[Modular] Some clean up for Modular tests (#12579)

* update

* update

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Author: DN6

tests/modular_pipelines/flux/test_modular_pipeline_flux.py

@@ -15,7 +15,6 @@
 
 import random
 import tempfile
-import unittest
 
 import numpy as np
 import PIL
@@ -34,21 +33,16 @@
 from ..test_modular_pipelines_common import ModularPipelineTesterMixin
 
 
-class FluxModularTests:
+class TestFluxModularPipelineFast(ModularPipelineTesterMixin):
     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
+    params = frozenset(["prompt", "height", "width", "guidance_scale"])
+    batch_params = frozenset(["prompt"])
 
-    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)
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(seed)
         inputs = {
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
@@ -57,36 +51,47 @@ def get_dummy_inputs(self, device, seed=0):
             "height": 8,
             "width": 8,
             "max_sequence_length": 48,
-            "output_type": "np",
+            "output_type": "pt",
         }
         return inputs
 
 
-class FluxModularPipelineFastTests(FluxModularTests, ModularPipelineTesterMixin, unittest.TestCase):
-    params = frozenset(["prompt", "height", "width", "guidance_scale"])
-    batch_params = frozenset(["prompt"])
-
+class TestFluxImg2ImgModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = FluxModularPipeline
+    pipeline_blocks_class = FluxAutoBlocks
+    repo = "hf-internal-testing/tiny-flux-modular"
 
-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
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "generator": generator,
+            "num_inference_steps": 4,
+            "guidance_scale": 5.0,
+            "height": 8,
+            "width": 8,
+            "max_sequence_length": 48,
+            "output_type": "pt",
+        }
+        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(torch_device)
+        image = image.cpu().permute(0, 2, 3, 1)[0]
+        init_image = PIL.Image.fromarray(np.uint8(image)).convert("RGB")
+
+        inputs["image"] = init_image
+        inputs["strength"] = 0.5
+
         return inputs
 
     def test_save_from_pretrained(self):
@@ -96,6 +101,7 @@ def test_save_from_pretrained(self):
 
         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)
@@ -105,26 +111,62 @@ def test_save_from_pretrained(self):
 
         image_slices = []
         for pipe in pipes:
-            inputs = self.get_dummy_inputs(torch_device)
+            inputs = self.get_dummy_inputs()
             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
+        assert torch.abs(image_slices[0] - image_slices[1]).max() < 1e-3
 
 
-class FluxKontextModularPipelineFastTests(FluxImg2ImgModularPipelineFastTests):
+class TestFluxKontextModularPipelineFast(ModularPipelineTesterMixin):
     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)
+    params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
+    batch_params = frozenset(["prompt", "image"])
+
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(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": "pt",
+        }
         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["max_area"] = inputs["height"] * inputs["width"]
         inputs["_auto_resize"] = False
+
         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()
+            image = pipe(**inputs, output="images")
+
+            image_slices.append(image[0, -3:, -3:, -1].flatten())
+
+        assert torch.abs(image_slices[0] - image_slices[1]).max() < 1e-3