Deploy GitHub a51e6f6df2b2d5093fd2526a7953c2ee6a422e37

.gitignore

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+__pycache__/
+*.py[cod]
+.pytest_cache/
+.coverage
+.venv/
+.env
+local/
+
+_vendor/
+custom_nodes/
+models/
+input/
+output/
+ComfyUI_temp/
+
+*.log
+.DS_Store

LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
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+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

NOTICE.md

@@ -0,0 +1,24 @@
+# 来源与修改声明
+
+本仓库是下列 GPL-3.0 项目的修改与整合版本：
+
+- RioShiina/ImageGen，核对版本：`3622e14a8b6587699eb3e0616c167d0c35649ca7`
+- Dekonstruktio/Fluxus，核对版本：`4aaca1f35ace66be1fb73de9bc46d390501f4ef6`
+- 两者共同祖先：`9dbb7e3b34150ab09eaa0b83b92c7ea7e0493860`
+
+Fluxus 在共同祖先之后只改动 README、MCP 启用开关和界面品牌文案，未形成独立推理引擎。本修改版因此以 ImageGen 为主干，并保留 Fluxus 的 MCP 关闭选项以及 `run_imagegen`、`get_chain_schema` 兼容接口。
+
+主要修改包括：
+
+- 将五套重复 Gradio 页面重构为一个动态工作台；
+- 新增中文界面、任务说明、模型语言提示与快捷模型方案；
+- 调整模型切换语义，避免覆盖用户 Prompt；
+- 为 UI、Gradio API 与 MCP 增加共享的有界 GPU 调度；
+- 将 MCP 裸线程改为有界执行器，并为任务表增加锁和容量限制；
+- 将临时文件改为 UUID，并为下载和共享目录写入增加进程锁；
+- 固定 ComfyUI/custom nodes commit，并与应用源码隔离；
+- 避免重复执行 ComfyUI SaveImage；
+- 增加输入/输出像素、批量和 URL 图片约束；
+- 增加测试、中文部署文档和 Fluxus 接口兼容层。
+
+本仓库保留原始 `LICENSE`，整体按 GPL-3.0 分发。这里列出的模型仅在运行时按需下载，其许可证与使用限制由各模型作者决定。ComfyUI、custom nodes、SageAttention 及其他第三方依赖也继续适用各自许可证；本声明不改变这些许可。

README.md

@@ -1,13 +1,343 @@
 ---
-title: ImageGen Studio
-emoji: 📊
-colorFrom: pink
-colorTo: blue
+title: ImageGen Studio 中文版
+emoji: 🖼
+colorFrom: indigo
+colorTo: purple
 sdk: gradio
-sdk_version: 6.23.1
-python_version: '3.12'
+sdk_version: "5.50.0"
 app_file: app.py
-pinned: false
+python_version: "3.12.12"
+startup_duration_timeout: 1h
+short_description: 中文优先的多任务图片生成与编辑工作台
+license: gpl-3.0
+pinned: true
+models:
+# This Space supports a wide variety of image generation pipelines. To maintain transparency, credit the original creators, and help users explore the Hugging Face ecosystem, we list and link several types of models in our metadata:
+# 1. **Directly Run Models:** Models and checkpoints actively loaded by our pipelines (configured via `yaml/file_list.yaml`).
+# 2. **Upstream Base Models:** The original foundation architectures from which our optimized ports, quantized versions, or wrappers are derived.
+  # Directly Run Models
+  - AiAF/Illustrious-XL-v0.1.safetensors
+  - alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1
+  - black-forest-labs/FLUX.1-Redux-dev
+  - black-forest-labs/FLUX.2-dev-NVFP4
+  - black-forest-labs/FLUX.2-klein-4b-nvfp4
+  - black-forest-labs/FLUX.2-klein-9b-nvfp4
+  - black-forest-labs/FLUX.2-klein-9b-kv-fp8
+  - black-forest-labs/FLUX.2-klein-base-4b-nvfp4
+  - black-forest-labs/FLUX.2-klein-base-9b-nvfp4
+  - bluepen5805/4nima_pencil-XL
+  - bluepen5805/anima-models
+  - bluepen5805/anima_pencil-XL
+  - bluepen5805/blue_pencil-XL
+  - bluepen5805/illustrious_pencil-XL
+  - bluepen5805/mellow_pencil-XL
+  - bluepen5805/noob_v_pencil-XL
+  - bluepen5805/pony_pencil-XL
+  - cagliostrolab/animagine-xl-3.1
+  - cagliostrolab/animagine-xl-4.0
+  - ChenkinNoob/ChenkinNoob-XL-V0.5
+  - circlestone-labs/Anima
+  - Clybius/Chroma-fp8-scaled
+  - comfyanonymous/ControlNet-v1-1_fp16_safetensors
+  - comfyanonymous/cosmos_1.0_text_encoder_and_VAE_ComfyUI
+  - comfyanonymous/flux_text_encoders
+  - Comfy-Org/Anima-LLLite
+  - Comfy-Org/Boogu-Image
+  - Comfy-Org/ERNIE-Image
+  - Comfy-Org/FLUX.1-Krea-dev_ComfyUI
+  - Comfy-Org/flux2-dev
+  - Comfy-Org/HiDream-I1_ComfyUI
+  - Comfy-Org/HiDream-O1-Image
+  - Comfy-Org/HunyuanImage_2.1_ComfyUI
+  - Comfy-Org/Ideogram-4
+  - Comfy-Org/Krea-2
+  - Comfy-Org/Lens
+  - Comfy-Org/LongCat-Image
+  - Comfy-Org/Lumina_Image_2.0_Repackaged
+  - Comfy-Org/Mage-Flow
+  - Comfy-Org/NewBie-image-Exp0.1_repackaged
+  - Comfy-Org/Omnigen2_ComfyUI_repackaged
+  - Comfy-Org/Ovis-Image
+  - Comfy-Org/PixelDiT
+  - Comfy-Org/Qwen-Image_ComfyUI
+  - Comfy-Org/Qwen-Image-Edit_ComfyUI
+  - Comfy-Org/sigclip_vision_384
+  - Comfy-Org/stable-diffusion-3.5-fp8
+  - Comfy-Org/vae-text-encorder-for-flux-klein-4b
+  - Comfy-Org/vae-text-encorder-for-flux-klein-9b
+  - Comfy-Org/Wan_2.1_ComfyUI_repackaged
+  - Comfy-Org/z_image
+  - Comfy-Org/z_image_turbo
+  - conradlocke/krea2-identity-edit
+  - cyberdelia/CyberRealisticPony
+  - diffusionmodels1254ani/hassakuAnima
+  - diffusionmodels1254ani/kirazuriAnima_v30AnimaBase1
+  - diffusionmodels1254ani/waiANIMA
+  - duongve/AnimaYume
+  - Eugeoter/noob-sdxl-controlnet-canny
+  - Eugeoter/noob-sdxl-controlnet-depth
+  - Eugeoter/noob-sdxl-controlnet-lineart_anime
+  - Eugeoter/noob-sdxl-controlnet-lineart_realistic
+  - Eugeoter/noob-sdxl-controlnet-manga_line
+  - Eugeoter/noob-sdxl-controlnet-normal
+  - Eugeoter/noob-sdxl-controlnet-softedge_hed
+  - Eugeoter/noob-sdxl-controlnet-tile
+  - fal/AuraFlow-v0.3
+  - frankjoshua/novaAnimeXL_ilV180
+  - h94/IP-Adapter
+  - h94/IP-Adapter-FaceID
+  - InstantX/FLUX.1-dev-IP-Adapter
+  - InstantX/Qwen-Image-ControlNet-Inpainting
+  - InstantX/Qwen-Image-ControlNet-Union
+  - InstantX/SD3.5-Large-IP-Adapter
+  - jdopensource/JoyAI-Image-Edit-ComfyUI
+  - jdopensource/JoyAI-Image-Edit-Plus-ComfyUI
+  - kandinskylab/Kandinsky-5.0-T2I-Lite
+  - Kijai/flux-fp8
+  - Laxhar/noob_openpose
+  - Laxhar/noobai-XL-1.1
+  - Laxhar/noobai-XL-Vpred-1.0
+  - licyk/sd_control_collection
+  - lightx2v/Qwen-Image-2512-Lightning
+  - lightx2v/Qwen-Image-Edit-2511-Lightning
+  - LyliaEngine/Pony_Diffusion_V6_XL
+  - MIC-Lab/illustriousXLv0.1_controlnet
+  - MIC-Lab/illustriousXLv1.1_controlnet
+  - misri/hassakuXLIllustrious_v30
+  - nvidia/Cosmos-Predict2-2B-Text2Image
+  - nvidia/Cosmos-Predict2-14B-Text2Image
+  - OnomaAIResearch/Illustrious-XL-v1.0
+  - OnomaAIResearch/Illustrious-XL-v1.1
+  - OnomaAIResearch/Illustrious-XL-v2.0
+  - ostris/krea2_turbo_style_reference
+  - Patil/Krea-2-depth-controlnet
+  - RedRayz/hikari_noob_v-pred_1.2.4
+  - Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0
+  - silveroxides/Chroma1-Radiance-fp8-scaled
+  - stabilityai/sdxl-turbo
+  - stabilityai/stable-diffusion-3.5-controlnets
+  - stabilityai/stable-diffusion-xl-base-1.0
+  - stable-diffusion-v1-5/stable-diffusion-v1-5
+  - tsukiyomi/krea2_raw-nvfp4
+  - Wenaka/NoobAI_XL_Inpainting_ControlNet_Full
+  - xinsir/anime-painter
+  - xinsir/controlnet-canny-sdxl-1.0
+  - xinsir/controlnet-depth-sdxl-1.0
+  - xinsir/controlnet-openpose-sdxl-1.0
+  - xinsir/controlnet-scribble-sdxl-1.0
+  - xinsir/controlnet-tile-sdxl-1.0
+  - xinsir/controlnet-union-sdxl-1.0
+  - XLabs-AI/flux-controlnet-collections
+  - zhenshipo/waiIllustriousSDXL_v170
+  # Upstream Base Models
+  - AIDC-AI/Ovis-Image-7B
+  - Alpha-VLLM/Lumina-Image-2.0
+  - baidu/ERNIE-Image
+  - baidu/ERNIE-Image-Turbo
+  - black-forest-labs/FLUX.1-dev
+  - black-forest-labs/FLUX.1-Krea-dev
+  - black-forest-labs/FLUX.1-schnell
+  - Boogu/Boogu-Image-0.1-Turbo
+  - Boogu/Boogu-Image-0.1-Base
+  - Boogu/Boogu-Image-0.1-Edit
+  - Boogu/Boogu-Image-0.1-Edit-Turbo
+  - HiDream-ai/HiDream-I1-Dev
+  - HiDream-ai/HiDream-I1-Fast
+  - HiDream-ai/HiDream-I1-Full
+  - HiDream-ai/HiDream-O1-Image
+  - HiDream-ai/HiDream-O1-Image-Dev
+  - ideogram-ai/ideogram-4-fp8
+  - kohya-ss/Anima-LLLite
+  - krea/Krea-2-Raw
+  - krea/Krea-2-Turbo
+  - lodestones/Chroma1-HD
+  - lodestones/Chroma1-Radiance
+  - mage-flow-community/Mage-Flow
+  - mage-flow-community/Mage-Flow-Base
+  - mage-flow-community/Mage-Flow-Edit
+  - mage-flow-community/Mage-Flow-Edit-Base
+  - mage-flow-community/Mage-Flow-Edit-Turbo
+  - mage-flow-community/Mage-Flow-Turbo
+  - meituan-longcat/LongCat-Image
+  - microsoft/Lens
+  - microsoft/Lens-Turbo
+  - NewBie-AI/NewBie-image-Exp0.1
+  - nvidia/PiD
+  - nvidia/PixelDiT-1300M-1024px
+  - OmniGen2/OmniGen2
+  - Qwen/Qwen-Image
+  - Qwen/Qwen-Image-2512
+  - Qwen/Qwen-Image-Edit
+  - Qwen/Qwen-Image-Edit-2509
+  - Qwen/Qwen-Image-Edit-2511
+  - stabilityai/stable-diffusion-3.5-large
+  - stabilityai/stable-diffusion-3.5-medium
+  - tencent/HunyuanImage-2.1
+  - Tongyi-MAI/Z-Image
+  - Tongyi-MAI/Z-Image-Turbo
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ImageGen Studio 中文版
+
+这是 [RioShiina/ImageGen](https://huggingface.co/spaces/RioShiina/ImageGen) 与 [Dekonstruktio/Fluxus](https://huggingface.co/spaces/Dekonstruktio/Fluxus) 的整合重构版，保留 YAML 配方、动态工作流和 92 个模型目录，重做任务切换、并发调度、中文指导与移动端体验。
+
+Fluxus 与 ImageGen 共享同一 Git 历史，Fluxus 当前版本只调整了品牌文案和 MCP 开关，没有第二套推理核心。因此本项目使用一个 ImageGen 引擎，并兼容两边的 API 契约，避免双后端、双状态和重复下载。
+
+## 主要改进
+
+| 项目 | 原版 | 本整合版 |
+|---|---|---|
+| 任务界面 | 5 个完整 Tab，重复创建全部高级控件 | 1 个共享工作台，动态切换 5 类任务 |
+| 任务切换 | 模型、Prompt 和素材分散在各 Tab | 保留模型、Prompt、参数与上传素材 |
+| 模型切换 | 会覆盖正负 Prompt 和推荐参数 | 保留用户 Prompt；推荐采样参数可自动跟随、关闭或一键恢复 |
+| 复杂度 | 约 4,033 组件 / 506 事件 | 烟测为 881 组件 / 120 事件 |
+| GPU 并发 | UI 与 MCP 可绕过彼此并发执行 | 各入口有界，最终汇合到公平的单 GPU 闸门 |
+| 异步 MCP | 每次请求创建无上限 daemon 线程 | 有界线程池、队列满错误、任务表上限与锁 |
+| 临时文件 | 4 位随机后缀，可能碰撞 | UUID 文件名 |
+| 启动依赖 | 运行时拉取最新版并覆盖项目根 | 固定 commit，隔离在 `_vendor/ComfyUI` |
+| 中文体验 | 英文界面，无模型语言提示 | 中文任务指导、模型用途说明、可操作错误信息 |
+| MCP 兼容 | ImageGen 与 Fluxus 接口名不同 | 同时保留新版接口与两个 Fluxus 旧别名 |
+
+## 支持能力
+
+- 任务：文生图、图生图、局部重绘、扩图、高清修复。
+- 模型：由 `yaml/model_list.yaml` 驱动，当前包含 30 类架构、92 个显示模型。
+- 扩展：LoRA、ControlNet、IP-Adapter、Embedding、区域提示、多图编辑、VAE、PiD 等。
+- 输出：PNG 内写入生成参数和完整 ComfyUI 工作流元数据。
+- 中文 Prompt：默认原样传递，不做隐式翻译；模型说明会区分中文自然语言和英文标签型模型。
+
+## 批量、多图与模型 PK
+
+这些能力只增加一个有上限的顺序编排层，不创建第二套推理引擎，也不会让多个大模型同时占用显存：
+
+| 运行方式 | 输入语义 | 输出语义 |
+|---|---|---|
+| 普通生成 | 当前任务的一组输入 | 1–4 张同组变体 |
+| 模型 PK | 同一 Prompt、尺寸、源图与已解析 Seed | 每个模型分别生成；默认最多 2 个模型 |
+| 多图独立 | A、B、C 是互不相关的源图 | A→A′、B→B′、C→C′ |
+| 多图 × 多模型 | 多张独立源图和两个模型 | 按模型分组顺序执行图片×模型组合 |
+| 多图融合 | 多张图共同作为一组参考 | 由兼容的编辑模型融合为 1–4 张结果 |
+
+- 多图独立目前支持图生图、扩图和高清修复；局部重绘需要逐张图片配对蒙版，暂不做批量。
+- 多图融合内部固定走“文生图 + 模型专属参考链”，不会与图生图 base latent 混用。Prompt 可按“参考图 1 / 2 / 3”说明各图角色。
+- 多图融合是生成式参考，不保证无损拼接、角色逐像素保留��确定性元素替换。
+- PK 默认采用各模型推荐采样参数；关闭后才严格复用当前步数、CFG、采样器和调度器。同一 Seed 在不同架构之间只是尽量控制变量，不表示初始噪声数学等价。
+- PK V1 会关闭 LoRA、ControlNet、IP-Adapter、参考链、自定义 VAE 和 PiD，只比较所有基础 checkpoint 都具备的 Prompt / 源图能力，避免某个模型偷偷多一层条件。
+- 单进程始终一次只执行一个 GPU 任务。模型文件按需缓存在磁盘；只在 PK 的实际模型边界和 GPU 异常后释放 ComfyUI 模型状态。
+- 默认上限为 2 个 PK 模型、4 张输入图、4 个顺序任务、8 张预计输出。某一组合失败时保留已成功结果；取消会停止尚未开始的后续组合。
+
+## 部署到 Hugging Face Space
+
+1. 新建 Gradio Space，硬件选择 ZeroGPU；README metadata 已固定 Python 3.12.12，并把冷启动上限设为 1 小时。
+2. 上传本仓库内容；Space 会按 `requirements.txt` 安装 Gradio 5.50 和 MCP 额外依赖。
+3. 按需要设置 Secret：
+   - `HF_TOKEN`：访问 gated/private Hugging Face 模型。
+   - `CIVITAI_API_KEY`：下载需要授权的 Civitai 资源。
+4. 首次启动会按 `vendor.lock.yaml` 拉取固定版本的 ComfyUI 与 5 个 custom nodes；首次使用某个模型时才下载其权重。
+
+部署边界：
+
+- 模型目录包含 92 个可选项，但这不等于 92 个模型可同时驻留。ZeroGPU 应使用按需磁盘缓存 + 单模型顺序执行。
+- Space 默认磁盘是临时盘，重启/重建后缓存可能消失。若需要长期保留模型，可把 Hugging Face Storage Bucket 挂载到 `/home/user/app/models`，并设置 `HF_HOME=/home/user/app/models/.hf-cache`，让权重实体和项目符号链接位于同一持久卷。
+- 下载前会读取远端文件大小并保留默认 3 GB 安全余量；空间不足时会在下载前拒绝，而不是写满磁盘。项目不自动删除 Hub cache，避免误删仍被符号链接引用的模型。
+- FLUX、SD3.5、Cosmos 等部分资源可能受访问条款限制。`HF_TOKEN` 所属账号必须先在对应模型页接受条款。
+- 公共 Space 建议精选 4–8 个常用模型，并设置 `IMAGEGEN_MAX_BATCH_SIZE=2`；完整 92 模型目录更适合挂载持久存储的专用部署。
+- ZeroGPU 单次 GPU 申请上限为 120 秒。大模型首次装载、超高分辨率或长采样仍可能超时；PK 会为每个模型分别申请 GPU，而不是占用一个超长租约。
+
+完整的适配状态、推荐变量与上线实测清单见 [`docs/HF_SPACE_DEPLOYMENT.md`](docs/HF_SPACE_DEPLOYMENT.md)。
+
+默认启动命令由 Space metadata 执行：
+
+```bash
+python app.py
+```
+
+## 本地运行
+
+需要 Python 3.12、Git，以及与目标模型相匹配的 NVIDIA GPU 环境：
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+python app.py
+```
+
+如已有 ComfyUI，可避免再次克隆：
+
+```bash
+COMFYUI_PATH=/absolute/path/to/ComfyUI python app.py
+```
+
+## 运行参数
+
+| 环境变量 | 默认值 | 说明 |
+|---|---:|---|
+| `IMAGEGEN_GPU_CONCURRENCY` | 固定 `1` | 为兼容旧部署保留名称；单进程始终串行，扩容请增加独立副本 |
+| `IMAGEGEN_QUEUE_MAX_SIZE` | `24` | Gradio 等待队列上限 |
+| `IMAGEGEN_MCP_MAX_PENDING` | `16` | MCP 后台任务上限 |
+| `IMAGEGEN_MCP_TASK_RETENTION` | `200` | 内存任务记录上限 |
+| `IMAGEGEN_MAX_BATCH_SIZE` | `4` | 单次最大图片数 |
+| `IMAGEGEN_MAX_PK_MODELS` | `2` | PK 模型总数上限（含当前模型） |
+| `IMAGEGEN_MAX_MULTI_IMAGES` | `4` | 批量输入/参考图的全局上传上限 |
+| `IMAGEGEN_MAX_PLAN_JOBS` | `4` | 一次提交可展开的顺序任务上限 |
+| `IMAGEGEN_MAX_PLAN_OUTPUTS` | `8` | 一次提交的预计输出总数上限 |
+| `IMAGEGEN_MAX_INPUT_MEGAPIXELS` | `4.2` | 输入图片/文生图画布上限 |
+| `IMAGEGEN_MAX_REFERENCE_MEGAPIXELS` | `12` | 一组上传图片的累计像素上限 |
+| `IMAGEGEN_MAX_REFERENCE_IMAGES` | `10` | 一次任务中全部参考图/控制图的合计上限 |
+| `IMAGEGEN_MAX_OUTPUT_MEGAPIXELS` | `16` | 扩图和高清修复预计输出上限 |
+| `IMAGEGEN_MIN_FREE_DISK_GB` | `3` | 模型下载后必须保留的磁盘余量 |
+| `IMAGEGEN_OUTPUT_RETENTION` | `80` | 本地保留的生成 PNG 数量 |
+| `IMAGEGEN_ENABLE_MCP` | `true` | 是否启动 MCP；设为 `false` 即采用 Fluxus 的关闭方式 |
+| `IMAGEGEN_STARTUP_GPU_PROBE` | `false` | 是否在启动时申请一次 GPU |
+| `IMAGEGEN_USE_SAGE_ATTENTION` | `auto` | `auto` / `true` / `false` |
+| `IMAGEGEN_SKIP_CUSTOM_NODES` | `false` | 本地调试时跳过 custom nodes 拉取 |
+| `IMAGEGEN_GIT_TIMEOUT_SECONDS` | `180` | 单次 Git clone/fetch 超时 |
+| `IMAGEGEN_GIT_ATTEMPTS` | `2` | Git 网络操作尝试次数 |
+
+## MCP / Gradio API
+
+公开 7 个主接口：
+
+- `get_task_list`
+- `get_model_architecture_list`
+- `get_model_list`
+- `get_feature_list`
+- `get_model_features`
+- `run`
+- `get_task_status`
+
+同时保留 Fluxus 客��端兼容别名：
+
+- `run_imagegen(json_params)` → `run(json_params)`
+- `get_chain_schema(chain_type)` → 返回单个扩展能力的完整 schema
+
+`get_feature_list()` 空参默认返回 Fluxus 兼容的完整 schema；如需节省 MCP token，传 `compact=true` 获取摘要。也可以传 `feature_name`，或使用 `get_chain_schema(chain_type)` 查询单项完整 schema。
+
+所有 UI 原子事件均隐藏，不作为公共 API 暴露。
+
+## 验证
+
+```bash
+python -m compileall -q .
+python -m unittest discover -s tests -v
+```
+
+界面烟测会以 stub ComfyUI 构建完整 Gradio 配置，因此不需要下载模型或占用 GPU。
+
+## 目录
+
+```text
+app.py                     # Space 入口与有界 Gradio 队列
+core/                      # 生成管线、工作流装配、统一调度
+ui/shared/studio_ui.py     # 单一动态工作台
+ui/events/                 # 切换、模型、扩展与生成事件
+mcp_tools/                 # 高层 API、兼容别名、任务状态
+yaml/                      # 模型、能力、默认参数与配方注册表
+vendor.lock.yaml           # ComfyUI / custom nodes 固定版本
+tests/                     # 并发、注册表、MCP 与 UI 烟测
+```
+
+## 许可与来源
+
+本项目是 ImageGen 的修改版，整体继续采用 GPL-3.0，并保留上游署名。Fluxus 也是同一 GPL-3.0 代码谱系。模型权重、ComfyUI 与 custom nodes 各自受其上游许可证约束；本仓库不重新分发模型权重。具体版本与修改声明见 `NOTICE.md`。

