SD3 IP-Adapter runtime checkpoint conversion (#10718)

* Added runtime checkpoint conversion

* Updated docs

* Fix for quantized model

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -77,7 +77,7 @@ from diffusers import StableDiffusion3Pipeline
 from transformers import SiglipVisionModel, SiglipImageProcessor
 
 image_encoder_id = "google/siglip-so400m-patch14-384"
-ip_adapter_id = "guiyrt/InstantX-SD3.5-Large-IP-Adapter-diffusers"
+ip_adapter_id = "InstantX/SD3.5-Large-IP-Adapter"
 
 feature_extractor = SiglipImageProcessor.from_pretrained(
     image_encoder_id,

src/diffusers/loaders/transformer_sd3.py

@@ -11,50 +11,66 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from contextlib import nullcontext
 from typing import Dict
 
 from ..models.attention_processor import SD3IPAdapterJointAttnProcessor2_0
 from ..models.embeddings import IPAdapterTimeImageProjection
 from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
+from ..utils import is_accelerate_available, is_torch_version, logging
+
+
+logger = logging.get_logger(__name__)
 
 
 class SD3Transformer2DLoadersMixin:
     """Load IP-Adapters and LoRA layers into a `[SD3Transformer2DModel]`."""
 
-    def _load_ip_adapter_weights(self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT) -> None:
-        """Sets IP-Adapter attention processors, image projection, and loads state_dict.
+    def _convert_ip_adapter_attn_to_diffusers(
+        self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT
+    ) -> Dict:
+        if low_cpu_mem_usage:
+            if is_accelerate_available():
+                from accelerate import init_empty_weights
+
+            else:
+                low_cpu_mem_usage = False
+                logger.warning(
+                    "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
+                    " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
+                    " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
+                    " install accelerate\n```\n."
+                )
+
+        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
+            raise NotImplementedError(
+                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                " `low_cpu_mem_usage=False`."
+            )
 
-        Args:
-            state_dict (`Dict`):
-                State dict with keys "ip_adapter", which contains parameters for attention processors, and
-                "image_proj", which contains parameters for image projection net.
-            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
-                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
-                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
-                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
-                argument to `True` will raise an error.
-        """
         # IP-Adapter cross attention parameters
         hidden_size = self.config.attention_head_dim * self.config.num_attention_heads
         ip_hidden_states_dim = self.config.attention_head_dim * self.config.num_attention_heads
-        timesteps_emb_dim = state_dict["ip_adapter"]["0.norm_ip.linear.weight"].shape[1]
+        timesteps_emb_dim = state_dict["0.norm_ip.linear.weight"].shape[1]
 
         # Dict where key is transformer layer index, value is attention processor's state dict
         # ip_adapter state dict keys example: "0.norm_ip.linear.weight"
         layer_state_dict = {idx: {} for idx in range(len(self.attn_processors))}
-        for key, weights in state_dict["ip_adapter"].items():
+        for key, weights in state_dict.items():
             idx, name = key.split(".", maxsplit=1)
             layer_state_dict[int(idx)][name] = weights
 
-        # Create IP-Adapter attention processor
+        # Create IP-Adapter attention processor & load state_dict
         attn_procs = {}
+        init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
         for idx, name in enumerate(self.attn_processors.keys()):
+            with init_context():
                 attn_procs[name] = SD3IPAdapterJointAttnProcessor2_0(
                     hidden_size=hidden_size,
                     ip_hidden_states_dim=ip_hidden_states_dim,
                     head_dim=self.config.attention_head_dim,
                     timesteps_emb_dim=timesteps_emb_dim,
-            ).to(self.device, dtype=self.dtype)
+                )
 
             if not low_cpu_mem_usage:
                 attn_procs[name].load_state_dict(layer_state_dict[idx], strict=True)
@@ -63,27 +79,90 @@ class SD3Transformer2DLoadersMixin:
                     attn_procs[name], layer_state_dict[idx], device=self.device, dtype=self.dtype
                 )
 
