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import gc |
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import unittest |
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import numpy as np |
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import torch |
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from PIL import Image |
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from transformers import AutoTokenizer, T5EncoderModel |
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from diffusers import ( |
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AutoPipelineForImage2Image, |
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AutoPipelineForText2Image, |
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Kandinsky3Pipeline, |
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Kandinsky3UNet, |
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VQModel, |
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) |
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from diffusers.image_processor import VaeImageProcessor |
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from diffusers.schedulers.scheduling_ddpm import DDPMScheduler |
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from diffusers.utils.testing_utils import ( |
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enable_full_determinism, |
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load_image, |
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require_torch_gpu, |
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slow, |
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) |
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from ..pipeline_params import ( |
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TEXT_TO_IMAGE_BATCH_PARAMS, |
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TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, |
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TEXT_TO_IMAGE_IMAGE_PARAMS, |
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TEXT_TO_IMAGE_PARAMS, |
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) |
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from ..test_pipelines_common import PipelineTesterMixin |
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enable_full_determinism() |
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class Kandinsky3PipelineFastTests(PipelineTesterMixin, unittest.TestCase): |
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pipeline_class = Kandinsky3Pipeline |
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params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} |
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batch_params = TEXT_TO_IMAGE_BATCH_PARAMS |
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image_params = TEXT_TO_IMAGE_IMAGE_PARAMS |
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image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS |
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callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS |
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test_xformers_attention = False |
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@property |
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def dummy_movq_kwargs(self): |
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return { |
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"block_out_channels": [32, 64], |
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"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], |
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"in_channels": 3, |
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"latent_channels": 4, |
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"layers_per_block": 1, |
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"norm_num_groups": 8, |
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"norm_type": "spatial", |
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"num_vq_embeddings": 12, |
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"out_channels": 3, |
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"up_block_types": [ |
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"AttnUpDecoderBlock2D", |
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"UpDecoderBlock2D", |
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], |
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"vq_embed_dim": 4, |
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} |
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@property |
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def dummy_movq(self): |
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torch.manual_seed(0) |
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model = VQModel(**self.dummy_movq_kwargs) |
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return model |
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def get_dummy_components(self, time_cond_proj_dim=None): |
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torch.manual_seed(0) |
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unet = Kandinsky3UNet( |
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in_channels=4, |
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time_embedding_dim=4, |
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groups=2, |
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attention_head_dim=4, |
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layers_per_block=3, |
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block_out_channels=(32, 64), |
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cross_attention_dim=4, |
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encoder_hid_dim=32, |
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) |
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scheduler = DDPMScheduler( |
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beta_start=0.00085, |
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beta_end=0.012, |
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steps_offset=1, |
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beta_schedule="squaredcos_cap_v2", |
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clip_sample=True, |
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thresholding=False, |
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) |
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torch.manual_seed(0) |
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movq = self.dummy_movq |
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torch.manual_seed(0) |
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text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5") |
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torch.manual_seed(0) |
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") |
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components = { |
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"unet": unet, |
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"scheduler": scheduler, |
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"movq": movq, |
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"text_encoder": text_encoder, |
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"tokenizer": tokenizer, |
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} |
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return components |
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def get_dummy_inputs(self, device, seed=0): |
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if str(device).startswith("mps"): |
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generator = torch.manual_seed(seed) |
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else: |
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generator = torch.Generator(device=device).manual_seed(seed) |
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inputs = { |
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"prompt": "A painting of a squirrel eating a burger", |
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"generator": generator, |
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"num_inference_steps": 2, |
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"guidance_scale": 6.0, |
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"output_type": "np", |
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"width": 16, |
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"height": 16, |
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} |
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return inputs |
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def test_kandinsky3(self): |
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device = "cpu" |
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components = self.get_dummy_components() |
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pipe = self.pipeline_class(**components) |
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pipe = pipe.to(device) |
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pipe.set_progress_bar_config(disable=None) |
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output = pipe(**self.get_dummy_inputs(device)) |
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image = output.images |
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image_slice = image[0, -3:, -3:, -1] |
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assert image.shape == (1, 16, 16, 3) |
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expected_slice = np.array([0.3768, 0.4373, 0.4865, 0.4890, 0.4299, 0.5122, 0.4921, 0.4924, 0.5599]) |
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assert ( |
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np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 |
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), f" expected_slice {expected_slice}, but got {image_slice.flatten()}" |
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def test_float16_inference(self): |
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super().test_float16_inference(expected_max_diff=1e-1) |
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def test_inference_batch_single_identical(self): |
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super().test_inference_batch_single_identical(expected_max_diff=1e-2) |
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@slow |
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@require_torch_gpu |
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class Kandinsky3PipelineIntegrationTests(unittest.TestCase): |
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def setUp(self): |
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super().setUp() |
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gc.collect() |
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torch.cuda.empty_cache() |
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def tearDown(self): |
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super().tearDown() |
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gc.collect() |
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torch.cuda.empty_cache() |
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def test_kandinskyV3(self): |
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pipe = AutoPipelineForText2Image.from_pretrained( |
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"kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16 |
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) |
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pipe.enable_model_cpu_offload() |
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pipe.set_progress_bar_config(disable=None) |
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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." |
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generator = torch.Generator(device="cpu").manual_seed(0) |
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image = pipe(prompt, num_inference_steps=5, generator=generator).images[0] |
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assert image.size == (1024, 1024) |
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expected_image = load_image( |
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png" |
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) |
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image_processor = VaeImageProcessor() |
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image_np = image_processor.pil_to_numpy(image) |
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expected_image_np = image_processor.pil_to_numpy(expected_image) |
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self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2)) |
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def test_kandinskyV3_img2img(self): |
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pipe = AutoPipelineForImage2Image.from_pretrained( |
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"kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16 |
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) |
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pipe.enable_model_cpu_offload() |
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pipe.set_progress_bar_config(disable=None) |
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generator = torch.Generator(device="cpu").manual_seed(0) |
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image = load_image( |
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png" |
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) |
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w, h = 512, 512 |
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image = image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1) |
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prompt = "A painting of the inside of a subway train with tiny raccoons." |
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image = pipe(prompt, image=image, strength=0.75, num_inference_steps=5, generator=generator).images[0] |
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assert image.size == (512, 512) |
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expected_image = load_image( |
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/i2i.png" |
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) |
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image_processor = VaeImageProcessor() |
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image_np = image_processor.pil_to_numpy(image) |
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expected_image_np = image_processor.pil_to_numpy(expected_image) |
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self.assertTrue(np.allclose(image_np, expected_image_np, atol=5e-2)) |
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