remove unneeded compile test.

Author: sayakpaul

tests/pipelines/controlnet/test_controlnet.py

@@ -15,7 +15,6 @@
 
 import gc
 import tempfile
-import traceback
 import unittest
 
 import numpy as np
@@ -39,13 +38,9 @@
     backend_reset_max_memory_allocated,
     backend_reset_peak_memory_stats,
     enable_full_determinism,
-    get_python_version,
-    is_torch_compile,
     load_image,
     load_numpy,
-    require_torch_2,
     require_torch_accelerator,
-    run_test_in_subprocess,
     slow,
     torch_device,
 )
@@ -68,52 +63,6 @@
 enable_full_determinism()
 
 
-# Will be run via run_test_in_subprocess
-def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
-    error = None
-    try:
-        _ = in_queue.get(timeout=timeout)
-
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-
-        pipe = StableDiffusionControlNetPipeline.from_pretrained(
-            "stable-diffusion-v1-5/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        pipe.unet.to(memory_format=torch.channels_last)
-        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-        pipe.controlnet.to(memory_format=torch.channels_last)
-        pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "bird"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        ).resize((512, 512))
-
-        output = pipe(prompt, image, num_inference_steps=10, generator=generator, output_type="np")
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy"
-        )
-        expected_image = np.resize(expected_image, (512, 512, 3))
-
-        assert np.abs(expected_image - image).max() < 1.0
-
-    except Exception:
-        error = f"{traceback.format_exc()}"
-
-    results = {"error": error}
-    out_queue.put(results, timeout=timeout)
-    out_queue.join()
-
-
 class ControlNetPipelineFastTests(
     IPAdapterTesterMixin,
     PipelineLatentTesterMixin,
@@ -1053,15 +1002,6 @@ def test_canny_guess_mode_euler(self):
         expected_slice = np.array([0.1655, 0.1721, 0.1623, 0.1685, 0.1711, 0.1646, 0.1651, 0.1631, 0.1494])
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
-    @is_torch_compile
-    @require_torch_2
-    @unittest.skipIf(
-        get_python_version == (3, 12),
-        reason="Torch Dynamo isn't yet supported for Python 3.12.",
-    )
-    def test_stable_diffusion_compile(self):
-        run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None)
-
     def test_v11_shuffle_global_pool_conditions(self):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_shuffle")
 

tests/pipelines/controlnet_xs/test_controlnetxs.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import gc
-import traceback
 import unittest
 
 import numpy as np
@@ -36,13 +35,9 @@
 from diffusers.utils.testing_utils import (
     backend_empty_cache,
     enable_full_determinism,
-    is_torch_compile,
     load_image,
-    load_numpy,
     require_accelerator,
-    require_torch_2,
     require_torch_accelerator,
-    run_test_in_subprocess,
     slow,
     torch_device,
 )
@@ -78,53 +73,6 @@ def to_np(tensor):
     return tensor
 
 
-# Will be run via run_test_in_subprocess
-def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
-    error = None
-    try:
-        _ = in_queue.get(timeout=timeout)
-
-        controlnet = ControlNetXSAdapter.from_pretrained(
-            "UmerHA/Testing-ConrolNetXS-SD2.1-canny", torch_dtype=torch.float16
-        )
-        pipe = StableDiffusionControlNetXSPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-2-1-base",
-            controlnet=controlnet,
-            safety_checker=None,
-            torch_dtype=torch.float16,
-        )
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        pipe.unet.to(memory_format=torch.channels_last)
-        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "bird"
-        image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-        ).resize((512, 512))
-
-        output = pipe(prompt, image, num_inference_steps=10, generator=generator, output_type="np")
-        image = output.images[0]
-
-        assert image.shape == (512, 512, 3)
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy"
-        )
-        expected_image = np.resize(expected_image, (512, 512, 3))
-
-        assert np.abs(expected_image - image).max() < 1.0
-
-    except Exception:
-        error = f"{traceback.format_exc()}"
-
-    results = {"error": error}
-    out_queue.put(results, timeout=timeout)
-    out_queue.join()
-
-
 class ControlNetXSPipelineFastTests(
     PipelineLatentTesterMixin,
     PipelineKarrasSchedulerTesterMixin,
@@ -402,8 +350,3 @@ def test_depth(self):
         original_image = image[-3:, -3:, -1].flatten()
         expected_image = np.array([0.4844, 0.4937, 0.4956, 0.4663, 0.5039, 0.5044, 0.4565, 0.4883, 0.4941])
         assert np.allclose(original_image, expected_image, atol=1e-04)
-
-    @is_torch_compile
-    @require_torch_2
-    def test_stable_diffusion_compile(self):
-        run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None)

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -17,7 +17,6 @@
 import gc
 import tempfile
 import time
-import traceback
 import unittest
 
 import numpy as np
@@ -49,16 +48,12 @@
     backend_reset_max_memory_allocated,
     backend_reset_peak_memory_stats,
     enable_full_determinism,
-    is_torch_compile,
-    load_image,
     load_numpy,
     nightly,
     numpy_cosine_similarity_distance,
     require_accelerate_version_greater,
-    require_torch_2,
     require_torch_accelerator,
     require_torch_multi_accelerator,
-    run_test_in_subprocess,
     skip_mps,
     slow,
     torch_device,
@@ -81,39 +76,6 @@
 enable_full_determinism()
 
