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Zero
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import os
from typing import List
import torch
from diffusers import StableDiffusionPipeline
from diffusers.pipelines.controlnet import MultiControlNetModel
from PIL import Image
from safetensors import safe_open
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from .attention_processor_faceid import LoRAAttnProcessor, LoRAIPAttnProcessor
class MLPProjModel(torch.nn.Module):
"""SD model with image prompt"""
def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.num_tokens = num_tokens
self.proj = torch.nn.Sequential(
torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
torch.nn.GELU(),
torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
)
self.norm = torch.nn.LayerNorm(cross_attention_dim)
def forward(self, id_embeds):
x = self.proj(id_embeds)
x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
x = self.norm(x)
return x
class IPAdapterFaceID:
def __init__(self, sd_pipe, ip_ckpt, device, lora_rank=128, num_tokens=4):
self.device = device
self.ip_ckpt = ip_ckpt
self.lora_rank = lora_rank
self.num_tokens = num_tokens
self.pipe = sd_pipe.to(self.device)
self.set_ip_adapter()
# image proj model
self.image_proj_model = self.init_proj()
self.load_ip_adapter()
def init_proj(self):
image_proj_model = MLPProjModel(
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
id_embeddings_dim=512,
num_tokens=self.num_tokens,
).to(self.device, dtype=torch.float16)
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] = LoRAAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank,
).to(self.device, dtype=torch.float16)
else:
attn_procs[name] = LoRAIPAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens,
).to(self.device, dtype=torch.float16)
unet.set_attn_processor(attn_procs)
def load_ip_adapter(self):
if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
state_dict = {"image_proj": {}, "ip_adapter": {}}
with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
for key in f.keys():
if key.startswith("image_proj."):
state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
elif key.startswith("ip_adapter."):
state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
else:
state_dict = torch.load(self.ip_ckpt, map_location="cpu")
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, faceid_embeds):
faceid_embeds = faceid_embeds.to(self.device, dtype=torch.float16)
image_prompt_embeds = self.image_proj_model(faceid_embeds)
uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_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, LoRAIPAttnProcessor):
attn_processor.scale = scale
def generate(
self,
faceid_embeds=None,
prompt=None,
negative_prompt=None,
scale=1.0,
num_samples=4,
seed=None,
guidance_scale=7.5,
num_inference_steps=30,
**kwargs,
):
self.set_scale(scale)
num_prompts = faceid_embeds.size(0)
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(faceid_embeds)
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_ = self.pipe.encode_prompt(
prompt,
device=self.device,
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,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
**kwargs,
).images
return images
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