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feat: 发布豆包Seedream 5.0/4.5生图节点

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- 新增宽高比（8种）× 分辨率档位（2K/3K/4K）选择，后端自动换算像素
- 5.0支持2K/3K，4.5支持2K/4K，搭配错误时明确报错
- 新增生图数量（1-10），2张以上自动并发请求，加快出图速度
- 移除旧版尺寸预设、宽度、高度输入

Co-Authored-By: Claude Sonnet 4.5 <[email protected]>
This commit is contained in:
o1key
2026-04-15 15:20:23 +08:00
co-authored by Claude Sonnet 4.5
parent 0b9d7583c7
commit 659f94656c
1 changed files with 257 additions and 127 deletions
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nodes/doubao_image.py
+257 -127
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@@ -1,56 +1,110 @@
"""
豆包生图节点
1:1 复刻字节跳动 Seedream 4 节点的前端外观（输入/输出/参数/样式）
后端通过 new-api 兼容层调用豆包官方 API
"""
import asyncio
import time
import numpy as np
import torch
from concurrent.futures import ThreadPoolExecutor
from PIL import Image
from typing import List
from typing import List, Optional
from ..clients.doubao_image_client import DoubaoImageClient
from ..utils.image_utils import tensor_to_pil
# ── 尺寸预设 ──────────────────────────────────────────────────────────────────
# (显示名, 宽, 高) —— 宽高用于构造 "WxH" size 字符串
RECOMMENDED_PRESETS_SEEDREAM_4 = [
("2048×2048 (1:1)", 2048, 2048),
("2304×1728 (4:3)", 2304, 1728),
("1728×2304 (3:4)", 1728, 2304),
("2560×1440 (16:9)", 2560, 1440),
("1440×2560 (9:16)", 1440, 2560),
("2496×1664 (3:2)", 2496, 1664),
("1664×2496 (2:3)", 1664, 2496),
("3024×1296 (21:9)", 3024, 1296),
("3072×3072 (1:1)", 3072, 3072),
("4096×4096 (1:1)", 4096, 4096),
("自定义", None, None),
]
_PRESET_LABELS = [label for label, _, _ in RECOMMENDED_PRESETS_SEEDREAM_4]
# ── 模型列表 ──────────────────────────────────────────────────────────────────
# 节点下拉选项 = new-api 后台配置的模型 ID（直接透传给 API）
_MODELS = [
"doubao-seedream-5-0-260128",
"doubao-seedream-4-5-251128",
]
# ── 宽高比列表 ─────────────────────────────────────────────────────────────────
_ASPECT_RATIOS = ["1:1", "4:3", "3:4", "16:9", "9:16", "3:2", "2:3", "21:9"]
# ── 分辨率档位（每个模型支持的档位不同）──────────────────────────────────────
# 5.0：2K / 3K
# 4.5：2K / 4K
_RESOLUTIONS = ["2K", "3K", "4K"]
# ── 像素对照表 ─────────────────────────────────────────────────────────────────
# 结构：{ 模型版本key: { 分辨率: { 宽高比: (宽, 高) } } }
_SIZE_TABLE = {
"5-0": {
"2K": {
"1:1": (2048, 2048),
"4:3": (2304, 1728),
"3:4": (1728, 2304),
"16:9": (2848, 1600),
"9:16": (1600, 2848),
"3:2": (2496, 1664),
"2:3": (1664, 2496),
"21:9": (3136, 1344),
},
"3K": {
"1:1": (3072, 3072),
"4:3": (3456, 2592),
"3:4": (2592, 3456),
"16:9": (4096, 2304),
"9:16": (2304, 4096),
"3:2": (3744, 2496),
"2:3": (2496, 3744),
"21:9": (4704, 2016),
},
},
"4-5": {
"2K": {
"1:1": (2048, 2048),
"4:3": (2304, 1728),
"3:4": (1728, 2304),
"16:9": (2848, 1600),
"9:16": (1600, 2848),
"3:2": (2496, 1664),
"2:3": (1664, 2496),
"21:9": (3136, 1344),
},
"4K": {
"1:1": (4096, 4096),
"4:3": (4704, 3520),
"3:4": (3520, 4704),
"16:9": (5504, 3040),
