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https://github.com/huggingface/diffusers.git
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SD3 IP-Adapter runtime checkpoint conversion (#10718)
* Added runtime checkpoint conversion * Updated docs * Fix for quantized model
This commit is contained in:
Daniel Regado
GitHub
@@ -77,7 +77,7 @@ from diffusers import StableDiffusion3Pipeline
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from transformers import SiglipVisionModel, SiglipImageProcessor
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image_encoder_id = "google/siglip-so400m-patch14-384"
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ip_adapter_id = "guiyrt/InstantX-SD3.5-Large-IP-Adapter-diffusers"
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ip_adapter_id = "InstantX/SD3.5-Large-IP-Adapter"
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feature_extractor = SiglipImageProcessor.from_pretrained(
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image_encoder_id,
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@@ -11,16 +11,143 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from contextlib import nullcontext
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from typing import Dict
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from ..models.attention_processor import SD3IPAdapterJointAttnProcessor2_0
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from ..models.embeddings import IPAdapterTimeImageProjection
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from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
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from ..utils import is_accelerate_available, is_torch_version, logging
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logger = logging.get_logger(__name__)
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class SD3Transformer2DLoadersMixin:
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"""Load IP-Adapters and LoRA layers into a `[SD3Transformer2DModel]`."""
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def _convert_ip_adapter_attn_to_diffusers(
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self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT
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) -> Dict:
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if low_cpu_mem_usage:
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if is_accelerate_available():
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from accelerate import init_empty_weights
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else:
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low_cpu_mem_usage = False
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logger.warning(
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"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
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" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
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" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
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" install accelerate\n```\n."
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)
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if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
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raise NotImplementedError(
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"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
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" `low_cpu_mem_usage=False`."
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)
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# IP-Adapter cross attention parameters
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hidden_size = self.config.attention_head_dim * self.config.num_attention_heads
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ip_hidden_states_dim = self.config.attention_head_dim * self.config.num_attention_heads
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timesteps_emb_dim = state_dict["0.norm_ip.linear.weight"].shape[1]
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# Dict where key is transformer layer index, value is attention processor's state dict
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# ip_adapter state dict keys example: "0.norm_ip.linear.weight"
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layer_state_dict = {idx: {} for idx in range(len(self.attn_processors))}
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for key, weights in state_dict.items():
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idx, name = key.split(".", maxsplit=1)
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layer_state_dict[int(idx)][name] = weights
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# Create IP-Adapter attention processor & load state_dict
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attn_procs = {}
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init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
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for idx, name in enumerate(self.attn_processors.keys()):
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with init_context():
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attn_procs[name] = SD3IPAdapterJointAttnProcessor2_0(
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hidden_size=hidden_size,
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ip_hidden_states_dim=ip_hidden_states_dim,
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head_dim=self.config.attention_head_dim,
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timesteps_emb_dim=timesteps_emb_dim,
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)
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if not low_cpu_mem_usage:
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attn_procs[name].load_state_dict(layer_state_dict[idx], strict=True)
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else:
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load_model_dict_into_meta(
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attn_procs[name], layer_state_dict[idx], device=self.device, dtype=self.dtype
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)
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return attn_procs
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def _convert_ip_adapter_image_proj_to_diffusers(
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self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT
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) -> IPAdapterTimeImageProjection:
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if low_cpu_mem_usage:
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if is_accelerate_available():
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from accelerate import init_empty_weights
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else:
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low_cpu_mem_usage = False
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logger.warning(
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"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
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" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
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" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
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" install accelerate\n```\n."
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)
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if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
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raise NotImplementedError(
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"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
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" `low_cpu_mem_usage=False`."