app.py

@@ -0,0 +1,127 @@
+import spaces
+import importlib.util
+import os
+import sys
+import site
+
+from core.runtime_config import CONFIG
+
+sage_mode = os.getenv("IMAGEGEN_USE_SAGE_ATTENTION", "auto").strip().lower()
+sage_available = importlib.util.find_spec("sageattention") is not None
+use_sage_attention = sage_mode in {"1", "true", "yes", "on"} or (
+    sage_mode == "auto" and sage_available
+)
+if use_sage_attention and "--use-sage-attention" not in sys.argv:
+    sys.argv.append("--use-sage-attention")
+    print("🚀 [SageAttention] Injected '--use-sage-attention' into sys.argv.")
+
+APP_DIR = os.path.dirname(os.path.abspath(__file__))
+if APP_DIR not in sys.path:
+    sys.path.insert(0, APP_DIR)
+    print(f"✅ Added project root '{APP_DIR}' to sys.path.")
+
+# Keep ComfyUI code isolated while pointing its model/input/output directories
+# at this Space. These arguments are consumed when ComfyUI is imported.
+if "--base-directory" not in sys.argv:
+    sys.argv.extend(["--base-directory", APP_DIR])
+
+SAGE_PATCH_APPLIED = False
+
+def apply_sage_attention_patch():
+    global SAGE_PATCH_APPLIED
+    if SAGE_PATCH_APPLIED:
+        return "SageAttention patch already applied."
+    if not use_sage_attention:
+        return "SageAttention disabled or unavailable; using the default attention backend."
+
+    try:
+        from comfy import model_management
+        import sageattention
+
+        print("--- [Runtime Patch] sageattention package found. Applying patch... ---")
+        model_management.sage_attention_enabled = lambda: True
+        model_management.pytorch_attention_enabled = lambda: False
+
+        SAGE_PATCH_APPLIED = True
+        return "✅ Successfully enabled SageAttention."
+    except ImportError:
+        SAGE_PATCH_APPLIED = False
+        msg = "--- [Runtime Patch] ⚠️ sageattention package not found. Continuing with default attention. ---"
+        print(msg)
+        return msg
+    except Exception as e:
+        SAGE_PATCH_APPLIED = False
+        msg = f"--- [Runtime Patch] ❌ An error occurred while applying SageAttention patch: {e} ---"
+        print(msg)
+        return msg
+
+@spaces.GPU
+def dummy_gpu_for_startup():
+    print("--- [GPU Startup] Dummy function for startup check initiated. ---")
+    patch_result = apply_sage_attention_patch()
+    print(f"--- [GPU Startup] {patch_result} ---")
+    print("--- [GPU Startup] Startup check passed. ---")
+    return "Startup check passed."
+
+
+def main():
+    os.chdir(APP_DIR)
+    from comfy_integration import setup as setup_comfyui
+    from imagegen_utils.app_utils import load_ipadapter_presets
+
+    print("--- [Setup] Starting ComfyUI initialization ---")
+    setup_comfyui.initialize_comfyui()
+
+    print("--- [Setup] Applying SageAttention Runtime Patch ---")
+    patch_result = apply_sage_attention_patch()
+    print(f"--- [Setup] {patch_result} ---")
+
+    print("--- [Setup] Reloading site-packages to detect newly installed packages... ---")
+    try:
+        site.main()
+        print("--- [Setup] ✅ Site-packages reloaded. ---")
+    except Exception as e:
+        print(f"--- [Setup] ⚠️  Warning: Could not fully reload site-packages: {e} ---")
+
+    if CONFIG.enable_startup_gpu_probe:
+        print("--- Initiating optional GPU startup check ---")
+        try:
+            dummy_gpu_for_startup()
+        except BaseException as e:
+            err_msg = f"{type(e).__name__}: {str(e)}"
+            print(f"--- [GPU Startup] ⚠️ Warning: Startup check failed: {err_msg} ---")
+
+    print("--- Starting Application Setup ---")
+
+    print("--- Loading IPAdapter presets ---")
+    load_ipadapter_presets()
+    print("--- ✅ IPAdapter setup complete. ---")
+
+
+    print("--- Environment configured. Proceeding with module imports. ---")
+    from ui.layout import build_ui
+    from ui.events import attach_event_handlers
+    if CONFIG.enable_mcp:
+        import mcp_tools as mcp
+        print(f"✅ Loaded MCP module with tools: {[fn.__name__ for fn in mcp.MCP_FUNCTIONS]}")
+    else:
+        print("ℹ️ MCP is disabled by IMAGEGEN_ENABLE_MCP.")
+
+    print(f"✅ Working directory is stable: {os.getcwd()}")
+
+    demo = build_ui(attach_event_handlers)
+
+    print(
+        "--- Launching Gradio Interface "
+        f"(GPU concurrency={CONFIG.gpu_concurrency}, queue={CONFIG.queue_max_size}, "
+        f"MCP={CONFIG.enable_mcp}) ---"
+    )
+    demo.queue(
+        default_concurrency_limit=1,
+        max_size=CONFIG.queue_max_size,
+        status_update_rate="auto",
+    ).launch(mcp_server=CONFIG.enable_mcp)
+
+
+if __name__ == "__main__":
+    main()

