Add torch_xla and from_single_file to instruct-pix2pix (#10444)

* Add torch_xla and from_single_file to instruct-pix2pix

* StableDiffusionInstructPix2PixPipelineSingleFileSlowTests

* StableDiffusionInstructPix2PixPipelineSingleFileSlowTests

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

src/diffusers/loaders/single_file_utils.py

@@ -109,6 +109,7 @@ CHECKPOINT_KEY_NAMES = {
     "autoencoder-dc-sana": "encoder.project_in.conv.bias",
     "mochi-1-preview": ["model.diffusion_model.blocks.0.attn.qkv_x.weight", "blocks.0.attn.qkv_x.weight"],
     "hunyuan-video": "txt_in.individual_token_refiner.blocks.0.adaLN_modulation.1.bias",
+    "instruct-pix2pix": "model.diffusion_model.input_blocks.0.0.weight",
 }
 
 DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
@@ -165,6 +166,7 @@ DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
     "autoencoder-dc-f32c32-sana": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers"},
     "mochi-1-preview": {"pretrained_model_name_or_path": "genmo/mochi-1-preview"},
     "hunyuan-video": {"pretrained_model_name_or_path": "hunyuanvideo-community/HunyuanVideo"},
+    "instruct-pix2pix": {"pretrained_model_name_or_path": "timbrooks/instruct-pix2pix"},
 }
 
 # Use to configure model sample size when original config is provided
@@ -633,6 +635,12 @@ def infer_diffusers_model_type(checkpoint):
     elif CHECKPOINT_KEY_NAMES["hunyuan-video"] in checkpoint:
         model_type = "hunyuan-video"
 
+    elif (
+        CHECKPOINT_KEY_NAMES["instruct-pix2pix"] in checkpoint
+        and checkpoint[CHECKPOINT_KEY_NAMES["instruct-pix2pix"]].shape[1] == 8
+    ):
+        model_type = "instruct-pix2pix"
+
     else:
         model_type = "v1"
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py

@@ -22,16 +22,23 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPV
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import FromSingleFileMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import PIL_INTERPOLATION, deprecate, logging
+from ...utils import PIL_INTERPOLATION, deprecate, is_torch_xla_available, logging
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -79,6 +86,7 @@ class StableDiffusionInstructPix2PixPipeline(
     TextualInversionLoaderMixin,
     StableDiffusionLoraLoaderMixin,
     IPAdapterMixin,
+    FromSingleFileMixin,
 ):
     r"""
     Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusion).
@@ -457,6 +465,9 @@ class StableDiffusionInstructPix2PixPipeline(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
             image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)

tests/single_file/test_stable_diffusion_single_file.py

@@ -4,11 +4,13 @@ import unittest
 
 import torch
 
-from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline
+from diffusers import EulerDiscreteScheduler, StableDiffusionInstructPix2PixPipeline, StableDiffusionPipeline
 from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name
+from diffusers.utils import load_image
 from diffusers.utils.testing_utils import (
     backend_empty_cache,
     enable_full_determinism,
+    nightly,
     require_torch_accelerator,
     slow,
     torch_device,
@@ -118,3 +120,44 @@ class StableDiffusion21PipelineSingleFileSlowTests(unittest.TestCase, SDSingleFi
 
     def test_single_file_format_inference_is_same_as_pretrained(self):
         super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)
+
+
+@nightly
+@slow
+@require_torch_accelerator
+class StableDiffusionInstructPix2PixPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+    pipeline_class = StableDiffusionInstructPix2PixPipeline
+    ckpt_path = "https://huggingface.co/timbrooks/instruct-pix2pix/blob/main/instruct-pix2pix-00-22000.safetensors"
+    original_config = (
+        "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/refs/heads/main/configs/generate.yaml"
+    )
+    repo_id = "timbrooks/instruct-pix2pix"
+
+    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)
+        image = load_image(
+            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"
+        )
+        inputs = {
+            "prompt": "turn him into a cyborg",
+            "image": image,
+            "generator": generator,
+            "num_inference_steps": 3,
+            "guidance_scale": 7.5,
+            "image_guidance_scale": 1.0,
+            "output_type": "np",
+        }
+        return inputs
+
+    def test_single_file_format_inference_is_same_as_pretrained(self):
+        super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)