 
-# Will be run via run_test_in_subprocess
-def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
-    error = None
-    try:
-        inputs = in_queue.get(timeout=timeout)
-        torch_device = inputs.pop("torch_device")
-        seed = inputs.pop("seed")
-        inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)
-
-        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
-        sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
-        sd_pipe = sd_pipe.to(torch_device)
-
-        sd_pipe.unet.to(memory_format=torch.channels_last)
-        sd_pipe.unet = torch.compile(sd_pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        image = sd_pipe(**inputs).images
-        image_slice = image[0, -3:, -3:, -1].flatten()
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.38019, 0.28647, 0.27321, 0.40377, 0.38290, 0.35446, 0.39218, 0.38165, 0.42239])
-
-        assert np.abs(image_slice - expected_slice).max() < 5e-3
-    except Exception:
-        error = f"{traceback.format_exc()}"
-
-    results = {"error": error}
-    out_queue.put(results, timeout=timeout)
-    out_queue.join()
-
-
 class StableDiffusionPipelineFastTests(
     IPAdapterTesterMixin,
     PipelineLatentTesterMixin,
@@ -1224,40 +1186,6 @@ def test_stable_diffusion_textual_inversion_with_sequential_cpu_offload(self):
         max_diff = np.abs(expected_image - image).max()
         assert max_diff < 8e-1
 
-    @is_torch_compile
-    @require_torch_2
-    def test_stable_diffusion_compile(self):
-        seed = 0
-        inputs = self.get_inputs(torch_device, seed=seed)
-        # Can't pickle a Generator object
-        del inputs["generator"]
-        inputs["torch_device"] = torch_device
-        inputs["seed"] = seed
-        run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=inputs)
-
-    def test_stable_diffusion_lcm(self):
-        unet = UNet2DConditionModel.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", subfolder="unet")
-        sd_pipe = StableDiffusionPipeline.from_pretrained("Lykon/dreamshaper-7", unet=unet).to(torch_device)
-        sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_inputs(torch_device)
-        inputs["num_inference_steps"] = 6
-        inputs["output_type"] = "pil"
-
-        image = sd_pipe(**inputs).images[0]
-
-        expected_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_full/stable_diffusion_lcm.png"
-        )
-
-        image = sd_pipe.image_processor.pil_to_numpy(image)
-        expected_image = sd_pipe.image_processor.pil_to_numpy(expected_image)
-
-        max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
-
-        assert max_diff < 1e-2
-
 
 @slow
 @require_torch_accelerator

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -15,7 +15,6 @@
 
 import gc
 import random
-import traceback
 import unittest
 
 import numpy as np
@@ -41,13 +40,10 @@
     backend_reset_peak_memory_stats,
     enable_full_determinism,
     floats_tensor,
-    is_torch_compile,
     load_image,
     load_numpy,
     nightly,
-    require_torch_2,
     require_torch_accelerator,
-    run_test_in_subprocess,
     skip_mps,
     slow,
     torch_device,
@@ -70,38 +66,6 @@
 enable_full_determinism()
 
 
-# Will be run via run_test_in_subprocess
-def _test_img2img_compile(in_queue, out_queue, timeout):
-    error = None
-    try:
-        inputs = in_queue.get(timeout=timeout)
-        torch_device = inputs.pop("torch_device")
-        seed = inputs.pop("seed")
-        inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)
-
-        pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
-        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-        pipe.unet.set_default_attn_processor()
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.unet.to(memory_format=torch.channels_last)
-        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-
-        image = pipe(**inputs).images
-        image_slice = image[0, -3:, -3:, -1].flatten()
-
-        assert image.shape == (1, 512, 768, 3)
-        expected_slice = np.array([0.0606, 0.0570, 0.0805, 0.0579, 0.0628, 0.0623, 0.0843, 0.1115, 0.0806])
-
-        assert np.abs(expected_slice - image_slice).max() < 1e-3
-    except Exception:
-        error = f"{traceback.format_exc()}"
-
-    results = {"error": error}
-    out_queue.put(results, timeout=timeout)
-    out_queue.join()
-
-
 class StableDiffusionImg2ImgPipelineFastTests(
     IPAdapterTesterMixin,
     PipelineLatentTesterMixin,
@@ -654,17 +618,6 @@ def test_img2img_safety_checker_works(self):
         assert out.nsfw_content_detected[0], f"Safety checker should work for prompt: {inputs['prompt']}"
         assert np.abs(out.images[0]).sum() < 1e-5  # should be all zeros
 
-    @is_torch_compile
-    @require_torch_2
-    def test_img2img_compile(self):
-        seed = 0
-        inputs = self.get_inputs(torch_device, seed=seed)
-        # Can't pickle a Generator object
-        del inputs["generator"]
-        inputs["torch_device"] = torch_device
-        inputs["seed"] = seed
-        run_test_in_subprocess(test_case=self, target_func=_test_img2img_compile, inputs=inputs)
-
 
 @nightly
 @require_torch_accelerator