chain_injectors/__init__.py

@@ -0,0 +1,50 @@
+import os
+import importlib
+import pkgutil
+
+def discover_injectors():
+    injectors = {}
+    package_dir = os.path.dirname(__file__)
+
+    for _, module_name, is_pkg in pkgutil.iter_modules([package_dir]):
+        if is_pkg or module_name.startswith('_'):
+            continue
+
+        full_module_name = f"chain_injectors.{module_name}"
+        try:
+            module = importlib.import_module(full_module_name)
+            if hasattr(module, 'inject') and callable(module.inject):
+                feature_name = getattr(module, 'FEATURE_NAME', None)
+                if not feature_name:
+                    feature_name = module_name[:-9] if module_name.endswith('_injector') else module_name
+
+                chain_type = getattr(module, 'CHAIN_TYPE', None)
+                if not chain_type:
+                    chain_type = f"dynamic_{feature_name}_chains"
+
+                injectors[chain_type] = module.inject
+            else:
+                print(f"Warning: Module '{full_module_name}' does not have a callable 'inject' function.")
+        except Exception as e:
+            print(f"Error importing injector module '{full_module_name}': {e}")
+
+    return injectors
+
+def get_registered_features():
+    features = {}
+    package_dir = os.path.dirname(__file__)
+
+    for _, module_name, is_pkg in pkgutil.iter_modules([package_dir]):
+        if is_pkg or module_name.startswith('_'):
+            continue
+
+        feature_name = module_name[:-9] if module_name.endswith('_injector') else module_name
+        full_module_name = f"chain_injectors.{module_name}"
+        chain_type = f"dynamic_{feature_name}_chains"
+
+        features[feature_name] = {
+            'module': full_module_name,
+            'chain_type': chain_type
+        }
+
+    return features

chain_injectors/anima_controlnet_lllite_injector.py

@@ -0,0 +1,53 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for Anima LLLite chain. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping.")
+        return
+
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+
+    for item_data in chain_items:
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        image_scaler_id = assembler._get_unique_id()
+        image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+        image_scaler_node['inputs']['upscale_method'] = 'nearest-exact'
+        image_scaler_node['inputs']['megapixels'] = 1.0
+        assembler.workflow[image_scaler_id] = image_scaler_node
+
+        patch_loader_id = assembler._get_unique_id()
+        patch_loader_node = assembler._get_node_template("ModelPatchLoader")
+        patch_loader_node['inputs']['name'] = item_data['control_net_name']
+        assembler.workflow[patch_loader_id] = patch_loader_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template("AnimaLLLiteApply")
+
+        apply_cn_node['inputs']['strength'] = item_data['strength']
+        apply_cn_node['inputs']['start_percent'] = item_data.get('start_percent', 0.0)
+        apply_cn_node['inputs']['end_percent'] = item_data.get('end_percent', 1.0)
+
+        apply_cn_node['inputs']['model'] = current_model_connection
+        apply_cn_node['inputs']['model_patch'] = [patch_loader_id, 0]
+        apply_cn_node['inputs']['image'] = [image_scaler_id, 0]
+
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_model_connection = [apply_cn_id, 0]
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = current_model_connection
+
+    print(f"Anima LLLite injector applied. KSampler model input re-routed through {len(chain_items)} LLLite(s).")

chain_injectors/boogu_image_edit_injector.py

@@ -0,0 +1,73 @@
+def create_node(assembler, class_type, title):
+    try:
+        node = assembler._get_node_template(class_type)
+    except Exception:
+        node = {
+            "inputs": {},
+            "class_type": class_type,
+            "_meta": {"title": title}
+        }
+    node['_meta']['title'] = title
+    return node
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    valid_images = []
+    for item in chain_items:
+        if not item:
+            continue
+        img_path = item
+        if isinstance(item, dict):
+            img_path = item.get('image') or item.get('filename') or item.get('path')
+        if img_path:
+            valid_images.append(img_path)
+
+    if not valid_images:
+        return
+
+    valid_images = valid_images[:10]
+
+    boogu_prompt_name = chain_definition.get('boogu_prompt_node', 'boogu_prompt')
+    vae_loader_name = chain_definition.get('vae_loader_node', 'vae_loader')
+
+    boogu_prompt_id = assembler.node_map.get(boogu_prompt_name)
+    if not boogu_prompt_id or boogu_prompt_id not in assembler.workflow:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict) and node.get('class_type') == 'TextEncodeBooguEdit':
+                boogu_prompt_id = node_id
+                break
+
+    if not boogu_prompt_id:
+        print(f"Warning: Target node '{boogu_prompt_name}' (TextEncodeBooguEdit) for Boogu Edit chain not found. Skipping.")
+        return
+
+    vae_id = assembler.node_map.get(vae_loader_name)
+    if not vae_id:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict) and node.get('class_type') == 'VAELoader':
+                vae_id = node_id
+                break
+
+    if vae_id:
+        assembler.workflow[boogu_prompt_id]['inputs']['vae'] = [vae_id, 0]
+
+    for i, img_filename in enumerate(valid_images):
+        load_id = assembler._get_unique_id()
+        load_node = create_node(assembler, "LoadImage", f"Load Reference Image {i+1}")
+        load_node['inputs']['image'] = img_filename
+        assembler.workflow[load_id] = load_node
+
+        scale_id = assembler._get_unique_id()
+        scale_node = create_node(assembler, "ImageScaleToTotalPixels", f"Scale Reference {i+1}")
+        scale_node['inputs']['upscale_method'] = "nearest-exact"
+        scale_node['inputs']['megapixels'] = 1
+        scale_node['inputs']['resolution_steps'] = 1
+        scale_node['inputs']['image'] = [load_id, 0]
+        assembler.workflow[scale_id] = scale_node
+
+        image_key = f"images.image_{i+1}"
+        assembler.workflow[boogu_prompt_id]['inputs'][image_key] = [scale_id, 0]
+
+    print(f"Boogu Edit injector applied with {len(valid_images)} reference image(s). Connected VAE dynamically.")

chain_injectors/conditioning_injector.py

@@ -0,0 +1,81 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+
+    target_node_id = None
+    target_input_name = None
+
+    if ksampler_name in assembler.node_map:
+        ksampler_id = assembler.node_map[ksampler_name]
+        if 'positive' in assembler.workflow[ksampler_id]['inputs']:
+            target_node_id = ksampler_id
+            target_input_name = 'positive'
+            print(f"Conditioning injector targeting KSampler node '{ksampler_name}'.")
+    else:
+        print(f"Warning: KSampler node '{ksampler_name}' for Conditioning chain not found. Skipping.")
+        return
+
+    if not target_node_id:
+        print("Warning: Conditioning chain could not find a valid injection point (KSampler may be missing 'positive' input). Skipping.")
+        return
+
+    clip_source_str = chain_definition.get('clip_source')
+    if not clip_source_str:
+        print("Warning: 'clip_source' definition missing in the recipe for the Conditioning chain. Skipping.")
+        return
+    clip_node_name, clip_idx_str = clip_source_str.split(':')
+    if clip_node_name not in assembler.node_map:
+        print(f"Warning: CLIP source node '{clip_node_name}' for Conditioning chain not found. Skipping.")
+        return
+    clip_connection = [assembler.node_map[clip_node_name], int(clip_idx_str)]
+
+    original_positive_connection = assembler.workflow[target_node_id]['inputs'][target_input_name]
+
+    area_conditioning_outputs = []
+
+    for item_data in chain_items:
+        prompt = item_data.get('prompt', '')
+        if not prompt or not prompt.strip():
+            continue
+
+        text_encode_id = assembler._get_unique_id()
+        text_encode_node = assembler._get_node_template("CLIPTextEncode")
+        text_encode_node['inputs']['text'] = prompt
+        text_encode_node['inputs']['clip'] = clip_connection
+        assembler.workflow[text_encode_id] = text_encode_node
+
+        set_area_id = assembler._get_unique_id()
+        set_area_node = assembler._get_node_template("ConditioningSetArea")
+        set_area_node['inputs']['width'] = item_data.get('width', 1024)
+        set_area_node['inputs']['height'] = item_data.get('height', 1024)
+        set_area_node['inputs']['x'] = item_data.get('x', 0)
+        set_area_node['inputs']['y'] = item_data.get('y', 0)
+        set_area_node['inputs']['strength'] = item_data.get('strength', 1.0)
+        set_area_node['inputs']['conditioning'] = [text_encode_id, 0]
+        assembler.workflow[set_area_id] = set_area_node
+
+        area_conditioning_outputs.append([set_area_id, 0])
+
+    if not area_conditioning_outputs:
+        return
+
+    current_combined_conditioning = area_conditioning_outputs[0]
+    if len(area_conditioning_outputs) > 1:
+        for i in range(1, len(area_conditioning_outputs)):
+            combine_id = assembler._get_unique_id()
+            combine_node = assembler._get_node_template("ConditioningCombine")
+            combine_node['inputs']['conditioning_1'] = current_combined_conditioning
+            combine_node['inputs']['conditioning_2'] = area_conditioning_outputs[i]
+            assembler.workflow[combine_id] = combine_node
+            current_combined_conditioning = [combine_id, 0]
+
+    final_combine_id = assembler._get_unique_id()
+    final_combine_node = assembler._get_node_template("ConditioningCombine")
+    final_combine_node['inputs']['conditioning_1'] = original_positive_connection
+    final_combine_node['inputs']['conditioning_2'] = current_combined_conditioning
+    assembler.workflow[final_combine_id] = final_combine_node
+
+    assembler.workflow[target_node_id]['inputs'][target_input_name] = [final_combine_id, 0]
+    print(f"Conditioning injector applied. Redirected '{target_input_name}' input with {len(area_conditioning_outputs)} regional prompts.")

chain_injectors/controlnet_injector.py

@@ -0,0 +1,60 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: Target node '{ksampler_name}' for ControlNet chain not found. Skipping chain injection.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'positive' not in assembler.workflow[ksampler_id]['inputs'] or \
+       'negative' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'positive' or 'negative' inputs. Skipping ControlNet chain.")
+        return
+
+    vae_source_str = chain_definition.get('vae_source')
+    if not vae_source_str:
+        print("Warning: 'vae_source' definition missing in the recipe for the ControlNet chain. Skipping.")
+        return
+    vae_node_name, vae_idx_str = vae_source_str.split(':')
+    if vae_node_name not in assembler.node_map:
+        print(f"Warning: VAE source node '{vae_node_name}' for ControlNet chain not found. Skipping.")
+        return
+    vae_connection = [assembler.node_map[vae_node_name], int(vae_idx_str)]
+
+    current_positive_connection = assembler.workflow[ksampler_id]['inputs']['positive']
+    current_negative_connection = assembler.workflow[ksampler_id]['inputs']['negative']
+
+    for item_data in chain_items:
+        cn_loader_id = assembler._get_unique_id()
+        cn_loader_node = assembler._get_node_template("ControlNetLoader")
+        cn_loader_node['inputs']['control_net_name'] = item_data['control_net_name']
+        assembler.workflow[cn_loader_id] = cn_loader_node
+
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template(chain_definition['template'])
+
+        apply_cn_node['inputs']['strength'] = item_data['strength']
+
+        apply_cn_node['inputs']['positive'] = current_positive_connection
+        apply_cn_node['inputs']['negative'] = current_negative_connection
+        apply_cn_node['inputs']['control_net'] = [cn_loader_id, 0]
+        apply_cn_node['inputs']['image'] = [image_loader_id, 0]
+        apply_cn_node['inputs']['vae'] = vae_connection
+
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_positive_connection = [apply_cn_id, 0]
+        current_negative_connection = [apply_cn_id, 1]
+
+    assembler.workflow[ksampler_id]['inputs']['positive'] = current_positive_connection
+    assembler.workflow[ksampler_id]['inputs']['negative'] = current_negative_connection
+
+    print(f"ControlNet injector applied. KSampler inputs redirected through {len(chain_items)} ControlNet nodes.")

