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Merged
yiyixuxu merged 4 commits into from
Dec 18, 2023
Merged

Add converter method for ip adapters #6150

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7660aee
Add converter method for ip adapters
fabiorigano Dec 12, 2023
dfda680
Move converter method
fabiorigano Dec 15, 2023
38896af
Merge branch 'main' into ipadapterconv
sayakpaul Dec 16, 2023
0b1bfc3
Update to image proj converter
fabiorigano Dec 16, 2023
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175 changes: 77 additions & 98 deletions src/diffusers/loaders/unet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections import OrderedDict, defaultdict
from collections import defaultdict
from contextlib import nullcontext
from typing import Callable, Dict, List, Optional, Union

Expand Down Expand Up @@ -664,6 +664,80 @@ def delete_adapters(self, adapter_names: Union[List[str], str]):
if hasattr(self, "peft_config"):
self.peft_config.pop(adapter_name, None)

def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict):
updated_state_dict = {}
image_projection = None

if "proj.weight" in state_dict:
# IP-Adapter
num_image_text_embeds = 4
clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
cross_attention_dim = state_dict["proj.weight"].shape[0] // 4

image_projection = ImageProjection(
cross_attention_dim=cross_attention_dim,
image_embed_dim=clip_embeddings_dim,
num_image_text_embeds=num_image_text_embeds,
)

for key, value in state_dict.items():
diffusers_name = key.replace("proj", "image_embeds")
updated_state_dict[diffusers_name] = value

elif "proj.3.weight" in state_dict:
# IP-Adapter Full
clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
cross_attention_dim = state_dict["proj.3.weight"].shape[0]

image_projection = MLPProjection(
cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim
)

for key, value in state_dict.items():
diffusers_name = key.replace("proj.0", "ff.net.0.proj")
diffusers_name = diffusers_name.replace("proj.2", "ff.net.2")
diffusers_name = diffusers_name.replace("proj.3", "norm")
updated_state_dict[diffusers_name] = value

else:
# IP-Adapter Plus
num_image_text_embeds = state_dict["latents"].shape[1]
embed_dims = state_dict["proj_in.weight"].shape[1]
output_dims = state_dict["proj_out.weight"].shape[0]
hidden_dims = state_dict["latents"].shape[2]
heads = state_dict["layers.0.0.to_q.weight"].shape[0] // 64

image_projection = Resampler(
embed_dims=embed_dims,
output_dims=output_dims,
hidden_dims=hidden_dims,
heads=heads,
num_queries=num_image_text_embeds,
)

for key, value in state_dict.items():
diffusers_name = key.replace("0.to", "2.to")
diffusers_name = diffusers_name.replace("1.0.weight", "3.0.weight")
diffusers_name = diffusers_name.replace("1.0.bias", "3.0.bias")
diffusers_name = diffusers_name.replace("1.1.weight", "3.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("1.3.weight", "3.1.net.2.weight")

if "norm1" in diffusers_name:
updated_state_dict[diffusers_name.replace("0.norm1", "0")] = value
elif "norm2" in diffusers_name:
updated_state_dict[diffusers_name.replace("0.norm2", "1")] = value
elif "to_kv" in diffusers_name:
v_chunk = value.chunk(2, dim=0)
updated_state_dict[diffusers_name.replace("to_kv", "to_k")] = v_chunk[0]
updated_state_dict[diffusers_name.replace("to_kv", "to_v")] = v_chunk[1]
elif "to_out" in diffusers_name:
updated_state_dict[diffusers_name.replace("to_out", "to_out.0")] = value
else:
updated_state_dict[diffusers_name] = value

image_projection.load_state_dict(updated_state_dict)
return image_projection

def _load_ip_adapter_weights(self, state_dict):
from ..models.attention_processor import (
AttnProcessor,
Expand Down Expand Up @@ -724,103 +798,8 @@ def _load_ip_adapter_weights(self, state_dict):

self.set_attn_processor(attn_procs)

