Allow stable diffusion attend and excite pipeline to work with any size output image. Re: #2476, #2603

Author: jcoffland

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py

@@ -75,7 +75,7 @@ def get_empty_store():
 
     def __call__(self, attn, is_cross: bool, place_in_unet: str):
         if self.cur_att_layer >= 0 and is_cross:
-            if attn.shape[1] == self.attn_res**2:
+            if attn.shape[1] == np.prod(self.attn_res):
                 self.step_store[place_in_unet].append(attn)
 
         self.cur_att_layer += 1
@@ -97,7 +97,7 @@ def aggregate_attention(self, from_where: List[str]) -> torch.Tensor:
         attention_maps = self.get_average_attention()
         for location in from_where:
             for item in attention_maps[location]:
-                cross_maps = item.reshape(-1, self.attn_res, self.attn_res, item.shape[-1])
+                cross_maps = item.reshape(-1, self.attn_res[0], self.attn_res[1], item.shape[-1])
                 out.append(cross_maps)
         out = torch.cat(out, dim=0)
         out = out.sum(0) / out.shape[0]
@@ -108,7 +108,7 @@ def reset(self):
         self.step_store = self.get_empty_store()
         self.attention_store = {}
 
-    def __init__(self, attn_res=16):
+    def __init__(self, width, height):
         """
         Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion
         process
@@ -118,7 +118,8 @@ def __init__(self, attn_res=16):
         self.step_store = self.get_empty_store()
         self.attention_store = {}
         self.curr_step_index = 0
-        self.attn_res = attn_res
+        self.attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32))
+
 
 
 class AttendExciteAttnProcessor:
@@ -715,7 +716,6 @@ def __call__(
         max_iter_to_alter: int = 25,
         thresholds: dict = {0: 0.05, 10: 0.5, 20: 0.8},
         scale_factor: int = 20,
-        attn_res: int = 16,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -787,8 +787,6 @@ def __call__(
                 Dictionary defining the iterations and desired thresholds to apply iterative latent refinement in.
             scale_factor (`int`, *optional*, default to 20):
                 Scale factor that controls the step size of each Attend and Excite update.
-            attn_res (`int`, *optional*, default to 16):
-                The resolution of most semantic attention map.
 
         Examples:
 
@@ -861,7 +859,7 @@ def __call__(
         # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
-        self.attention_store = AttentionStore(attn_res=attn_res)
+        self.attention_store = AttentionStore(width, height)
         self.register_attention_control()
 
         # default config for step size from original repo