fix(api): complete panorama tiles for SD pipeline

api/onnx_web/diffusers/pipelines/panorama.py

@@ -26,9 +26,15 @@ from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMSchedu
 from diffusers.utils import PIL_INTERPOLATION, deprecate, logging
 from transformers import CLIPImageProcessor, CLIPTokenizer
 
-from onnx_web.chain.tile import make_tile_mask
+from ...chain.tile import make_tile_mask
-
+from ...params import Size
-from ..utils import LATENT_CHANNELS, LATENT_FACTOR, parse_regions, repair_nan
+from ..utils import (
+    LATENT_CHANNELS,
+    LATENT_FACTOR,
+    expand_latents,
+    parse_regions,
+    repair_nan,
+)
 
 logger = logging.get_logger(__name__)
 
@@ -373,7 +379,7 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
             w_end = w_start + window_size
             views.append((h_start, h_end, w_start, w_end))
 
-        return views
+        return (views, (h_end, w_end))
 
     @torch.no_grad()
     def text2img(
@@ -552,10 +558,17 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
         timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
 
         # panorama additions
-        views = self.get_views(height, width, self.window, self.stride)
+        views, resize = self.get_views(height, width, self.window, self.stride)
         count = np.zeros_like(latents)
         value = np.zeros_like(latents)
 
+        latents = expand_latents(
+            latents,
+            generator.randint(),
+            Size(width, height),
+            sigma=self.scheduler.init_noise_sigma,
+        )
+
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
             last = i == (len(self.scheduler.timesteps) - 1)
             count.fill(0)
@@ -707,6 +720,9 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
             if callback is not None and i % callback_steps == 0:
                 callback(i, t, latents)
 
+        # remove extra margins
+        latents = latents[:, :, 0:height, 0:width]
+
         latents = np.clip(latents, -4, +4)
         latents = 1 / 0.18215 * latents
         # image = self.vae_decoder(latent_sample=latents)[0]

api/onnx_web/diffusers/pipelines/panorama_xl.py

@@ -391,7 +391,12 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
         value = np.zeros_like((latents[0], latents[1], *resize))
 
         # adjust latents
-        latents = expand_latents(latents, generator.randint(), Size(width, height))
+        latents = expand_latents(
+            latents,
+            generator.randint(),
+            Size(width, height),
+            sigma=self.scheduler.init_noise_sigma,
+        )
 
         # 8. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

api/onnx_web/diffusers/utils.py

@@ -280,11 +280,12 @@ def expand_latents(
     latents: np.ndarray,
     seed: int,
     size: Size,
+    sigma: float = 1.0,
 ) -> np.ndarray:
     batch, _channels, height, width = latents.shape
     extra_latents = get_latents_from_seed(seed, size, batch=batch)
     extra_latents[:, :, 0:height, 0:width] = latents
-    return extra_latents
+    return extra_latents * np.float64(sigma)
 
 
 def get_tile_latents(