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Update image and video workflow nodes

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o1key
2026-05-28 16:44:30 +08:00
parent 3f0f4099fb
commit 5d9aff9ca7
21 changed files with 3507 additions and 381 deletions
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clients/gpt_image_client.py
+330 -84
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@@ -6,9 +6,8 @@ GPT Image API 客户端
设计原则：
- 与 doubao_image_client.py 保持相同的异步 + 同步双入口模式
- 图像以 multipart/form-data 方式上传（edits 接口）
- generations 接口使用 JSON 请求体，图像以 data URI base64 内联传递
- 响应支持 url 和 b64_json 两种格式，优先处理 b64_json（避免二次下载）
- generations / edits 接口均使用 multipart/form-data
- 响应兼容 SSE 流式、JSON、url 和 b64_json
"""
import asyncio
@@ -28,11 +27,6 @@ from ..utils.config import get_api_key_or_raise, get_api_base_url
from ..utils.image_utils import tensor_to_pil, encode_image_to_base64
from ..utils.http_error import RETRYABLE_STATUS_CODES, HTTP_ERROR_MESSAGES, _compute_delay, DEFAULT_MAX_RETRIES, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR, get_friendly_message
# GPT Image 专属错误文案
_GPT_ERROR_MESSAGES = {
500: "触发内容风控，或服务器繁忙！",
}
try:
from comfy.model_management import processing_interrupted, InterruptProcessingException
_INTERRUPT_AVAILABLE = True
@@ -43,7 +37,7 @@ except ImportError:
# ── 接口端点 ──────────────────────────────────────────────────────────────────
_ENDPOINT_GENERATIONS = "/v1/images/generations/"
_ENDPOINT_EDITS = "/v1/images/edits/"
_ENDPOINT_EDITS = "/v1/images/edits"
# ── 模型名映射（UI 显示名 → API 实际参数名）─────────────────────────────────
_MODEL_NAME_MAP = {
@@ -60,11 +54,10 @@ class GptImageClient:
GPT Image API 客户端
接口说明：
generations：JSON body，支持 quality / size / n / model
edits：multipart/form-data，必须包含 image（PNG），可选 mask（PNG）
generations：multipart/form-data，支持 quality / size / n / model
edits：multipart/form-data，图片和 mask 使用 PNG 文件上传
两个接口的响应格式相同：
{ "data": [ {"url": "..."} | {"b64_json": "..."} ] }
响应支持 JSON 和 SSE 流式格式。
"""
def __init__(self):
@@ -82,6 +75,26 @@ class GptImageClient:
"Content-Type": "application/json",
}
@staticmethod
def _new_multipart_form() -> aiohttp.FormData:
try:
return aiohttp.FormData(default_to_multipart=True)
except TypeError:
form = aiohttp.FormData()
form._is_multipart = True
return form
@staticmethod
def _add_form_fields(form: aiohttp.FormData, fields: dict) -> None:
for key, value in fields.items():
if value is None:
continue
if isinstance(value, bool):
value = "true" if value else "false"
elif isinstance(value, (dict, list)):
value = json.dumps(value, ensure_ascii=False)
form.add_field(key, str(value))
# ── 图像转换工具 ──────────────────────────────────────────────────────────
# ── 请求体大小限制 ────────────────────────────────────────────────────────
@@ -208,6 +221,10 @@ class GptImageClient:
raise RuntimeError(f"API 返回错误: {msg}")
data_list = resp_json.get("data")
if data_list is None:
data_list = resp_json.get("images")
if data_list is None and (resp_json.get("b64_json") or resp_json.get("url")):
data_list = [resp_json]
if not data_list:
raise RuntimeError(
f"API 响应中未找到 data 字段，完整响应：\n"
@@ -247,6 +264,203 @@ class GptImageClient:
return images
@staticmethod
def _decode_b64_image(b64: str, label: str) -> Image.Image:
try:
img_bytes = base64.b64decode(b64)
img = Image.open(BytesIO(img_bytes))
print(f"[o1key GPT Image] {label} base64 解码完成 ({img.size[0]}×{img.size[1]})")
return img
except Exception as e:
raise RuntimeError(f"{label} base64 解码失败: {e}") from None
async def _append_images_from_payload(
self,
payload: dict,
session: aiohttp.ClientSession,
images: List[Image.Image],
event_name: str = "",
) -> bool:
if isinstance(payload, dict) and "error" in payload:
err = payload["error"]
msg = (
err.get("message") or err.get("msg") or json.dumps(err, ensure_ascii=False)
if isinstance(err, dict)
else str(err)
)
raise RuntimeError(get_friendly_message(500, msg)) from None
if not isinstance(payload, dict):
return False
event_type = payload.get("type") or event_name
if "partial_image" in event_type:
return False
for key in ("data", "images"):
data_list = payload.get(key)
if isinstance(data_list, list):
parsed = await self._parse_response({"data": data_list}, session)
images.extend(parsed)
return True
if payload.get("b64_json") or payload.get("url"):
parsed = await self._parse_response({"data": [payload]}, session)
images.extend(parsed)
return True
image_obj = payload.get("image")
if isinstance(image_obj, dict) and (image_obj.get("b64_json") or image_obj.get("url")):
parsed = await self._parse_response({"data": [image_obj]}, session)
images.extend(parsed)
return True
return False
async def _parse_edit_stream_response(
self,
resp: aiohttp.ClientResponse,
session: aiohttp.ClientSession,
) -> List[Image.Image]:
"""
Parse /v1/images/edits SSE events and return final completed images.
