# coding=utf-8 # Copyright 2022 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 time import unittest import numpy as np import torch from diffusers import ( AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel, UNet2DModel, VQModel, ) from diffusers.utils import floats_tensor, load_image, slow, torch_device from diffusers.utils.testing_utils import require_torch_gpu from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from ...test_pipelines_common import PipelineTesterMixin torch.backends.cuda.matmul.allow_tf32 = False class StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() @property 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 @property def dummy_uncond_unet(self): torch.manual_seed(0) model = UNet2DModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) return model @property def dummy_cond_unet(self): torch.manual_seed(0) model = 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, ) return model @property def dummy_cond_unet_inpaint(self): torch.manual_seed(0) model = UNet2DConditionModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=9, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) return model @property def dummy_vq_model(self): torch.manual_seed(0) model = VQModel( block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=3, ) return model @property def dummy_vae(self): torch.manual_seed(0) model = 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, ) return model @property 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, ) return CLIPTextModel(config) @property def dummy_extractor(self): def extract(*args, **kwargs): class Out: def __init__(self): self.pixel_values = torch.ones([0]) def to(self, device): self.pixel_values.to(device) return self return Out() return extract def test_stable_diffusion_ddim(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) 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], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") image = output.images generator = torch.Generator(device=device).manual_seed(0) image_from_tuple = sd_pipe( [prompt], generator=generator, guidance_scale=6.0, 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] assert image.shape == (1, 128, 128, 3) expected_slice = np.array([0.5112, 0.4692, 0.4715, 0.5206, 0.4894, 0.5114, 0.5096, 0.4932, 0.4755]) 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_ddim_factor_8(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) 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], generator=generator, guidance_scale=6.0, height=536, width=536, num_inference_steps=2, output_type="np", ) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 134, 134, 3) expected_slice = np.array([0.7834, 0.5488, 0.5781, 0.46, 0.3609, 0.5369, 0.542, 0.4855, 0.5557]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_pndm(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) 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], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") image = output.images generator = torch.Generator(device=device).manual_seed(0) image_from_tuple = sd_pipe( [prompt], generator=generator, guidance_scale=6.0, 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] assert image.shape == (1, 128, 128, 3) expected_slice = np.array([0.4937, 0.4649, 0.4716, 0.5145, 0.4889, 0.513, 0.513, 0.4905, 0.4738]) 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_no_safety_checker(self): pipe = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None ) assert isinstance(pipe, StableDiffusionPipeline) assert isinstance(pipe.scheduler, LMSDiscreteScheduler) assert pipe.safety_checker is None image = pipe("example prompt", num_inference_steps=2).images[0] assert image is not None def test_stable_diffusion_k_lms(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear") vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) 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], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") image = output.images generator = torch.Generator(device=device).manual_seed(0) image_from_tuple = sd_pipe( [prompt], generator=generator, guidance_scale=6.0, 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] assert image.shape == (1, 128, 128, 3) expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785]) 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_attention_chunk(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear") vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) 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_1 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") # make sure chunking the attention yields the same result sd_pipe.enable_attention_slicing(slice_size=1) generator = torch.Generator(device=device).manual_seed(0) output_2 = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 1e-4 def test_stable_diffusion_negative_prompt(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" negative_prompt = "french fries" generator = torch.Generator(device=device).manual_seed(0) output = sd_pipe( prompt, negative_prompt=negative_prompt, generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np", ) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 128, 128, 3) expected_slice = np.array([0.4851, 0.4617, 0.4765, 0.5127, 0.4845, 0.5153, 0.5141, 0.4886, 0.4719]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_num_images_per_prompt(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) prompt = "A painting of a squirrel eating a burger" # test num_images_per_prompt=1 (default) images = sd_pipe(prompt, num_inference_steps=2, output_type="np").images assert images.shape == (1, 128, 128, 3) # test num_images_per_prompt=1 (default) for batch of prompts batch_size = 2 images = sd_pipe([prompt] * batch_size, num_inference_steps=2, output_type="np").images assert images.shape == (batch_size, 128, 128, 3) # test num_images_per_prompt for single prompt num_images_per_prompt = 2 images = sd_pipe( prompt, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt ).images assert images.shape == (num_images_per_prompt, 128, 128, 3) # test num_images_per_prompt for batch of prompts batch_size = 2 images = sd_pipe( [prompt] * batch_size, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt ).images assert images.shape == (batch_size * num_images_per_prompt, 128, 128, 3) @unittest.skipIf(torch_device != "cuda", "This test requires a GPU") def test_stable_diffusion_fp16(self): """Test that stable diffusion works with fp16""" unet = self.dummy_cond_unet scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") # put models in fp16 unet = unet.half() vae = vae.half() bert = bert.half() # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) 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.Generator(device=torch_device).manual_seed(0) image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images