import os import glob import numpy as np from PIL import Image import torch import torch.nn as nn from pipeline_flux_ipa import FluxPipeline from transformer_flux import FluxTransformer2DModel from attention_processor import IPAFluxAttnProcessor2_0 from transformers import AutoProcessor, SiglipVisionModel def resize_img(input_image, max_side=1280, min_side=1024, size=None, pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64): w, h = input_image.size if size is not None: w_resize_new, h_resize_new = size else: ratio = min_side / min(h, w) w, h = round(ratio*w), round(ratio*h) ratio = max_side / max(h, w) input_image = input_image.resize([round(ratio*w), round(ratio*h)], mode) w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number input_image = input_image.resize([w_resize_new, h_resize_new], mode) if pad_to_max_side: res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255 offset_x = (max_side - w_resize_new) // 2 offset_y = (max_side - h_resize_new) // 2 res[offset_y:offset_y+h_resize_new, offset_x:offset_x+w_resize_new] = np.array(input_image) input_image = Image.fromarray(res) return input_image 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 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 = SiglipVisionModel.from_pretrained(image_encoder_path).to(self.device, dtype=torch.bfloat16) self.clip_image_processor = AutoProcessor.from_pretrained(self.image_encoder_path) # 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.transformer.config.joint_attention_dim, # 4096 id_embeddings_dim=1152, num_tokens=self.num_tokens, ).to(self.device, dtype=torch.bfloat16) return image_proj_model def set_ip_adapter(self): transformer = self.pipe.transformer ip_attn_procs = {} # 19+38=57 for name in transformer.attn_processors.keys(): if name.startswith("transformer_blocks.") or name.startswith("single_transformer_blocks"): ip_attn_procs[name] = IPAFluxAttnProcessor2_0( hidden_size=transformer.config.num_attention_heads * transformer.config.attention_head_dim, cross_attention_dim=transformer.config.joint_attention_dim, num_tokens=self.num_tokens, ).to(self.device, dtype=torch.bfloat16) else: ip_attn_procs[name] = transformer.attn_processors[name] transformer.set_attn_processor(ip_attn_procs) 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"], strict=True) ip_layers = torch.nn.ModuleList(self.pipe.transformer.attn_processors.values()) ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False) @torch.inference_mode() def get_image_embeds(self, pil_image=None, clip_image_embeds=None): if pil_image is not None: 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=self.image_encoder.dtype)).pooler_output clip_image_embeds = clip_image_embeds.to(dtype=torch.bfloat16) else: clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.bfloat16) image_prompt_embeds = self.image_proj_model(clip_image_embeds) return image_prompt_embeds def set_scale(self, scale): for attn_processor in self.pipe.transformer.attn_processors.values(): if isinstance(attn_processor, IPAFluxAttnProcessor2_0): attn_processor.scale = scale def generate( self, pil_image=None, clip_image_embeds=None, prompt=None, scale=1.0, num_samples=1, seed=None, guidance_scale=3.5, num_inference_steps=24, **kwargs, ): self.set_scale(scale) image_prompt_embeds = self.get_image_embeds( pil_image=pil_image, clip_image_embeds=clip_image_embeds ) if seed is None: generator = None else: generator = torch.Generator(self.device).manual_seed(seed) images = self.pipe( prompt=prompt, image_emb=image_prompt_embeds, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, **kwargs, ).images return images if __name__ == '__main__': model_path = "black-forest-labs/FLUX.1-dev" image_encoder_path = "google/siglip-so400m-patch14-384" ipadapter_path = "./ip-adapter.bin" transformer = FluxTransformer2DModel.from_pretrained( model_path, subfolder="transformer", torch_dtype=torch.bfloat16 ) pipe = FluxPipeline.from_pretrained( model_path, transformer=transformer, torch_dtype=torch.bfloat16 ) ip_model = IPAdapter(pipe, image_encoder_path, ipadapter_path, device="cuda", num_tokens=128) image_dir = "./assets/images/2.jpg" image_name = image_dir.split("/")[-1] image = Image.open(image_dir).convert("RGB") image = resize_img(image) prompt = "a young girl" images = ip_model.generate( pil_image=image, prompt=prompt, scale=0.7, width=960, height=1280, seed=42 ) images[0].save(f"results/{image_name}")