Partial images are intentionally ignored so the node output stays unchanged.
"""
images: List[Image.Image] = []
buffer = ""
event_name = ""
data_lines = []
partial_count = 0
debug_body_parts = []
async def _handle_event():
nonlocal event_name, data_lines, partial_count, images
if not data_lines:
event_name = ""
return
data_str = "\n".join(data_lines).strip()
event_name = event_name.strip()
data_lines = []
if not data_str or data_str == "[DONE]":
return
try:
payload = json.loads(data_str)
except Exception:
raise RuntimeError(get_friendly_message(500, data_str)) from None
if isinstance(payload, dict):
event_type = payload.get("type") or event_name
else:
event_type = event_name
if "partial_image" in event_type:
partial_count += 1
return
await self._append_images_from_payload(payload, session, images, event_name)
async for raw_chunk in resp.content.iter_any():
chunk_text = raw_chunk.decode("utf-8", errors="ignore")
debug_body_parts.append(chunk_text)
buffer += chunk_text
while "\n" in buffer:
line, buffer = buffer.split("\n", 1)
line = line.rstrip("\r")
if line == "":
await _handle_event()
event_name = ""
continue
if line.startswith(":"):
continue
if line.startswith("event:"):
event_name = line[len("event:"):].strip()
elif line.startswith("data:"):
data_lines.append(line[len("data:"):].lstrip())
if buffer.strip():
data_lines.append(buffer.strip())
await _handle_event()
if partial_count:
print(f"[o1key GPT Image] 流式中间图 {partial_count} 张（已忽略，仅输出最终图）")
if not images:
raise RuntimeError("流式响应结束，但未收到最终图片")
return images
async def _parse_stream_text_response(
self,
text: str,
session: aiohttp.ClientSession,
) -> List[Image.Image]:
images: List[Image.Image] = []
partial_count = 0
event_name = ""
data_lines = []
async def _handle_event():
nonlocal event_name, data_lines, partial_count, images
if not data_lines:
event_name = ""
return
data_str = "\n".join(data_lines).strip()
event_name = event_name.strip()
data_lines = []
if not data_str or data_str == "[DONE]":
return
payload = json.loads(data_str)
if isinstance(payload, dict):
event_type = payload.get("type") or event_name
else:
event_type = event_name
if "partial_image" in event_type:
partial_count += 1
return
await self._append_images_from_payload(payload, session, images, event_name)
for raw_line in text.splitlines():
line = raw_line.rstrip("\r")
if line == "":
await _handle_event()
event_name = ""
continue
if line.startswith(":"):
continue
if line.startswith("event:"):
event_name = line[len("event:"):].strip()
elif line.startswith("data:"):
data_lines.append(line[len("data:"):].lstrip())
await _handle_event()
if partial_count:
print(f"[o1key GPT Image] 流式中间图 {partial_count} 张（已忽略，仅输出最终图）")
if not images:
raise RuntimeError("流式响应结束，但未收到最终图片")
return images
async def _parse_success_response(
self,
resp: aiohttp.ClientResponse,
session: aiohttp.ClientSession,
label: str = "",
) -> List[Image.Image]:
content_type = resp.headers.get("Content-Type", "").lower()
if "event-stream" in content_type:
return await self._parse_edit_stream_response(resp, session)
text = await resp.text()
stripped = text.lstrip()
if stripped.startswith("data:") or stripped.startswith("event:"):
return await self._parse_stream_text_response(text, session)
try:
resp_json = json.loads(text)
except Exception:
raise RuntimeError(f"响应 JSON 解析失败，原始内容：{text[:500]}")
return await self._parse_response(resp_json, session)
# ── 中断轮询 ──────────────────────────────────────────────────────────────
@staticmethod
@@ -311,13 +525,15 @@ class GptImageClient:
"quality": quality,
"n": n,
"moderation": "low",
"partial_images": 0,
}
body["size"] = size if size else "auto"
# 图生图：将 tensor 列表转成 data URI 内联
image_files = []
# 图生图：multipart 方式上传参考图
if image_list is not None:
data_urls = []
for idx_img, img_tensor in enumerate(image_list):
pil_images = tensor_to_pil(img_tensor)
img = pil_images[0]
