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| # 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 unittest | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from transformers import CLIPTokenizer | |
| from transformers.models.blip_2.configuration_blip_2 import Blip2Config | |
| from transformers.models.clip.configuration_clip import CLIPTextConfig | |
| from diffusers import AutoencoderKL, BlipDiffusionPipeline, PNDMScheduler, UNet2DConditionModel | |
| from diffusers.utils.testing_utils import enable_full_determinism | |
| from src.diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor | |
| from src.diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel | |
| from src.diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel | |
| from ..test_pipelines_common import PipelineTesterMixin | |
| enable_full_determinism() | |
| class BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase): | |
| pipeline_class = BlipDiffusionPipeline | |
| params = [ | |
| "prompt", | |
| "reference_image", | |
| "source_subject_category", | |
| "target_subject_category", | |
| ] | |
| batch_params = [ | |
| "prompt", | |
| "reference_image", | |
| "source_subject_category", | |
| "target_subject_category", | |
| ] | |
| required_optional_params = [ | |
| "generator", | |
| "height", | |
| "width", | |
| "latents", | |
| "guidance_scale", | |
| "num_inference_steps", | |
| "neg_prompt", | |
| "guidance_scale", | |
| "prompt_strength", | |
| "prompt_reps", | |
| ] | |
| def get_dummy_components(self): | |
| torch.manual_seed(0) | |
| text_encoder_config = CLIPTextConfig( | |
| vocab_size=1000, | |
| hidden_size=16, | |
| intermediate_size=16, | |
| projection_dim=16, | |
| num_hidden_layers=1, | |
| num_attention_heads=1, | |
| max_position_embeddings=77, | |
| ) | |
| text_encoder = ContextCLIPTextModel(text_encoder_config) | |
| vae = AutoencoderKL( | |
| in_channels=4, | |
| out_channels=4, | |
| down_block_types=("DownEncoderBlock2D",), | |
| up_block_types=("UpDecoderBlock2D",), | |
| block_out_channels=(32,), | |
| layers_per_block=1, | |
| act_fn="silu", | |
| latent_channels=4, | |
| norm_num_groups=16, | |
| sample_size=16, | |
| ) | |
| blip_vision_config = { | |
| "hidden_size": 16, | |
| "intermediate_size": 16, | |
| "num_hidden_layers": 1, | |
| "num_attention_heads": 1, | |
| "image_size": 224, | |
| "patch_size": 14, | |
| "hidden_act": "quick_gelu", | |
| } | |
| blip_qformer_config = { | |
| "vocab_size": 1000, | |
| "hidden_size": 16, | |
| "num_hidden_layers": 1, | |
| "num_attention_heads": 1, | |
| "intermediate_size": 16, | |
| "max_position_embeddings": 512, | |
| "cross_attention_frequency": 1, | |
| "encoder_hidden_size": 16, | |
| } | |
| qformer_config = Blip2Config( | |
| vision_config=blip_vision_config, | |
| qformer_config=blip_qformer_config, | |
| num_query_tokens=16, | |
| tokenizer="hf-internal-testing/tiny-random-bert", | |
| ) | |
| qformer = Blip2QFormerModel(qformer_config) | |
| unet = UNet2DConditionModel( | |
| block_out_channels=(16, 32), | |
| norm_num_groups=16, | |
| layers_per_block=1, | |
| sample_size=16, | |
| in_channels=4, | |
| out_channels=4, | |
| down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), | |
| up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), | |
| cross_attention_dim=16, | |
| ) | |
| tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") | |
| scheduler = PNDMScheduler( | |
| beta_start=0.00085, | |
| beta_end=0.012, | |
| beta_schedule="scaled_linear", | |
| set_alpha_to_one=False, | |
| skip_prk_steps=True, | |
| ) | |
| vae.eval() | |
| qformer.eval() | |
| text_encoder.eval() | |
| image_processor = BlipImageProcessor() | |
| components = { | |
| "text_encoder": text_encoder, | |
| "vae": vae, | |
| "qformer": qformer, | |
| "unet": unet, | |
| "tokenizer": tokenizer, | |
| "scheduler": scheduler, | |
| "image_processor": image_processor, | |
| } | |
| return components | |
| def get_dummy_inputs(self, device, seed=0): | |
| np.random.seed(seed) | |
| reference_image = np.random.rand(32, 32, 3) * 255 | |
| reference_image = Image.fromarray(reference_image.astype("uint8")).convert("RGBA") | |
| if str(device).startswith("mps"): | |
| generator = torch.manual_seed(seed) | |
| else: | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| inputs = { | |
| "prompt": "swimming underwater", | |
| "generator": generator, | |
| "reference_image": reference_image, | |
| "source_subject_category": "dog", | |
| "target_subject_category": "dog", | |
| "height": 32, | |
| "width": 32, | |
| "guidance_scale": 7.5, | |
| "num_inference_steps": 2, | |
| "output_type": "np", | |
| } | |
| return inputs | |
| def test_blipdiffusion(self): | |
| device = "cpu" | |
| components = self.get_dummy_components() | |
| pipe = self.pipeline_class(**components) | |
| pipe = pipe.to(device) | |
| pipe.set_progress_bar_config(disable=None) | |
| image = pipe(**self.get_dummy_inputs(device))[0] | |
| image_slice = image[0, -3:, -3:, 0] | |
| assert image.shape == (1, 16, 16, 4) | |
| expected_slice = np.array([0.7096, 0.5900, 0.6703, 0.4032, 0.7766, 0.3629, 0.5447, 0.4149, 0.8172]) | |
| assert ( | |
| np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
| ), f" expected_slice {image_slice.flatten()}, but got {image_slice.flatten()}" | |