# create image projection layers.
if "proj.weight" in state_dict["image_proj"]:
# IP-Adapter
clip_embeddings_dim = state_dict["image_proj"]["proj.weight"].shape[-1]
cross_attention_dim = state_dict["image_proj"]["proj.weight"].shape[0] // 4

image_projection = ImageProjection(
cross_attention_dim=cross_attention_dim,
image_embed_dim=clip_embeddings_dim,
num_image_text_embeds=num_image_text_embeds,
)
image_projection.to(dtype=self.dtype, device=self.device)

# load image projection layer weights
image_proj_state_dict = {}
image_proj_state_dict.update(
{
"image_embeds.weight": state_dict["image_proj"]["proj.weight"],
"image_embeds.bias": state_dict["image_proj"]["proj.bias"],
"norm.weight": state_dict["image_proj"]["norm.weight"],
"norm.bias": state_dict["image_proj"]["norm.bias"],
}
)
image_projection.load_state_dict(image_proj_state_dict)
del image_proj_state_dict

elif "proj.3.weight" in state_dict["image_proj"]:
clip_embeddings_dim = state_dict["image_proj"]["proj.0.weight"].shape[0]
cross_attention_dim = state_dict["image_proj"]["proj.3.weight"].shape[0]

image_projection = MLPProjection(
cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim
)
image_projection.to(dtype=self.dtype, device=self.device)

# load image projection layer weights
image_proj_state_dict = {}
image_proj_state_dict.update(
{
"ff.net.0.proj.weight": state_dict["image_proj"]["proj.0.weight"],
"ff.net.0.proj.bias": state_dict["image_proj"]["proj.0.bias"],
"ff.net.2.weight": state_dict["image_proj"]["proj.2.weight"],
"ff.net.2.bias": state_dict["image_proj"]["proj.2.bias"],
"norm.weight": state_dict["image_proj"]["proj.3.weight"],
"norm.bias": state_dict["image_proj"]["proj.3.bias"],
}
)
image_projection.load_state_dict(image_proj_state_dict)
del image_proj_state_dict

else:
# IP-Adapter Plus
embed_dims = state_dict["image_proj"]["proj_in.weight"].shape[1]
output_dims = state_dict["image_proj"]["proj_out.weight"].shape[0]
hidden_dims = state_dict["image_proj"]["latents"].shape[2]
heads = state_dict["image_proj"]["layers.0.0.to_q.weight"].shape[0] // 64

image_projection = Resampler(
embed_dims=embed_dims,
output_dims=output_dims,
hidden_dims=hidden_dims,
heads=heads,
num_queries=num_image_text_embeds,
)

image_proj_state_dict = state_dict["image_proj"]

new_sd = OrderedDict()
for k, v in image_proj_state_dict.items():
if "0.to" in k:
k = k.replace("0.to", "2.to")
elif "1.0.weight" in k:
k = k.replace("1.0.weight", "3.0.weight")
elif "1.0.bias" in k:
k = k.replace("1.0.bias", "3.0.bias")
elif "1.1.weight" in k:
k = k.replace("1.1.weight", "3.1.net.0.proj.weight")
elif "1.3.weight" in k:
k = k.replace("1.3.weight", "3.1.net.2.weight")

if "norm1" in k:
new_sd[k.replace("0.norm1", "0")] = v
elif "norm2" in k:
new_sd[k.replace("0.norm2", "1")] = v
elif "to_kv" in k:
v_chunk = v.chunk(2, dim=0)
new_sd[k.replace("to_kv", "to_k")] = v_chunk[0]
new_sd[k.replace("to_kv", "to_v")] = v_chunk[1]
elif "to_out" in k:
new_sd[k.replace("to_out", "to_out.0")] = v
else:
new_sd[k] = v

image_projection.load_state_dict(new_sd)
del image_proj_state_dict
# convert IP-Adapter Image Projection layers to diffusers
image_projection = self._convert_ip_adapter_image_proj_to_diffusers(state_dict["image_proj"])

self.encoder_hid_proj = image_projection.to(device=self.device, dtype=self.dtype)
self.config.encoder_hid_dim_type = "ip_image_proj"

delete_adapter_layers