chain_injectors/diffsynth_controlnet_injector.py

@@ -0,0 +1,75 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    model_sampler_name = chain_definition.get('model_sampler_node')
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+
+    target_node_id = None
+    target_input_name = 'model'
+
+    if model_sampler_name and model_sampler_name in assembler.node_map:
+        model_sampler_id = assembler.node_map[model_sampler_name]
+        if target_input_name in assembler.workflow[model_sampler_id]['inputs']:
+            target_node_id = model_sampler_id
+            print(f"ControlNet Model Patch injector targeting ModelSamplingAuraFlow node '{model_sampler_name}'.")
+
+    if not target_node_id:
+        if ksampler_name in assembler.node_map:
+            ksampler_id = assembler.node_map[ksampler_name]
+            if target_input_name in assembler.workflow[ksampler_id]['inputs']:
+                target_node_id = ksampler_id
+                print(f"ControlNet Model Patch injector targeting KSampler node '{ksampler_name}'.")
+        else:
+            print(f"Warning: Neither ModelSamplingAuraFlow node '{model_sampler_name}' nor KSampler node '{ksampler_name}' found for ControlNet patch chain. Skipping.")
+            return
+
+    if not target_node_id:
+        print(f"Warning: Could not find a valid 'model' input on target nodes. Skipping ControlNet patch chain.")
+        return
+
+    current_model_connection = assembler.workflow[target_node_id]['inputs'][target_input_name]
+
+    vae_source_str = chain_definition.get('vae_source')
+    vae_connection = None
+    if vae_source_str:
+        try:
+            vae_node_name, vae_idx_str = vae_source_str.split(':')
+            if vae_node_name in assembler.node_map:
+                vae_connection = [assembler.node_map[vae_node_name], int(vae_idx_str)]
+            else:
+                print(f"Warning: VAE source node '{vae_node_name}' not found for ControlNet patch chain. VAE will not be connected.")
+        except ValueError:
+            print(f"Warning: Invalid 'vae_source' format '{vae_source_str}' for ControlNet patch chain. Expected 'node_name:index'. VAE will not be connected.")
+    else:
+        print(f"Warning: 'vae_source' not defined for ControlNet patch chain definition. VAE may not be connected.")
+
+    for item_data in chain_items:
+        patch_loader_id = assembler._get_unique_id()
+        patch_loader_node = assembler._get_node_template("ModelPatchLoader")
+        patch_loader_node['inputs']['name'] = item_data['control_net_name']
+        assembler.workflow[patch_loader_id] = patch_loader_node
+
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template(chain_definition['template'])
+
+        apply_cn_node['inputs']['strength'] = item_data.get('strength', 1.0)
+        apply_cn_node['inputs']['model'] = current_model_connection
+        apply_cn_node['inputs']['model_patch'] = [patch_loader_id, 0]
+        apply_cn_node['inputs']['image'] = [image_loader_id, 0]
+
+        if 'vae' in apply_cn_node['inputs'] and vae_connection:
+            apply_cn_node['inputs']['vae'] = vae_connection
+
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_model_connection = [apply_cn_id, 0]
+
+    assembler.workflow[target_node_id]['inputs'][target_input_name] = current_model_connection
+
+    print(f"ControlNet Model Patch injector applied. Target 'model' input re-routed through {len(chain_items)} patch(es).")

chain_injectors/flux1_ipadapter_injector.py

@@ -0,0 +1,46 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for Flux1 IPAdapter chain. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping Flux1 IPAdapter chain.")
+        return
+
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+
+    for item_data in chain_items:
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        ipadapter_loader_id = assembler._get_unique_id()
+        ipadapter_loader_node = assembler._get_node_template("IPAdapterFluxLoader")
+        ipadapter_loader_node['inputs']['ipadapter'] = "ip-adapter.bin"
+        ipadapter_loader_node['inputs']['clip_vision'] = "google/siglip-so400m-patch14-384"
+        ipadapter_loader_node['inputs']['provider'] = "cuda"
+        assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+
+        apply_ipa_id = assembler._get_unique_id()
+        apply_ipa_node = assembler._get_node_template("ApplyIPAdapterFlux")
+
+        apply_ipa_node['inputs']['weight'] = item_data['weight']
+        apply_ipa_node['inputs']['start_percent'] = item_data.get('start_percent', 0.0)
+        apply_ipa_node['inputs']['end_percent'] = item_data.get('end_percent', 0.6)
+
+        apply_ipa_node['inputs']['model'] = current_model_connection
+        apply_ipa_node['inputs']['ipadapter_flux'] = [ipadapter_loader_id, 0]
+        apply_ipa_node['inputs']['image'] = [image_loader_id, 0]
+
+        assembler.workflow[apply_ipa_id] = apply_ipa_node
+        current_model_connection = [apply_ipa_id, 0]
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = current_model_connection
+    print(f"Flux1 IPAdapter injector applied. KSampler model input re-routed through {len(chain_items)} IPAdapter(s).")

chain_injectors/hidream_o1_reference_injector.py

@@ -0,0 +1,54 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for HiDream-O1 Reference chain. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'positive' not in assembler.workflow[ksampler_id]['inputs'] or 'negative' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' missing positive/negative inputs. Skipping.")
+        return
+
+    current_pos_conditioning = assembler.workflow[ksampler_id]['inputs']['positive']
+    current_neg_conditioning = assembler.workflow[ksampler_id]['inputs']['negative']
+
+    ref_images_id = assembler._get_unique_id()
+    ref_images_node = assembler._get_node_template("HiDreamO1ReferenceImages")
+
+    if 'images' in ref_images_node['inputs']:
+        del ref_images_node['inputs']['images']
+
+    ref_images_node['inputs']['positive'] = current_pos_conditioning
+    ref_images_node['inputs']['negative'] = current_neg_conditioning
+
+    for i, img_filename in enumerate(chain_items):
+        if i >= 10:
+            break
+
+        load_id = assembler._get_unique_id()
+        load_node = assembler._get_node_template("LoadImage")
+        load_node['inputs']['image'] = img_filename
+        load_node['_meta']['title'] = f"Load Reference Image {i+1}"
+        assembler.workflow[load_id] = load_node
+
+        scale_id = assembler._get_unique_id()
+        scale_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        scale_node['inputs']['megapixels'] = 1.0
+        scale_node['inputs']['upscale_method'] = "lanczos"
+        scale_node['inputs']['image'] = [load_id, 0]
+        scale_node['_meta']['title'] = f"Scale Reference {i+1}"
+        assembler.workflow[scale_id] = scale_node
+
+        ref_images_node['inputs'][f'images.image_{i+1}'] = [scale_id, 0]
+
+    assembler.workflow[ref_images_id] = ref_images_node
+
+    assembler.workflow[ksampler_id]['inputs']['positive'] = [ref_images_id, 0]
+    assembler.workflow[ksampler_id]['inputs']['negative'] = [ref_images_id, 1]
+
+    print(f"HiDream-O1 Reference injector applied. Re-routed inputs through {min(len(chain_items), 10)} reference images.")

chain_injectors/hidream_o1_smoothing_injector.py

@@ -0,0 +1,39 @@
+from copy import deepcopy
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    target_node_name = chain_definition.get('target_node')
+    if not target_node_name or target_node_name not in assembler.node_map:
+        print(f"Warning: Target node '{target_node_name}' not found for HiDream-O1 Smoothing. Skipping.")
+        return
+
+    target_node_id = assembler.node_map[target_node_name]
+
+    if 'model' not in assembler.workflow[target_node_id]['inputs']:
+        print(f"Warning: Target node '{target_node_name}' has no 'model' input. Skipping.")
+        return
+
+    current_model_connection = assembler.workflow[target_node_id]['inputs']['model']
+
+    for _ in chain_items:
+        template = assembler._get_node_template("HiDreamO1PatchSeamSmoothing")
+        node_data = deepcopy(template)
+
+        node_data['inputs']['start_percent'] = 0.8
+        node_data['inputs']['end_percent'] = 1.0
+        node_data['inputs']['pattern'] = "single_shift"
+        node_data['inputs']['passes'] = "ramp_2_4"
+        node_data['inputs']['blend'] = "median"
+        node_data['inputs']['strength'] = 1.0
+
+        node_data['inputs']['model'] = current_model_connection
+
+        new_node_id = assembler._get_unique_id()
+        assembler.workflow[new_node_id] = node_data
+
+        current_model_connection = [new_node_id, 0]
+
+    assembler.workflow[target_node_id]['inputs']['model'] = current_model_connection
+    print("HiDream-O1 Patch Seam Smoothing injector applied.")

chain_injectors/ipadapter_injector.py

@@ -0,0 +1,151 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    final_settings = {}
+    if chain_items and isinstance(chain_items[-1], dict) and chain_items[-1].get('is_final_settings'):
+        final_settings = chain_items.pop()
+
+    if not chain_items:
+        return
+
+    end_node_name = chain_definition.get('end')
+    if not end_node_name or end_node_name not in assembler.node_map:
+        print(f"Warning: Target node '{end_node_name}' for IPAdapter chain not found. Skipping chain injection.")
+        return
+
+    end_node_id = assembler.node_map[end_node_name]
+
+    if 'model' not in assembler.workflow[end_node_id]['inputs']:
+        print(f"Warning: Target node '{end_node_name}' is missing 'model' input. Skipping IPAdapter chain.")
+        return
+
+    current_model_connection = assembler.workflow[end_node_id]['inputs']['model']
+
+    model_type = final_settings.get('model_type', 'sdxl')
+    megapixels = 1.05 if model_type == 'sdxl' else 0.39
+
+    first_preset = chain_items[0].get('preset', '')
+    is_faceid_chain = 'FACEID' in first_preset.upper()
+
+    if is_faceid_chain:
+        for i, item_data in enumerate(chain_items):
+            image_loader_id = assembler._get_unique_id()
+            image_loader_node = assembler._get_node_template("LoadImage")
+            image_loader_node['inputs']['image'] = item_data['image']
+            assembler.workflow[image_loader_id] = image_loader_node
+
+            image_scaler_id = assembler._get_unique_id()
+            image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+            image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+            image_scaler_node['inputs']['megapixels'] = megapixels
+            image_scaler_node['inputs']['upscale_method'] = "lanczos"
+            assembler.workflow[image_scaler_id] = image_scaler_node
+
+            ipadapter_loader_id = assembler._get_unique_id()
+            ipadapter_loader_node = assembler._get_node_template("IPAdapterUnifiedLoaderFaceID")
+            ipadapter_loader_node['inputs']['model'] = current_model_connection
+            ipadapter_loader_node['inputs']['preset'] = item_data['preset']
+            ipadapter_loader_node['inputs']['lora_strength'] = item_data.get('lora_strength', 0.6)
+            ipadapter_loader_node['inputs']['provider'] = "CUDA"
+            assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+
+            apply_id = assembler._get_unique_id()
+            apply_node = assembler._get_node_template("IPAdapterFaceID")
+            apply_node['inputs']['model'] = [ipadapter_loader_id, 0]
+            apply_node['inputs']['ipadapter'] = [ipadapter_loader_id, 1]
+            apply_node['inputs']['image'] = [image_scaler_id, 0]
+            apply_node['inputs']['weight'] = item_data['weight']
+            apply_node['inputs']['weight_faceidv2'] = final_settings.get('final_lora_strength', 0.6)
+            apply_node['inputs']['weight_type'] = "linear"
+            apply_node['inputs']['combine_embeds'] = final_settings.get('final_combine_method', 'concat')
+            apply_node['inputs']['start_at'] = item_data.get('start_percent', 0.0)
+            apply_node['inputs']['end_at'] = item_data.get('end_percent', 1.0)
+            apply_node['inputs']['embeds_scaling'] = final_settings.get('final_embeds_scaling', 'V only')
+
+            assembler.workflow[apply_id] = apply_node
+            current_model_connection = [apply_id, 0]
+
+        assembler.workflow[end_node_id]['inputs']['model'] = current_model_connection
+        print(f"IPAdapter FaceID injector applied (Direct Apply). Redirected '{end_node_name}' model input through {len(chain_items)} FaceID node(s).")
+        return
+
+    else:
+        pos_embed_outputs = []
+        neg_embed_outputs = []
+
+        for i, item_data in enumerate(chain_items):
+            loader_type = 'FaceID' if 'FACEID' in item_data.get('preset', '') else 'Unified'
+            loader_template_name = "IPAdapterUnifiedLoader"
+            if loader_type == 'FaceID':
+                loader_template_name = "IPAdapterUnifiedLoaderFaceID"
+
+            image_loader_id = assembler._get_unique_id()
+            image_loader_node = assembler._get_node_template("LoadImage")
+            image_loader_node['inputs']['image'] = item_data['image']
+            assembler.workflow[image_loader_id] = image_loader_node
+
+            image_scaler_id = assembler._get_unique_id()
+            image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+            image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+            image_scaler_node['inputs']['megapixels'] = megapixels
+            image_scaler_node['inputs']['upscale_method'] = "lanczos"
+            assembler.workflow[image_scaler_id] = image_scaler_node
+
+            ipadapter_loader_id = assembler._get_unique_id()
+            ipadapter_loader_node = assembler._get_node_template(loader_template_name)
+            ipadapter_loader_node['inputs']['model'] = current_model_connection
+            ipadapter_loader_node['inputs']['preset'] = item_data['preset']
+            if loader_type == 'FaceID':
+                 ipadapter_loader_node['inputs']['lora_strength'] = item_data.get('lora_strength', 0.6)
+            assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+
+            encoder_id = assembler._get_unique_id()
+            encoder_node = assembler._get_node_template("IPAdapterEncoder")
+            encoder_node['inputs']['weight'] = item_data['weight']
+            encoder_node['inputs']['ipadapter'] = [ipadapter_loader_id, 1]
+            encoder_node['inputs']['image'] = [image_scaler_id, 0]
+            assembler.workflow[encoder_id] = encoder_node
+
+            pos_embed_outputs.append([encoder_id, 0])
+            neg_embed_outputs.append([encoder_id, 1])
+
+        pos_combiner_id = assembler._get_unique_id()
+        pos_combiner_node = assembler._get_node_template("IPAdapterCombineEmbeds")
+        pos_combiner_node['inputs']['method'] = final_settings.get('final_combine_method', 'concat')
+        for i, conn in enumerate(pos_embed_outputs):
+            pos_combiner_node['inputs'][f'embed{i+1}'] = conn
+        assembler.workflow[pos_combiner_id] = pos_combiner_node
+
+        neg_combiner_id = assembler._get_unique_id()
+        neg_combiner_node = assembler._get_node_template("IPAdapterCombineEmbeds")
+        neg_combiner_node['inputs']['method'] = final_settings.get('final_combine_method', 'concat')
+        for i, conn in enumerate(neg_embed_outputs):
+            neg_combiner_node['inputs'][f'embed{i+1}'] = conn
+        assembler.workflow[neg_combiner_id] = neg_combiner_node
+
+        final_loader_type = 'FaceID' if 'FACEID' in final_settings.get('final_preset', '') else 'Unified'
+        final_loader_template_name = "IPAdapterUnifiedLoader"
+        if final_loader_type == 'FaceID':
+            final_loader_template_name = "IPAdapterUnifiedLoaderFaceID"
+
+        final_loader_id = assembler._get_unique_id()
+        final_loader_node = assembler._get_node_template(final_loader_template_name)
+        final_loader_node['inputs']['model'] = current_model_connection
+        final_loader_node['inputs']['preset'] = final_settings.get('final_preset', 'STANDARD (medium strength)')
+        if final_loader_type == 'FaceID':
+            final_loader_node['inputs']['lora_strength'] = final_settings.get('final_lora_strength', 0.6)
+        assembler.workflow[final_loader_id] = final_loader_node
+
+        apply_embeds_id = assembler._get_unique_id()
+        apply_embeds_node = assembler._get_node_template("IPAdapterEmbeds")
+        apply_embeds_node['inputs']['weight'] = final_settings.get('final_weight', 1.0)
+        apply_embeds_node['inputs']['embeds_scaling'] = final_settings.get('final_embeds_scaling', 'V only')
+        apply_embeds_node['inputs']['model'] = [final_loader_id, 0]
+        apply_embeds_node['inputs']['ipadapter'] = [final_loader_id, 1]
+        apply_embeds_node['inputs']['pos_embed'] = [pos_combiner_id, 0]
+        apply_embeds_node['inputs']['neg_embed'] = [neg_combiner_id, 0]
+        assembler.workflow[apply_embeds_id] = apply_embeds_node
+
+        assembler.workflow[end_node_id]['inputs']['model'] = [apply_embeds_id, 0]
+        print(f"IPAdapter Unified injector applied. Redirected '{end_node_name}' model input through {len(chain_items)} reference image(s).")