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)
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init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
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# Convert to diffusers
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updated_state_dict = {}
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for key, value in state_dict.items():
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# InstantX/SD3.5-Large-IP-Adapter
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if key.startswith("layers."):
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idx = key.split(".")[1]
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key = key.replace(f"layers.{idx}.0.norm1", f"layers.{idx}.ln0")
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key = key.replace(f"layers.{idx}.0.norm2", f"layers.{idx}.ln1")
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key = key.replace(f"layers.{idx}.0.to_q", f"layers.{idx}.attn.to_q")
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key = key.replace(f"layers.{idx}.0.to_kv", f"layers.{idx}.attn.to_kv")
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key = key.replace(f"layers.{idx}.0.to_out", f"layers.{idx}.attn.to_out.0")
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key = key.replace(f"layers.{idx}.1.0", f"layers.{idx}.adaln_norm")
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key = key.replace(f"layers.{idx}.1.1", f"layers.{idx}.ff.net.0.proj")
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key = key.replace(f"layers.{idx}.1.3", f"layers.{idx}.ff.net.2")
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key = key.replace(f"layers.{idx}.2.1", f"layers.{idx}.adaln_proj")
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updated_state_dict[key] = value
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# Image projetion parameters
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embed_dim = updated_state_dict["proj_in.weight"].shape[1]
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output_dim = updated_state_dict["proj_out.weight"].shape[0]
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hidden_dim = updated_state_dict["proj_in.weight"].shape[0]
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heads = updated_state_dict["layers.0.attn.to_q.weight"].shape[0] // 64
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num_queries = updated_state_dict["latents"].shape[1]
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timestep_in_dim = updated_state_dict["time_embedding.linear_1.weight"].shape[1]
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# Image projection
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with init_context():
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image_proj = IPAdapterTimeImageProjection(
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embed_dim=embed_dim,
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output_dim=output_dim,
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hidden_dim=hidden_dim,
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heads=heads,
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num_queries=num_queries,
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timestep_in_dim=timestep_in_dim,
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)
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if not low_cpu_mem_usage:
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image_proj.load_state_dict(updated_state_dict, strict=True)
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else:
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load_model_dict_into_meta(image_proj, updated_state_dict, device=self.device, dtype=self.dtype)
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return image_proj
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def _load_ip_adapter_weights(self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT) -> None:
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"""Sets IP-Adapter attention processors, image projection, and loads state_dict.
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@@ -34,56 +161,8 @@ class SD3Transformer2DLoadersMixin:
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Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
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argument to `True` will raise an error.
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"""
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# IP-Adapter cross attention parameters
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hidden_size = self.config.attention_head_dim * self.config.num_attention_heads
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ip_hidden_states_dim = self.config.attention_head_dim * self.config.num_attention_heads
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timesteps_emb_dim = state_dict["ip_adapter"]["0.norm_ip.linear.weight"].shape[1]
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# Dict where key is transformer layer index, value is attention processor's state dict
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# ip_adapter state dict keys example: "0.norm_ip.linear.weight"
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layer_state_dict = {idx: {} for idx in range(len(self.attn_processors))}
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for key, weights in state_dict["ip_adapter"].items():
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idx, name = key.split(".", maxsplit=1)
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layer_state_dict[int(idx)][name] = weights
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# Create IP-Adapter attention processor
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attn_procs = {}
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for idx, name in enumerate(self.attn_processors.keys()):
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attn_procs[name] = SD3IPAdapterJointAttnProcessor2_0(
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hidden_size=hidden_size,
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ip_hidden_states_dim=ip_hidden_states_dim,
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head_dim=self.config.attention_head_dim,
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timesteps_emb_dim=timesteps_emb_dim,
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).to(self.device, dtype=self.dtype)
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if not low_cpu_mem_usage:
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attn_procs[name].load_state_dict(layer_state_dict[idx], strict=True)
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else:
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load_model_dict_into_meta(
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attn_procs[name], layer_state_dict[idx], device=self.device, dtype=self.dtype
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)
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attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dict["ip_adapter"], low_cpu_mem_usage)
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self.set_attn_processor(attn_procs)
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# Image projetion parameters
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embed_dim = state_dict["image_proj"]["proj_in.weight"].shape[1]
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output_dim = state_dict["image_proj"]["proj_out.weight"].shape[0]
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hidden_dim = state_dict["image_proj"]["proj_in.weight"].shape[0]
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heads = state_dict["image_proj"]["layers.0.attn.to_q.weight"].shape[0] // 64
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num_queries = state_dict["image_proj"]["latents"].shape[1]
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timestep_in_dim = state_dict["image_proj"]["time_embedding.linear_1.weight"].shape[1]
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# Image projection
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self.image_proj = IPAdapterTimeImageProjection(
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embed_dim=embed_dim,
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output_dim=output_dim,
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hidden_dim=hidden_dim,
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heads=heads,
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num_queries=num_queries,
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timestep_in_dim=timestep_in_dim,
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).to(device=self.device, dtype=self.dtype)
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if not low_cpu_mem_usage:
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self.image_proj.load_state_dict(state_dict["image_proj"], strict=True)
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else:
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load_model_dict_into_meta(self.image_proj, state_dict["image_proj"], device=self.device, dtype=self.dtype)
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self.image_proj = self._convert_ip_adapter_image_proj_to_diffusers(state_dict["image_proj"], low_cpu_mem_usage)
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