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tango2-full
/
diffusers
/tests
/pipelines
/stable_diffusion
/test_stable_diffusion_image_variation.py
| # coding=utf-8 | |
| # Copyright 2023 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 random | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection | |
| from diffusers import ( | |
| AutoencoderKL, | |
| DPMSolverMultistepScheduler, | |
| PNDMScheduler, | |
| StableDiffusionImageVariationPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device | |
| from diffusers.utils.testing_utils import require_torch_gpu | |
| from ...pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS | |
| from ...test_pipelines_common import PipelineTesterMixin | |
| torch.backends.cuda.matmul.allow_tf32 = False | |
| class StableDiffusionImageVariationPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = StableDiffusionImageVariationPipeline | |
| params = IMAGE_VARIATION_PARAMS | |
| batch_params = IMAGE_VARIATION_BATCH_PARAMS | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| unet = UNet2DConditionModel( | |
| block_out_channels=(32, 64), | |
| layers_per_block=2, | |
| sample_size=32, | |
| in_channels=4, | |
| out_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
| cross_attention_dim=32, | |
| ) | |
| scheduler = PNDMScheduler(skip_prk_steps=True) | |
| torch.manual_seed(0) | |
| vae = AutoencoderKL( | |
| block_out_channels=[32, 64], | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| latent_channels=4, | |
| ) | |
| torch.manual_seed(0) | |
| image_encoder_config = CLIPVisionConfig( | |
| hidden_size=32, | |
| projection_dim=32, | |
| intermediate_size=37, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=4, | |
| num_hidden_layers=5, | |
| image_size=32, | |
| patch_size=4, | |
| ) | |
| image_encoder = CLIPVisionModelWithProjection(image_encoder_config) | |
| feature_extractor = CLIPImageProcessor(crop_size=32, size=32) | |
| components = { | |
| "unet": unet, | |
| "scheduler": scheduler, | |
| "vae": vae, | |
| "image_encoder": image_encoder, | |
| "feature_extractor": feature_extractor, | |
| "safety_checker": None, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)) | |
| image = image.cpu().permute(0, 2, 3, 1)[0] | |
| image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32)) | |
| if str(device).startswith("mps"): | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| inputs = { | |
| "image": image, | |
| "generator": generator, | |
| "num_inference_steps": 2, | |
| "guidance_scale": 6.0, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_stable_diffusion_img_variation_default_case(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionImageVariationPipeline(**components) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| image = sd_pipe(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1] | |
| assert image.shape == (1, 64, 64, 3) | |
| expected_slice = np.array([0.5167, 0.5746, 0.4835, 0.4914, 0.5605, 0.4691, 0.5201, 0.4898, 0.4958]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| def test_stable_diffusion_img_variation_multiple_images(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| components = self.get_dummy_components() | |
| sd_pipe = StableDiffusionImageVariationPipeline(**components) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_dummy_inputs(device) | |
| inputs["image"] = 2 * [inputs["image"]] | |
| output = sd_pipe(**inputs) | |
| image = output.images | |
| image_slice = image[-1, -3:, -3:, -1] | |
| assert image.shape == (2, 64, 64, 3) | |
| expected_slice = np.array([0.6568, 0.5470, 0.5684, 0.5444, 0.5945, 0.6221, 0.5508, 0.5531, 0.5263]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 | |
| class StableDiffusionImageVariationPipelineSlowTests(unittest.TestCase): | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): | |
| generator = torch.Generator(device=generator_device).manual_seed(seed) | |
| init_image = load_image( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_imgvar/input_image_vermeer.png" | |
| ) | |
| latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64)) | |
| latents = torch.from_numpy(latents).to(device=device, dtype=dtype) | |
| inputs = { | |
| "image": init_image, | |
| "latents": latents, | |
| "generator": generator, | |
| "num_inference_steps": 3, | |
| "guidance_scale": 7.5, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_stable_diffusion_img_variation_pipeline_default(self): | |
| sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained( | |
| "lambdalabs/sd-image-variations-diffusers", safety_checker=None | |
| ) | |
| sd_pipe = sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device) | |
| image = sd_pipe(**inputs).images | |
| image_slice = image[0, -3:, -3:, -1].flatten() | |
| assert image.shape == (1, 512, 512, 3) | |
| expected_slice = np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]) | |
| assert np.abs(image_slice - expected_slice).max() < 1e-4 | |
| def test_stable_diffusion_img_variation_intermediate_state(self): | |
| number_of_steps = 0 | |
| def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: | |
| callback_fn.has_been_called = True | |
| nonlocal number_of_steps | |
| number_of_steps += 1 | |
| if step == 1: | |
| latents = latents.detach().cpu().numpy() | |
| assert latents.shape == (1, 4, 64, 64) | |
| latents_slice = latents[0, -3:, -3:, -1] | |
| expected_slice = np.array( | |
| [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309] | |
| ) | |
| assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
| elif step == 2: | |
| latents = latents.detach().cpu().numpy() | |
| assert latents.shape == (1, 4, 64, 64) | |
| latents_slice = latents[0, -3:, -3:, -1] | |
| expected_slice = np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]) | |
| assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
| callback_fn.has_been_called = False | |
| pipe = StableDiffusionImageVariationPipeline.from_pretrained( | |
| "fusing/sd-image-variations-diffusers", | |
| safety_checker=None, | |
| torch_dtype=torch.float16, | |
| ) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
| pipe(**inputs, callback=callback_fn, callback_steps=1) | |
| assert callback_fn.has_been_called | |
| assert number_of_steps == inputs["num_inference_steps"] | |
| def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| model_id = "fusing/sd-image-variations-diffusers" | |
| pipe = StableDiffusionImageVariationPipeline.from_pretrained( | |
| model_id, safety_checker=None, torch_dtype=torch.float16 | |
| ) | |
| pipe = pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing(1) | |
| pipe.enable_sequential_cpu_offload() | |
| inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
| _ = pipe(**inputs) | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| # make sure that less than 2.6 GB is allocated | |
| assert mem_bytes < 2.6 * 10**9 | |
| class StableDiffusionImageVariationPipelineNightlyTests(unittest.TestCase): | |
| def tearDown(self): | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): | |
| generator = torch.Generator(device=generator_device).manual_seed(seed) | |
| init_image = load_image( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_imgvar/input_image_vermeer.png" | |
| ) | |
| latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64)) | |
| latents = torch.from_numpy(latents).to(device=device, dtype=dtype) | |
| inputs = { | |
| "image": init_image, | |
| "latents": latents, | |
| "generator": generator, | |
| "num_inference_steps": 50, | |
| "guidance_scale": 7.5, | |
| "output_type": "numpy", | |
| } | |
| return inputs | |
| def test_img_variation_pndm(self): | |
| sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers") | |
| sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device) | |
| image = sd_pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |
| def test_img_variation_dpm(self): | |
| sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers") | |
| sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config) | |
| sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| inputs = self.get_inputs(torch_device) | |
| inputs["num_inference_steps"] = 25 | |
| image = sd_pipe(**inputs).images[0] | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
| "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy" | |
| ) | |
| max_diff = np.abs(expected_image - image).max() | |
| assert max_diff < 1e-3 | |