chain_injectors/joyai_image_injector.py

@@ -0,0 +1,63 @@
+import os
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    valid_images = []
+    for item in chain_items:
+        if not item:
+            continue
+        img_path = item
+        if isinstance(item, dict):
+            img_path = item.get('image') or item.get('filename') or item.get('path')
+        if img_path:
+            valid_images.append(img_path)
+
+    if not valid_images:
+        return
+
+    valid_images = valid_images[:2]
+
+    pos_prompt_name = chain_definition.get('pos_prompt_node', 'pos_prompt')
+    neg_prompt_name = chain_definition.get('neg_prompt_node', 'neg_prompt')
+    vae_node_name = chain_definition.get('vae_node', 'vae_loader')
+
+    if pos_prompt_name not in assembler.node_map:
+        print(f"Warning: Positive prompt node '{pos_prompt_name}' not found for JoyAI Reference chain. Skipping.")
+        return
+
+    if vae_node_name not in assembler.node_map:
+        print(f"Warning: VAE loader node '{vae_node_name}' not found for JoyAI Reference chain. Skipping.")
+        return
+
+    pos_prompt_id = assembler.node_map[pos_prompt_name]
+    neg_prompt_id = assembler.node_map.get(neg_prompt_name)
+    vae_node_id = assembler.node_map[vae_node_name]
+
+    assembler.workflow[pos_prompt_id]['inputs']['vae'] = [vae_node_id, 0]
+    if neg_prompt_id and neg_prompt_id in assembler.workflow:
+        assembler.workflow[neg_prompt_id]['inputs']['vae'] = [vae_node_id, 0]
+
+    for i, img_filename in enumerate(valid_images):
+        load_id = assembler._get_unique_id()
+        load_node = assembler._get_node_template("LoadImage")
+        load_node['inputs']['image'] = img_filename
+        load_node['_meta']['title'] = f"Load Reference Image {i+1}"
+        assembler.workflow[load_id] = load_node
+
+        scale_id = assembler._get_unique_id()
+        scale_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        scale_node['inputs']['megapixels'] = 1.0
+        scale_node['inputs']['upscale_method'] = "nearest-exact"
+        scale_node['inputs']['resolution_steps'] = 1
+        scale_node['inputs']['image'] = [load_id, 0]
+        scale_node['_meta']['title'] = f"Scale Reference {i+1}"
+        assembler.workflow[scale_id] = scale_node
+
+        input_key = f"images.image{i}"
+        assembler.workflow[pos_prompt_id]['inputs'][input_key] = [scale_id, 0]
+        if neg_prompt_id and neg_prompt_id in assembler.workflow:
+            assembler.workflow[neg_prompt_id]['inputs'][input_key] = [scale_id, 0]
+
+    print(f"JoyAI Reference injector applied. Injected {len(valid_images)} reference images to JoyAI text encoding nodes.")

chain_injectors/krea2_controlnet_injector.py

@@ -0,0 +1,78 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: Target node '{ksampler_name}' for Krea2 ControlNet chain not found. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping.")
+        return
+
+    vae_source_str = chain_definition.get('vae_source')
+    vae_connection = None
+    if vae_source_str:
+        vae_node_name, vae_idx_str = vae_source_str.split(':')
+        if vae_node_name in assembler.node_map:
+            vae_connection = [assembler.node_map[vae_node_name], int(vae_idx_str)]
+
+    latent_connection = assembler.workflow[ksampler_id]['inputs'].get('latent_image')
+    if not latent_connection:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'latent_image' input. Krea2 ControlNet requires it. Skipping.")
+        return
+
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+
+    for item_data in chain_items:
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        image_scaler_id = assembler._get_unique_id()
+        image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+        image_scaler_node['inputs']['upscale_method'] = 'nearest-exact'
+        image_scaler_node['inputs']['megapixels'] = 1.0
+        image_scaler_node['inputs']['resolution_steps'] = 1
+        assembler.workflow[image_scaler_id] = image_scaler_node
+
+        lora_loader_id = assembler._get_unique_id()
+        lora_loader_node = assembler._get_node_template("Krea2ControlLoRALoader")
+        lora_loader_node['inputs']['lora_name'] = item_data['control_net_name']
+        lora_loader_node['inputs']['strength'] = item_data.get('strength', 1.0)
+        lora_loader_node['inputs']['model'] = current_model_connection
+        assembler.workflow[lora_loader_id] = lora_loader_node
+
+        img_encode_id = assembler._get_unique_id()
+        img_encode_node = assembler._get_node_template("Krea2ControlImageEncode")
+        img_encode_node['inputs']['resize'] = "match_latent_size"
+        img_encode_node['inputs']['upscale_method'] = "lanczos"
+        img_encode_node['inputs']['crop'] = "center"
+        img_encode_node['inputs']['channel_mode'] = "rgb"
+        img_encode_node['inputs']['normalize'] = "none"
+        img_encode_node['inputs']['invert'] = False
+        img_encode_node['inputs']['batch_mode'] = "independent_images"
+        img_encode_node['inputs']['control_image'] = [image_scaler_id, 0]
+        if vae_connection:
+            img_encode_node['inputs']['vae'] = vae_connection
+        if latent_connection:
+            img_encode_node['inputs']['latent'] = latent_connection
+        assembler.workflow[img_encode_id] = img_encode_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template("Krea2ControlApply")
+        apply_cn_node['inputs']['model'] = [lora_loader_id, 0]
+        apply_cn_node['inputs']['control_latent'] = [img_encode_id, 0]
+
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_model_connection = [apply_cn_id, 0]
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = current_model_connection
+
+    print(f"Krea2 ControlNet injector applied. KSampler model input redirected through {len(chain_items)} Krea2 ControlNet nodes.")

chain_injectors/krea2_identity_edit_injector.py

@@ -0,0 +1,173 @@
+import os
+from imagegen_utils.app_utils import ensure_file_downloaded
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    valid_images = []
+    for item in chain_items:
+        if not item:
+            continue
+        img_path = item
+        if isinstance(item, dict):
+            img_path = item.get('image') or item.get('filename') or item.get('path')
+        if img_path:
+            valid_images.append(img_path)
+
+    if not valid_images:
+        return
+
+    valid_images = valid_images[:2]
+
+    lora_filename = "krea2_identity_edit_v1_2.safetensors"
+    try:
+        ensure_file_downloaded(lora_filename)
+    except Exception as e:
+        print(f"Warning: Failed to ensure '{lora_filename}' downloaded: {e}")
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    pos_prompt_name = chain_definition.get('pos_prompt_node', 'pos_prompt')
+    neg_prompt_name = chain_definition.get('neg_prompt_node', 'neg_prompt')
+    clip_loader_name = chain_definition.get('clip_loader_node', 'clip_loader')
+    vae_loader_name = chain_definition.get('vae_loader_node', 'vae_loader')
+
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: Target node '{ksampler_name}' for Krea2 Identity Edit chain not found. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping.")
+        return
+
+    latent_connection = assembler.workflow[ksampler_id]['inputs'].get('latent_image')
+    if not latent_connection:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'latent_image' input. Skipping.")
+        return
+
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+
+    vae_connection = None
+    if vae_loader_name in assembler.node_map:
+        vae_connection = [assembler.node_map[vae_loader_name], 0]
+
+    clip_connection = None
+    if clip_loader_name in assembler.node_map:
+        clip_connection = [assembler.node_map[clip_loader_name], 0]
+    elif pos_prompt_name in assembler.node_map:
+        pos_id = assembler.node_map[pos_prompt_name]
+        clip_connection = assembler.workflow[pos_id]['inputs'].get('clip')
+
+    lora_loader_id = assembler._get_unique_id()
+    lora_loader_node = assembler._get_node_template("LoraLoaderModelOnly")
+    lora_loader_node['inputs']['lora_name'] = lora_filename
+    lora_loader_node['inputs']['strength_model'] = 1.0
+    lora_loader_node['inputs']['model'] = current_model_connection
+    lora_loader_node['_meta']['title'] = "Load LoRA (Krea2 Identity Edit)"
+    assembler.workflow[lora_loader_id] = lora_loader_node
+
+    image_ids = []
+    vae_encode_ids = []
+
+    for i, img_filename in enumerate(valid_images):
+        load_id = assembler._get_unique_id()
+        load_node = assembler._get_node_template("LoadImage")
+        load_node['inputs']['image'] = img_filename
+        load_node['_meta']['title'] = f"Load Image (Ref {i+1})"
+        assembler.workflow[load_id] = load_node
+        image_ids.append(load_id)
+
+        vae_enc_id = assembler._get_unique_id()
+        vae_enc_node = assembler._get_node_template("VAEEncode")
+        vae_enc_node['inputs']['pixels'] = [load_id, 0]
+        if vae_connection:
+            vae_enc_node['inputs']['vae'] = vae_connection
+        vae_enc_node['_meta']['title'] = f"VAE Encode (Ref {i+1})"
+        assembler.workflow[vae_enc_id] = vae_enc_node
+        vae_encode_ids.append(vae_enc_id)
+
+    patch_id = assembler._get_unique_id()
+    patch_node = assembler._get_node_template("Krea2EditModelPatch")
+    patch_node['inputs']['ref_boost'] = 4
+    patch_node['inputs']['ref_boost_a'] = 1
+    patch_node['inputs']['fit_mode'] = "fit"
+    patch_node['inputs']['model'] = [lora_loader_id, 0]
+    patch_node['inputs']['source_latent'] = [vae_encode_ids[0], 0]
+    if vae_connection:
+        patch_node['inputs']['vae'] = vae_connection
+    patch_node['inputs']['source_image'] = [image_ids[0], 0]
+    patch_node['inputs']['target_latent'] = latent_connection
+
+    if len(valid_images) > 1:
+        patch_node['inputs']['source_latent_b'] = [vae_encode_ids[1], 0]
+        patch_node['inputs']['source_image_b'] = [image_ids[1], 0]
+
+    patch_node['_meta']['title'] = "Krea2 Edit (source patch)"
+    assembler.workflow[patch_id] = patch_node
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = [patch_id, 0]
+
+    pos_prompt_id = assembler.node_map.get(pos_prompt_name)
+    neg_prompt_id = assembler.node_map.get(neg_prompt_name)
+
+    pos_text = ""
+    if pos_prompt_id and pos_prompt_id in assembler.workflow:
+        pos_text = assembler.workflow[pos_prompt_id]['inputs'].get('text', '')
+    elif hasattr(assembler, 'ui_values') and isinstance(assembler.ui_values, dict):
+        pos_text = assembler.ui_values.get('positive_prompt') or assembler.ui_values.get('prompt') or ''
+
+    if not pos_text:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict):
+                cls = node.get('class_type', '')
+                if cls in ['Krea2EditGroundedEncode', 'TextEncodeQwenImageEditPlus', 'CLIPTextEncode']:
+                    t = node.get('inputs', {}).get('prompt') or node.get('inputs', {}).get('text')
+                    if t:
+                        pos_text = t
+                        break
+
+    neg_text = ""
+    if neg_prompt_id and neg_prompt_id in assembler.workflow:
+        neg_text = assembler.workflow[neg_prompt_id]['inputs'].get('text', '')
+    elif hasattr(assembler, 'ui_values') and isinstance(assembler.ui_values, dict):
+        neg_text = assembler.ui_values.get('negative_prompt') or assembler.ui_values.get('neg_prompt') or ''
+
+    pos_grounded_id = assembler._get_unique_id()
+    pos_grounded_node = assembler._get_node_template("Krea2EditGroundedEncode")
+    pos_grounded_node['inputs']['prompt'] = pos_text
+    pos_grounded_node['inputs']['grounding_px'] = 768
+    pos_grounded_node['inputs']['system_prompt'] = ""
+    if clip_connection:
+        pos_grounded_node['inputs']['clip'] = clip_connection
+    pos_grounded_node['inputs']['image'] = [image_ids[0], 0]
+    if len(valid_images) > 1:
+        pos_grounded_node['inputs']['image_b'] = [image_ids[1], 0]
+    pos_grounded_node['_meta']['title'] = "Krea2 Edit (grounded encode positive)"
+    assembler.workflow[pos_grounded_id] = pos_grounded_node
+
+    assembler.workflow[ksampler_id]['inputs']['positive'] = [pos_grounded_id, 0]
+
+    neg_grounded_id = assembler._get_unique_id()
+    neg_grounded_node = assembler._get_node_template("Krea2EditGroundedEncode")
+    neg_grounded_node['inputs']['prompt'] = neg_text
+    neg_grounded_node['inputs']['grounding_px'] = 768
+    neg_grounded_node['inputs']['system_prompt'] = ""
+    if clip_connection:
+        neg_grounded_node['inputs']['clip'] = clip_connection
+    neg_grounded_node['inputs']['image'] = [image_ids[0], 0]
+    if len(valid_images) > 1:
+        neg_grounded_node['inputs']['image_b'] = [image_ids[1], 0]
+    neg_grounded_node['_meta']['title'] = "Krea2 Edit (grounded encode negative)"
+    assembler.workflow[neg_grounded_id] = neg_grounded_node
+
+    assembler.workflow[ksampler_id]['inputs']['negative'] = [neg_grounded_id, 0]
+
+    if pos_prompt_id and pos_prompt_id in assembler.workflow:
+        del assembler.workflow[pos_prompt_id]
+
+    if neg_prompt_id and neg_prompt_id in assembler.workflow:
+        del assembler.workflow[neg_prompt_id]
+
+    print(f"Krea2 Identity Edit injector applied with {len(valid_images)} reference image(s). Original CLIPTextEncode nodes removed.")

