Remove unnecessary single file tests for SD Cascade UNet (#7996)

update

tests/models/unets/test_models_unet_stable_cascade.py

-# coding=utf-8
-# Copyright 2024 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 gc
-import unittest
-
-import torch
-
-from diffusers import StableCascadeUNet
-from diffusers.utils import logging
-from diffusers.utils.testing_utils import (
-    enable_full_determinism,
-    numpy_cosine_similarity_distance,
-    require_torch_gpu,
-    slow,
-)
-from diffusers.utils.torch_utils import randn_tensor
-
-
-logger = logging.get_logger(__name__)
-
-enable_full_determinism()
-
-
-@slow
-class StableCascadeUNetModelSlowTests(unittest.TestCase):
-    def tearDown(self) -> None:
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_stable_cascade_unet_prior_single_file_components(self):
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url)
-
-        single_file_unet_config = single_file_unet.config
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade-prior", subfolder="prior", variant="bf16")
-        unet_config = unet.config
-        del unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in single_file_unet_config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-
-            assert unet_config[param_name] == param_value
-
-    def test_stable_cascade_unet_decoder_single_file_components(self):
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors"
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url)
-
-        single_file_unet_config = single_file_unet.config
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade", subfolder="decoder", variant="bf16")
-        unet_config = unet.config
-        del unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in single_file_unet_config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-
-            assert unet_config[param_name] == param_value
-
-    def test_stable_cascade_unet_config_loading(self):
-        config = StableCascadeUNet.load_config(
-            pretrained_model_name_or_path="diffusers/stable-cascade-configs", subfolder="prior"
-        )
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
-
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url, config=config)
-        single_file_unet_config = single_file_unet.config
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-
-            assert single_file_unet_config[param_name] == param_value
-
-    @require_torch_gpu
-    def test_stable_cascade_unet_single_file_prior_forward_pass(self):
-        dtype = torch.bfloat16
-        generator = torch.Generator("cpu")
-
-        model_inputs = {
-            "sample": randn_tensor((1, 16, 24, 24), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "timestep_ratio": torch.tensor([1]).to("cuda", dtype),
-            "clip_text_pooled": randn_tensor((1, 1, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "clip_text": randn_tensor((1, 77, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "clip_img": randn_tensor((1, 1, 768), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "pixels": randn_tensor((1, 3, 8, 8), generator=generator.manual_seed(0)).to("cuda", dtype),
-        }
-
-        unet = StableCascadeUNet.from_pretrained(
-            "stabilityai/stable-cascade-prior",
-            subfolder="prior",
-            revision="refs/pr/2",
-            variant="bf16",
-            torch_dtype=dtype,
-        )
-        unet.to("cuda")
-        with torch.no_grad():
-            prior_output = unet(**model_inputs).sample.float().cpu().numpy()
-
-        # Remove UNet from GPU memory before loading the single file UNet model
-        del unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url, torch_dtype=dtype)
-        single_file_unet.to("cuda")
-        with torch.no_grad():
-            prior_single_file_output = single_file_unet(**model_inputs).sample.float().cpu().numpy()
-
-        # Remove UNet from GPU memory before loading the single file UNet model
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        max_diff = numpy_cosine_similarity_distance(prior_output.flatten(), prior_single_file_output.flatten())
-        assert max_diff < 8e-3
-
-    @require_torch_gpu
-    def test_stable_cascade_unet_single_file_decoder_forward_pass(self):
-        dtype = torch.float32
-        generator = torch.Generator("cpu")
-
-        model_inputs = {
-            "sample": randn_tensor((1, 4, 256, 256), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "timestep_ratio": torch.tensor([1]).to("cuda", dtype),
-            "clip_text": randn_tensor((1, 77, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "clip_text_pooled": randn_tensor((1, 1, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "pixels": randn_tensor((1, 3, 8, 8), generator=generator.manual_seed(0)).to("cuda", dtype),
-        }
-
-        unet = StableCascadeUNet.from_pretrained(
-            "stabilityai/stable-cascade",
-            subfolder="decoder",
-            revision="refs/pr/44",
-            torch_dtype=dtype,
-        )
-        unet.to("cuda")
-        with torch.no_grad():
-            prior_output = unet(**model_inputs).sample.float().cpu().numpy()
-
-        # Remove UNet from GPU memory before loading the single file UNet model
-        del unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b.safetensors"
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url, torch_dtype=dtype)
-        single_file_unet.to("cuda")
-        with torch.no_grad():
-            prior_single_file_output = single_file_unet(**model_inputs).sample.float().cpu().numpy()
-
-        # Remove UNet from GPU memory before loading the single file UNet model
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        max_diff = numpy_cosine_similarity_distance(prior_output.flatten(), prior_single_file_output.flatten())
-        assert max_diff < 1e-4