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| # 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 CLIPTextConfig, CLIPTextModel, CLIPTokenizer | |
| from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel | |
| from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device | |
| from diffusers.utils.testing_utils import require_torch_gpu | |
| torch.backends.cuda.matmul.allow_tf32 = False | |
| class StableDiffusionUpscalePipelineFastTests(unittest.TestCase): | |
| def tearDown(self): | |
| # clean up the VRAM after each test | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def dummy_image(self): | |
| batch_size = 1 | |
| num_channels = 3 | |
| sizes = (32, 32) | |
| image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) | |
| return image | |
| def dummy_cond_unet_upscale(self): | |
| torch.manual_seed(0) | |
| model = UNet2DConditionModel( | |
| block_out_channels=(32, 32, 64), | |
| layers_per_block=2, | |
| sample_size=32, | |
| in_channels=7, | |
| out_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"), | |
| cross_attention_dim=32, | |
| # SD2-specific config below | |
| attention_head_dim=8, | |
| use_linear_projection=True, | |
| only_cross_attention=(True, True, False), | |
| num_class_embeds=100, | |
| ) | |
| return model | |
| def dummy_vae(self): | |
| torch.manual_seed(0) | |
| model = AutoencoderKL( | |
| block_out_channels=[32, 32, 64], | |
| in_channels=3, | |
| out_channels=3, | |
| down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"], | |
| up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"], | |
| latent_channels=4, | |
| ) | |
| return model | |
| def dummy_text_encoder(self): | |
| torch.manual_seed(0) | |
| config = CLIPTextConfig( | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| hidden_size=32, | |
| intermediate_size=37, | |
| layer_norm_eps=1e-05, | |
| num_attention_heads=4, | |
| num_hidden_layers=5, | |
| pad_token_id=1, | |
| vocab_size=1000, | |
| # SD2-specific config below | |
| hidden_act="gelu", | |
| projection_dim=512, | |
| ) | |
| return CLIPTextModel(config) | |
| def test_stable_diffusion_upscale(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| unet = self.dummy_cond_unet_upscale | |
| low_res_scheduler = DDPMScheduler() | |
| scheduler = DDIMScheduler(prediction_type="v_prediction") | |
| vae = self.dummy_vae | |
| text_encoder = self.dummy_text_encoder | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] | |
| low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) | |
| # make sure here that pndm scheduler skips prk | |
| sd_pipe = StableDiffusionUpscalePipeline( | |
| unet=unet, | |
| low_res_scheduler=low_res_scheduler, | |
| scheduler=scheduler, | |
| vae=vae, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| max_noise_level=350, | |
| ) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| output = sd_pipe( | |
| [prompt], | |
| image=low_res_image, | |
| generator=generator, | |
| guidance_scale=6.0, | |
| noise_level=20, | |
| num_inference_steps=2, | |
| output_type="np", | |
| ) | |
| image = output.images | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| image_from_tuple = sd_pipe( | |
| [prompt], | |
| image=low_res_image, | |
| generator=generator, | |
| guidance_scale=6.0, | |
| noise_level=20, | |
| num_inference_steps=2, | |
| output_type="np", | |
| return_dict=False, | |
| )[0] | |
| image_slice = image[0, -3:, -3:, -1] | |
| image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1] | |
| expected_height_width = low_res_image.size[0] * 4 | |
| assert image.shape == (1, expected_height_width, expected_height_width, 3) | |
| expected_slice = np.array([0.2562, 0.3606, 0.4204, 0.4469, 0.4822, 0.4647, 0.5315, 0.5748, 0.5606]) | |
| assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 | |
| def test_stable_diffusion_upscale_batch(self): | |
| device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
| unet = self.dummy_cond_unet_upscale | |
| low_res_scheduler = DDPMScheduler() | |
| scheduler = DDIMScheduler(prediction_type="v_prediction") | |
| vae = self.dummy_vae | |
| text_encoder = self.dummy_text_encoder | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] | |
| low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) | |
| # make sure here that pndm scheduler skips prk | |
| sd_pipe = StableDiffusionUpscalePipeline( | |
| unet=unet, | |
| low_res_scheduler=low_res_scheduler, | |
| scheduler=scheduler, | |
| vae=vae, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| max_noise_level=350, | |
| ) | |
| sd_pipe = sd_pipe.to(device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| output = sd_pipe( | |