chain_injectors/krea2_style_reference_injector.py

@@ -0,0 +1,168 @@
+import os
+from imagegen_utils.app_utils import ensure_file_downloaded
+
+def create_node(assembler, class_type, title):
+    try:
+        node = assembler._get_node_template(class_type)
+    except Exception:
+        node = {
+            "inputs": {},
+            "class_type": class_type,
+            "_meta": {"title": title}
+        }
+    node['_meta']['title'] = title
+    return node
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    valid_images = []
+    for item in chain_items:
+        if not item:
+            continue
+        img_path = item
+        if isinstance(item, dict):
+            img_path = item.get('image') or item.get('filename') or item.get('path')
+        if img_path:
+            valid_images.append(img_path)
+
+    if not valid_images:
+        return
+
+    valid_images = valid_images[:3]
+
+    lora_filename = "krea2_style_reference.safetensors"
+    try:
+        ensure_file_downloaded(lora_filename)
+    except Exception as e:
+        print(f"Warning: Failed to ensure '{lora_filename}' downloaded: {e}")
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    pos_prompt_name = chain_definition.get('pos_prompt_node', 'pos_prompt')
+    neg_prompt_name = chain_definition.get('neg_prompt_node', 'neg_prompt')
+    clip_loader_name = chain_definition.get('clip_loader_node', 'clip_loader')
+    vae_loader_name = chain_definition.get('vae_loader_node', 'vae_loader')
+
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: Target node '{ksampler_name}' for Krea2 Style Reference Edit chain not found. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping.")
+        return
+
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+
+    vae_connection = None
+    if vae_loader_name in assembler.node_map:
+        vae_connection = [assembler.node_map[vae_loader_name], 0]
+    else:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict) and node.get('class_type') == 'VAELoader':
+                vae_connection = [node_id, 0]
+                break
+
+    clip_connection = None
+    if clip_loader_name in assembler.node_map:
+        clip_connection = [assembler.node_map[clip_loader_name], 0]
+    elif pos_prompt_name in assembler.node_map:
+        pos_id = assembler.node_map[pos_prompt_name]
+        clip_connection = assembler.workflow[pos_id]['inputs'].get('clip')
+
+    scaled_image_ids = []
+    for i, img_filename in enumerate(valid_images):
+        load_id = assembler._get_unique_id()
+        load_node = create_node(assembler, "LoadImage", f"Load Reference Image {i+1}")
+        load_node['inputs']['image'] = img_filename
+        assembler.workflow[load_id] = load_node
+
+        scale_id = assembler._get_unique_id()
+        scale_node = create_node(assembler, "ImageScaleToTotalPixels", f"Scale Reference {i+1}")
+        scale_node['inputs']['upscale_method'] = "nearest-exact"
+        scale_node['inputs']['megapixels'] = 1
+        scale_node['inputs']['resolution_steps'] = 1
+        scale_node['inputs']['image'] = [load_id, 0]
+        assembler.workflow[scale_id] = scale_node
+        scaled_image_ids.append(scale_id)
+
+    lora_loader_id = assembler._get_unique_id()
+    lora_loader_node = create_node(assembler, "LoraLoaderModelOnly", "Load LoRA (Krea2 Style Reference)")
+    lora_loader_node['inputs']['lora_name'] = lora_filename
+    lora_loader_node['inputs']['strength_model'] = 1.0
+    lora_loader_node['inputs']['model'] = current_model_connection
+    assembler.workflow[lora_loader_id] = lora_loader_node
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = [lora_loader_id, 0]
+
+    pos_prompt_id = assembler.node_map.get(pos_prompt_name)
+    neg_prompt_id = assembler.node_map.get(neg_prompt_name)
+
+    pos_text = ""
+    if pos_prompt_id and pos_prompt_id in assembler.workflow:
+        pos_text = assembler.workflow[pos_prompt_id]['inputs'].get('text', '')
+    elif hasattr(assembler, 'ui_values') and isinstance(assembler.ui_values, dict):
+        pos_text = assembler.ui_values.get('positive_prompt') or assembler.ui_values.get('prompt') or ''
+
+    if not pos_text:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict):
+                cls = node.get('class_type', '')
+                if cls in ['Krea2EditGroundedEncode', 'TextEncodeQwenImageEditPlus', 'CLIPTextEncode']:
+                    t = node.get('inputs', {}).get('prompt') or node.get('inputs', {}).get('text')
+                    if t:
+                        pos_text = t
+                        break
+
+    neg_text = ""
+    if neg_prompt_id and neg_prompt_id in assembler.workflow:
+        neg_text = assembler.workflow[neg_prompt_id]['inputs'].get('text', '')
+    elif hasattr(assembler, 'ui_values') and isinstance(assembler.ui_values, dict):
+        neg_text = assembler.ui_values.get('negative_prompt') or assembler.ui_values.get('neg_prompt') or ''
+
+    pos_encode_id = assembler._get_unique_id()
+    pos_encode_node = create_node(assembler, "TextEncodeQwenImageEditPlus", "TextEncodeQwenImageEditPlus (Positive)")
+    pos_encode_node['inputs']['prompt'] = pos_text
+    if clip_connection:
+        pos_encode_node['inputs']['clip'] = clip_connection
+    if vae_connection:
+        pos_encode_node['inputs']['vae'] = vae_connection
+    for idx, s_id in enumerate(scaled_image_ids):
+        pos_encode_node['inputs'][f"image{idx+1}"] = [s_id, 0]
+    assembler.workflow[pos_encode_id] = pos_encode_node
+
+    neg_encode_id = assembler._get_unique_id()
+    neg_encode_node = create_node(assembler, "TextEncodeQwenImageEditPlus", "TextEncodeQwenImageEditPlus (Negative)")
+    neg_encode_node['inputs']['prompt'] = neg_text
+    if clip_connection:
+        neg_encode_node['inputs']['clip'] = clip_connection
+    if vae_connection:
+        neg_encode_node['inputs']['vae'] = vae_connection
+    for idx, s_id in enumerate(scaled_image_ids):
+        neg_encode_node['inputs'][f"image{idx+1}"] = [s_id, 0]
+    assembler.workflow[neg_encode_id] = neg_encode_node
+
+    pos_ref_id = assembler._get_unique_id()
+    pos_ref_node = create_node(assembler, "FluxKontextMultiReferenceLatentMethod", "Edit Model Reference Method")
+    pos_ref_node['inputs']['reference_latents_method'] = "index_timestep_zero"
+    pos_ref_node['inputs']['conditioning'] = [pos_encode_id, 0]
+    assembler.workflow[pos_ref_id] = pos_ref_node
+
+    neg_ref_id = assembler._get_unique_id()
+    neg_ref_node = create_node(assembler, "FluxKontextMultiReferenceLatentMethod", "Edit Model Reference Method")
+    neg_ref_node['inputs']['reference_latents_method'] = "index_timestep_zero"
+    neg_ref_node['inputs']['conditioning'] = [neg_encode_id, 0]
+    assembler.workflow[neg_ref_id] = neg_ref_node
+
+    assembler.workflow[ksampler_id]['inputs']['positive'] = [pos_ref_id, 0]
+    assembler.workflow[ksampler_id]['inputs']['negative'] = [neg_ref_id, 0]
+
+    if pos_prompt_id and pos_prompt_id in assembler.workflow:
+        del assembler.workflow[pos_prompt_id]
+
+    if neg_prompt_id and neg_prompt_id in assembler.workflow:
+        del assembler.workflow[neg_prompt_id]
+
+    print(f"Krea2 Style Reference Edit injector applied with {len(valid_images)} reference image(s). Original CLIPTextEncode nodes replaced.")

chain_injectors/lora_injector.py

@@ -0,0 +1,67 @@
+from copy import deepcopy
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    start_node_name = chain_definition.get('start')
+    start_node_id = None
+    if start_node_name:
+        if start_node_name not in assembler.node_map:
+            print(f"Warning: Start node '{start_node_name}' for dynamic LoRA chain not found. Skipping chain.")
+            return
+        start_node_id = assembler.node_map[start_node_name]
+
+    output_map = chain_definition.get('output_map', {})
+    current_connections = {}
+    for key, type_name in output_map.items():
+        if ':' in str(key):
+            node_name, idx_str = key.split(':')
+            if node_name not in assembler.node_map:
+                print(f"Warning: Node '{node_name}' in chain's output_map not found. Skipping.")
+                continue
+            node_id = assembler.node_map[node_name]
+            start_output_idx = int(idx_str)
+            current_connections[type_name] = [node_id, start_output_idx]
+        elif start_node_id:
+            start_output_idx = int(key)
+            current_connections[type_name] = [start_node_id, start_output_idx]
+        else:
+            print(f"Warning: LoRA chain has no 'start' node defined, and an output_map key '{key}' is not in 'node:index' format. Skipping this connection.")
+
+
+    input_map = chain_definition.get('input_map', {})
+    chain_output_map = chain_definition.get('template_output_map', { "0": "model", "1": "clip" })
+
+    for item_data in chain_items:
+        template_name = chain_definition['template']
+        template = assembler._get_node_template(template_name)
+        node_data = deepcopy(template)
+
+        for param_name, value in item_data.items():
+            if param_name in node_data['inputs']:
+                node_data['inputs'][param_name] = value
+
+        for type_name, input_name in input_map.items():
+            if type_name in current_connections:
+                node_data['inputs'][input_name] = current_connections[type_name]
+
+        new_node_id = assembler._get_unique_id()
+        assembler.workflow[new_node_id] = node_data
+
+        for idx_str, type_name in chain_output_map.items():
+            current_connections[type_name] = [new_node_id, int(idx_str)]
+
+    end_input_map = chain_definition.get('end_input_map', {})
+    for type_name, targets in end_input_map.items():
+        if type_name in current_connections:
+            if not isinstance(targets, list):
+                targets = [targets]
+
+            for target_str in targets:
+                end_node_name, end_input_name = target_str.split(':')
+                if end_node_name in assembler.node_map:
+                    end_node_id = assembler.node_map[end_node_name]
+                    assembler.workflow[end_node_id]['inputs'][end_input_name] = current_connections[type_name]
+                else:
+                    print(f"Warning: End node '{end_node_name}' for dynamic chain not found. Skipping connection.")

