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import os
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from typing import List
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import torch
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from diffusers import StableDiffusionPipeline
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from diffusers.pipelines.controlnet import MultiControlNetModel
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from PIL import Image
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from safetensors import safe_open
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from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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from .attention_processor_faceid import LoRAAttnProcessor, LoRAIPAttnProcessor
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from .utils import is_torch2_available, get_generator
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USE_DAFAULT_ATTN = False
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if is_torch2_available() and (not USE_DAFAULT_ATTN):
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from .attention_processor_faceid import (
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LoRAAttnProcessor2_0 as LoRAAttnProcessor,
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)
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from .attention_processor_faceid import (
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LoRAIPAttnProcessor2_0 as LoRAIPAttnProcessor,
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)
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else:
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from .attention_processor_faceid import LoRAAttnProcessor, LoRAIPAttnProcessor
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from .resampler import PerceiverAttention, FeedForward
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class FacePerceiverResampler(torch.nn.Module):
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def __init__(
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self,
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*,
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dim=768,
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depth=4,
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dim_head=64,
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heads=16,
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embedding_dim=1280,
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output_dim=768,
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ff_mult=4,
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):
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super().__init__()
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self.proj_in = torch.nn.Linear(embedding_dim, dim)
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self.proj_out = torch.nn.Linear(dim, output_dim)
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self.norm_out = torch.nn.LayerNorm(output_dim)
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self.layers = torch.nn.ModuleList([])
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for _ in range(depth):
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self.layers.append(
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torch.nn.ModuleList(
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[
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PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
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FeedForward(dim=dim, mult=ff_mult),
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]
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)
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)
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def forward(self, latents, x):
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x = self.proj_in(x)
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for attn, ff in self.layers:
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latents = attn(x, latents) + latents
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latents = ff(latents) + latents
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latents = self.proj_out(latents)
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return self.norm_out(latents)
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class MLPProjModel(torch.nn.Module):
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def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.num_tokens = num_tokens
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self.proj = torch.nn.Sequential(
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torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
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torch.nn.GELU(),
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torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
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)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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def forward(self, id_embeds):
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x = self.proj(id_embeds)
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x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
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x = self.norm(x)
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return x
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class ProjPlusModel(torch.nn.Module):
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def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, clip_embeddings_dim=1280, num_tokens=4):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.num_tokens = num_tokens
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self.proj = torch.nn.Sequential(
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torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
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torch.nn.GELU(),
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torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
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)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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self.perceiver_resampler = FacePerceiverResampler(
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dim=cross_attention_dim,
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depth=4,
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dim_head=64,
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heads=cross_attention_dim // 64,
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embedding_dim=clip_embeddings_dim,
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output_dim=cross_attention_dim,
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ff_mult=4,
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)
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def forward(self, id_embeds, clip_embeds, shortcut=False, scale=1.0):
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x = self.proj(id_embeds)
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x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
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x = self.norm(x)
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out = self.perceiver_resampler(x, clip_embeds)
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if shortcut:
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out = x + scale * out
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return out
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class IPAdapterFaceID:
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def __init__(self, sd_pipe, ip_ckpt, device, lora_rank=128, num_tokens=4, torch_dtype=torch.float16):
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self.device = device
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self.ip_ckpt = ip_ckpt
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self.lora_rank = lora_rank
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self.num_tokens = num_tokens
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self.torch_dtype = torch_dtype
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self.pipe = sd_pipe.to(self.device)
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self.set_ip_adapter()
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self.image_proj_model = self.init_proj()
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self.load_ip_adapter()
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def init_proj(self):
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image_proj_model = MLPProjModel(
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cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
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id_embeddings_dim=512,
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num_tokens=self.num_tokens,
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).to(self.device, dtype=self.torch_dtype)
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return image_proj_model
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def set_ip_adapter(self):
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unet = self.pipe.unet
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attn_procs = {}
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for name in unet.attn_processors.keys():
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cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
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if name.startswith("mid_block"):
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hidden_size = unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = unet.config.block_out_channels[block_id]
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if cross_attention_dim is None:
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attn_procs[name] = LoRAAttnProcessor(
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hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank,
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).to(self.device, dtype=self.torch_dtype)
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else:
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attn_procs[name] = LoRAIPAttnProcessor(
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hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens,
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).to(self.device, dtype=self.torch_dtype)
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unet.set_attn_processor(attn_procs)
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def load_ip_adapter(self):
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if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
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state_dict = {"image_proj": {}, "ip_adapter": {}}
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with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
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for key in f.keys():
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if key.startswith("image_proj."):
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state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
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elif key.startswith("ip_adapter."):
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state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
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else:
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state_dict = torch.load(self.ip_ckpt, map_location="cpu")
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self.image_proj_model.load_state_dict(state_dict["image_proj"])
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ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
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ip_layers.load_state_dict(state_dict["ip_adapter"])
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@torch.inference_mode()
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def get_image_embeds(self, faceid_embeds):
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faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
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image_prompt_embeds = self.image_proj_model(faceid_embeds)
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uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds))
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return image_prompt_embeds, uncond_image_prompt_embeds
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def set_scale(self, scale):
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for attn_processor in self.pipe.unet.attn_processors.values():
