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7f921f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from typing import Optional, Sequence, Union
import torch
import numpy as np
from PIL import Image
from pipelines.flux_image_new import FluxImagePipeline
from models.utils import load_state_dict,parse_flux_model_configs
from models.unified_dataset import UnifiedDataset, gen_points
from models.flux_dit import FluxDiTStateDictConverter
import matplotlib.pyplot as plt
converter = FluxDiTStateDictConverter()
# 模型配置
MODEL_CONFIGS = {
"depth": "ckpts/depth.safetensors",
"normal": "ckpts/normal.safetensors",
"matting": "ckpts/matting.safetensors",
"depth_lora": "ckpts/depth_lora.safetensors",
"normal_lora": "ckpts/normal_lora.safetensors",
"matting_lora": "ckpts/matting_lora.safetensors",
}
def inference(
model_root: str,
task: str,
input_paths: Union[str, Sequence[str]],
resolution: int = 768,
num_inference_steps: int = 8,
seed: int = 42,
output_path: Optional[str] = None,
torch_dtype: torch.dtype = torch.bfloat16,
device: str = "cuda",
) -> None:
"""Run inference on one or more images."""
# 获取模型路径
if task not in MODEL_CONFIGS:
raise ValueError(f"task must be one of {list(MODEL_CONFIGS.keys())}")
state_dict_path = MODEL_CONFIGS[task]
# 加载模型
print("Loading FluxImagePipeline ...")
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch_dtype,
device=device,
model_configs=parse_flux_model_configs(model_root),
)
if "lora" in task:
pipe.load_lora(pipe.dit, state_dict_path, hotload=False)
else:
state_dict = load_state_dict(state_dict_path)
pipe.dit.load_state_dict(state_dict)
print("Model loaded successfully!")
# 准备图片列表
if isinstance(input_paths, str):
images = [path for path in input_paths.split(",") if path]
else:
images = list(input_paths)
transform = UnifiedDataset.default_image_operator(height=resolution, width=resolution)
task = task.replace("_lora", "")
# 处理每张图片
for image_path in images:
if task in {"depth", "normal"}:
out_np = pipe(
prompt=f"Transform to {task} map while maintaining original composition",
kontext_images=transform(image_path),
height=768,
width=768,
embedded_guidance=1,
num_inference_steps=num_inference_steps,
seed=seed,
output_type="np",
rand_device=device,
task=task,
)
else: # matting
alpha = np.zeros((resolution, resolution), dtype=np.float32)
alpha[resolution // 4 : resolution * 3 // 4, resolution // 4 : resolution * 3 // 4] = 1.0
points, coords = gen_points(alpha, num_points=10, radius=30)
img = Image.open(image_path).convert("RGB").resize((resolution, resolution))
for i in range(coords.shape[0] // 2):
x, y = coords[i * 2], coords[i * 2 + 1]
for dx in range(-9, 10):
for dy in range(-9, 10):
if dx * dx + dy * dy <= 81:
px = int(x * resolution) + dx
py = int(y * resolution) + dy
if 0 <= px < resolution and 0 <= py < resolution:
img.putpixel((px, py), (255, 0, 0))
img.save(image_path.replace(".jpg", ".png").replace(".png", "_debug_points.png"))
points = torch.from_numpy(points * 2 - 1).repeat(3, 1, 1).to(device)
out_np = pipe(
prompt="Extract the foreground object and generate alpha matte",
kontext_images=[transform(image_path), points],
height=768,
width=768,
embedded_guidance=1,
num_inference_steps=10,
seed=seed,
output_type="np",
rand_device=device,
task=task,
)
# 后处理
if task == "depth":
if out_np.ndim == 3:
out_np = np.mean(out_np, axis=2)
cmap = plt.get_cmap("Spectral")
out_np = cmap(out_np)[:, :, :3]
out_np = (out_np * 255).astype(np.uint8)
elif task == "normal":
out_np = (out_np + 1) / 2 * 255.0
out_np = out_np.astype(np.uint8)
else: # matting
out_np = (out_np * 255.0).astype(np.uint8)
# 保存结果
if output_path is None:
save_path = image_path.replace(".jpg", ".png").replace(".png", f"_{task}.png")
else:
save_path = output_path
Image.fromarray(out_np).save(save_path)
print(f"Saved output to {save_path}")
if __name__ == "__main__":
# Please Change the model root path below to your own model directory
model_root = "./FLUX.1-Kontext-dev"
inference(
model_root=model_root,
task="depth", # Options: "depth", "normal", "matting"
input_paths="samples/cutecat.jpg"
) |