# 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 random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, AutoencoderTiny, AutoPipelineForImage2Image, EulerDiscreteScheduler, StableDiffusionImg2ImgPipeline, StableDiffusionPAGImg2ImgPipeline, UNet2DConditionModel, ) from ...testing_utils import ( backend_empty_cache, enable_full_determinism, floats_tensor, load_image, require_torch_accelerator, slow, torch_device, ) from ..pipeline_params import ( IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, ) from ..test_pipelines_common import ( IPAdapterTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, ) enable_full_determinism() class StableDiffusionPAGImg2ImgPipelineFastTests( IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase, ): pipeline_class = StableDiffusionPAGImg2ImgPipeline params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"pag_scale", "pag_adaptive_scale"}) - {"height", "width"} required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"} batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS def get_dummy_components(self, time_cond_proj_dim=None): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(32, 64), 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"), cross_attention_dim=32, ) 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, ) text_encoder_config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, 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_tiny_autoencoder(self): return AutoencoderTiny(in_channels=3, out_channels=3, latent_channels=4) def get_dummy_inputs(self, device, seed=0): image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) image = image / 2 + 0.5 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", "image": image, "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "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 = StableDiffusionImg2ImgPipeline(**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_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, 32, 32, 3, ), f"the shape of the output image should be (1, 32, 32, 3) but got {image.shape}" expected_slice = np.array( [0.4508819, 0.49191576, 0.42664427, 0.66448534, 0.5606137, 0.43760118, 0.58251137, 0.5944448, 0.51642907] ) 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 StableDiffusionPAGImg2ImgPipelineIntegrationTests(unittest.TestCase): pipeline_class = StableDiffusionPAGImg2ImgPipeline repo_id = "Jiali/stable-diffusion-1.5" 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", dtype=torch.float32, seed=0): generator = torch.Generator(device=generator_device).manual_seed(seed) init_image = load_image( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "/stable_diffusion_img2img/sketch-mountains-input.png" ) inputs = { "prompt": "a fantasy landscape, concept art, high resolution", "image": init_image, "generator": generator, "num_inference_steps": 3, "strength": 0.75, "guidance_scale": 7.5, "pag_scale": 3.0, "output_type": "np", } return inputs def test_pag_cfg(self): pipeline = AutoPipelineForImage2Image.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 = AutoPipelineForImage2Image.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()}" )