add PAG support for SD Img2Img

docs/source/en/api/pipelines/pag.md

@@ -53,6 +53,11 @@ Since RegEx is supported as a way for matching layer identifiers, it is crucial
 	- all
 	- __call__
 
+## StableDiffusionPAGImg2ImgPipeline
+[[autodoc]] StableDiffusionPAGImg2ImgPipeline
+	- all
+	- __call__
+
 ## StableDiffusionControlNetPAGPipeline
 [[autodoc]] StableDiffusionControlNetPAGPipeline
 

src/diffusers/__init__.py

@@ -344,6 +344,7 @@
             "StableDiffusionLatentUpscalePipeline",
             "StableDiffusionLDM3DPipeline",
             "StableDiffusionModelEditingPipeline",
+            "StableDiffusionPAGImg2ImgPipeline",
             "StableDiffusionPAGPipeline",
             "StableDiffusionPanoramaPipeline",
             "StableDiffusionParadigmsPipeline",
@@ -795,6 +796,7 @@
             StableDiffusionLatentUpscalePipeline,
             StableDiffusionLDM3DPipeline,
             StableDiffusionModelEditingPipeline,
+            StableDiffusionPAGImg2ImgPipeline,
             StableDiffusionPAGPipeline,
             StableDiffusionPanoramaPipeline,
             StableDiffusionParadigmsPipeline,

src/diffusers/pipelines/__init__.py

@@ -164,6 +164,7 @@
             "HunyuanDiTPAGPipeline",
             "StableDiffusion3PAGPipeline",
             "StableDiffusionPAGPipeline",
+            "StableDiffusionPAGImg2ImgPipeline",
             "StableDiffusionControlNetPAGPipeline",
             "StableDiffusionXLPAGPipeline",
             "StableDiffusionXLPAGInpaintPipeline",
@@ -569,6 +570,7 @@
             StableDiffusion3PAGPipeline,
             StableDiffusionControlNetPAGInpaintPipeline,
             StableDiffusionControlNetPAGPipeline,
+            StableDiffusionPAGImg2ImgPipeline,
             StableDiffusionPAGPipeline,
             StableDiffusionXLControlNetPAGImg2ImgPipeline,
             StableDiffusionXLControlNetPAGPipeline,

src/diffusers/pipelines/auto_pipeline.py

@@ -63,6 +63,7 @@
     StableDiffusion3PAGPipeline,
     StableDiffusionControlNetPAGInpaintPipeline,
     StableDiffusionControlNetPAGPipeline,
+    StableDiffusionPAGImg2ImgPipeline,
     StableDiffusionPAGPipeline,
     StableDiffusionXLControlNetPAGImg2ImgPipeline,
     StableDiffusionXLControlNetPAGPipeline,
@@ -131,6 +132,7 @@
         ("kandinsky22", KandinskyV22Img2ImgCombinedPipeline),
         ("kandinsky3", Kandinsky3Img2ImgPipeline),
         ("stable-diffusion-controlnet", StableDiffusionControlNetImg2ImgPipeline),
+        ("stable-diffusion-pag", StableDiffusionPAGImg2ImgPipeline),
         ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetImg2ImgPipeline),
         ("stable-diffusion-xl-pag", StableDiffusionXLPAGImg2ImgPipeline),
         ("stable-diffusion-xl-controlnet-pag", StableDiffusionXLControlNetPAGImg2ImgPipeline),

src/diffusers/pipelines/pag/__init__.py

@@ -32,6 +32,7 @@
     _import_structure["pipeline_pag_sd"] = ["StableDiffusionPAGPipeline"]
     _import_structure["pipeline_pag_sd_3"] = ["StableDiffusion3PAGPipeline"]
     _import_structure["pipeline_pag_sd_animatediff"] = ["AnimateDiffPAGPipeline"]
+    _import_structure["pipeline_pag_sd_img2img"] = ["StableDiffusionPAGImg2ImgPipeline"]
     _import_structure["pipeline_pag_sd_xl"] = ["StableDiffusionXLPAGPipeline"]
     _import_structure["pipeline_pag_sd_xl_img2img"] = ["StableDiffusionXLPAGImg2ImgPipeline"]
     _import_structure["pipeline_pag_sd_xl_inpaint"] = ["StableDiffusionXLPAGInpaintPipeline"]
@@ -54,6 +55,7 @@
         from .pipeline_pag_sd import StableDiffusionPAGPipeline
         from .pipeline_pag_sd_3 import StableDiffusion3PAGPipeline
         from .pipeline_pag_sd_animatediff import AnimateDiffPAGPipeline
+        from .pipeline_pag_sd_img2img import StableDiffusionPAGImg2ImgPipeline
         from .pipeline_pag_sd_xl import StableDiffusionXLPAGPipeline
         from .pipeline_pag_sd_xl_img2img import StableDiffusionXLPAGImg2ImgPipeline
         from .pipeline_pag_sd_xl_inpaint import StableDiffusionXLPAGInpaintPipeline

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1592,6 +1592,21 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class StableDiffusionPAGImg2ImgPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class StableDiffusionPAGPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/pag/test_pag_sd_img2img.py

@@ -0,0 +1,282 @@
+# 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 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 diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    floats_tensor,
+    load_image,
+    require_torch_gpu,
+    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(
+            [0.44203848, 0.49598145, 0.42248967, 0.6707724, 0.5683791, 0.43603387, 0.58316565, 0.60077155, 0.5174199]
+        )
+        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
+        self.assertLessEqual(max_diff, 1e-3)
+
+
+@slow
+@require_torch_gpu
+class StableDiffusionPAGImg2ImgPipelineIntegrationTests(unittest.TestCase):
+    pipeline_class = StableDiffusionPAGImg2ImgPipeline
+    repo_id = "Jiali/stable-diffusion-1.5"
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    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()
+        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)
+        print(image_slice.flatten())
+        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()
+        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]
+        )
+        print(image_slice.flatten())
+        assert (
+            np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
+        ), f"output is different from expected, {image_slice.flatten()}"