"9:16": (3040, 5504),
"3:2": (4992, 3328),
"2:3": (3328, 4992),
"21:9": (6240, 2656),
},
},
}
# 每个模型版本支持的分辨率档位
_MODEL_RESOLUTIONS = {
"5-0": ["2K", "3K"],
"4-5": ["2K", "4K"],
}
# 并发请求超时（秒）
_CONCURRENT_TIMEOUT = 330
def _model_key(model: str) -> str:
"""从模型 ID 中提取版本 key（'5-0' 或 '4-5'）。"""
for key in _SIZE_TABLE:
if key in model:
return key
raise ValueError(f"无法识别模型版本：{model}，支持的模型：{_MODELS}")
def _pil_list_to_tensor(images: List[Image.Image]) -> torch.Tensor:
"""
PIL Image 列表 → ComfyUI IMAGE tensor [B, H, W, C]，值域 [0, 1]。
多张尺寸不同时，以最大尺寸为准，较小图像丢弃（与项目其他节点策略一致）。
多张尺寸不同时，以最大尺寸为准，较小图像丢弃。
"""
if not images:
placeholder = Image.new("RGB", (512, 512), color=(128, 128, 128))
images = [placeholder]
# 找最大尺寸
base_size = max(images, key=lambda img: img.size[0] * img.size[1]).size
matched = [img for img in images if img.size == base_size]
skipped = len(images) - len(matched)
@@ -62,11 +116,11 @@ def _pil_list_to_tensor(images: List[Image.Image]) -> torch.Tensor:
arr = np.array(img.convert("RGB")).astype(np.float32) / 255.0
tensors.append(torch.from_numpy(arr))
return torch.stack(tensors, dim=0) # [B, H, W, C]
return torch.stack(tensors, dim=0) # [B, H, W, C]
class DoubaoImage:
"""豆包生图 —— 1:1 复刻字节跳动 Seedream 4 节点前端，后端对接豆包官方 API"""
"""豆包生图 —— 通过宽高比 + 分辨率档位选择尺寸，后端自动换算真实像素"""
@classmethod
def INPUT_TYPES(cls):
@@ -84,55 +138,33 @@ class DoubaoImage:
"tooltip": "用于创建或编辑图像的文本提示",
},
),
"尺寸预设": (
_PRESET_LABELS,
"宽高比": (
_ASPECT_RATIOS,
{
"default": _PRESET_LABELS[0],
"tooltip": '选择推荐尺寸。选择"自定义"可使用下方的宽度和高度',
"default": "1:1",
"tooltip": "图像宽高比。所有分辨率档位均支持这些比例",
},
),
"宽度": (
"INT",
"分辨率": (
_RESOLUTIONS,
{
"default": 2048,
"min": 1024,
"max": 6240,
"step": 64,
"tooltip": '图像的自定义宽度。仅当尺寸预设设置为"自定义"时生效',
},
),
"高度": (
"INT",
{
"default": 2048,
"min": 1024,
"max": 4992,
"step": 64,
"tooltip": '图像的自定义高度。仅当尺寸预设设置为"自定义"时生效',
},
),
"顺序图像生成": (
["disabled", "auto"],
{
"default": "disabled",
"default": "2K",
"tooltip": (
'分组图像生成模式。'
'"disabled"生成单张图像；'
'"auto"让模型决定是否生成多张相关图像（如故事场景、角色变体）'
"图像分辨率档位。\n"
"• Seedream 5.0：支持 2K / 3K\n"
"• Seedream 4.5：支持 2K / 4K\n"
"（3K 与 4.5 或 4K 与 5.0 搭配时将报错）"
),
},
),
"最大图片数": (
"生图数量": (
"INT",
{
"default": 1,
"min": 1,
"max": 15,
"max": 10,
"step": 1,
"tooltip": (
"当顺序图像生成='auto'时生成的最大图像数量。"
"总图像数（输入+生成）不能超过15张"
),
"tooltip": "生成图像的数量。2-10 张时自动并发请求，加快出图速度",
},
),
"种子": (
@@ -150,7 +182,10 @@ class DoubaoImage:
"BOOLEAN",
{
"default": True,
"tooltip": "如果启用，当任何请求的图像缺失或返回错误时将中止执行",
"tooltip": (
"启用时：任意一张失败即抛出错误并中止。\n"
"禁用时：返回已成功生成的图像，忽略失败项"
),
},
),
},
@@ -172,106 +207,201 @@ class DoubaoImage:
FUNCTION = "generate"
CATEGORY = "comfyui_o1key/豆包"
# ── 核心生成方法 ──────────────────────────────────────────────────────────
# ── 并发核心：在新 event loop 里 gather N 个 _generate_async ─────────────
async def _run_concurrent(
self,
client: DoubaoImageClient,
生图数量: int,
model: str,
prompt: str,
size: str,
seed: int,
image_tensor,
pbar,
) -> List[dict]:
