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import os
from typing import List
import torch
from diffusers import StableDiffusionPipeline
from diffusers.pipelines.controlnet import MultiControlNetModel
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from PIL import Image
from .utils import is_torch2_available
if is_torch2_available():
from .attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor, CNAttnProcessor2_0 as CNAttnProcessor
else:
from .attention_processor import IPAttnProcessor, AttnProcessor, CNAttnProcessor
from .resampler import Resampler
class ImageProjModel(torch.nn.Module):
"""Projection Model"""
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.clip_extra_context_tokens = clip_extra_context_tokens
self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
self.norm = torch.nn.LayerNorm(cross_attention_dim)
def forward(self, image_embeds):
embeds = image_embeds
clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
return clip_extra_context_tokens
class IPAdapter:
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4):
self.device = device
self.image_encoder_path = image_encoder_path
self.ip_ckpt = ip_ckpt
self.num_tokens = num_tokens
self.pipe = sd_pipe.to(self.device)
self.set_ip_adapter()
# load image encoder
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(self.device, dtype=torch.bfloat16)
self.clip_image_processor = CLIPImageProcessor()
# image proj model
self.image_proj_model = self.init_proj()
self.load_ip_adapter()
def init_proj(self):
image_proj_model = ImageProjModel(
cross_attention_dim=self.pipe.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.bfloat16)
return image_proj_model
def set_ip_adapter(self):
unet = self.pipe.unet
attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
attn_procs[name] = AttnProcessor()
else:
attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim,
scale=1.0,num_tokens= self.num_tokens).to(self.device, dtype=torch.bfloat16)
unet.set_attn_processor(attn_procs)
if hasattr(self.pipe, "controlnet"):
if isinstance(self.pipe.controlnet, MultiControlNetModel):
for controlnet in self.pipe.controlnet.nets:
controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
else:
self.pipe.controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
def update_state_dict(self, state_dict):
image_proj_dict = {}
ip_adapter_dict = {}
for k in state_dict.keys():
if k.startswith("image_proj_model"):
image_proj_dict[k.replace("image_proj_model.", "")] = state_dict[k]
if k.startswith("adapter_modules"):
ip_adapter_dict[k.replace("adapter_modules.", "")] = state_dict[k]
dict = {'image_proj': image_proj_dict,
'ip_adapter' : ip_adapter_dict
}
return dict
def load_ip_adapter(self):
state_dict = torch.load(self.ip_ckpt, map_location="cpu")
if "image_proj_model.proj.weight" in state_dict.keys():
state_dict = self.update_state_dict(state_dict)
self.image_proj_model.load_state_dict(state_dict["image_proj"])
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
ip_layers.load_state_dict(state_dict["ip_adapter"])
@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_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.bfloat16)).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):
for attn_processor in self.pipe.unet.attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.scale = scale
def generate(
self,
pil_image,
prompt=None,
negative_prompt=None,
scale=1.0,
num_samples=4,
seed=-1,
guidance_scale=7.5,
num_inference_steps=30,
**kwargs,
):
self.set_scale(scale)
if isinstance(pil_image, List):
num_prompts = len(pil_image)
else:
num_prompts = 1
# if isinstance(pil_image, Image.Image):
# num_prompts = 1
# else:
# num_prompts = len(pil_image)
# print("num promp", num_prompts)
if prompt is None:
prompt = "best quality, high quality"
if negative_prompt is None:
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
if not isinstance(prompt, List):
prompt = [prompt] * num_prompts
if not isinstance(negative_prompt, List):
negative_prompt = [negative_prompt] * num_prompts
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
bs_embed, seq_len, _ = image_prompt_embeds.shape
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
with torch.inference_mode():
prompt_embeds = self.pipe._encode_prompt(
prompt, device=self.device, num_images_per_prompt=num_samples, do_classifier_free_guidance=True, negative_prompt=negative_prompt)
negative_prompt_embeds_, prompt_embeds_ = prompt_embeds.chunk(2)
prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
images = self.pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
**kwargs,
).images
return images
class IPAdapterXL(IPAdapter):
"""SDXL"""
def generate(
self,
pil_image,
prompt=None,
negative_prompt=None,
scale=1.0,
num_samples=4,
seed=-1,
num_inference_steps=30,
**kwargs,
):
self.set_scale(scale)
if isinstance(pil_image, Image.Image):
num_prompts = 1
else:
num_prompts = len(pil_image)
if prompt is None:
prompt = "best quality, high quality"
if negative_prompt is None:
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
if not isinstance(prompt, List):
prompt = [prompt] * num_prompts
if not isinstance(negative_prompt, List):
negative_prompt = [negative_prompt] * num_prompts
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
bs_embed, seq_len, _ = image_prompt_embeds.shape
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
with torch.inference_mode():
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = self.pipe.encode_prompt(
prompt, num_images_per_prompt=num_samples, do_classifier_free_guidance=True, negative_prompt=negative_prompt)
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
images = self.pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
num_inference_steps=num_inference_steps,
generator=generator,
**kwargs,
).images
return images
class IPAdapterPlus(IPAdapter):
"""IP-Adapter with fine-grained features"""
def init_proj(self):
image_proj_model = Resampler(
dim=self.pipe.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,
output_dim=self.pipe.unet.config.cross_attention_dim,
ff_mult=4
).to(self.device, dtype=torch.bfloat16)
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.bfloat16)
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)
return image_prompt_embeds, uncond_image_prompt_embeds
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