# coding=utf-8 # Copyright 2024 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 inspect import unittest import numpy as np import torch from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, KolorsPAGPipeline, KolorsPipeline, UNet2DConditionModel, ) from diffusers.pipelines.kolors import ChatGLMModel, ChatGLMTokenizer from diffusers.utils.testing_utils import enable_full_determinism 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 ( PipelineFromPipeTesterMixin, PipelineTesterMixin, ) enable_full_determinism() class KolorsPAGPipelineFastTests( PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase, ): pipeline_class = KolorsPAGPipeline params = TEXT_TO_IMAGE_PARAMS.union({"pag_scale", "pag_adaptive_scale"}) 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.union({"add_text_embeds", "add_time_ids"}) # Copied from tests.pipelines.kolors.test_kolors.KolorsPipelineFastTests.get_dummy_components def get_dummy_components(self, time_cond_proj_dim=None): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(2, 4), layers_per_block=2, time_cond_proj_dim=time_cond_proj_dim, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), # specific config below attention_head_dim=(2, 4), use_linear_projection=True, addition_embed_type="text_time", addition_time_embed_dim=8, transformer_layers_per_block=(1, 2), projection_class_embeddings_input_dim=56, cross_attention_dim=8, norm_num_groups=1, ) scheduler = EulerDiscreteScheduler( beta_start=0.00085, beta_end=0.012, steps_offset=1, beta_schedule="scaled_linear", timestep_spacing="leading", ) 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 = ChatGLMModel.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b") tokenizer = ChatGLMTokenizer.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b") components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "image_encoder": 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 painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 5.0, "pag_scale": 0.9, "output_type": "np", } return inputs def test_pag_disable_enable(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() # base pipeline (expect same output when pag is disabled) pipe_sd = KolorsPipeline(**components) pipe_sd = pipe_sd.to(device) pipe_sd.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) del inputs["pag_scale"] assert ( "pag_scale" not in inspect.signature(pipe_sd.__call__).parameters ), f"`pag_scale` should not be a call parameter of the base pipeline {pipe_sd.__class__.__name__}." out = pipe_sd(**inputs).images[0, -3:, -3:, -1] # pag disabled with pag_scale=0.0 pipe_pag = self.pipeline_class(**components) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) inputs["pag_scale"] = 0.0 out_pag_disabled = pipe_pag(**inputs).images[0, -3:, -3:, -1] # pag enabled pipe_pag = self.pipeline_class(**components, pag_applied_layers=["mid", "up", "down"]) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) out_pag_enabled = pipe_pag(**inputs).images[0, -3:, -3:, -1] assert np.abs(out.flatten() - out_pag_disabled.flatten()).max() < 1e-3 assert np.abs(out.flatten() - out_pag_enabled.flatten()).max() > 1e-3 def test_pag_applied_layers(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() # base pipeline pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) # pag_applied_layers = ["mid","up","down"] should apply to all self-attention layers all_self_attn_layers = [k for k in pipe.unet.attn_processors.keys() if "attn1" in k] original_attn_procs = pipe.unet.attn_processors pag_layers = ["mid", "down", "up"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_layers) all_self_attn_mid_layers = [ "mid_block.attentions.0.transformer_blocks.0.attn1.processor", "mid_block.attentions.0.transformer_blocks.1.attn1.processor", ] pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["mid"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers) pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["mid_block"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers) pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["mid_block.attentions.0"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers) # pag_applied_layers = ["mid.block_0.attentions_1"] does not exist in the model pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["mid_block.attentions.1"] with self.assertRaises(ValueError): pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) # pag_applied_layers = "down" should apply to all self-attention layers in down_blocks pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["down"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert len(pipe.pag_attn_processors) == 4 pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["down_blocks.0"] with self.assertRaises(ValueError): pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["down_blocks.1"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert len(pipe.pag_attn_processors) == 4 pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["down_blocks.1.attentions.1"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert len(pipe.pag_attn_processors) == 2 def test_pag_inference(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() pipe_pag = self.pipeline_class(**components, pag_applied_layers=["mid", "up", "down"]) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = pipe_pag(**inputs).images image_slice = image[0, -3:, -3:, -1] assert image.shape == ( 1, 64, 64, 3, ), f"the shape of the output image should be (1, 64, 64, 3) but got {image.shape}" expected_slice = np.array( [0.26030684, 0.43192005, 0.4042826, 0.4189067, 0.5181305, 0.3832534, 0.472135, 0.4145031, 0.43726248] ) max_diff = np.abs(image_slice.flatten() - expected_slice).max() self.assertLessEqual(max_diff, 1e-3) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(expected_max_diff=3e-3)