"""
并发发起 生图数量 个独立请求，每完成一个推进一格进度条。
返回结果列表：[{"index": int, "images": [...], "error": str|None}]
"""
# 固定参数（顺序生成功能暂时隐藏）
seq = "disabled"
max_img = 1
async def _one(idx: int) -> dict:
try:
imgs = await client._generate_async(
model=model,
prompt=prompt,
size=size,
seed=seed,
sequential_image_generation=seq,
max_images=max_img,
image_tensor=image_tensor,
)
return {"index": idx, "images": imgs, "error": None}
except Exception as e:
return {"index": idx, "images": [], "error": str(e)}
# 用 as_completed 方式逐个推进进度条
tasks = [asyncio.create_task(_one(i)) for i in range(生图数量)]
results = [None] * 生图数量
completed = 0
for coro in asyncio.as_completed(tasks):
res = await coro
results[res["index"]] = res
completed += 1
status = "✓" if res["error"] is None else f"✗ {res['error']}"
print(f"[豆包生图] [{completed}/{生图数量}] 第 {res['index'] + 1} 张 → {status}")
if pbar is not None:
pbar.update(1)
return results
# ── 节点主入口 ────────────────────────────────────────────────────────────
def generate(
self,
模型: str,
提示词: str,
尺寸预设: str,
宽度: int,
高度: int,
顺序图像生成: str,
最大图片数: int,
宽高比: str,
分辨率: str,
生图数量: int,
种子: int,
部分失败时停止: bool,
图像=None,
):
start_time = time.time()
# 顺序图像生成功能暂时隐藏，固定使用默认值
顺序图像生成 = "disabled"
最大图片数 = 1
# ── 1. 校验提示词 ─────────────────────────────────────────────────────
if not 提示词.strip():
raise ValueError("提示词不能为空，请输入图像描述后重试。")
# ── 2. 解析尺寸 ───────────────────────────────────────────────────────
w, h = None, None
for label, tw, th in RECOMMENDED_PRESETS_SEEDREAM_4:
if label == 尺寸预设:
w, h = tw, th
break
# ── 2. 解析模型版本并校验分辨率兼容性 ────────────────────────────────
try:
mkey = _model_key(模型)
except ValueError as e:
raise ValueError(str(e)) from None
if w is None or h is None:
# 自定义尺寸
w, h = 宽度, 高度
print(f"[豆包生图] 自定义尺寸：{w}×{h}")
supported = _MODEL_RESOLUTIONS[mkey]
if 分辨率 not in supported:
raise ValueError(
f"模型 {模型} 不支持 {分辨率} 分辨率。\n"
f"该模型支持：{' / '.join(supported)}"
)
# ── 3. 查表换算真实像素 ───────────────────────────────────────────────
w, h = _SIZE_TABLE[mkey][分辨率][宽高比]
size_str = f"{w}x{h}"
# ── 3. 打印概要 ───────────────────────────────────────────────────────
# ── 4. 打印概要 ───────────────────────────────────────────────────────
mode_str = "图生图" if 图像 is not None else "文生图"
seq_str = f" | 顺序生成=auto(最多{最大图片数}张)" if 顺序图像生成 == "auto" else ""
print(
f"[豆包生图] {mode_str} | 模型={模型} | 尺寸={size_str}"
f" | 种子={种子}{seq_str}"
f"[豆包生图] {mode_str} | 模型={模型} | {分辨率} {宽高比} → {size_str}"
f" | 数量={生图数量} | 种子={种子}"
)
# ── 4. 进度条 ─────────────────────────────────────────────────────────
try:
from comfy.utils import ProgressBar
pbar = ProgressBar(100)
except Exception:
pbar = None
def _pb(pct: int):
if pbar:
pbar.update_absolute(pct, 100)
_pb(0)
# ── 5. 调用客户端 ─────────────────────────────────────────────────────
# ── 5. 初始化客户端 ───────────────────────────────────────────────────
try:
client = DoubaoImageClient()
except ValueError as e:
raise ValueError(str(e)) from None
_pb(5)
