| 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 .utils import is_torch2_available, get_generator
|
|
|
| USE_DAFAULT_ATTN = False
|
| if is_torch2_available() and (not USE_DAFAULT_ATTN):
|
| from .attention_processor import (
|
| AttnProcessor2_0 as AttnProcessor,
|
| )
|
| from .attention_processor import (
|
| IPAttnProcessor2_0 as IPAttnProcessor,
|
| )
|
| else:
|
| from .attention_processor import AttnProcessor, IPAttnProcessor
|
| from .resampler import PerceiverAttention, FeedForward
|
|
|
|
|
| class FacePerceiverResampler(torch.nn.Module):
|
| def __init__(
|
| self,
|
| *,
|
| dim=768,
|
| depth=4,
|
| dim_head=64,
|
| heads=16,
|
| embedding_dim=1280,
|
| output_dim=768,
|
| ff_mult=4,
|
| ):
|
| super().__init__()
|
|
|
| self.proj_in = torch.nn.Linear(embedding_dim, dim)
|
| self.proj_out = torch.nn.Linear(dim, output_dim)
|
| self.norm_out = torch.nn.LayerNorm(output_dim)
|
| self.layers = torch.nn.ModuleList([])
|
| for _ in range(depth):
|
| self.layers.append(
|
| torch.nn.ModuleList(
|
| [
|
| PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
|
| FeedForward(dim=dim, mult=ff_mult),
|
| ]
|
| )
|
| )
|
|
|
| def forward(self, latents, x):
|
| x = self.proj_in(x)
|
| for attn, ff in self.layers:
|
| latents = attn(x, latents) + latents
|
| latents = ff(latents) + latents
|
| latents = self.proj_out(latents)
|
| return self.norm_out(latents)
|
|
|
|
|
| class MLPProjModel(torch.nn.Module):
|
| 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 ProjPlusModel(torch.nn.Module):
|
| def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, clip_embeddings_dim=1280, 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)
|
|
|
| self.perceiver_resampler = FacePerceiverResampler(
|
| dim=cross_attention_dim,
|
| depth=4,
|
| dim_head=64,
|
| heads=cross_attention_dim // 64,
|
| embedding_dim=clip_embeddings_dim,
|
| output_dim=cross_attention_dim,
|
| ff_mult=4,
|
| )
|
|
|
| def forward(self, id_embeds, clip_embeds, shortcut=False, scale=1.0):
|
|
|
| x = self.proj(id_embeds)
|
| x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
|
| x = self.norm(x)
|
| out = self.perceiver_resampler(x, clip_embeds)
|
| if shortcut:
|
| out = x + scale * out
|
| return out
|
|
|
|
|
| class IPAdapterFaceID:
|
| def __init__(self, sd_pipe, ip_ckpt, device, num_tokens=4, n_cond=1, torch_dtype=torch.float16):
|
| self.device = device
|
| self.ip_ckpt = ip_ckpt
|
| self.num_tokens = num_tokens
|
| self.n_cond = n_cond
|
| self.torch_dtype = torch_dtype
|
|
|
| self.pipe = sd_pipe.to(self.device)
|
| self.set_ip_adapter()
|
|
|
|
|
| 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=self.torch_dtype)
|
| 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*self.n_cond,
|
| ).to(self.device, dtype=self.torch_dtype)
|
| 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"], strict=False)
|
|
|
| @torch.inference_mode()
|
| def get_image_embeds(self, faceid_embeds):
|
|
|
| multi_face = False
|
| if faceid_embeds.dim() == 3:
|
| multi_face = True
|
| b, n, c = faceid_embeds.shape
|
| faceid_embeds = faceid_embeds.reshape(b*n, c)
|
|
|
| faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
|
| image_prompt_embeds = self.image_proj_model(faceid_embeds)
|
| uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds))
|
| if multi_face:
|
| c = image_prompt_embeds.size(-1)
|
| image_prompt_embeds = image_prompt_embeds.reshape(b, -1, c)
|
| uncond_image_prompt_embeds = uncond_image_prompt_embeds.reshape(b, -1, c)
|
|
|
| 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,
|
| 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
|
| else:
|
| faceid_embeds = faceid_embeds.repeat(num_samples, 1, 1)
|
| num_samples = 1
|
|
|
| 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 = get_generator(seed, self.device)
|
|
|
| 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,
