Merge branch 'main' of https://github.com/Stability-AI/generative-models into main

README.md

@@ -4,6 +4,9 @@
 
 ## News
 
+**July 4, 2023**
+- A technical report on SDXL is now available [here](assets/sdxl_report.pdf).
+
 **June 22, 2023**
 
 

scripts/demo/detect.py

@@ -83,7 +83,7 @@ class GetWatermarkMatch:
     def __call__(self, x: np.ndarray) -> np.ndarray:
         """
         Detects the number of matching bits the predefined watermark with one
-        or multiple images. Images should be in cv2 format, e.g. h x w x c.
+        or multiple images. Images should be in cv2 format, e.g. h x w x c BGR.
 
         Args:
             x: ([B], h w, c) in range [0, 255]
@@ -94,7 +94,6 @@ class GetWatermarkMatch:
         squeeze = len(x.shape) == 3
         if squeeze:
             x = x[None, ...]
-        x = np.flip(x, axis=-1)
 
         bs = x.shape[0]
         detected = np.empty((bs, self.num_bits), dtype=bool)

scripts/demo/sampling.py

@@ -311,8 +311,9 @@ if __name__ == "__main__":
         samples, samples_z = out
     else:
         samples = out
+        samples_z = None
 
-    if add_pipeline:
+    if add_pipeline and samples_z is not None:
         st.write("**Running Refinement Stage**")
         samples = apply_refiner(
             samples_z,

scripts/demo/streamlit_helpers.py

@@ -43,19 +43,19 @@ class WatermarkEmbedder:
         Returns:
             same as input but watermarked
         """
-        # watermarking libary expects input as cv2 format
+        # watermarking libary expects input as cv2 BGR format
         squeeze = len(image.shape) == 4
         if squeeze:
             image = image[None, ...]
         n = image.shape[0]
         image_np = rearrange(
             (255 * image).detach().cpu(), "n b c h w -> (n b) h w c"
-        ).numpy()
+        ).numpy()[:, :, :, ::-1]
         # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
         for k in range(image_np.shape[0]):
             image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
         image = torch.from_numpy(
-            rearrange(image_np, "(n b) h w c -> n b c h w", n=n)
+            rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)
         ).to(image.device)
         image = torch.clamp(image / 255, min=0.0, max=1.0)
         if squeeze: