[Core] fix img2img pipeline for Playground (#7627)

* playground vae encoding should use std and mean of the vae.

* style.

* fix-copies.

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -898,6 +898,12 @@ def prepare_latents(
                 f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
             )
 
+        latents_mean = latents_std = None
+        if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
         # Offload text encoder if `enable_model_cpu_offload` was enabled
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.text_encoder_2.to("cpu")
@@ -935,7 +941,12 @@ def prepare_latents(
                 self.vae.to(dtype)
 
             init_latents = init_latents.to(dtype)
-            init_latents = self.vae.config.scaling_factor * init_latents
+            if latents_mean is not None and latents_std is not None:
+                latents_mean = latents_mean.to(device=self.device, dtype=dtype)
+                latents_std = latents_std.to(device=self.device, dtype=dtype)
+                init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std
+            else:
+                init_latents = self.vae.config.scaling_factor * init_latents
 
         if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
             # expand init_latents for batch_size

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -665,6 +665,12 @@ def prepare_latents(
                 f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
             )
 
+        latents_mean = latents_std = None
+        if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
         # Offload text encoder if `enable_model_cpu_offload` was enabled
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.text_encoder_2.to("cpu")
@@ -702,7 +708,12 @@ def prepare_latents(
                 self.vae.to(dtype)
 
             init_latents = init_latents.to(dtype)
-            init_latents = self.vae.config.scaling_factor * init_latents
+            if latents_mean is not None and latents_std is not None:
+                latents_mean = latents_mean.to(device=self.device, dtype=dtype)
+                latents_std = latents_std.to(device=self.device, dtype=dtype)
+                init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std
+            else:
+                init_latents = self.vae.config.scaling_factor * init_latents
 
         if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
             # expand init_latents for batch_size