@@ -333,10 +549,8 @@ class GptImageClient:
png_budget = int(per_image_budget * 3 / 4)
label = f"第{idx_img + 1}张" if len(image_list) > 1 else ""
png_bytes = self._shrink_png_to_limit(png_bytes, png_budget, label)
b64 = base64.b64encode(png_bytes).decode("utf-8")
data_urls.append(f"data:image/png;base64,{b64}")
body["image"] = data_urls[0] if len(data_urls) == 1 else data_urls
mode = f"图生图（参考图 {len(data_urls)} 张）"
image_files.append(png_bytes)
mode = f"图生图（参考图 {len(image_files)} 张）"
else:
mode = "文生图"
@@ -347,25 +561,40 @@ class GptImageClient:
connector = aiohttp.TCPConnector(ssl=False, force_close=True)
timeout = aiohttp.ClientTimeout(total=_REQUEST_TIMEOUT)
def _build_multipart_form() -> aiohttp.FormData:
form = self._new_multipart_form()
self._add_form_fields(form, body)
image_field = "image[]" if len(image_files) > 1 else "image"
for idx_img, png_bytes in enumerate(image_files):
form.add_field(
image_field,
png_bytes,
filename=f"image_{idx_img + 1}.png",
content_type="image/png",
)
return form
async def _do_request():
async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
last_status = None
for attempt in range(DEFAULT_MAX_RETRIES + 1):
t0 = time.time()
async with session.post(url, json=body, headers=self._json_headers()) as resp:
async with session.post(
url,
data=_build_multipart_form(),
headers=self._auth_headers(),
) as resp:
elapsed = time.time() - t0
text = await resp.text()
if resp.status != 200:
last_status = resp.status
text = await resp.text()
if resp.status in RETRYABLE_STATUS_CODES and attempt < DEFAULT_MAX_RETRIES:
friendly = HTTP_ERROR_MESSAGES.get(resp.status)
friendly = get_friendly_message(resp.status)
delay = _compute_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR)
print(f"[o1key GPT Image] {friendly} {delay:.1f}s 后重试 ({attempt+1}/{DEFAULT_MAX_RETRIES})...")
await asyncio.sleep(delay)
continue
if resp.status in _GPT_ERROR_MESSAGES:
raise RuntimeError(_GPT_ERROR_MESSAGES[resp.status])
if resp.status in HTTP_ERROR_MESSAGES:
raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
try:
@@ -380,13 +609,8 @@ class GptImageClient:
msg = text
raise RuntimeError(get_friendly_message(resp.status, msg))
try:
resp_json = json.loads(text)
except Exception:
raise RuntimeError(f"响应 JSON 解析失败，原始内容：{text[:500]}")
print(f"[o1key GPT Image] API 响应耗时 {elapsed:.1f}s")
return await self._parse_response(resp_json, session)
print(f"[o1key GPT Image] API 响应耗时 {elapsed:.1f}s")
return await self._parse_success_response(resp, session, "GENERATIONS")
if last_status and last_status in HTTP_ERROR_MESSAGES:
raise RuntimeError(HTTP_ERROR_MESSAGES[last_status])
@@ -394,7 +618,7 @@ class GptImageClient:
return await self._run_with_interrupt(_do_request())
# ── 图像编辑（edits 接口，multipart/form-data）──────────────────────────
# ── 图像编辑（edits 接口，multipart/form-data）──────────────────────
async def _edit_async(
self,
@@ -408,7 +632,7 @@ class GptImageClient:
mask_tensor: Optional[torch.Tensor] = None,
) -> List[Image.Image]:
"""
调用 /v1/images/edits/ 接口（multipart/form-data）。
调用 /v1/images/edits 接口（multipart/form-data）。
"""
# 模型名映射：UI 显示名 → API 参数名
api_model = _MODEL_NAME_MAP.get(model, model)
@@ -421,15 +645,9 @@ class GptImageClient:
normalized_tensors.append(t)
num_images = len(normalized_tensors)
form = aiohttp.FormData()
form.add_field("model", api_model)
form.add_field("prompt", prompt)
form.add_field("n", str(n))
form.add_field("quality", quality)
image_files = []
form.add_field("size", size if size else "auto")
# 多图：用 image[] 数组字段逐张附加，支持 gpt-image-1.5 最多 16 张
# 多图：用 multipart image/image[] 字段逐张上传
# 预算：20MB 按图数平摊，蒙版预留 1MB
mask_reserve = 1024 * 1024 if mask_tensor is not None else 0
per_image_budget = max(
@@ -440,29 +658,51 @@ class GptImageClient:
img_bytes = self._tensor_to_png_bytes(frame)
label = f"第{i + 1}张" if num_images > 1 else ""
img_bytes = self._shrink_png_to_limit(img_bytes, per_image_budget, label)
