Support InstantStyle

docs/source/en/using-diffusers/ip_adapter.md

@@ -640,3 +640,87 @@ image
 <div class="flex justify-center">
     <img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ipa-controlnet-out.png" />
 </div>
+
+### Style & layout control
+
+[InstantStyle](https://arxiv.org/abs/2404.02733) is a plug-and-play method on top of IP-Adapter, which disentangles style and layout from image prompt to control image generation. This is achieved by only inserting IP-Adapters to some specific part of the model. 
+
+By default IP-Adapters are inserted to all layers of the model. Use the [`~loaders.IPAdapterMixin.set_ip_adapter_scale`] method with a dictionary to assign scales to IP-Adapter at different layers.
+
+```py
+from diffusers import AutoPipelineForImage2Image
+from diffusers.utils import load_image
+import torch
+
+pipeline = AutoPipelineForImage2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
+pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+
+scale = {
+    "down": {"block_2": [0.0, 1.0]},
+    "up": {"block_0": [0.0, 1.0, 0.0]},
+}
+pipeline.set_ip_adapter_scale(scale)
+```
+
+This will activate IP-Adapter at the second layer in the model's down-part block 2 and up-part block 0. The former is the layer where IP-Adapter injects layout information and the latter injects style. Inserting IP-Adapter to these two layers you can generate images following the style and layout of image prompt, but with contents more aligned to text prompt.
+
+```py
+style_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg")
+
+generator = torch.Generator(device="cpu").manual_seed(42)
+image = pipeline(
+    prompt="a cat, masterpiece, best quality, high quality",
+    image=style_image,
+    negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
+    guidance_scale=5,
+    num_inference_steps=30,
+    generator=generator,
+).images[0]
+image
+```
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit_style_layout_cat.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
+  </div>
+</div>
+
+In contrast, inserting IP-Adapter to all layers will often generate images that overly focus on image prompt and diminish diversity.
+
+Activate IP-Adapter only in the style layer and then call the pipeline again.
+
+```py
+scale = {
+    "up": {"block_0": [0.0, 1.0, 0.0]},
+}
+pipeline.set_ip_adapter_scale(scale)
+
+generator = torch.Generator(device="cpu").manual_seed(42)
+image = pipeline(
+    prompt="a cat, masterpiece, best quality, high quality",
+    image=style_image,
+    negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
+    guidance_scale=5,
+    num_inference_steps=30,
+    generator=generator,
+).images[0]
+image
+```
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit_style_cat.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter only in style layer</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/30518dfe089e6bf50008875077b44cb98fb2065c/diffusers/default_out.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter in all layers</figcaption>
+  </div>
+</div>
+
+Note that you don't have to specify all layers in the dictionary. Those not included in the dictionary will be set to scale 0 which means disable IP-Adapter by default.

src/diffusers/loaders/ip_adapter.py

@@ -28,6 +28,7 @@
     is_transformers_available,
     logging,
 )
+from .unet_loader_utils import _maybe_expand_lora_scales
 
 
 if is_transformers_available():
@@ -243,25 +244,55 @@ def load_ip_adapter(
 
     def set_ip_adapter_scale(self, scale):
         """
-        Sets the conditioning scale between text and image.
+        Set IP-Adapter scales per-transformer block. Input `scale` could be a single config or a list of configs for
+        granular control over each IP-Adapter behavior. A config can be a float or a dictionary.
 
         Example:
 
         ```py
-        pipeline.set_ip_adapter_scale(0.5)
+        # To use original IP-Adapter
+        scale = 1.0
+        pipeline.set_ip_adapter_scale(scale)
+
+        # To use style block only
+        scale = {
+            "up": {"block_0": [0.0, 1.0, 0.0]},
+        }
+        pipeline.set_ip_adapter_scale(scale)
+
+        # To use style+layout blocks
+        scale = {
+            "down": {"block_2": [0.0, 1.0]},
+            "up": {"block_0": [0.0, 1.0, 0.0]},
+        }
+        pipeline.set_ip_adapter_scale(scale)
+
+        # To use style and layout from 2 reference images
+        scales = [{"down": {"block_2": [0.0, 1.0]}}, {"up": {"block_0": [0.0, 1.0, 0.0]}}]
+        pipeline.set_ip_adapter_scale(scales)
         ```
         """
         unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
-        for attn_processor in unet.attn_processors.values():
+        if not isinstance(scale, list):
+            scale = [scale]
+        scale_configs = _maybe_expand_lora_scales(unet, scale, default_scale=0.0)
+
+        for attn_name, attn_processor in unet.attn_processors.items():
             if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
-                if not isinstance(scale, list):
-                    scale = [scale] * len(attn_processor.scale)
-                if len(attn_processor.scale) != len(scale):
+                if len(scale_configs) != len(attn_processor.scale):
                     raise ValueError(
-                        f"`scale` should be a list of same length as the number if ip-adapters "
-                        f"Expected {len(attn_processor.scale)} but got {len(scale)}."
+                        f"Cannot assign {len(scale_configs)} scale_configs to "
+                        f"{len(attn_processor.scale)} IP-Adapter."
                     )
-                attn_processor.scale = scale
+                elif len(scale_configs) == 1:
+                    scale_configs = scale_configs * len(attn_processor.scale)
+                for i, scale_config in enumerate(scale_configs):
+                    if isinstance(scale_config, dict):
+                        for k, s in scale_config.items():
+                            if attn_name.startswith(k):
+                                attn_processor.scale[i] = s
+                    else:
+                        attn_processor.scale[i] = scale_config
 