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if isinstance(attn_processor, LoRAIPAttnProcessor):
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attn_processor.scale = scale
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def generate(
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self,
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faceid_embeds=None,
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prompt=None,
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negative_prompt=None,
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scale=1.0,
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num_samples=4,
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seed=None,
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guidance_scale=7.5,
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num_inference_steps=30,
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**kwargs,
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):
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self.set_scale(scale)
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num_prompts = faceid_embeds.size(0)
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if prompt is None:
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prompt = "best quality, high quality"
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if negative_prompt is None:
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
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if not isinstance(prompt, List):
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prompt = [prompt] * num_prompts
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if not isinstance(negative_prompt, List):
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negative_prompt = [negative_prompt] * num_prompts
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image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(faceid_embeds)
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bs_embed, seq_len, _ = image_prompt_embeds.shape
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image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
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image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
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with torch.inference_mode():
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prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
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prompt,
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device=self.device,
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num_images_per_prompt=num_samples,
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do_classifier_free_guidance=True,
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negative_prompt=negative_prompt,
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)
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prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
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negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
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generator = get_generator(seed, self.device)
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images = self.pipe(
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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generator=generator,
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**kwargs,
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).images
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return images
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class IPAdapterFaceIDPlus:
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def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, lora_rank=128, num_tokens=4, torch_dtype=torch.float16):
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self.device = device
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self.image_encoder_path = image_encoder_path
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self.ip_ckpt = ip_ckpt
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self.lora_rank = lora_rank
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self.num_tokens = num_tokens
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self.torch_dtype = torch_dtype
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self.pipe = sd_pipe.to(self.device)
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self.set_ip_adapter()
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self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
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self.device, dtype=self.torch_dtype
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)
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self.clip_image_processor = CLIPImageProcessor()
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self.image_proj_model = self.init_proj()
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self.load_ip_adapter()
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def init_proj(self):
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image_proj_model = ProjPlusModel(
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cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
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id_embeddings_dim=512,
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clip_embeddings_dim=self.image_encoder.config.hidden_size,
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num_tokens=self.num_tokens,
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).to(self.device, dtype=self.torch_dtype)
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return image_proj_model
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def set_ip_adapter(self):
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unet = self.pipe.unet
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attn_procs = {}
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for name in unet.attn_processors.keys():
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cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
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if name.startswith("mid_block"):
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hidden_size = unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = unet.config.block_out_channels[block_id]
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if cross_attention_dim is None:
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attn_procs[name] = LoRAAttnProcessor(
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hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank,
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).to(self.device, dtype=self.torch_dtype)
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else:
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attn_procs[name] = LoRAIPAttnProcessor(
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hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens,
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).to(self.device, dtype=self.torch_dtype)
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unet.set_attn_processor(attn_procs)
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def load_ip_adapter(self):
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if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
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state_dict = {"image_proj": {}, "ip_adapter": {}}
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with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
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for key in f.keys():
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if key.startswith("image_proj."):
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state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
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elif key.startswith("ip_adapter."):
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state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
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else:
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state_dict = torch.load(self.ip_ckpt, map_location="cpu")
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self.image_proj_model.load_state_dict(state_dict["image_proj"])
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ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
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ip_layers.load_state_dict(state_dict["ip_adapter"])
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@torch.inference_mode()
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def get_image_embeds(self, faceid_embeds, face_image, s_scale, shortcut):
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if isinstance(face_image, Image.Image):
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pil_image = [face_image]
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clip_image = self.clip_image_processor(images=face_image, return_tensors="pt").pixel_values
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clip_image = clip_image.to(self.device, dtype=self.torch_dtype)
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clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
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uncond_clip_image_embeds = self.image_encoder(
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torch.zeros_like(clip_image), output_hidden_states=True
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).hidden_states[-2]
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faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
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image_prompt_embeds = self.image_proj_model(faceid_embeds, clip_image_embeds, shortcut=shortcut, scale=s_scale)
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uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds), uncond_clip_image_embeds, shortcut=shortcut, scale=s_scale)
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return image_prompt_embeds, uncond_image_prompt_embeds
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def set_scale(self, scale):
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for attn_processor in self.pipe.unet.attn_processors.values():
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if isinstance(attn_processor, LoRAIPAttnProcessor):
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attn_processor.scale = scale
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def generate(
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self,
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face_image=None,
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faceid_embeds=None,
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prompt=None,
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negative_prompt=None,
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scale=1.0,
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num_samples=4,
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seed=None,
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guidance_scale=7.5,
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num_inference_steps=30,
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s_scale=1.0,
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shortcut=False,
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**kwargs,
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):
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self.set_scale(scale)
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num_prompts = faceid_embeds.size(0)
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if prompt is None:
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prompt = "best quality, high quality"
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if negative_prompt is None:
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
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if not isinstance(prompt, List):
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prompt = [prompt] * num_prompts
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if not isinstance(negative_prompt, List):
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negative_prompt = [negative_prompt] * num_prompts
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image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(faceid_embeds, face_image, s_scale, shortcut)
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bs_embed, seq_len, _ = image_prompt_embeds.shape
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|
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
|
|
|
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,
|
|
|
**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
|
|
|
|
