# 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 tempfile import unittest import numpy as np import torch from diffusers import ( AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel, logging, ) from diffusers.utils import load_numpy, slow, torch_device from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from ...test_pipelines_common import PipelineTesterMixin torch.backends.cuda.matmul.allow_tf32 = False class StableDiffusion2PipelineFastTests(PipelineTesterMixin, unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() @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, # SD2-specific config below attention_head_dim=(2, 4, 8, 8), use_linear_projection=True, ) 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, sample_size=128, ) 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, # SD2-specific config below hidden_act="gelu", projection_dim=512, ) return CLIPTextModel(config) def test_save_pretrained_from_pretrained(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=None, requires_safety_checker=False, ) 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 with tempfile.TemporaryDirectory() as tmpdirname: sd_pipe.save_pretrained(tmpdirname) sd_pipe = StableDiffusionPipeline.from_pretrained(tmpdirname) sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) generator = generator.manual_seed(0) output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np") new_image = output.images assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass" 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=None, requires_safety_checker=False, ) 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, 64, 64, 3) expected_slice = np.array([0.5649, 0.6022, 0.4804, 0.5270, 0.5585, 0.4643, 0.5159, 0.4963, 0.4793]) 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_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=None, requires_safety_checker=False, ) 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, 64, 64, 3) expected_slice = np.array([0.5099, 0.5677, 0.4671, 0.5128, 0.5697, 0.4676, 0.5277, 0.4964, 0.4946]) 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_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=None, requires_safety_checker=False, ) 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, 64, 64, 3) expected_slice = np.array([0.4717, 0.5376, 0.4568, 0.5225, 0.5734, 0.4797, 0.5467, 0.5074, 0.5043]) 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_k_euler_ancestral(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = EulerAncestralDiscreteScheduler(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=None, requires_safety_checker=False, ) 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, 64, 64, 3) expected_slice = np.array([0.4715, 0.5376, 0.4569, 0.5224, 0.5734, 0.4797, 0.5465, 0.5074, 0.5046]) 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_k_euler(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator unet = self.dummy_cond_unet scheduler = EulerDiscreteScheduler(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=None, requires_safety_checker=False, ) 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, 64, 64, 3) expected_slice = np.array([0.4717, 0.5376, 0.4568, 0.5225, 0.5734, 0.4797, 0.5467, 0.5074, 0.5043]) 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=None, requires_safety_checker=False, ) 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 @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=None, requires_safety_checker=False, ) 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, 64, 64, 3) def test_stable_diffusion_long_prompt(self): 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=None, requires_safety_checker=False, ) sd_pipe = sd_pipe.to(torch_device) sd_pipe.set_progress_bar_config(disable=None) do_classifier_free_guidance = True negative_prompt = None num_images_per_prompt = 1 logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion") prompt = 25 * "@" with CaptureLogger(logger) as cap_logger_3: text_embeddings_3 = sd_pipe._encode_prompt( prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt ) prompt = 100 * "@" with CaptureLogger(logger) as cap_logger: text_embeddings = sd_pipe._encode_prompt( prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt ) negative_prompt = "Hello" with CaptureLogger(logger) as cap_logger_2: text_embeddings_2 = sd_pipe._encode_prompt( prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt ) assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape assert text_embeddings.shape[1] == 77 assert cap_logger.out == cap_logger_2.out # 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25 assert cap_logger.out.count("@") == 25 assert cap_logger_3.out == "" @slow @require_torch_gpu class StableDiffusion2PipelineIntegrationTests(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): sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base") 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) output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np") image = output.images image_slice = image[0, 253:256, 253:256, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.0788, 0.0823, 0.1091, 0.1165, 0.1263, 0.1459, 0.1317, 0.1507, 0.1551]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_ddim(self): scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-base", subfolder="scheduler") sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base", scheduler=scheduler) 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) output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type="numpy") image = output.images image_slice = image[0, 253:256, 253:256, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.0642, 0.0382, 0.0408, 0.0395, 0.0227, 0.0942, 0.0749, 0.0669, 0.0248]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_k_lms(self): scheduler = LMSDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2-base", subfolder="scheduler") sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base", scheduler=scheduler) sd_pipe = sd_pipe.to(torch_device) sd_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) image = sd_pipe( [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="numpy" ).images image_slice = image[0, 253:256, 253:256, -1] assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.0548, 0.0626, 0.0612, 0.0611, 0.0706, 0.0586, 0.0843, 0.0333, 0.1197]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_attention_slicing(self): torch.cuda.reset_peak_memory_stats() model_id = "stabilityai/stable-diffusion-2-base" pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16) 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_same_quality(self): torch.cuda.reset_peak_memory_stats() model_id = "stabilityai/stable-diffusion-2-base" pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16) pipe = pipe.to(torch_device) pipe.enable_attention_slicing() 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 pipe = StableDiffusionPipeline.from_pretrained(model_id) pipe = pipe.to(torch_device) generator = torch.Generator(device=torch_device).manual_seed(0) 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() < 5e-2 def test_stable_diffusion_text2img_pipeline_default(self): expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-text2img/astronaut_riding_a_horse.npy" ) model_id = "stabilityai/stable-diffusion-2-base" pipe = StableDiffusionPipeline.from_pretrained(model_id, safety_checker=None) 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, 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() < 5e-3 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([1.8606, 1.3169, -0.0691, 1.2374, -2.309, 1.077, -0.1084, -0.6774, -2.9594]) assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-3 elif step == 20: latents = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 64) latents_slice = latents[0, -3:, -3:, -1] expected_slice = np.array([1.0757, 1.1860, 1.1410, 0.4645, -0.2476, 0.6100, -0.7755, -0.8841, -0.9497]) assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 test_callback_fn.has_been_called = False pipe = StableDiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-2-base", revision="fp16", torch_dtype=torch.float16 ) 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=20, guidance_scale=7.5, generator=generator, callback=test_callback_fn, callback_steps=1, ) assert test_callback_fn.has_been_called assert number_of_steps == 20 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() pipeline_id = "stabilityai/stable-diffusion-2-base" prompt = "Andromeda galaxy in a bottle" pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, revision="fp16", torch_dtype=torch.float16) pipeline = pipeline.to(torch_device) pipeline.enable_attention_slicing(1) pipeline.enable_sequential_cpu_offload() generator = torch.Generator(device=torch_device).manual_seed(0) _ = pipeline(prompt, generator=generator, num_inference_steps=5) mem_bytes = torch.cuda.max_memory_allocated() # make sure that less than 2.8 GB is allocated assert mem_bytes < 2.8 * 10**9