# 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 diffusers import DDIMScheduler, KandinskyV22Pipeline, KandinskyV22PriorPipeline, UNet2DConditionModel, VQModel from diffusers.utils.testing_utils import ( enable_full_determinism, floats_tensor, load_numpy, require_torch_gpu, slow, torch_device, ) from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class Dummies: @property def text_embedder_hidden_size(self): return 32 @property def time_input_dim(self): return 32 @property def block_out_channels_0(self): return self.time_input_dim @property def time_embed_dim(self): return self.time_input_dim * 4 @property def cross_attention_dim(self): return 32 @property def dummy_unet(self): torch.manual_seed(0) model_kwargs = { "in_channels": 4, # Out channels is double in channels because predicts mean and variance "out_channels": 8, "addition_embed_type": "image", "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"), "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"), "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", "block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2), "layers_per_block": 1, "encoder_hid_dim": self.text_embedder_hidden_size, "encoder_hid_dim_type": "image_proj", "cross_attention_dim": self.cross_attention_dim, "attention_head_dim": 4, "resnet_time_scale_shift": "scale_shift", "class_embed_type": None, } model = UNet2DConditionModel(**model_kwargs) return model @property def dummy_movq_kwargs(self): return { "block_out_channels": [32, 64], "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": [ "AttnUpDecoderBlock2D", "UpDecoderBlock2D", ], "vq_embed_dim": 4, } @property def dummy_movq(self): torch.manual_seed(0) model = VQModel(**self.dummy_movq_kwargs) return model def get_dummy_components(self): unet = self.dummy_unet movq = self.dummy_movq scheduler = DDIMScheduler( num_train_timesteps=1000, beta_schedule="linear", beta_start=0.00085, beta_end=0.012, clip_sample=False, set_alpha_to_one=False, steps_offset=1, prediction_type="epsilon", thresholding=False, ) components = { "unet": unet, "scheduler": scheduler, "movq": movq, } return components def get_dummy_inputs(self, device, seed=0): image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device) negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to( device ) if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "image_embeds": image_embeds, "negative_image_embeds": negative_image_embeds, "generator": generator, "height": 64, "width": 64, "guidance_scale": 4.0, "num_inference_steps": 2, "output_type": "np", } return inputs class KandinskyV22PipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = KandinskyV22Pipeline params = [ "image_embeds", "negative_image_embeds", ] batch_params = ["image_embeds", "negative_image_embeds"] required_optional_params = [ "generator", "height", "width", "latents", "guidance_scale", "num_inference_steps", "return_dict", "guidance_scale", "num_images_per_prompt", "output_type", "return_dict", ] test_xformers_attention = False def get_dummy_inputs(self, device, seed=0): dummies = Dummies() return dummies.get_dummy_inputs(device=device, seed=seed) def get_dummy_components(self): dummies = Dummies() return dummies.get_dummy_components() def test_kandinsky(self): device = "cpu" components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) output = pipe(**self.get_dummy_inputs(device)) image = output.images image_from_tuple = pipe( **self.get_dummy_inputs(device), 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([0.3420, 0.9505, 0.3919, 1.0000, 0.5188, 0.3109, 0.6139, 0.5624, 0.6811]) assert ( np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}" assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2 ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}" def test_float16_inference(self): super().test_float16_inference(expected_max_diff=1e-1) @slow @require_torch_gpu class KandinskyV22PipelineIntegrationTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_kandinsky_text2img(self): expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy" ) pipe_prior = KandinskyV22PriorPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 ) pipe_prior.to(torch_device) pipeline = KandinskyV22Pipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16 ) pipeline = pipeline.to(torch_device) pipeline.set_progress_bar_config(disable=None) prompt = "red cat, 4k photo" generator = torch.Generator(device="cuda").manual_seed(0) image_emb, zero_image_emb = pipe_prior( prompt, generator=generator, num_inference_steps=5, negative_prompt="", ).to_tuple() generator = torch.Generator(device="cuda").manual_seed(0) output = pipeline( image_embeds=image_emb, negative_image_embeds=zero_image_emb, generator=generator, num_inference_steps=100, output_type="np", ) image = output.images[0] assert image.shape == (512, 512, 3) assert_mean_pixel_difference(image, expected_image)