Added Lora Support to SD3 Img2Img Pipeline (#9659)

* add lora

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import inspect
-from typing import Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import PIL.Image
 import torch
@@ -25,7 +25,7 @@ from transformers import (
 )
 
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import SD3LoraLoaderMixin
+from ...loaders import FromSingleFileMixin, SD3LoraLoaderMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.transformers import SD3Transformer2DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
@@ -149,7 +149,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
+class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin):
     r"""
     Args:
         transformer ([`SD3Transformer2DModel`]):
@@ -680,6 +680,10 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
     def guidance_scale(self):
         return self._guidance_scale
 
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+
     @property
     def clip_skip(self):
         return self._clip_skip
@@ -723,6 +727,7 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
         negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
         clip_skip: Optional[int] = None,
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
@@ -797,6 +802,10 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
                 of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+               A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
             callback_on_step_end (`Callable`, *optional*):
                 A function that calls at the end of each denoising steps during the inference. The function is called
                 with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
@@ -835,6 +844,7 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
 
         self._guidance_scale = guidance_scale
         self._clip_skip = clip_skip
+        self._joint_attention_kwargs = joint_attention_kwargs
         self._interrupt = False
 
         # 2. Define call parameters
@@ -847,6 +857,10 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
 
         device = self._execution_device
 
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+
         (
             prompt_embeds,
             negative_prompt_embeds,
@@ -868,6 +882,7 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
             clip_skip=self.clip_skip,
             num_images_per_prompt=num_images_per_prompt,
             max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
         )
 
         if self.do_classifier_free_guidance:
@@ -912,6 +927,7 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline):
                     timestep=timestep,
                     encoder_hidden_states=prompt_embeds,
                     pooled_projections=pooled_prompt_embeds,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
                     return_dict=False,
                 )[0]
 

tests/lora/test_lora_layers_sd3.py

@@ -12,13 +12,29 @@
 # 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 sys
 import unittest
 
+import numpy as np
+import torch
 from transformers import AutoTokenizer, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
 
-from diffusers import FlowMatchEulerDiscreteScheduler, SD3Transformer2DModel, StableDiffusion3Pipeline
-from diffusers.utils.testing_utils import is_peft_available, require_peft_backend, require_torch_gpu, torch_device
+from diffusers import (
+    FlowMatchEulerDiscreteScheduler,
+    SD3Transformer2DModel,
+    StableDiffusion3Img2ImgPipeline,
+    StableDiffusion3Pipeline,
+)
+from diffusers.utils import load_image
+from diffusers.utils.import_utils import is_accelerate_available
+from diffusers.utils.testing_utils import (
+    is_peft_available,
+    numpy_cosine_similarity_distance,
+    require_peft_backend,
+    require_torch_gpu,
+    torch_device,
+)
 
 
 if is_peft_available():
@@ -29,6 +45,10 @@ sys.path.append(".")
 from utils import PeftLoraLoaderMixinTests  # noqa: E402
 
 
+if is_accelerate_available():
+    from accelerate.utils import release_memory
+
+
 @require_peft_backend
 class SD3LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     pipeline_class = StableDiffusion3Pipeline
@@ -108,3 +128,88 @@ class SD3LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     @unittest.skip("Not supported in SD3.")
     def test_modify_padding_mode(self):
         pass
+
+
+@require_torch_gpu
+@require_peft_backend
+class LoraSD3IntegrationTests(unittest.TestCase):
+    pipeline_class = StableDiffusion3Img2ImgPipeline
+    repo_id = "stabilityai/stable-diffusion-3-medium-diffusers"
+
+    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, seed=0):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
+        )
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device="cpu").manual_seed(seed)
+
+        return {
+            "prompt": "corgi",
+            "num_inference_steps": 2,
+            "guidance_scale": 5.0,
+            "output_type": "np",
+            "generator": generator,
+            "image": init_image,
+        }
+
+    def test_sd3_img2img_lora(self):
+        pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16)
+        pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors")
+        pipe.enable_sequential_cpu_offload()
+
+        inputs = self.get_inputs(torch_device)
+
+        image = pipe(**inputs).images[0]
+        image_slice = image[0, :10, :10]
+        expected_slice = np.array(
+            [
+                0.47827148,
+                0.5,
+                0.71972656,
+                0.3955078,
+                0.4194336,
+                0.69628906,
+                0.37036133,
+                0.40820312,
+                0.6923828,
+                0.36450195,
+                0.40429688,
+                0.6904297,
+                0.35595703,
+                0.39257812,
+                0.68652344,
+                0.35498047,
+                0.3984375,
+                0.68310547,
+                0.34716797,
+                0.3996582,
+                0.6855469,
+                0.3388672,
+                0.3959961,
+                0.6816406,
+                0.34033203,
+                0.40429688,
+                0.6845703,
+                0.34228516,
+                0.4086914,
+                0.6870117,
+            ]
+        )
+
+        max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
+
+        assert max_diff < 1e-4, f"Outputs are not close enough, got {max_diff}"
+        pipe.unload_lora_weights()
+        release_memory(pipe)