# 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 inspect import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, AutoPipelineForText2Image, DDIMScheduler, StableDiffusionPAGPipeline, StableDiffusionPipeline, UNet2DConditionModel, ) from ...testing_utils import ( backend_empty_cache, enable_full_determinism, 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 ( IPAdapterTesterMixin, PipelineFromPipeTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, ) enable_full_determinism() class StableDiffusionPAGPipelineFastTests( PipelineTesterMixin, IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase, ): pipeline_class = StableDiffusionPAGPipeline 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"}) def get_dummy_components(self, time_cond_proj_dim=None): cross_attention_dim = 8 torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(4, 8), layers_per_block=2, sample_size=32, time_cond_proj_dim=time_cond_proj_dim, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=cross_attention_dim, norm_num_groups=2, ) scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) torch.manual_seed(0) vae = AutoencoderKL( block_out_channels=[4, 8], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=4, norm_num_groups=2, ) torch.manual_seed(0) text_encoder_config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=cross_attention_dim, intermediate_size=16, layer_norm_eps=1e-05, num_attention_heads=2, num_hidden_layers=2, pad_token_id=1, vocab_size=1000, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, "image_encoder": 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 = StableDiffusionPipeline(**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 = [ "down", "mid", "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) # pag_applied_layers = ["mid"], or ["mid.block_0"] or ["mid.block_0.attentions_0"] should apply to all self-attention layers in mid_block, i.e. # mid_block.attentions.0.transformer_blocks.0.attn1.processor # mid_block.attentions.0.transformer_blocks.1.attn1.processor 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 # down_blocks.1.attentions.0.transformer_blocks.0.attn1.processor # down_blocks.1.attentions.0.transformer_blocks.1.attn1.processor # down_blocks.1.attentions.0.transformer_blocks.0.attn1.processor 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) == 2 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) == 2 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) == 1 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( [0.23171297, 0.44669262, 0.48407662, 0.29981518, 0.36721927, 0.46788025, 0.46333545, 0.3314417, 0.42078137] ) max_diff = np.abs(image_slice.flatten() - expected_slice).max() self.assertLessEqual(max_diff, 1e-3) def test_encode_prompt_works_in_isolation(self): extra_required_param_value_dict = { "device": torch.device(torch_device).type, "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0, } return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict) @slow @require_torch_accelerator class StableDiffusionPAGPipelineIntegrationTests(unittest.TestCase): pipeline_class = StableDiffusionPAGPipeline repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" def setUp(self): super().setUp() gc.collect() backend_empty_cache(torch_device) def tearDown(self): super().tearDown() gc.collect() backend_empty_cache(torch_device) def get_inputs(self, device, generator_device="cpu", seed=1, guidance_scale=7.0): generator = torch.Generator(device=generator_device).manual_seed(seed) inputs = { "prompt": "a polar bear sitting in a chair drinking a milkshake", "negative_prompt": "deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality", "generator": generator, "num_inference_steps": 3, "guidance_scale": guidance_scale, "pag_scale": 3.0, "output_type": "np", } return inputs def test_pag_cfg(self): pipeline = AutoPipelineForText2Image.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16) pipeline.enable_model_cpu_offload(device=torch_device) pipeline.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device) image = pipeline(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array( [0.58251953, 0.5722656, 0.5683594, 0.55029297, 0.52001953, 0.52001953, 0.49951172, 0.45410156, 0.50146484] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3, ( f"output is different from expected, {image_slice.flatten()}" ) def test_pag_uncond(self): pipeline = AutoPipelineForText2Image.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16) pipeline.enable_model_cpu_offload(device=torch_device) pipeline.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device, guidance_scale=0.0) image = pipeline(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array( [0.5986328, 0.52441406, 0.3972168, 0.4741211, 0.34985352, 0.22705078, 0.4128418, 0.2866211, 0.31713867] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3, ( f"output is different from expected, {image_slice.flatten()}" )