Add support Karras sigmas for StableDiffusionKDiffusionPipeline

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py

@@ -17,6 +17,7 @@
 
 import torch
 from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
+from k_diffusion.sampling import get_sigmas_karras
 
 from ...pipelines import DiffusionPipeline
 from ...schedulers import LMSDiscreteScheduler
@@ -400,6 +401,7 @@ def __call__(
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
+        use_karras_sigmas: Optional[bool] = False,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -456,7 +458,9 @@ def __call__(
             callback_steps (`int`, *optional*, defaults to 1):
                 The frequency at which the `callback` function will be called. If not specified, the callback will be
                 called at every step.
-
+            use_karras_sigmas (`bool`, *optional*, defaults to `False`):
+                Use karras sigmas. For example, specifying `sample_dpmpp_2m` to `set_scheduler` will be equivalent to
+                `DPM++2M` in stable-diffusion-webui. On top of that, setting this option to True will make it `DPM++2M Karras`.
         Returns:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
             [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
@@ -494,10 +498,18 @@ def __call__(
 
         # 4. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=prompt_embeds.device)
-        sigmas = self.scheduler.sigmas
+
+        # 5. Prepare sigmas
+        if use_karras_sigmas:
+            sigma_min: float = self.k_diffusion_model.sigmas[0].item()
+            sigma_max: float = self.k_diffusion_model.sigmas[-1].item()
+            sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max)
+            sigmas = sigmas.to(device)
+        else:
+            sigmas = self.scheduler.sigmas
         sigmas = sigmas.to(prompt_embeds.dtype)
 
-        # 5. Prepare latent variables
+        # 6. Prepare latent variables
         num_channels_latents = self.unet.in_channels
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
@@ -513,7 +525,7 @@ def __call__(
         self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
         self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)
 
-        # 6. Define model function
+        # 7. Define model function
         def model_fn(x, t):
             latent_model_input = torch.cat([x] * 2)
             t = torch.cat([t] * 2)
@@ -524,16 +536,16 @@ def model_fn(x, t):
             noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             return noise_pred
 
-        # 7. Run k-diffusion solver
+        # 8. Run k-diffusion solver
         latents = self.sampler(model_fn, latents, sigmas)
 
-        # 8. Post-processing
+        # 9. Post-processing
         image = self.decode_latents(latents)
 
-        # 9. Run safety checker
+        # 10. Run safety checker
         image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
 
-        # 10. Convert to PIL
+        # 11. Convert to PIL
         if output_type == "pil":
             image = self.numpy_to_pil(image)
 

tests/pipelines/stable_diffusion/test_stable_diffusion_k_diffusion.py

@@ -75,3 +75,32 @@ def test_stable_diffusion_2(self):
         expected_slice = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1
+
+    def test_stable_diffusion_karras_sigmas(self):
+        sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
+        sd_pipe = sd_pipe.to(torch_device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        sd_pipe.set_scheduler("sample_dpmpp_2m")
+
+        prompt = "A painting of a squirrel eating a burger"
+        generator = torch.manual_seed(0)
+        output = sd_pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=7.5,
+            num_inference_steps=15,
+            output_type="np",
+            use_karras_sigmas=True,
+        )
+
+        image = output.images
+
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 512, 512, 3)
+        expected_slice = np.array(
+            [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048]
+        )
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2