Upload infer_flux_ipa_siglip.py

infer_flux_ipa_siglip.py

@@ -0,0 +1,190 @@
+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}")