# ── 6. 进度条（按张数计）──────────────────────────────────────────────
try:
pil_images: List[Image.Image] = client.generate_sync(
model=模型,
prompt=提示词,
size=size_str,
seed=种子,
sequential_image_generation=顺序图像生成,
max_images=最大图片数,
image_tensor=图像,
)
except RuntimeError as e:
raise RuntimeError(str(e)) from None
except Exception as e:
raise RuntimeError(f"豆包生图请求失败: {e}") from None
from comfy.utils import ProgressBar
pbar = ProgressBar(生图数量)
except Exception:
pbar = None
_pb(90)
# ── 6. 部分失败判断 ───────────────────────────────────────────────────
if 顺序图像生成 == "auto" and 部分失败时停止:
if len(pil_images) < 最大图片数:
raise RuntimeError(
f"部分图像生成失败：期望 {最大图片数} 张，"
f"实际返回 {len(pil_images)} 张。"
"（可将【部分失败时停止】设为 False 以接受不完整结果）"
# ── 7. 单张 / 多张分支 ────────────────────────────────────────────────
if 生图数量 == 1:
# 单张：走原有同步路径
try:
pil_images: List[Image.Image] = client.generate_sync(
model=模型,
prompt=提示词,
size=size_str,
seed=种子,
sequential_image_generation=顺序图像生成,
max_images=最大图片数,
image_tensor=图像,
)
except RuntimeError as e:
raise RuntimeError(str(e)) from None
except Exception as e:
raise RuntimeError(f"豆包生图请求失败: {e}") from None
# ── 7. PIL → tensor ───────────────────────────────────────────────────
if pbar is not None:
pbar.update(1)
else:
# 多张：并发请求
def _run_in_thread():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
return loop.run_until_complete(
self._run_concurrent(
client=client,
生图数量=生图数量,
model=模型,
prompt=提示词,
size=size_str,
seed=种子,
image_tensor=图像,
pbar=pbar,
)
)
finally:
loop.close()
with ThreadPoolExecutor(max_workers=1) as executor:
future = executor.submit(_run_in_thread)
try:
results = future.result(timeout=_CONCURRENT_TIMEOUT)
except TimeoutError:
raise RuntimeError(
f"并发生图超时（>{_CONCURRENT_TIMEOUT}s），请检查网络或减少生图数量"
)
# 统计成功 / 失败
success_results = [r for r in results if r and r["error"] is None]
failed_results = [r for r in results if r and r["error"] is not None]
if failed_results:
fail_info = "；".join(
f"第{r['index']+1}张: {r['error']}" for r in failed_results
)
if 部分失败时停止:
raise RuntimeError(
f"{len(failed_results)}/{生图数量} 张生成失败：{fail_info}\n"
"（可将【部分失败时停止】设为 False 以返回已成功的图像）"
)
else:
print(f"[豆包生图] 警告：{len(failed_results)}/{生图数量} 张失败，已忽略：{fail_info}")
if not success_results:
raise RuntimeError("所有图像均生成失败，请检查网络或 API 配置。")
# 按原始 index 排序，展平为 PIL 列表
success_results.sort(key=lambda r: r["index"])
pil_images = []
for r in success_results:
pil_images.extend(r["images"])
# ── 8. PIL → tensor ───────────────────────────────────────────────────
output_tensor = _pil_list_to_tensor(pil_images)
_pb(100)
# ── 8. 完成日志 ───────────────────────────────────────────────────────
# ── 9. 完成日志 ───────────────────────────────────────────────────────
elapsed = time.time() - start_time
time_str = f"{elapsed:.1f}s"
print(
f"[豆包生图] 完成！耗时 {time_str}，"
f"[豆包生图] 完成！耗时 {elapsed:.1f}s，"
f"输出 {output_tensor.shape[0]} 张 "
f"{output_tensor.shape[2]}×{output_tensor.shape[1]}"
)