form.add_field(
"image[]",
img_bytes,
filename=f"image_{i}.png",
content_type="image/png",
)
image_files.append(img_bytes)
# 蒙版尺寸校验以第一张图为基准
first_tensor = normalized_tensors[0]
ih, iw = first_tensor.shape[1], first_tensor.shape[2]
mask_png = None
if mask_tensor is not None:
mask_png = self._mask_tensor_to_rgba_png_bytes(mask_tensor, (ih, iw))
form.add_field(
"mask",
mask_png,
filename="mask.png",
content_type="image/png",
)
mode = "图像编辑（带蒙版）"
else:
mode = "图像编辑（无蒙版）"
form_fields = {
"model": api_model,
"prompt": prompt,
"partial_images": 0,
"n": n,
"quality": quality,
"size": size if size else "auto",
"output_format": "png",
"background": "opaque",
"moderation": "low",
}
def _build_multipart_form() -> aiohttp.FormData:
form = self._new_multipart_form()
self._add_form_fields(form, form_fields)
for idx_img, img_bytes in enumerate(image_files):
form.add_field(
"image[]",
img_bytes,
filename=f"image_{idx_img + 1}.png",
content_type="image/png",
)
if mask_png is not None:
form.add_field(
"mask",
mask_png,
filename="mask.png",
content_type="image/png",
)
return form
url = f"{self.base_url}{_ENDPOINT_EDITS}"
print(f"[o1key GPT Image] {mode} | 模型={model} | 参考图={num_images}张 | "
f"quality={quality} | size={size} | n={n}")
@@ -472,39 +712,45 @@ class GptImageClient:
async def _do_request():
async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
t0 = time.time()
async with session.post(
url,
data=form,
headers=self._auth_headers(),
) as resp:
elapsed = time.time() - t0
text = await resp.text()
last_status = None
for attempt in range(DEFAULT_MAX_RETRIES + 1):
t0 = time.time()
async with session.post(
url,
data=_build_multipart_form(),
headers=self._auth_headers(),
) as resp:
elapsed = time.time() - t0
if resp.status != 200:
if resp.status in _GPT_ERROR_MESSAGES:
raise RuntimeError(_GPT_ERROR_MESSAGES[resp.status])
if resp.status in HTTP_ERROR_MESSAGES:
raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
try:
err_json = json.loads(text)
err_obj = err_json.get("error", {})
msg = (
err_obj.get("message") or err_obj.get("msg") or text
if isinstance(err_obj, dict)
else str(err_obj) or text
)
except Exception:
msg = text
raise RuntimeError(get_friendly_message(resp.status, msg))
if resp.status != 200:
text = await resp.text()
last_status = resp.status
if resp.status in RETRYABLE_STATUS_CODES and attempt < DEFAULT_MAX_RETRIES:
friendly = get_friendly_message(resp.status)
delay = _compute_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR)
print(f"[o1key GPT Image] {friendly} retrying in {delay:.1f}s ({attempt+1}/{DEFAULT_MAX_RETRIES})...")
await asyncio.sleep(delay)
continue
if resp.status in HTTP_ERROR_MESSAGES:
raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
try:
err_json = json.loads(text)
err_obj = err_json.get("error", {})
msg = (
err_obj.get("message") or err_obj.get("msg") or text
if isinstance(err_obj, dict)
else str(err_obj) or text
)
except Exception:
msg = text
raise RuntimeError(get_friendly_message(resp.status, msg))
try:
resp_json = json.loads(text)
except Exception:
raise RuntimeError(f"响应 JSON 解析失败，原始内容：{text[:500]}")
print(f"[o1key GPT Image] API 响应耗时 {elapsed:.1f}s")
return await self._parse_success_response(resp, session, "EDITS")
print(f"[o1key GPT Image] API 响应耗时 {elapsed:.1f}s")
return await self._parse_response(resp_json, session)
if last_status and last_status in HTTP_ERROR_MESSAGES:
raise RuntimeError(HTTP_ERROR_MESSAGES[last_status])
raise RuntimeError(f"Request failed after {DEFAULT_MAX_RETRIES} retries")
return await self._run_with_interrupt(_do_request())
@@ -525,7 +771,7 @@ class GptImageClient:
同步入口，在独立线程中运行事件循环，避免与 ComfyUI 主循环冲突。
路由逻辑：
- 无 image_tensor → generations 接口（文生图，JSON body）
- 无 image_tensor → generations 接口（文生图，multipart/form-data）
- 有 image_tensor → edits 接口（图生图/编辑，multipart/form-data）
"""
use_edits = (image_tensor is not None)