chain_injectors/pid_injector.py

@@ -0,0 +1,292 @@
+import os
+import yaml
+import random
+
+def load_pid_config():
+    project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    pid_path = os.path.join(project_root, 'yaml', 'pid.yaml')
+    with open(pid_path, 'r', encoding='utf-8') as f:
+        return yaml.safe_load(f) or {}
+
+def load_model_config():
+    project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    model_list_path = os.path.join(project_root, 'yaml', 'model_list.yaml')
+    with open(model_list_path, 'r', encoding='utf-8') as f:
+        return yaml.safe_load(f) or {}
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    pid_config = {}
+    try:
+        pid_config = load_pid_config() or {}
+    except Exception as e:
+        print(f"Error loading PiD config: {e}")
+
+    pid_items = pid_config.get("PiD", [])
+    architectures_settings = {}
+    default_settings = {"unet_name": "pid_flux1_1024_to_4096_4step_mxfp8.safetensors", "latent_format": "flux"}
+
+    for item in pid_items:
+        unet_name = item.get("filepath")
+        latent_format = item.get("latent_format")
+        archs = item.get("architectures", [])
+        for arch in archs:
+            architectures_settings[arch] = {
+                "unet_name": unet_name,
+                "latent_format": latent_format
+            }
+            if arch == "flux1":
+                default_settings = {
+                    "unet_name": unet_name,
+                    "latent_format": latent_format
+                }
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: [PiD Injector] KSampler node '{ksampler_name}' not found. Skipping.")
+        return
+
+    original_ksampler_id = assembler.node_map[ksampler_name]
+
+    original_vae_loader_id = assembler.node_map.get('vae_loader')
+    original_vae_decode_id = assembler.node_map.get('vae_decode')
+    original_pos_prompt_id = assembler.node_map.get('pos_prompt')
+    original_neg_prompt_id = assembler.node_map.get('neg_prompt')
+
+    if not original_vae_loader_id:
+        for node_id, node_data in assembler.workflow.items():
+            if node_data.get('class_type') == 'VAELoader':
+                original_vae_loader_id = node_id
+                break
+
+    if not original_vae_decode_id:
+        for node_id, node_data in assembler.workflow.items():
+            if node_data.get('class_type') == 'VAEDecode':
+                original_vae_decode_id = node_id
+                break
+
+    if not original_pos_prompt_id or not original_neg_prompt_id:
+        for node_id, node_data in assembler.workflow.items():
+            if node_data.get('class_type') == 'CLIPTextEncode':
+                title = node_data.get('_meta', {}).get('title', '')
+                if 'Positive' in title:
+                    if not original_pos_prompt_id:
+                        original_pos_prompt_id = node_id
+                elif 'Negative' in title:
+                    if not original_neg_prompt_id:
+                        original_neg_prompt_id = node_id
+
+    pos_text = ""
+    if original_pos_prompt_id and original_pos_prompt_id in assembler.workflow:
+        pos_text = assembler.workflow[original_pos_prompt_id]['inputs'].get('text', '')
+
+    neg_text = ""
+    if original_neg_prompt_id and original_neg_prompt_id in assembler.workflow:
+        neg_text = assembler.workflow[original_neg_prompt_id]['inputs'].get('text', '')
+
+    clip_loader_id = assembler._get_unique_id()
+    clip_loader_node = assembler._get_node_template("CLIPLoader")
+    clip_loader_node['inputs']['clip_name'] = "gemma_2_2b_it_elm_fp8_scaled.safetensors"
+    clip_loader_node['inputs']['type'] = "pixeldit"
+    clip_loader_node['inputs']['device'] = "default"
+    assembler.workflow[clip_loader_id] = clip_loader_node
+
+    pos_text_encode_id = assembler._get_unique_id()
+    pos_text_encode_node = assembler._get_node_template("CLIPTextEncode")
+    pos_text_encode_node['inputs']['text'] = pos_text
+    pos_text_encode_node['inputs']['clip'] = [clip_loader_id, 0]
+    assembler.workflow[pos_text_encode_id] = pos_text_encode_node
+
+    neg_text_encode_id = assembler._get_unique_id()
+    neg_text_encode_node = assembler._get_node_template("CLIPTextEncode")
+    neg_text_encode_node['inputs']['text'] = neg_text
+    neg_text_encode_node['inputs']['clip'] = [clip_loader_id, 0]
+    assembler.workflow[neg_text_encode_id] = neg_text_encode_node
+
+    active_model_file = None
+    for node_id, node_data in assembler.workflow.items():
+        class_type = node_data.get('class_type')
+        if class_type == 'UNETLoader':
+            active_model_file = node_data.get('inputs', {}).get('unet_name')
+            if active_model_file:
+                break
+        elif class_type == 'CheckpointLoaderSimple':
+            active_model_file = node_data.get('inputs', {}).get('ckpt_name')
+            if active_model_file:
+                break
+
+    architecture = None
+    if active_model_file:
+        try:
+            model_config = load_model_config()
+            checkpoints = model_config.get("Checkpoints", {})
+            for arch_name, arch_data in checkpoints.items():
+                models_list = arch_data.get("models", [])
+                for model_entry in models_list:
+                    if model_entry.get('path') == active_model_file:
+                        architecture = arch_name
+                        break
+                    components_dict = model_entry.get('components', {})
+                    if active_model_file in components_dict.values():
+                        architecture = arch_name
+                        break
+                if architecture:
+                    break
+        except Exception as e:
+            print(f"Error looking up model architecture in PiD injector: {e}")
+
+        if architecture:
+            architecture = architecture.lower().replace(" ", "-").replace(".", "")
+        else:
+            file_lower = active_model_file.lower().replace("-", "").replace("_", "").replace(".", "")
+            for arch in sorted(architectures_settings.keys(), key=len, reverse=True):
+                candidates = [arch]
+                if "-image" in arch:
+                    candidates.append(arch.replace("-image", ""))
+                if "-i1" in arch:
+                    candidates.append(arch.replace("-i1", ""))
+                if "-kv" in arch:
+                    candidates.append(arch.replace("-kv", ""))
+
+                matched = False
+                for cand in candidates:
+                    if cand.replace("-", "").replace(".", "") in file_lower:
+                        architecture = arch
+                        matched = True
+                        break
+                if matched:
+                    break
+
+    unet_name = default_settings.get("unet_name")
+    latent_format = default_settings.get("latent_format")
+
+    if architecture in architectures_settings:
+        arch_config = architectures_settings[architecture]
+        unet_name = arch_config.get("unet_name", unet_name)
+        latent_format = arch_config.get("latent_format", latent_format)
+    else:
+        print(f"[PiD Injector] Warning: Model architecture '{architecture}' (file: '{active_model_file}') not explicitly mapped. Using default settings.")
+
+    pid_pos_id = assembler._get_unique_id()
+    pid_pos_node = assembler._get_node_template("PiDConditioning")
+    pid_pos_node['inputs']['latent_format'] = latent_format
+    pid_pos_node['inputs']['degrade_sigma'] = 0
+    pid_pos_node['inputs']['positive'] = [pos_text_encode_id, 0]
+    pid_pos_node['inputs']['latent'] = [original_ksampler_id, 0]
+    assembler.workflow[pid_pos_id] = pid_pos_node
+
+    pid_neg_id = assembler._get_unique_id()
+    pid_neg_node = assembler._get_node_template("PiDConditioning")
+    pid_neg_node['inputs']['latent_format'] = latent_format
+    pid_neg_node['inputs']['degrade_sigma'] = 0
+    pid_neg_node['inputs']['positive'] = [neg_text_encode_id, 0]
+    pid_neg_node['inputs']['latent'] = [original_ksampler_id, 0]
+    assembler.workflow[pid_neg_id] = pid_neg_node
+
+    pid_unet_loader_id = assembler._get_unique_id()
+    pid_unet_loader_node = assembler._get_node_template("UNETLoader")
+    pid_unet_loader_node['inputs']['unet_name'] = unet_name
+    pid_unet_loader_node['inputs']['weight_dtype'] = "default"
+    assembler.workflow[pid_unet_loader_id] = pid_unet_loader_node
+
+    orig_width = 1024
+    orig_height = 1024
+    original_latent_source_id = assembler.node_map.get('latent_source')
+    if original_latent_source_id in assembler.workflow:
+        node_inputs = assembler.workflow[original_latent_source_id].get('inputs', {})
+        if 'width' in node_inputs and 'height' in node_inputs:
+            orig_width = node_inputs['width']
+            orig_height = node_inputs['height']
+        else:
+            for node_data in assembler.workflow.values():
+                inputs = node_data.get('inputs', {})
+                if 'width' in inputs and 'height' in inputs and isinstance(inputs['width'], (int, float)) and isinstance(inputs['height'], (int, float)):
+                    if 256 <= inputs['width'] <= 4096 and 256 <= inputs['height'] <= 4096:
+                        orig_width = inputs['width']
+                        orig_height = inputs['height']
+                        break
+    else:
+        for node_data in assembler.workflow.values():
+            inputs = node_data.get('inputs', {})
+            if 'width' in inputs and 'height' in inputs and isinstance(inputs['width'], (int, float)) and isinstance(inputs['height'], (int, float)):
+                if 256 <= inputs['width'] <= 4096 and 256 <= inputs['height'] <= 4096:
+                    orig_width = inputs['width']
+                    orig_height = inputs['height']
+                    break
+
+    empty_latent_id = assembler._get_unique_id()
+    empty_latent_node = assembler._get_node_template("EmptyChromaRadianceLatentImage")
+    empty_latent_node['inputs']['width'] = int(orig_width) * 4
+    empty_latent_node['inputs']['height'] = int(orig_height) * 4
+    empty_latent_node['inputs']['batch_size'] = 1
+
+    if original_latent_source_id in assembler.workflow:
+        orig_batch_size = assembler.workflow[original_latent_source_id]['inputs'].get('batch_size') or assembler.workflow[original_latent_source_id]['inputs'].get('amount')
+        if orig_batch_size:
+            empty_latent_node['inputs']['batch_size'] = orig_batch_size
+
+    assembler.workflow[empty_latent_id] = empty_latent_node
+
+    orig_seed = 0
+    if original_ksampler_id in assembler.workflow:
+        orig_seed = assembler.workflow[original_ksampler_id]['inputs'].get('seed', 0)
+        if orig_seed == -1:
+            orig_seed = random.randint(0, 2**32 - 1)
+        else:
+            orig_seed = (orig_seed + 1) % (2**32)
+
+    new_ksampler_id = assembler._get_unique_id()
+    new_ksampler_node = assembler._get_node_template("KSampler")
+    new_ksampler_node['inputs']['seed'] = orig_seed
+    new_ksampler_node['inputs']['steps'] = 4
+    new_ksampler_node['inputs']['cfg'] = 1
+    new_ksampler_node['inputs']['sampler_name'] = "lcm"
+    new_ksampler_node['inputs']['scheduler'] = "simple"
+    new_ksampler_node['inputs']['denoise'] = 1.0
+    new_ksampler_node['inputs']['model'] = [pid_unet_loader_id, 0]
+    new_ksampler_node['inputs']['positive'] = [pid_pos_id, 0]
+    new_ksampler_node['inputs']['negative'] = [pid_neg_id, 0]
+    new_ksampler_node['inputs']['latent_image'] = [empty_latent_id, 0]
+    assembler.workflow[new_ksampler_id] = new_ksampler_node
+
+    pid_vae_loader_id = assembler._get_unique_id()
+    pid_vae_loader_node = assembler._get_node_template("VAELoader")
+    pid_vae_loader_node['inputs']['vae_name'] = "pixel_space"
+    assembler.workflow[pid_vae_loader_id] = pid_vae_loader_node
+
+    pid_vae_decode_id = assembler._get_unique_id()
+    pid_vae_decode_node = assembler._get_node_template("VAEDecode")
+    pid_vae_decode_node['inputs']['samples'] = [new_ksampler_id, 0]
+    pid_vae_decode_node['inputs']['vae'] = [pid_vae_loader_id, 0]
+    assembler.workflow[pid_vae_decode_id] = pid_vae_decode_node
+
+    if original_vae_decode_id:
+        for node_id, node_data in assembler.workflow.items():
+            if 'inputs' in node_data:
+                for input_name, input_val in list(node_data['inputs'].items()):
+                    if isinstance(input_val, list) and len(input_val) == 2:
+                        if input_val[0] == original_vae_decode_id:
+                            node_data['inputs'][input_name] = [pid_vae_decode_id, 0]
+
+    is_vae_loader_referenced = False
+    if original_vae_loader_id:
+        for node_id, node_data in assembler.workflow.items():
+            if node_id == original_vae_loader_id:
+                continue
+            for input_val in node_data.get('inputs', {}).values():
+                if isinstance(input_val, list) and len(input_val) == 2:
+                    if input_val[0] == original_vae_loader_id:
+                        is_vae_loader_referenced = True
+                        break
+            if is_vae_loader_referenced:
+                break
+
+    if original_vae_loader_id in assembler.workflow and not is_vae_loader_referenced:
+        del assembler.workflow[original_vae_loader_id]
+
+    if original_vae_decode_id in assembler.workflow:
+        del assembler.workflow[original_vae_decode_id]
+
+    print("[PiD Injector] Successfully injected PiD pipeline and replaced VAE decode/loader.")

chain_injectors/qwen_image_edit_injector.py

@@ -0,0 +1,113 @@
+def create_node(assembler, class_type, title):
+    try:
+        node = assembler._get_node_template(class_type)
+    except Exception:
+        node = {
+            "inputs": {},
+            "class_type": class_type,
+            "_meta": {"title": title}
+        }
+    node['_meta']['title'] = title
+    return node
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    valid_images = []
+    for item in chain_items:
+        if not item:
+            continue
+        img_path = item
+        if isinstance(item, dict):
+            img_path = item.get('image') or item.get('filename') or item.get('path')
+        if img_path:
+            valid_images.append(img_path)
+
+    if not valid_images:
+        return
+
+    valid_images = valid_images[:3]
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    pos_prompt_name = chain_definition.get('pos_prompt_node', 'pos_prompt')
+    neg_prompt_name = chain_definition.get('neg_prompt_node', 'neg_prompt')
+    vae_loader_name = chain_definition.get('vae_loader_node', 'vae_loader')
+    model_sampler_name = chain_definition.get('model_sampler_node', 'model_sampler')
+
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: Target node '{ksampler_name}' for Qwen-Image Edit chain not found. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+    pos_prompt_id = assembler.node_map.get(pos_prompt_name)
+    neg_prompt_id = assembler.node_map.get(neg_prompt_name)
+
+    if not pos_prompt_id or not neg_prompt_id:
+        print("Warning: Positive or negative prompt node not found for Qwen-Image Edit chain. Skipping.")
+        return
+
+    vae_id = assembler.node_map.get(vae_loader_name)
+    if not vae_id:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict) and node.get('class_type') == 'VAELoader':
+                vae_id = node_id
+                break
+
+    if vae_id:
+        assembler.workflow[pos_prompt_id]['inputs']['vae'] = [vae_id, 0]
+        assembler.workflow[neg_prompt_id]['inputs']['vae'] = [vae_id, 0]
+
+    for i, img_filename in enumerate(valid_images):
+        load_id = assembler._get_unique_id()
+        load_node = create_node(assembler, "LoadImage", f"Load Reference Image {i+1}")
+        load_node['inputs']['image'] = img_filename
+        assembler.workflow[load_id] = load_node
+
+        scale_id = assembler._get_unique_id()
+        scale_node = create_node(assembler, "ImageScaleToTotalPixels", f"Scale Reference {i+1}")
+        scale_node['inputs']['upscale_method'] = "lanczos"
+        scale_node['inputs']['megapixels'] = 1
+        scale_node['inputs']['resolution_steps'] = 1
+        scale_node['inputs']['image'] = [load_id, 0]
+        assembler.workflow[scale_id] = scale_node
+
+        image_key = f"image{i+1}"
+        assembler.workflow[pos_prompt_id]['inputs'][image_key] = [scale_id, 0]
+        assembler.workflow[neg_prompt_id]['inputs'][image_key] = [scale_id, 0]
+
+    pos_ref_id = assembler._get_unique_id()
+    pos_ref_node = create_node(assembler, "FluxKontextMultiReferenceLatentMethod", "Edit Model Reference Method")
+    pos_ref_node['inputs']['reference_latents_method'] = "index_timestep_zero"
+    pos_ref_node['inputs']['conditioning'] = [pos_prompt_id, 0]
+    assembler.workflow[pos_ref_id] = pos_ref_node
+
+    neg_ref_id = assembler._get_unique_id()
+    neg_ref_node = create_node(assembler, "FluxKontextMultiReferenceLatentMethod", "Edit Model Reference Method")
+    neg_ref_node['inputs']['reference_latents_method'] = "index_timestep_zero"
+    neg_ref_node['inputs']['conditioning'] = [neg_prompt_id, 0]
+    assembler.workflow[neg_ref_id] = neg_ref_node
+
+    assembler.workflow[ksampler_id]['inputs']['positive'] = [pos_ref_id, 0]
+    assembler.workflow[ksampler_id]['inputs']['negative'] = [neg_ref_id, 0]
+
+    model_sampler_id = assembler.node_map.get(model_sampler_name)
+    if not model_sampler_id:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict) and node.get('class_type') == 'ModelSamplingAuraFlow':
+                model_sampler_id = node_id
+                break
+
+    if model_sampler_id and model_sampler_id in assembler.workflow:
+        assembler.workflow[model_sampler_id]['inputs']['shift'] = 3
+
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+    cfg_norm_id = assembler._get_unique_id()
+    cfg_norm_node = create_node(assembler, "CFGNorm", "CFGNorm")
+    cfg_norm_node['inputs']['strength'] = 1
+    cfg_norm_node['inputs']['pre_cfg'] = False
+    cfg_norm_node['inputs']['model'] = current_model_connection
+    assembler.workflow[cfg_norm_id] = cfg_norm_node
+    assembler.workflow[ksampler_id]['inputs']['model'] = [cfg_norm_id, 0]
+
+    print(f"Qwen-Image Edit injector applied with {len(valid_images)} reference image(s). Connected VAE dynamically.")