|
| num_images_per_prompt=num_samples,
|
| **kwargs,
|
| ).images
|
|
|
| return images
|
|
|
|
|
| class IPAdapterFaceIDPlus:
|
| def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, torch_dtype=torch.float16):
|
| self.device = device
|
| self.image_encoder_path = image_encoder_path
|
| self.ip_ckpt = ip_ckpt
|
| self.num_tokens = num_tokens
|
| self.torch_dtype = torch_dtype
|
|
|
| self.pipe = sd_pipe.to(self.device)
|
| self.set_ip_adapter()
|
|
|
|
|
| self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
| self.device, dtype=self.torch_dtype
|
| )
|
| self.clip_image_processor = CLIPImageProcessor()
|
|
|
| self.image_proj_model = self.init_proj()
|
|
|
| self.load_ip_adapter()
|
|
|
| def init_proj(self):
|
| image_proj_model = ProjPlusModel(
|
| cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
| id_embeddings_dim=512,
|
| clip_embeddings_dim=self.image_encoder.config.hidden_size,
|
| num_tokens=self.num_tokens,
|
| ).to(self.device, dtype=self.torch_dtype)
|
| 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=self.torch_dtype)
|
| 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"], strict=False)
|
|
|
| @torch.inference_mode()
|
| def get_image_embeds(self, faceid_embeds, face_image, s_scale, shortcut):
|
| if isinstance(face_image, Image.Image):
|
| pil_image = [face_image]
|
| clip_image = self.clip_image_processor(images=face_image, return_tensors="pt").pixel_values
|
| clip_image = clip_image.to(self.device, dtype=self.torch_dtype)
|
| clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
| uncond_clip_image_embeds = self.image_encoder(
|
| torch.zeros_like(clip_image), output_hidden_states=True
|
| ).hidden_states[-2]
|
|
|
| faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
|
| image_prompt_embeds = self.image_proj_model(faceid_embeds, clip_image_embeds, shortcut=shortcut, scale=s_scale)
|
| uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds), uncond_clip_image_embeds, shortcut=shortcut, scale=s_scale)
|
| 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,
|
| face_image=None,
|
| faceid_embeds=None,
|
| prompt=None,
|
| negative_prompt=None,
|
| scale=1.0,
|
| num_samples=4,
|
| seed=None,
|
| guidance_scale=7.5,
|
| num_inference_steps=30,
|
| s_scale=1.0,
|
| shortcut=False,
|
| **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, face_image, s_scale, shortcut)
|
|
|
| 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 = get_generator(seed, self.device)
|
|
|
| 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 IPAdapterFaceIDXL(IPAdapterFaceID):
|
| """SDXL"""
|
|
|
| def generate(
|
| self,
|
| faceid_embeds=None,
|
| prompt=None,
|
| negative_prompt=None,
|
| scale=1.0,
|
| num_samples=4,
|
| seed=None,
|
| 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
|
| else:
|
| faceid_embeds = faceid_embeds.repeat(num_samples, 1, 1)
|
| num_samples = 1
|
|
|
| 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,
|
| 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 = get_generator(seed, self.device)
|
|
|
| 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,
|
| num_images_per_prompt=num_samples,
|
| **kwargs,
|
| ).images
|
|
|
| return images
|
|
|
|
|
| class IPAdapterFaceIDPlusXL(IPAdapterFaceIDPlus):
|
| """SDXL"""
|
|
|
| def generate(
|
| self,
|
| face_image=None,
|
| faceid_embeds=None,
|
| prompt=None,
|
| negative_prompt=None,
|
| scale=1.0,
|
| num_samples=4,
|
| seed=None,
|
| guidance_scale=7.5,
|
| num_inference_steps=30,
|
| s_scale=1.0,
|
| shortcut=True,
|
| **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, face_image, s_scale, shortcut)
|
|
|
| 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 = get_generator(seed, self.device)
|
|
|
| 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,
|
| guidance_scale=guidance_scale,
|
| **kwargs,
|
| ).images
|
|
|
| return images
|
|
|
