comfyui-txt2img

ComfyUI 文生图 (txt2img)

⚠️ 操作前必须先阅读 comfyui-operation-rules

完整工作流代码

可直接复制使用，修改 prompt 和参数即可。

import random

# Step 1: 查询可用模型
ckpts = folder_paths.get_filename_list("checkpoints")
print("Available checkpoints:")
for c in ckpts:
    print(f"  {c}")
ckpt_name = ckpts[0]  # 选择第一个，或按需指定
print(f"\nUsing: {ckpt_name}")

import random

# Step 2: 构建 workflow
ckpt_name = "..."  # 上一步选择的模型名
seed = random.randint(0, 2**63)

wf = {}

# 1. 加载 Checkpoint → MODEL(0), CLIP(1), VAE(2)
wf["1"] = {
    "class_type": "CheckpointLoaderSimple",
    "inputs": {"ckpt_name": ckpt_name}
}

# 2. 正向提示词 → CONDITIONING(0)
wf["2"] = {
    "class_type": "CLIPTextEncode",
    "inputs": {
        "text": "a majestic mountain landscape at golden hour, dramatic clouds, photorealistic, 8k",
        "clip": ["1", 1]
    }
}

# 3. 负向提示词 → CONDITIONING(0)
wf["3"] = {
    "class_type": "CLIPTextEncode",
    "inputs": {
        "text": "ugly, blurry, low quality, deformed, watermark, text",
        "clip": ["1", 1]
    }
}

# 4. 空 Latent → LATENT(0)
wf["4"] = {
    "class_type": "EmptyLatentImage",
    "inputs": {"width": 1024, "height": 1024, "batch_size": 1}
}

# 5. 采样 → LATENT(0)
wf["5"] = {
    "class_type": "KSampler",
    "inputs": {
        "model": ["1", 0],
        "positive": ["2", 0],
        "negative": ["3", 0],
        "latent_image": ["4", 0],
        "seed": seed,
        "steps": 20,
        "cfg": 7.0,
        "sampler_name": "euler",
        "scheduler": "normal",
        "denoise": 1.0
    }
}

# 6. VAE 解码 → IMAGE(0)
wf["6"] = {
    "class_type": "VAEDecode",
    "inputs": {"samples": ["5", 0], "vae": ["1", 2]}
}

# 7. 保存图片
wf["7"] = {
    "class_type": "SaveImage",
    "inputs": {"images": ["6", 0], "filename_prefix": "txt2img"}
}

# Step 3: 提交执行
result = submit_workflow(wf)
print(f"Done! Images: {len(result['images'])}")

# Step 4: 展示结果
for img in result["images"]:
    img_bytes = client.get_image(img["filename"], img["subfolder"], img["type"])
    save_preview(img_bytes, f"txt2img_{img['filename']}")