[fix] multi t2i adapter set total_downscale_factor (huggingface#4621)

* [fix] multi t2i adapter set total_downscale_factor

* move image checks into check inputs

* remove copied from

src/diffusers/models/adapter.py

@@ -41,6 +41,31 @@ def __init__(self, adapters: List["T2IAdapter"]):
         self.num_adapter = len(adapters)
         self.adapters = nn.ModuleList(adapters)
 
+        if len(adapters) == 0:
+            raise ValueError("Expecting at least one adapter")
+
+        if len(adapters) == 1:
+            raise ValueError("For a single adapter, please use the `T2IAdapter` class instead of `MultiAdapter`")
+
+        # The outputs from each adapter are added together with a weight
+        # This means that the change in dimenstions from downsampling must
+        # be the same for all adapters. Inductively, it also means the total
+        # downscale factor must also be the same for all adapters.
+
+        first_adapter_total_downscale_factor = adapters[0].total_downscale_factor
+
+        for idx in range(1, len(adapters)):
+            adapter_idx_total_downscale_factor = adapters[idx].total_downscale_factor
+
+            if adapter_idx_total_downscale_factor != first_adapter_total_downscale_factor:
+                raise ValueError(
+                    f"Expecting all adapters to have the same total_downscale_factor, "
+                    f"but got adapters[0].total_downscale_factor={first_adapter_total_downscale_factor} and "
+                    f"adapter[`{idx}`]={adapter_idx_total_downscale_factor}"
+                )
+
+        self.total_downscale_factor = adapters[0].total_downscale_factor
+
     def forward(self, xs: torch.Tensor, adapter_weights: Optional[List[float]] = None) -> List[torch.Tensor]:
         r"""
         Args:
@@ -56,14 +81,8 @@ def forward(self, xs: torch.Tensor, adapter_weights: Optional[List[float]] = Non
         else:
             adapter_weights = torch.tensor(adapter_weights)
 
-        if xs.shape[1] % self.num_adapter != 0:
-            raise ValueError(
-                f"Expecting multi-adapter's input have number of channel that cab be evenly divisible "
-                f"by num_adapter: {xs.shape[1]} % {self.num_adapter} != 0"
-            )
-        x_list = torch.chunk(xs, self.num_adapter, dim=1)
         accume_state = None
-        for x, w, adapter in zip(x_list, adapter_weights, self.adapters):
+        for x, w, adapter in zip(xs, adapter_weights, self.adapters):
             features = adapter(x)
             if accume_state is None:
                 accume_state = features

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py

@@ -453,13 +453,13 @@ def prepare_extra_step_kwargs(self, generator, eta):
             extra_step_kwargs["generator"] = generator
         return extra_step_kwargs
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
     def check_inputs(
         self,
         prompt,
         height,
         width,
         callback_steps,
+        image,
         negative_prompt=None,
         prompt_embeds=None,
         negative_prompt_embeds=None,
@@ -501,6 +501,17 @@ def check_inputs(
                     f" {negative_prompt_embeds.shape}."
                 )
 
+        if isinstance(self.adapter, MultiAdapter):
+            if not isinstance(image, list):
+                raise ValueError(
+                    "MultiAdapter is enabled, but `image` is not a list. Please pass a list of images to `image`."
+                )
+
+            if len(image) != len(self.adapter.adapters):
+                raise ValueError(
+                    f"MultiAdapter requires passing the same number of images as adapters. Given {len(image)} images and {len(self.adapter.adapters)} adapters."
+                )
+
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
         shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
@@ -653,17 +664,19 @@ def __call__(
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
-            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
+            prompt, height, width, callback_steps, image, negative_prompt, prompt_embeds, negative_prompt_embeds
         )
 
-        is_multi_adapter = isinstance(self.adapter, MultiAdapter)
-        if is_multi_adapter:
-            adapter_input = [_preprocess_adapter_image(img, height, width).to(device) for img in image]
-            n, c, h, w = adapter_input[0].shape
-            adapter_input = torch.stack([x.reshape([n * c, h, w]) for x in adapter_input])
+        if isinstance(self.adapter, MultiAdapter):
+            adapter_input = []
+
+            for one_image in image:
+                one_image = _preprocess_adapter_image(one_image, height, width)
+                one_image = one_image.to(device=device, dtype=self.adapter.dtype)
+                adapter_input.append(one_image)
         else:
-            adapter_input = _preprocess_adapter_image(image, height, width).to(device)
-        adapter_input = adapter_input.to(self.adapter.dtype)
+            adapter_input = _preprocess_adapter_image(image, height, width)
+            adapter_input = adapter_input.to(device=device, dtype=self.adapter.dtype)
 
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):