# 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 unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMParallelScheduler, DDPMParallelScheduler, StableDiffusionParadigmsPipeline, UNet2DConditionModel, ) from diffusers.utils.testing_utils import ( enable_full_determinism, nightly, require_torch_gpu, torch_device, ) from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class StableDiffusionParadigmsPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase): pipeline_class = StableDiffusionParadigmsPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = TEXT_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_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, # SD2-specific config below attention_head_dim=(2, 4), use_linear_projection=True, ) scheduler = DDIMParallelScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) 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, sample_size=128, ) torch.manual_seed(0) text_encoder_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, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": "a photograph of an astronaut riding a horse", "generator": generator, "num_inference_steps": 10, "guidance_scale": 6.0, "output_type": "numpy", "parallel": 3, "debug": True, } return inputs def test_stable_diffusion_paradigms_default_case(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionParadigmsPipeline(**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([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_stable_diffusion_paradigms_default_case_ddpm(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() torch.manual_seed(0) components["scheduler"] = DDPMParallelScheduler() torch.manual_seed(0) sd_pipe = StableDiffusionParadigmsPipeline(**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([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 # override to speed the overall test timing up. def test_inference_batch_consistent(self): super().test_inference_batch_consistent(batch_sizes=[1, 2]) # override to speed the overall test timing up. def test_inference_batch_single_identical(self): super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=3e-3) def test_stable_diffusion_paradigms_negative_prompt(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() sd_pipe = StableDiffusionParadigmsPipeline(**components) sd_pipe = sd_pipe.to(device) sd_pipe.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) negative_prompt = "french fries" output = sd_pipe(**inputs, negative_prompt=negative_prompt) image = output.images image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) expected_slice = np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @nightly @require_torch_gpu class StableDiffusionParadigmsPipelineSlowTests(unittest.TestCase): def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def get_inputs(self, seed=0): generator = torch.Generator(device=torch_device).manual_seed(seed) inputs = { "prompt": "a photograph of an astronaut riding a horse", "generator": generator, "num_inference_steps": 10, "guidance_scale": 7.5, "output_type": "numpy", "parallel": 3, "debug": True, } return inputs def test_stable_diffusion_paradigms_default(self): model_ckpt = "stabilityai/stable-diffusion-2-base" scheduler = DDIMParallelScheduler.from_pretrained(model_ckpt, subfolder="scheduler") pipe = StableDiffusionParadigmsPipeline.from_pretrained(model_ckpt, scheduler=scheduler, safety_checker=None) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() inputs = self.get_inputs() image = pipe(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]) assert np.abs(expected_slice - image_slice).max() < 1e-2