assert image.shape == (1, 128, 128, 3) @slow @require_torch_gpu class StableDiffusionPipelineIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_stable_diffusion(self): # make sure here that pndm scheduler skips prk sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", device_map="auto") 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.Generator(device=torch_device).manual_seed(0) with torch.autocast("cuda"): output = sd_pipe( [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np" ) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_fast_ddim(self): sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", device_map="auto") sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) sd_pipe.scheduler = scheduler prompt = "A painting of a squirrel eating a burger" generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast("cuda"): output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy") image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.9326, 0.923, 0.951, 0.9365, 0.9214, 0.951, 0.9365, 0.9414, 0.918]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_lms_stable_diffusion_pipeline(self): model_id = "CompVis/stable-diffusion-v1-1" pipe = StableDiffusionPipeline.from_pretrained(model_id, device_map="auto").to(torch_device) pipe.set_progress_bar_config(disable=None) scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler") pipe.scheduler = scheduler prompt = "a photograph of an astronaut riding a horse" generator = torch.Generator(device=torch_device).manual_seed(0) image = pipe( [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy" ).images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_memory_chunking(self): torch.cuda.reset_peak_memory_stats() model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionPipeline.from_pretrained( model_id, revision="fp16", torch_dtype=torch.float16, device_map="auto" ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) prompt = "a photograph of an astronaut riding a horse" # make attention efficient pipe.enable_attention_slicing() generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): output_chunked = pipe( [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy" ) image_chunked = output_chunked.images mem_bytes = torch.cuda.max_memory_allocated() torch.cuda.reset_peak_memory_stats() # make sure that less than 3.75 GB is allocated assert mem_bytes < 3.75 * 10**9 # disable chunking pipe.disable_attention_slicing() generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): output = pipe( [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy" ) image = output.images # make sure that more than 3.75 GB is allocated mem_bytes = torch.cuda.max_memory_allocated() assert mem_bytes > 3.75 * 10**9 assert np.abs(image_chunked.flatten() - image.flatten()).max() < 1e-3 def test_stable_diffusion_text2img_pipeline_fp16(self): torch.cuda.reset_peak_memory_stats() model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionPipeline.from_pretrained( model_id, revision="fp16", device_map="auto", torch_dtype=torch.float16 ) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) prompt = "a photograph of an astronaut riding a horse" generator = torch.Generator(device=torch_device).manual_seed(0) output_chunked = pipe( [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy" ) image_chunked = output_chunked.images generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): output = pipe( [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy" ) image = output.images # Make sure results are close enough diff = np.abs(image_chunked.flatten() - image.flatten()) # They ARE different since ops are not run always at the same precision # however, they should be extremely close. assert diff.mean() < 2e-2 def test_stable_diffusion_text2img_pipeline(self): expected_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/text2img/astronaut_riding_a_horse.png" ) expected_image = np.array(expected_image, dtype=np.float32) / 255.0 model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionPipeline.from_pretrained( model_id, safety_checker=None, device_map="auto", ) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "astronaut riding a horse" generator = torch.Generator(device=torch_device).manual_seed(0) output = pipe(prompt=prompt, strength=0.75, guidance_scale=7.5, generator=generator, output_type="np") image = output.images[0] assert image.shape == (512, 512, 3) assert np.abs(expected_image - image).max() < 1e-2 def test_stable_diffusion_text2img_intermediate_state(self): number_of_steps = 0 def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: test_callback_fn.has_been_called = True nonlocal number_of_steps number_of_steps += 1 if step == 0: latents = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array( [1.8285, 1.2857, -0.1024, 1.2406, -2.3068, 1.0747, -0.0818, -0.6520, -2.9506] ) assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3 elif step == 50: latents = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array( [1.1078, 1.5803, 0.2773, -0.0589, -1.7928, -0.3665, -0.4695, -1.0727, -1.1601] ) assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2 test_callback_fn.has_been_called = False pipe = StableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, device_map="auto" ) pipe = pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() prompt = "Andromeda galaxy in a bottle" generator = torch.Generator(device=torch_device).manual_seed(0) with torch.autocast(torch_device): pipe( prompt=prompt, num_inference_steps=50, guidance_scale=7.5, generator=generator, callback=test_callback_fn, callback_steps=1, ) assert test_callback_fn.has_been_called assert number_of_steps == 51 def test_stable_diffusion_accelerate_auto_device(self): pipeline_id = "CompVis/stable-diffusion-v1-4" start_time = time.time() pipeline_normal_load = StableDiffusionPipeline.from_pretrained( pipeline_id, revision="fp16", torch_dtype=torch.float16 ) pipeline_normal_load.to(torch_device) normal_load_time = time.time() - start_time start_time = time.time() _ = StableDiffusionPipeline.from_pretrained( pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, device_map="auto" ) meta_device_load_time = time.time() - start_time assert 2 * meta_device_load_time < normal_load_time @unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU") def test_stable_diffusion_pipeline_with_unet_on_gpu_only(self): torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() pipeline_id = "CompVis/stable-diffusion-v1-4" prompt = "Andromeda galaxy in a bottle" pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, revision="fp16", torch_dtype=torch.float16) pipeline.enable_attention_slicing(1) pipeline.enable_sequential_cpu_offload() _ = pipeline(prompt, num_inference_steps=5) mem_bytes = torch.cuda.max_memory_allocated() # make sure that less than 1.5 GB is allocated assert mem_bytes < 1.5 * 10**9