device
+from ..test_modular_pipelines_common import ModularPipelineTesterMixin
+
+
+class FluxModularTests:
+    pipeline_class = FluxModularPipeline
+    pipeline_blocks_class = FluxAutoBlocks
+    repo = "hf-internal-testing/tiny-flux-modular"
+
+    def get_pipeline(self, components_manager=None, torch_dtype=torch.float32):
+        pipeline = self.pipeline_blocks_class().init_pipeline(self.repo, components_manager=components_manager)
+        pipeline.load_components(torch_dtype=torch_dtype)
+        return pipeline
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 5.0,
+            "height": 8,
+            "width": 8,
+            "max_sequence_length": 48,
+            "output_type": "np",
+        }
+        return inputs
+
+
+class FluxModularPipelineFastTests(FluxModularTests, ModularPipelineTesterMixin, unittest.TestCase):
+    params = frozenset(["prompt", "height", "width", "guidance_scale"])
+    batch_params = frozenset(["prompt"])
+
+
+class FluxImg2ImgModularPipelineFastTests(FluxModularTests, ModularPipelineTesterMixin, unittest.TestCase):
+    params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
+    batch_params = frozenset(["prompt", "image"])
+
+    def get_pipeline(self, components_manager=None, torch_dtype=torch.float32):
+        pipeline = super().get_pipeline(components_manager, torch_dtype)
+        # Override `vae_scale_factor` here as currently, `image_processor` is initialized with
+        # fixed constants instead of
+        # https://github.com/huggingface/diffusers/blob/d54622c2679d700b425ad61abce9b80fc36212c0/src/diffusers/pipelines/flux/pipeline_flux_img2img.py#L230C9-L232C10
+        pipeline.image_processor = VaeImageProcessor(vae_scale_factor=2)
+        return pipeline
+
+    def get_dummy_inputs(self, device, seed=0):
+        inputs = super().get_dummy_inputs(device, seed)
+        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
+        image = image / 2 + 0.5
+        inputs["image"] = image
+        inputs["strength"] = 0.8
+        inputs["height"] = 8
+        inputs["width"] = 8
+        return inputs
+
+    def test_save_from_pretrained(self):
+        pipes = []
+        base_pipe = self.get_pipeline().to(torch_device)
+        pipes.append(base_pipe)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            base_pipe.save_pretrained(tmpdirname)
+            pipe = ModularPipeline.from_pretrained(tmpdirname).to(torch_device)
+            pipe.load_components(torch_dtype=torch.float32)
+            pipe.to(torch_device)
+            pipe.image_processor = VaeImageProcessor(vae_scale_factor=2)
+
+        pipes.append(pipe)
+
+        image_slices = []
+        for pipe in pipes:
+            inputs = self.get_dummy_inputs(torch_device)
+            image = pipe(**inputs, output="images")
+
+            image_slices.append(image[0, -3:, -3:, -1].flatten())
+
+        assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
+
+
+class FluxKontextModularPipelineFastTests(FluxImg2ImgModularPipelineFastTests):
+    pipeline_class = FluxKontextModularPipeline
+    pipeline_blocks_class = FluxKontextAutoBlocks
+    repo = "hf-internal-testing/tiny-flux-kontext-pipe"
+
+    def get_dummy_inputs(self, device, seed=0):
+        inputs = super().get_dummy_inputs(device, seed)
+        image = PIL.Image.new("RGB", (32, 32), 0)
+        _ = inputs.pop("strength")
+        inputs["image"] = image
+        inputs["height"] = 8
+        inputs["width"] = 8
+        inputs["max_area"] = 8 * 8
+        inputs["_auto_resize"] = False
+        return inputs

tests/modular_pipelines/flux/test_modular_pipeline_flux.py

@@ -21,24 +21,12 @@
 import torch
 from PIL import Image
 
-from diffusers import (
-    ClassifierFreeGuidance,
-    StableDiffusionXLAutoBlocks,
-    StableDiffusionXLModularPipeline,
-)
+from diffusers import ClassifierFreeGuidance, StableDiffusionXLAutoBlocks, StableDiffusionXLModularPipeline
 from diffusers.loaders import ModularIPAdapterMixin
 
-from ...models.unets.test_models_unet_2d_condition import (
-    create_ip_adapter_state_dict,
-)
-from ...testing_utils import (
-    enable_full_determinism,
-    floats_tensor,
-    torch_device,
-)
-from ..test_modular_pipelines_common import (
-    ModularPipelineTesterMixin,
-)
+from ...models.unets.test_models_unet_2d_condition import create_ip_adapter_state_dict
+from ...testing_utils import enable_full_determinism, floats_tensor, torch_device
+from ..test_modular_pipelines_common import ModularPipelineTesterMixin
 
 
 enable_full_determinism()