Tiamat AI 2025 新版多模态平台：EasyControl 框架下的高质量图像生成全攻略

import torch
from PIL import Image
from src.pipeline import FluxPipeline

# 初始化图像处理器
base_path = "black-forest-labs/FLUX.1-dev"
pipe = FluxPipeline.from_pretrained(base_path, torch_dtype=torch.bfloat16)
pipe.to("cuda")

# 定义生成参数
prompt = "图书馆中的 SKS"
subject_image = Image.open("./test_imgs/subject_0.png").convert("RGB")

# 生成图像
image = pipe(
    prompt,
    height=,
    width=,
    guidance_scale=3.5,
    num_inference_steps=,
    max_sequence_length=,
    generator=torch.Generator("cpu").manual_seed(),
    subject_images=(subject_image),
    cond_size=,
).images[]

# 保存图像
image.save("output.png")

from src.lora_helper import set_multi_lora

# 加载多个LoRA模型
paths = (control_models("subject"), control_models("inpainting"))
set_multi_lora(pipe.transformer, paths, lora_weights=((), ()), cond_size=)

# 定义多条件输入
prompt = "车上的 SKS"
subject_images = (Image.open("./test_imgs/subject_1.png").convert("RGB"))
spatial_images = (Image.open("./test_imgs/inpainting.png").convert("RGB"))

# 生成图像
image = pipe(
    prompt,
    height=,
    width=,
    guidance_scale=3.5,
    num_inference_steps=,
    max_sequence_length=,
    generator=torch.Generator("cpu").manual_seed(),
    subject_images=subject_images,
    spatial_images=spatial_images,
    cond_size=,
).images[]