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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}") |