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Tidy IPAdapter. Add types, improve field/method naming.

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This commit is contained in:
Ryan Dick 2023-09-08 16:00:58 -04:00
parent 3f7d5b4e0f
commit 6ca6cf713c
1 changed files with 40 additions and 33 deletions
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73
invokeai/backend/ip_adapter/ip_adapter.py
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@ -21,7 +21,7 @@ from .resampler import Resampler
class ImageProjModel(torch.nn.Module):
"""Projection Model"""
"""Image Projection Model"""
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
super().__init__()
@ -43,31 +43,38 @@ class ImageProjModel(torch.nn.Module):
class IPAdapter:
"""IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
def __init__(self, unet: UNet2DConditionModel, image_encoder_path, ip_ckpt, device, num_tokens=4):
def __init__(
self,
unet: UNet2DConditionModel,
image_encoder_path: str,
ip_adapter_ckpt_path: str,
device: torch.device,
num_tokens: int = 4,
):
self._unet = unet
self.device = device
self.image_encoder_path = image_encoder_path
self.ip_ckpt = ip_ckpt
self.num_tokens = num_tokens
self._device = device
self._image_encoder_path = image_encoder_path
self._ip_adapter_ckpt_path = ip_adapter_ckpt_path
self._num_tokens = num_tokens
self._attn_processors = self._prepare_attention_processors()
# load image encoder
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
self.device, dtype=torch.float16
self._image_encoder = CLIPVisionModelWithProjection.from_pretrained(self._image_encoder_path).to(
self._device, dtype=torch.float16
)
self.clip_image_processor = CLIPImageProcessor()
self._clip_image_processor = CLIPImageProcessor()
# image proj model
self.image_proj_model = self.init_proj()
self._image_proj_model = self._init_image_proj_model()
self.load_ip_adapter()
self._load_weights()
def init_proj(self):
def _init_image_proj_model(self):
image_proj_model = ImageProjModel(
cross_attention_dim=self._unet.config.cross_attention_dim,
clip_embeddings_dim=self.image_encoder.config.projection_dim,
clip_extra_context_tokens=self.num_tokens,
).to(self.device, dtype=torch.float16)
clip_embeddings_dim=self._image_encoder.config.projection_dim,
clip_extra_context_tokens=self._num_tokens,
).to(self._device, dtype=torch.float16)
return image_proj_model
def _prepare_attention_processors(self):
@ -92,7 +99,7 @@ class IPAdapter:
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
).to(self.device, dtype=torch.float16)
).to(self._device, dtype=torch.float16)
return attn_procs
@contextmanager
@ -109,9 +116,9 @@ class IPAdapter:
finally:
self._unet.set_attn_processor(orig_attn_processors)
def load_ip_adapter(self):
state_dict = torch.load(self.ip_ckpt, map_location="cpu")
self.image_proj_model.load_state_dict(state_dict["image_proj"])
def _load_weights(self):
state_dict = torch.load(self._ip_adapter_ckpt_path, map_location="cpu")
self._image_proj_model.load_state_dict(state_dict["image_proj"])
ip_layers = torch.nn.ModuleList(self._attn_processors.values())
ip_layers.load_state_dict(state_dict["ip_adapter"])
@ -119,10 +126,10 @@ class IPAdapter:
def get_image_embeds(self, pil_image):
if isinstance(pil_image, Image.Image):
pil_image = [pil_image]
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
clip_image = self._clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
clip_image_embeds = self._image_encoder(clip_image.to(self._device, dtype=torch.float16)).image_embeds
image_prompt_embeds = self._image_proj_model(clip_image_embeds)
uncond_image_prompt_embeds = self._image_proj_model(torch.zeros_like(clip_image_embeds))
return image_prompt_embeds, uncond_image_prompt_embeds
def set_scale(self, scale):
@ -134,29 +141,29 @@ class IPAdapter:
class IPAdapterPlus(IPAdapter):
"""IP-Adapter with fine-grained features"""
def init_proj(self):
def _init_image_proj_model(self):
image_proj_model = Resampler(
dim=self._unet.config.cross_attention_dim,
depth=4,
dim_head=64,
heads=12,
num_queries=self.num_tokens,
embedding_dim=self.image_encoder.config.hidden_size,
num_queries=self._num_tokens,
embedding_dim=self._image_encoder.config.hidden_size,
output_dim=self._unet.config.cross_attention_dim,
ff_mult=4,
).to(self.device, dtype=torch.float16)
).to(self._device, dtype=torch.float16)
return image_proj_model
@torch.inference_mode()
def get_image_embeds(self, pil_image):
if isinstance(pil_image, Image.Image):
pil_image = [pil_image]
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
clip_image = clip_image.to(self.device, dtype=torch.float16)
clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
uncond_clip_image_embeds = self.image_encoder(
clip_image = self._clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
clip_image = clip_image.to(self._device, dtype=torch.float16)
clip_image_embeds = self._image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
image_prompt_embeds = self._image_proj_model(clip_image_embeds)
uncond_clip_image_embeds = self._image_encoder(
torch.zeros_like(clip_image), output_hidden_states=True
).hidden_states[-2]
uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
uncond_image_prompt_embeds = self._image_proj_model(uncond_clip_image_embeds)
return image_prompt_embeds, uncond_image_prompt_embeds