-        self.set_attn_processor(attn_procs)
+        return attn_procs
+
+    def _convert_ip_adapter_image_proj_to_diffusers(
+        self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT
+    ) -> IPAdapterTimeImageProjection:
+        if low_cpu_mem_usage:
+            if is_accelerate_available():
+                from accelerate import init_empty_weights
+
+            else:
+                low_cpu_mem_usage = False
+                logger.warning(
+                    "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
+                    " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
+                    " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
+                    " install accelerate\n```\n."
+                )
+
+        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
+            raise NotImplementedError(
+                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                " `low_cpu_mem_usage=False`."
+            )
+
+        init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
+
+        # Convert to diffusers
+        updated_state_dict = {}
+        for key, value in state_dict.items():
+            # InstantX/SD3.5-Large-IP-Adapter
+            if key.startswith("layers."):
+                idx = key.split(".")[1]
+                key = key.replace(f"layers.{idx}.0.norm1", f"layers.{idx}.ln0")
+                key = key.replace(f"layers.{idx}.0.norm2", f"layers.{idx}.ln1")
+                key = key.replace(f"layers.{idx}.0.to_q", f"layers.{idx}.attn.to_q")
+                key = key.replace(f"layers.{idx}.0.to_kv", f"layers.{idx}.attn.to_kv")
+                key = key.replace(f"layers.{idx}.0.to_out", f"layers.{idx}.attn.to_out.0")
+                key = key.replace(f"layers.{idx}.1.0", f"layers.{idx}.adaln_norm")
+                key = key.replace(f"layers.{idx}.1.1", f"layers.{idx}.ff.net.0.proj")
+                key = key.replace(f"layers.{idx}.1.3", f"layers.{idx}.ff.net.2")
+                key = key.replace(f"layers.{idx}.2.1", f"layers.{idx}.adaln_proj")
+            updated_state_dict[key] = value
 
         # Image projetion parameters
-        embed_dim = state_dict["image_proj"]["proj_in.weight"].shape[1]
-        output_dim = state_dict["image_proj"]["proj_out.weight"].shape[0]
-        hidden_dim = state_dict["image_proj"]["proj_in.weight"].shape[0]
-        heads = state_dict["image_proj"]["layers.0.attn.to_q.weight"].shape[0] // 64
-        num_queries = state_dict["image_proj"]["latents"].shape[1]
-        timestep_in_dim = state_dict["image_proj"]["time_embedding.linear_1.weight"].shape[1]
+        embed_dim = updated_state_dict["proj_in.weight"].shape[1]
+        output_dim = updated_state_dict["proj_out.weight"].shape[0]
+        hidden_dim = updated_state_dict["proj_in.weight"].shape[0]
+        heads = updated_state_dict["layers.0.attn.to_q.weight"].shape[0] // 64
+        num_queries = updated_state_dict["latents"].shape[1]
+        timestep_in_dim = updated_state_dict["time_embedding.linear_1.weight"].shape[1]
 
         # Image projection
-        self.image_proj = IPAdapterTimeImageProjection(
+        with init_context():
+            image_proj = IPAdapterTimeImageProjection(
                 embed_dim=embed_dim,
                 output_dim=output_dim,
                 hidden_dim=hidden_dim,
                 heads=heads,
                 num_queries=num_queries,
                 timestep_in_dim=timestep_in_dim,
-        ).to(device=self.device, dtype=self.dtype)
+            )
 
         if not low_cpu_mem_usage:
-            self.image_proj.load_state_dict(state_dict["image_proj"], strict=True)
+            image_proj.load_state_dict(updated_state_dict, strict=True)
         else:
-            load_model_dict_into_meta(self.image_proj, state_dict["image_proj"], device=self.device, dtype=self.dtype)
+            load_model_dict_into_meta(image_proj, updated_state_dict, device=self.device, dtype=self.dtype)
+
+        return image_proj
+
+    def _load_ip_adapter_weights(self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT) -> None:
+        """Sets IP-Adapter attention processors, image projection, and loads state_dict.
+
+        Args:
+            state_dict (`Dict`):
+                State dict with keys "ip_adapter", which contains parameters for attention processors, and
+                "image_proj", which contains parameters for image projection net.
+            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
+                Speed up model loading only loading the pretrained weights and not initializing the weights. This also
+                tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
+                Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
+                argument to `True` will raise an error.
+        """
+
+        attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dict["ip_adapter"], low_cpu_mem_usage)
+        self.set_attn_processor(attn_procs)
+
+        self.image_proj = self._convert_ip_adapter_image_proj_to_diffusers(state_dict["image_proj"], low_cpu_mem_usage)