Fix panorama to support all schedulers (#3546)

* refactor blocks init

* refactor blocks loop

* remove unused function and warnings

* fix scheduler update location

* reformat code

* reformat code again

* fix PNDM test case

* reformat pndm test case

Author: Isotr0py

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py

@@ -11,6 +11,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
 import inspect
 import warnings
 from typing import Any, Callable, Dict, List, Optional, Union
@@ -21,7 +22,7 @@
 from ...image_processor import VaeImageProcessor
 from ...loaders import TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import DDIMScheduler, PNDMScheduler
+from ...schedulers import DDIMScheduler
 from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring
 from ..pipeline_utils import DiffusionPipeline
 from . import StableDiffusionPipelineOutput
@@ -96,9 +97,6 @@ def __init__(
     ):
         super().__init__()
 
-        if isinstance(scheduler, PNDMScheduler):
-            logger.error("PNDMScheduler for this pipeline is currently not supported.")
-
         if safety_checker is None and requires_safety_checker:
             logger.warning(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
@@ -612,7 +610,7 @@ def __call__(
 
         # 6. Define panorama grid and initialize views for synthesis.
         views = self.get_views(height, width)
-        blocks_model_outputs = [None] * len(views)
+        views_scheduler_status = [copy.deepcopy(self.scheduler.__dict__)] * len(views)
         count = torch.zeros_like(latents)
         value = torch.zeros_like(latents)
 
@@ -637,6 +635,9 @@ def __call__(
                     # get the latents corresponding to the current view coordinates
                     latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]
 
+                    # rematch block's scheduler status
+                    self.scheduler.__dict__.update(views_scheduler_status[j])
+
                     # expand the latents if we are doing classifier free guidance
                     latent_model_input = (
                         torch.cat([latents_for_view] * 2) if do_classifier_free_guidance else latents_for_view
@@ -657,21 +658,13 @@ def __call__(
                         noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                     # compute the previous noisy sample x_t -> x_t-1
-                    if hasattr(self.scheduler, "model_outputs"):
-                        # rematch model_outputs in each block
-                        if i >= 1:
-                            self.scheduler.model_outputs = blocks_model_outputs[j]
-                        latents_view_denoised = self.scheduler.step(
-                            noise_pred, t, latents_for_view, **extra_step_kwargs
-                        ).prev_sample
-                        # collect model_outputs
-                        blocks_model_outputs[j] = [
-                            output if output is not None else None for output in self.scheduler.model_outputs
-                        ]
-                    else:
-                        latents_view_denoised = self.scheduler.step(
-                            noise_pred, t, latents_for_view, **extra_step_kwargs
-                        ).prev_sample
+                    latents_view_denoised = self.scheduler.step(
+                        noise_pred, t, latents_for_view, **extra_step_kwargs
+                    ).prev_sample
+
+                    # save views scheduler status after sample
+                    views_scheduler_status[j] = copy.deepcopy(self.scheduler.__dict__)
+
                     value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                     count[:, :, h_start:h_end, w_start:w_end] += 1
 

tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py

@@ -174,15 +174,22 @@ def test_stable_diffusion_panorama_euler(self):
     def test_stable_diffusion_panorama_pndm(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()
-        components["scheduler"] = PNDMScheduler()
+        components["scheduler"] = PNDMScheduler(
+            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
+        )
         sd_pipe = StableDiffusionPanoramaPipeline(**components)
         sd_pipe = sd_pipe.to(device)
         sd_pipe.set_progress_bar_config(disable=None)
 
         inputs = self.get_dummy_inputs(device)
-        # the pipeline does not expect pndm so test if it raises error.
-        with self.assertRaises(ValueError):
-            _ = sd_pipe(**inputs).images
+        image = sd_pipe(**inputs).images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 64, 64, 3)
+
+        expected_slice = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
 
 @slow