| 2 * [prompt], | |
| image=2 * [low_res_image], | |
| guidance_scale=6.0, | |
| noise_level=20, | |
| num_inference_steps=2, | |
| output_type="np", | |
| ) | |
| image = output.images | |
| assert image.shape[0] == 2 | |
| generator = torch.Generator(device=device).manual_seed(0) | |
| output = sd_pipe( | |
| [prompt], | |
| image=low_res_image, | |
| generator=generator, | |
| num_images_per_prompt=2, | |
| guidance_scale=6.0, | |
| noise_level=20, | |
| num_inference_steps=2, | |
| output_type="np", | |
| ) | |
| image = output.images | |
| assert image.shape[0] == 2 | |
| def test_stable_diffusion_upscale_fp16(self): | |
| """Test that stable diffusion upscale works with fp16""" | |
| unet = self.dummy_cond_unet_upscale | |
| low_res_scheduler = DDPMScheduler() | |
| scheduler = DDIMScheduler(prediction_type="v_prediction") | |
| vae = self.dummy_vae | |
| text_encoder = self.dummy_text_encoder | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0] | |
| low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) | |
| # put models in fp16, except vae as it overflows in fp16 | |
| unet = unet.half() | |
| text_encoder = text_encoder.half() | |
| # make sure here that pndm scheduler skips prk | |
| sd_pipe = StableDiffusionUpscalePipeline( | |
| unet=unet, | |
| low_res_scheduler=low_res_scheduler, | |
| scheduler=scheduler, | |
| vae=vae, | |
| text_encoder=text_encoder, | |
| tokenizer=tokenizer, | |
| max_noise_level=350, | |
| ) | |
| sd_pipe = sd_pipe.to(torch_device) | |
| sd_pipe.set_progress_bar_config(disable=None) | |
| prompt = "A painting of a squirrel eating a burger" | |
| generator = torch.manual_seed(0) | |
| image = sd_pipe( | |
| [prompt], | |
| image=low_res_image, | |
| generator=generator, | |
| num_inference_steps=2, | |
| output_type="np", | |
| ).images | |
| expected_height_width = low_res_image.size[0] * 4 | |
| assert image.shape == (1, expected_height_width, expected_height_width, 3) | |
| class StableDiffusionUpscalePipelineIntegrationTests(unittest.TestCase): | |
| def tearDown(self): | |
| # clean up the VRAM after each test | |
| super().tearDown() | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| def test_stable_diffusion_upscale_pipeline(self): | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
| "/sd2-upscale/low_res_cat.png" | |
| ) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale" | |
| "/upsampled_cat.npy" | |
| ) | |
| model_id = "stabilityai/stable-diffusion-x4-upscaler" | |
| pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| prompt = "a cat sitting on a park bench" | |
| generator = torch.manual_seed(0) | |
| output = pipe( | |
| prompt=prompt, | |
| image=image, | |
| generator=generator, | |
| output_type="np", | |
| ) | |
| image = output.images[0] | |
| assert image.shape == (512, 512, 3) | |
| assert np.abs(expected_image - image).max() < 1e-3 | |
| def test_stable_diffusion_upscale_pipeline_fp16(self): | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
| "/sd2-upscale/low_res_cat.png" | |
| ) | |
| expected_image = load_numpy( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale" | |
| "/upsampled_cat_fp16.npy" | |
| ) | |
| model_id = "stabilityai/stable-diffusion-x4-upscaler" | |
| pipe = StableDiffusionUpscalePipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16, | |
| ) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing() | |
| prompt = "a cat sitting on a park bench" | |
| generator = torch.manual_seed(0) | |
| output = pipe( | |
| prompt=prompt, | |
| image=image, | |
| generator=generator, | |
| output_type="np", | |
| ) | |
| image = output.images[0] | |
| assert image.shape == (512, 512, 3) | |
| assert np.abs(expected_image - image).max() < 5e-1 | |
| 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() | |
| image = load_image( | |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" | |
| "/sd2-upscale/low_res_cat.png" | |
| ) | |
| model_id = "stabilityai/stable-diffusion-x4-upscaler" | |
| pipe = StableDiffusionUpscalePipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16, | |
| ) | |
| pipe.to(torch_device) | |
| pipe.set_progress_bar_config(disable=None) | |
| pipe.enable_attention_slicing(1) | |
| pipe.enable_sequential_cpu_offload() | |
| prompt = "a cat sitting on a park bench" | |
| generator = torch.manual_seed(0) | |
| _ = pipe( | |
| prompt=prompt, | |
| image=image, | |
| generator=generator, | |
| num_inference_steps=5, | |
| output_type="np", | |
| ) | |
| mem_bytes = torch.cuda.max_memory_allocated() | |
| # make sure that less than 2.9 GB is allocated | |
| assert mem_bytes < 2.9 * 10**9 | |