     def unload_ip_adapter(self):
         """

src/diffusers/loaders/unet_loader_utils.py

@@ -38,7 +38,9 @@ def _translate_into_actual_layer_name(name):
     return ".".join((updown, block, attn))
 
 
-def _maybe_expand_lora_scales(unet: "UNet2DConditionModel", weight_scales: List[Union[float, Dict]]):
+def _maybe_expand_lora_scales(
+    unet: "UNet2DConditionModel", weight_scales: List[Union[float, Dict]], default_scale=1.0
+):
     blocks_with_transformer = {
         "down": [i for i, block in enumerate(unet.down_blocks) if hasattr(block, "attentions")],
         "up": [i for i, block in enumerate(unet.up_blocks) if hasattr(block, "attentions")],
@@ -47,7 +49,11 @@ def _maybe_expand_lora_scales(unet: "UNet2DConditionModel", weight_scales: List[
 
     expanded_weight_scales = [
         _maybe_expand_lora_scales_for_one_adapter(
-            weight_for_adapter, blocks_with_transformer, transformer_per_block, unet.state_dict()
+            weight_for_adapter,
+            blocks_with_transformer,
+            transformer_per_block,
+            unet.state_dict(),
+            default_scale=default_scale,
         )
         for weight_for_adapter in weight_scales
     ]
@@ -60,6 +66,7 @@ def _maybe_expand_lora_scales_for_one_adapter(
     blocks_with_transformer: Dict[str, int],
     transformer_per_block: Dict[str, int],
     state_dict: None,
+    default_scale: float = 1.0,
 ):
     """
     Expands the inputs into a more granular dictionary. See the example below for more details.
@@ -108,21 +115,36 @@ def _maybe_expand_lora_scales_for_one_adapter(
     scales = copy.deepcopy(scales)
 
     if "mid" not in scales:
-        scales["mid"] = 1
+        scales["mid"] = default_scale
+    elif isinstance(scales["mid"], list):
+        if len(scales["mid"]) == 1:
+            scales["mid"] = scales["mid"][0]
+        else:
+            raise ValueError(f"Expected 1 scales for mid, got {len(scales['mid'])}.")
 
     for updown in ["up", "down"]:
         if updown not in scales:
-            scales[updown] = 1
+            scales[updown] = default_scale
 
         # eg {"down": 1} to {"down": {"block_1": 1, "block_2": 1}}}
         if not isinstance(scales[updown], dict):
-            scales[updown] = {f"block_{i}": scales[updown] for i in blocks_with_transformer[updown]}
+            scales[updown] = {f"block_{i}": copy.deepcopy(scales[updown]) for i in blocks_with_transformer[updown]}
 
-        # eg {"down": "block_1": 1}} to {"down": "block_1": [1, 1]}}
+        # eg {"down": {"block_1": 1}} to {"down": {"block_1": [1, 1]}}
         for i in blocks_with_transformer[updown]:
             block = f"block_{i}"
+            # set not assigned blocks to default scale
+            if block not in scales[updown]:
+                scales[updown][block] = default_scale
             if not isinstance(scales[updown][block], list):
                 scales[updown][block] = [scales[updown][block] for _ in range(transformer_per_block[updown])]
+            elif len(scales[updown][block]) == 1:
+                # a list specifying scale to each masked IP input
+                scales[updown][block] = scales[updown][block] * transformer_per_block[updown]
+            elif len(scales[updown][block]) != transformer_per_block[updown]:
+                raise ValueError(
+                    f"Expected {transformer_per_block[updown]} scales for {updown}.{block}, got {len(scales[updown][block])}."
+                )
 
         # eg {"down": "block_1": [1, 1]}}  to {"down.block_1.0": 1, "down.block_1.1": 1}
         for i in blocks_with_transformer[updown]: