Add the K-LMS scheduler to the inpainting pipeline + tests (#587)

* Add the K-LMS scheduler to the inpainting pipeline + tests

* Remove redundant casts

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -213,7 +213,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
 
         # add noise to latents using the timesteps
         noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
-        init_latents = self.scheduler.add_noise(init_latents, noise, timesteps).to(self.device)
+        init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
 
         # get prompt text embeddings
         text_input = self.tokenizer(
@@ -265,8 +265,6 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
                 sigma = self.scheduler.sigmas[t_index]
                 # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
                 latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
-                latent_model_input = latent_model_input.to(self.unet.dtype)
-                t = t.to(self.unet.dtype)
 
             # predict the noise residual
             noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -284,7 +282,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
 
         # scale and decode the image latents with vae
         latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents.to(self.vae.dtype)).sample
+        image = self.vae.decode(latents).sample
 
         image = (image / 2 + 0.5).clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).numpy()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -12,7 +12,7 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import DDIMScheduler, PNDMScheduler
+from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from ...utils import logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
@@ -78,7 +78,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler],
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPFeatureExtractor,
     ):
@@ -241,8 +241,13 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
         offset = self.scheduler.config.get("steps_offset", 0)
         init_timestep = int(num_inference_steps * strength) + offset
         init_timestep = min(init_timestep, num_inference_steps)
-        timesteps = self.scheduler.timesteps[-init_timestep]
-        timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            timesteps = torch.tensor(
+                [num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
+            )
+        else:
+            timesteps = self.scheduler.timesteps[-init_timestep]
+            timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
 
         # add noise to latents using the timesteps
         noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
@@ -287,8 +292,13 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
         latents = init_latents
         t_start = max(num_inference_steps - init_timestep + offset, 0)
         for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
+            t_index = t_start + i
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            if isinstance(self.scheduler, LMSDiscreteScheduler):
+                sigma = self.scheduler.sigmas[t_index]
+                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
+                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
 
             # predict the noise residual
             noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -299,10 +309,15 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            if isinstance(self.scheduler, LMSDiscreteScheduler):
+                latents = self.scheduler.step(noise_pred, t_index, latents, **extra_step_kwargs).prev_sample
+                # masking
+                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor(t_index))
+            else:
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+                # masking
+                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
 
-            # masking
-            init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
             latents = (init_latents_proper * mask) + (latents * (1 - mask))
 
         # scale and decode the image latents with vae

tests/test_pipelines.py

@@ -1325,6 +1325,53 @@ class PipelineTesterMixin(unittest.TestCase):
         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image).max() < 1e-2
 
+    @slow
+    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
+    def test_stable_diffusion_inpaint_pipeline_k_lms(self):
+        init_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/overture-creations-5sI6fQgYIuo.png"
+        )
+        mask_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
+        )
+        expected_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
+            "/in_paint/red_cat_sitting_on_a_park_bench_k_lms.png"
+        )
+        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
+
+        lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+
+        model_id = "CompVis/stable-diffusion-v1-4"
+        pipe = StableDiffusionInpaintPipeline.from_pretrained(
+            model_id,
+            scheduler=lms,
+            safety_checker=self.dummy_safety_checker,
+            use_auth_token=True,
+        )
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.enable_attention_slicing()
+
+        prompt = "A red cat sitting on a park bench"
+
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        output = pipe(
+            prompt=prompt,
+            init_image=init_image,
+            mask_image=mask_image,
+            strength=0.75,
+            guidance_scale=7.5,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        assert image.shape == (512, 512, 3)
+        assert np.abs(expected_image - image).max() < 1e-2
+
     @slow
     def test_stable_diffusion_onnx(self):
         from scripts.convert_stable_diffusion_checkpoint_to_onnx import convert_models