# coding=utf-8 # Copyright 2026 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 PIL import Image from transformers import AutoConfig, AutoTokenizer, T5EncoderModel from diffusers import ( AutoPipelineForImage2Image, AutoPipelineForText2Image, Kandinsky3Pipeline, Kandinsky3UNet, VQModel, ) from diffusers.image_processor import VaeImageProcessor from diffusers.schedulers.scheduling_ddpm import DDPMScheduler from ...testing_utils import ( backend_empty_cache, enable_full_determinism, load_image, require_torch_accelerator, slow, torch_device, ) from ..pipeline_params import ( TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS, ) from ..test_pipelines_common import PipelineTesterMixin enable_full_determinism() class Kandinsky3PipelineFastTests(PipelineTesterMixin, unittest.TestCase): pipeline_class = Kandinsky3Pipeline params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = TEXT_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS test_xformers_attention = False @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, time_cond_proj_dim=None): torch.manual_seed(0) unet = Kandinsky3UNet( in_channels=4, time_embedding_dim=4, groups=2, attention_head_dim=4, layers_per_block=3, block_out_channels=(32, 64), cross_attention_dim=4, encoder_hid_dim=32, ) scheduler = DDPMScheduler( beta_start=0.00085, beta_end=0.012, steps_offset=1, beta_schedule="squaredcos_cap_v2", clip_sample=True, thresholding=False, ) torch.manual_seed(0) movq = self.dummy_movq torch.manual_seed(0) config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") text_encoder = T5EncoderModel(config).eval() torch.manual_seed(0) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") components = { "unet": unet, "scheduler": scheduler, "movq": movq, "text_encoder": text_encoder, "tokenizer": tokenizer, } 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 painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "np", "width": 16, "height": 16, } return inputs def test_kandinsky3(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_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 16, 16, 3) expected_slice = np.array([0.3944, 0.3680, 0.4842, 0.5333, 0.4412, 0.4812, 0.5089, 0.5381, 0.5578]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1, ( f" expected_slice {expected_slice}, but got {image_slice.flatten()}" ) def test_float16_inference(self): super().test_float16_inference(expected_max_diff=1e-1) def test_inference_batch_single_identical(self): super().test_inference_batch_single_identical(expected_max_diff=1e-2) @slow @require_torch_accelerator class Kandinsky3PipelineIntegrationTests(unittest.TestCase): def setUp(self): # clean up the VRAM before each test super().setUp() gc.collect() backend_empty_cache(torch_device) def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() backend_empty_cache(torch_device) def test_kandinskyV3(self): pipe = AutoPipelineForText2Image.from_pretrained( "kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16 ) pipe.enable_model_cpu_offload(device=torch_device) pipe.set_progress_bar_config(disable=None) prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background." generator = torch.Generator(device="cpu").manual_seed(0) image = pipe(prompt, num_inference_steps=5, generator=generator).images[0] assert image.size == (1024, 1024) expected_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png" ) image_processor = VaeImageProcessor() image_np = image_processor.pil_to_numpy(image) expected_image_np = image_processor.pil_to_numpy(expected_image) self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2)) def test_kandinskyV3_img2img(self): pipe = AutoPipelineForImage2Image.from_pretrained( "kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16 ) pipe.enable_model_cpu_offload(device=torch_device) pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png" ) w, h = 512, 512 image = image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1) prompt = "A painting of the inside of a subway train with tiny raccoons." image = pipe(prompt, image=image, strength=0.75, num_inference_steps=5, generator=generator).images[0] assert image.size == (512, 512) expected_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/i2i.png" ) image_processor = VaeImageProcessor() image_np = image_processor.pil_to_numpy(image) expected_image_np = image_processor.pil_to_numpy(expected_image) self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2))