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import gc |
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import tempfile |
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import unittest |
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import torch |
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from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline |
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from diffusers.utils import load_image |
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from diffusers.utils.testing_utils import ( |
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enable_full_determinism, |
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numpy_cosine_similarity_distance, |
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require_torch_gpu, |
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slow, |
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torch_device, |
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) |
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from .single_file_testing_utils import ( |
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SDXLSingleFileTesterMixin, |
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download_diffusers_config, |
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download_single_file_checkpoint, |
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) |
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enable_full_determinism() |
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@slow |
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@require_torch_gpu |
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class StableDiffusionXLControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin): |
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pipeline_class = StableDiffusionXLControlNetPipeline |
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ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors" |
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repo_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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original_config = ( |
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"https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml" |
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) |
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def setUp(self): |
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super().setUp() |
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gc.collect() |
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torch.cuda.empty_cache() |
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def tearDown(self): |
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super().tearDown() |
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gc.collect() |
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torch.cuda.empty_cache() |
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def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): |
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generator = torch.Generator(device=generator_device).manual_seed(seed) |
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image = load_image( |
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png" |
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) |
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inputs = { |
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"prompt": "Stormtrooper's lecture", |
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"image": image, |
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"generator": generator, |
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"num_inference_steps": 2, |
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"strength": 0.75, |
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"guidance_scale": 7.5, |
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"output_type": "np", |
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} |
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return inputs |
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def test_single_file_format_inference_is_same_as_pretrained(self): |
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controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16) |
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pipe_single_file = self.pipeline_class.from_single_file( |
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self.ckpt_path, controlnet=controlnet, torch_dtype=torch.float16 |
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) |
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pipe_single_file.unet.set_default_attn_processor() |
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pipe_single_file.enable_model_cpu_offload() |
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pipe_single_file.set_progress_bar_config(disable=None) |
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inputs = self.get_inputs(torch_device) |
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single_file_images = pipe_single_file(**inputs).images[0] |
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pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet, torch_dtype=torch.float16) |
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pipe.unet.set_default_attn_processor() |
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pipe.enable_model_cpu_offload() |
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inputs = self.get_inputs(torch_device) |
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images = pipe(**inputs).images[0] |
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assert images.shape == (512, 512, 3) |
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assert single_file_images.shape == (512, 512, 3) |
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max_diff = numpy_cosine_similarity_distance(images[0].flatten(), single_file_images[0].flatten()) |
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assert max_diff < 5e-2 |
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def test_single_file_components(self): |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
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) |
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pipe = self.pipeline_class.from_pretrained( |
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self.repo_id, |
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variant="fp16", |
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controlnet=controlnet, |
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torch_dtype=torch.float16, |
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) |
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pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, controlnet=controlnet) |
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super().test_single_file_components(pipe, pipe_single_file) |
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def test_single_file_components_local_files_only(self): |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
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) |
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pipe = self.pipeline_class.from_pretrained( |
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self.repo_id, |
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variant="fp16", |
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controlnet=controlnet, |
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torch_dtype=torch.float16, |
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) |
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with tempfile.TemporaryDirectory() as tmpdir: |
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ckpt_filename = self.ckpt_path.split("/")[-1] |
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local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir) |
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single_file_pipe = self.pipeline_class.from_single_file( |
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local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True |
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) |
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self._compare_component_configs(pipe, single_file_pipe) |
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def test_single_file_components_with_original_config(self): |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
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) |
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pipe = self.pipeline_class.from_pretrained( |
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self.repo_id, |
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variant="fp16", |
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controlnet=controlnet, |
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torch_dtype=torch.float16, |
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) |
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pipe_single_file = self.pipeline_class.from_single_file( |
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self.ckpt_path, |
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original_config=self.original_config, |
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controlnet=controlnet, |
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) |
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self._compare_component_configs(pipe, pipe_single_file) |
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def test_single_file_components_with_original_config_local_files_only(self): |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
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) |
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pipe = self.pipeline_class.from_pretrained( |
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self.repo_id, |
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variant="fp16", |
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controlnet=controlnet, |
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torch_dtype=torch.float16, |
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) |
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with tempfile.TemporaryDirectory() as tmpdir: |
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ckpt_filename = self.ckpt_path.split("/")[-1] |
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local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir) |
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pipe_single_file = self.pipeline_class.from_single_file( |
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local_ckpt_path, |
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safety_checker=None, |
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controlnet=controlnet, |
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local_files_only=True, |
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) |
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self._compare_component_configs(pipe, pipe_single_file) |
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def test_single_file_components_with_diffusers_config(self): |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
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) |
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pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) |
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pipe_single_file = self.pipeline_class.from_single_file( |
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self.ckpt_path, controlnet=controlnet, config=self.repo_id |
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) |
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super()._compare_component_configs(pipe, pipe_single_file) |
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def test_single_file_components_with_diffusers_config_local_files_only(self): |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
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) |
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pipe = self.pipeline_class.from_pretrained( |
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self.repo_id, |
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controlnet=controlnet, |
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) |
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with tempfile.TemporaryDirectory() as tmpdir: |
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ckpt_filename = self.ckpt_path.split("/")[-1] |
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local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir) |
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local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) |
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pipe_single_file = self.pipeline_class.from_single_file( |
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local_ckpt_path, |
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config=local_diffusers_config, |
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safety_checker=None, |
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controlnet=controlnet, |
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local_files_only=True, |
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) |
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super()._compare_component_configs(pipe, pipe_single_file) |
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