update for SDXL Turbo support

examples/stable-diffusion/README.md

@@ -131,7 +131,7 @@ python text_to_image_generation.py \
     --use_habana \
     --use_hpu_graphs \
     --gaudi_config Habana/stable-diffusion \
-    --bf16    
+    --bf16
 ```
 
 > HPU graphs are recommended when generating images by batches to get the fastest possible generations.
@@ -192,7 +192,10 @@ python text_to_image_generation.py \
     --use_habana \
     --use_hpu_graphs \
     --gaudi_config Habana/stable-diffusion \
-    --bf16
+    --bf16 \
+    --num_inference_steps 1 \
+    --guidance_scale 0.0 \
+    --timestep_spacing trailing
 ```
 
 > HPU graphs are recommended when generating images by batches to get the fastest possible generations.

examples/stable-diffusion/text_to_image_generation.py

@@ -61,6 +61,13 @@ def main():
         help="Name of scheduler",
     )
 
+    parser.add_argument(
+        "--timestep_spacing",
+        default="linspace",
+        choices=["linspace", "leading", "trailing"],
+        type=str,
+        help="The way the timesteps should be scaled.",
+    )
     # Pipeline arguments
     parser.add_argument(
         "--prompts",
@@ -207,14 +214,17 @@ def main():
     logger.setLevel(logging.INFO)
 
     # Initialize the scheduler and the generation pipeline
+    kwargs = {"timestep_spacing": args.timestep_spacing}
     if args.scheduler == "euler_discrete":
-        scheduler = GaudiEulerDiscreteScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler")
+        scheduler = GaudiEulerDiscreteScheduler.from_pretrained(
+            args.model_name_or_path, subfolder="scheduler", **kwargs
+        )
     elif args.scheduler == "euler_ancestral_discrete":
         scheduler = GaudiEulerAncestralDiscreteScheduler.from_pretrained(
-            args.model_name_or_path, subfolder="scheduler"
+            args.model_name_or_path, subfolder="scheduler", **kwargs
         )
     else:
-        scheduler = GaudiDDIMScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler")
+        scheduler = GaudiDDIMScheduler.from_pretrained(args.model_name_or_path, subfolder="scheduler", **kwargs)
 
     kwargs = {
         "scheduler": scheduler,

tests/test_diffusers.py

@@ -818,7 +818,7 @@ class GaudiStableDiffusionXLPipelineTester(TestCase):
     Tests the StableDiffusionXLPipeline for Gaudi.
     """
 
-    def get_dummy_components(self, time_cond_proj_dim=None):
+    def get_dummy_components(self, time_cond_proj_dim=None, timestep_spacing="leading"):
         torch.manual_seed(0)
         unet = UNet2DConditionModel(
             block_out_channels=(2, 4),
@@ -844,7 +844,7 @@ def get_dummy_components(self, time_cond_proj_dim=None):
             beta_end=0.012,
             steps_offset=1,
             beta_schedule="scaled_linear",
-            timestep_spacing="leading",
+            timestep_spacing=timestep_spacing,
         )
         torch.manual_seed(0)
         vae = AutoencoderKL(
@@ -933,6 +933,25 @@ def test_stable_diffusion_xl_euler_ancestral(self):
         expected_slice = np.array([0.4675, 0.5173, 0.4611, 0.4067, 0.5250, 0.4674, 0.5446, 0.5094, 0.4791])
         self.assertLess(np.abs(image_slice.flatten() - expected_slice).max(), 1e-2)
 
+    def test_stable_diffusion_xl_turbo_euler_ancestral(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components(timestep_spacing="trailing")
+        gaudi_config = GaudiConfig(use_torch_autocast=False)
+
+        sd_pipe = GaudiStableDiffusionXLPipeline(use_habana=True, gaudi_config=gaudi_config, **components)
+        sd_pipe.scheduler = GaudiEulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
+
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images[0]
+
+        image_slice = image[-3:, -3:, -1]
+
+        self.assertEqual(image.shape, (64, 64, 3))
+        expected_slice = np.array([0.4675, 0.5173, 0.4611, 0.4067, 0.5250, 0.4674, 0.5446, 0.5094, 0.4791])
+        self.assertLess(np.abs(image_slice.flatten() - expected_slice).max(), 1e-2)
+
     @parameterized.expand(["pil", "np", "latent"])
     def test_stable_diffusion_xl_output_types(self, output_type):
         components = self.get_dummy_components()