chain_injectors/reference_image_injector.py

@@ -0,0 +1,64 @@
+import os
+
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    valid_images = []
+    for item in chain_items:
+        if not item:
+            continue
+        img_path = item
+        if isinstance(item, dict):
+            img_path = item.get('image') or item.get('filename') or item.get('path')
+        if img_path:
+            valid_images.append(img_path)
+
+    if not valid_images:
+        return
+
+    text_encode_name = chain_definition.get('text_encode_node')
+    text_encode_id = None
+    if text_encode_name and text_encode_name in assembler.node_map:
+        text_encode_id = assembler.node_map[text_encode_name]
+    else:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict) and node.get('class_type') == 'TextEncodeMageFlowEdit':
+                text_encode_id = node_id
+                break
+
+    if not text_encode_id or text_encode_id not in assembler.workflow:
+        print("Warning: TextEncodeMageFlowEdit node not found for Reference Image chain. Skipping.")
+        return
+
+    vae_node_name = chain_definition.get('vae_node', 'vae_loader')
+    vae_node_id = assembler.node_map.get(vae_node_name)
+    if not vae_node_id:
+        for node_id, node in assembler.workflow.items():
+            if isinstance(node, dict) and node.get('class_type') == 'VAELoader':
+                vae_node_id = node_id
+                break
+
+    if vae_node_id:
+        assembler.workflow[text_encode_id]['inputs']['vae'] = [vae_node_id, 0]
+
+    for i, img_filename in enumerate(valid_images):
+        load_id = assembler._get_unique_id()
+        load_node = assembler._get_node_template("LoadImage")
+        load_node['inputs']['image'] = img_filename
+        load_node['_meta']['title'] = f"Load Reference Image {i+1}"
+        assembler.workflow[load_id] = load_node
+
+        scale_id = assembler._get_unique_id()
+        scale_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        scale_node['inputs']['megapixels'] = 1.0
+        scale_node['inputs']['upscale_method'] = "nearest-exact"
+        scale_node['inputs']['resolution_steps'] = 1
+        scale_node['inputs']['image'] = [load_id, 0]
+        scale_node['_meta']['title'] = f"Scale Reference {i+1}"
+        assembler.workflow[scale_id] = scale_node
+
+        input_key = f"images.image_{i+1}"
+        assembler.workflow[text_encode_id]['inputs'][input_key] = [scale_id, 0]
+
+    print(f"Reference Image injector applied. Injected {len(valid_images)} reference images to TextEncodeMageFlowEdit node '{text_encode_id}'.")

chain_injectors/reference_latent_injector.py

@@ -0,0 +1,157 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    guider_node_name = chain_definition.get('guider_node')
+    guider_target_inputs = chain_definition.get('guider_target_inputs', [])
+    start_connections_map = chain_definition.get('start_connections', {})
+    vae_node_name = chain_definition.get('vae_node', 'vae_loader')
+
+    if guider_node_name and guider_node_name in assembler.node_map and guider_target_inputs:
+        guider_id = assembler.node_map[guider_node_name]
+        if vae_node_name not in assembler.node_map:
+            print(f"Warning: VAE node '{vae_node_name}' not found for Guider chain. Skipping.")
+            return
+        vae_node_id = assembler.node_map[vae_node_name]
+
+        print(f"ReferenceLatent injector targeting DualCFGGuider node '{guider_node_name}'.")
+
+        current_connections = {}
+        for target_input in guider_target_inputs:
+            conn_str = start_connections_map.get(target_input)
+            if not conn_str:
+                print(f"Warning: No start connection defined for '{target_input}' in Guider chain. Skipping this input.")
+                continue
+            try:
+                node_name, idx_str = conn_str.split(':')
+                node_id = assembler.node_map[node_name]
+                current_connections[target_input] = [node_id, int(idx_str)]
+            except (ValueError, KeyError):
+                print(f"Warning: Invalid start connection '{conn_str}' for '{target_input}'. Skipping.")
+
+        encoded_latents = []
+        for i, img_filename in enumerate(chain_items):
+            load_id = assembler._get_unique_id()
+            load_node = assembler._get_node_template("LoadImage")
+            load_node['inputs']['image'] = img_filename
+            assembler.workflow[load_id] = load_node
+
+            scale_id = assembler._get_unique_id()
+            scale_node = assembler._get_node_template("ImageScaleToTotalPixels")
+            scale_node['inputs']['megapixels'] = 1.0
+            scale_node['inputs']['upscale_method'] = "lanczos"
+            scale_node['inputs']['image'] = [load_id, 0]
+            assembler.workflow[scale_id] = scale_node
+
+            vae_encode_id = assembler._get_unique_id()
+            vae_encode_node = assembler._get_node_template("VAEEncode")
+            vae_encode_node['inputs']['pixels'] = [scale_id, 0]
+            vae_encode_node['inputs']['vae'] = [vae_node_id, 0]
+            assembler.workflow[vae_encode_id] = vae_encode_node
+            encoded_latents.append([vae_encode_id, 0])
+
+        for target_input_name, start_connection in current_connections.items():
+            current_chain_head = start_connection
+            for i, latent_conn in enumerate(encoded_latents):
+                ref_latent_id = assembler._get_unique_id()
+                ref_latent_node = assembler._get_node_template("ReferenceLatent")
+                ref_latent_node['inputs']['conditioning'] = current_chain_head
+                ref_latent_node['inputs']['latent'] = latent_conn
+                ref_latent_node['_meta']['title'] = f"{target_input_name} RefLatent {i+1}"
+                assembler.workflow[ref_latent_id] = ref_latent_node
+                current_chain_head = [ref_latent_id, 0]
+
+            assembler.workflow[guider_id]['inputs'][target_input_name] = current_chain_head
+            print(f"  - Input '{target_input_name}' of node '{guider_node_name}' re-routed through {len(chain_items)} reference images.")
+
+        return
+
+    flux_guidance_name = chain_definition.get('flux_guidance_node')
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for ReferenceLatent chain. Skipping.")
+        return
+    if vae_node_name not in assembler.node_map:
+        print(f"Warning: VAE loader node '{vae_node_name}' not found for ReferenceLatent chain. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+    vae_node_id = assembler.node_map[vae_node_name]
+
+    pos_target_node_id = None
+    pos_target_input_name = None
+    if flux_guidance_name and flux_guidance_name in assembler.node_map:
+        flux_guidance_id = assembler.node_map[flux_guidance_name]
+        if 'conditioning' in assembler.workflow[flux_guidance_id]['inputs']:
+            pos_target_node_id = flux_guidance_id
+            pos_target_input_name = 'conditioning'
+            print(f"ReferenceLatent injector targeting FluxGuidance node '{flux_guidance_name}' for positive chain.")
+
+    if not pos_target_node_id:
+        if 'positive' in assembler.workflow[ksampler_id]['inputs']:
+            pos_target_node_id = ksampler_id
+            pos_target_input_name = 'positive'
+            print(f"ReferenceLatent injector targeting KSampler node '{ksampler_name}' for positive chain.")
+        else:
+            print(f"Warning: Could not find a valid positive injection point for ReferenceLatent chain. Skipping.")
+            return
+
+    current_pos_conditioning = assembler.workflow[pos_target_node_id]['inputs'][pos_target_input_name]
+
+    neg_target_node_id = ksampler_id
+    neg_target_input_name = 'negative'
+    if 'negative' not in assembler.workflow[neg_target_node_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' has no 'negative' input. Skipping negative ReferenceLatent chain.")
+        neg_target_node_id = None
+
+    current_neg_conditioning = None
+    if neg_target_node_id:
+        current_neg_conditioning = assembler.workflow[neg_target_node_id]['inputs'][neg_target_input_name]
+
+    for i, img_filename in enumerate(chain_items):
+        load_id = assembler._get_unique_id()
+        load_node = assembler._get_node_template("LoadImage")
+        load_node['inputs']['image'] = img_filename
+        load_node['_meta']['title'] = f"Load Reference Image {i+1}"
+        assembler.workflow[load_id] = load_node
+
+        scale_id = assembler._get_unique_id()
+        scale_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        scale_node['inputs']['megapixels'] = 1.0
+        scale_node['inputs']['upscale_method'] = "lanczos"
+        scale_node['inputs']['image'] = [load_id, 0]
+        scale_node['_meta']['title'] = f"Scale Reference {i+1}"
+        assembler.workflow[scale_id] = scale_node
+
+        vae_encode_id = assembler._get_unique_id()
+        vae_encode_node = assembler._get_node_template("VAEEncode")
+        vae_encode_node['inputs']['pixels'] = [scale_id, 0]
+        vae_encode_node['inputs']['vae'] = [vae_node_id, 0]
+        vae_encode_node['_meta']['title'] = f"VAE Encode Reference {i+1}"
+        assembler.workflow[vae_encode_id] = vae_encode_node
+
+        latent_conn = [vae_encode_id, 0]
+
+        pos_ref_latent_id = assembler._get_unique_id()
+        pos_ref_latent_node = assembler._get_node_template("ReferenceLatent")
+        pos_ref_latent_node['inputs']['conditioning'] = current_pos_conditioning
+        pos_ref_latent_node['inputs']['latent'] = latent_conn
+        pos_ref_latent_node['_meta']['title'] = f"Positive ReferenceLatent {i+1}"
+        assembler.workflow[pos_ref_latent_id] = pos_ref_latent_node
+        current_pos_conditioning = [pos_ref_latent_id, 0]
+
+        if neg_target_node_id:
+            neg_ref_latent_id = assembler._get_unique_id()
+            neg_ref_latent_node = assembler._get_node_template("ReferenceLatent")
+            neg_ref_latent_node['inputs']['conditioning'] = current_neg_conditioning
+            neg_ref_latent_node['inputs']['latent'] = latent_conn
+            neg_ref_latent_node['_meta']['title'] = f"Negative ReferenceLatent {i+1}"
+            assembler.workflow[neg_ref_latent_id] = neg_ref_latent_node
+            current_neg_conditioning = [neg_ref_latent_id, 0]
+
+    assembler.workflow[pos_target_node_id]['inputs'][pos_target_input_name] = current_pos_conditioning
+    if neg_target_node_id:
+        assembler.workflow[neg_target_node_id]['inputs'][neg_target_input_name] = current_neg_conditioning
+
+    print(f"ReferenceLatent injector applied. Re-routed inputs through {len(chain_items)} reference images.")

chain_injectors/sd3_ipadapter_injector.py

@@ -0,0 +1,66 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: KSampler node '{ksampler_name}' not found for SD3 IPAdapter chain. Skipping.")
+        return
+
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'model' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'model' input. Skipping SD3 IPAdapter chain.")
+        return
+
+    current_model_connection = assembler.workflow[ksampler_id]['inputs']['model']
+
+    clip_vision_loader_id = assembler._get_unique_id()
+    clip_vision_loader_node = assembler._get_node_template("CLIPVisionLoader")
+    clip_vision_loader_node['inputs']['clip_name'] = "sigclip_vision_patch14_384.safetensors"
+    assembler.workflow[clip_vision_loader_id] = clip_vision_loader_node
+
+    ipadapter_loader_id = assembler._get_unique_id()
+    ipadapter_loader_node = assembler._get_node_template("IPAdapterSD3Loader")
+    ipadapter_loader_node['inputs']['ipadapter'] = "ip-adapter_sd35l_instantx.bin"
+    ipadapter_loader_node['inputs']['provider'] = "cuda"
+    assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+
+    for item_data in chain_items:
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        image_scaler_id = assembler._get_unique_id()
+        image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+        image_scaler_node['inputs']['upscale_method'] = 'nearest-exact'
+        image_scaler_node['inputs']['megapixels'] = 1.0
+        assembler.workflow[image_scaler_id] = image_scaler_node
+
+        clip_vision_encode_id = assembler._get_unique_id()
+        clip_vision_encode_node = assembler._get_node_template("CLIPVisionEncode")
+        clip_vision_encode_node['inputs']['crop'] = "center"
+        clip_vision_encode_node['inputs']['clip_vision'] = [clip_vision_loader_id, 0]
+        clip_vision_encode_node['inputs']['image'] = [image_scaler_id, 0]
+        assembler.workflow[clip_vision_encode_id] = clip_vision_encode_node
+
+        apply_ipa_id = assembler._get_unique_id()
+        apply_ipa_node = assembler._get_node_template("ApplyIPAdapterSD3")
+
+        apply_ipa_node['inputs']['weight'] = item_data.get('weight', 1.0)
+        apply_ipa_node['inputs']['start_percent'] = item_data.get('start_percent', 0.0)
+        apply_ipa_node['inputs']['end_percent'] = item_data.get('end_percent', 1.0)
+
+        apply_ipa_node['inputs']['model'] = current_model_connection
+        apply_ipa_node['inputs']['ipadapter'] = [ipadapter_loader_id, 0]
+        apply_ipa_node['inputs']['image_embed'] = [clip_vision_encode_id, 0]
+
+        assembler.workflow[apply_ipa_id] = apply_ipa_node
+
+        current_model_connection = [apply_ipa_id, 0]
+
+    assembler.workflow[ksampler_id]['inputs']['model'] = current_model_connection
+
+    print(f"SD3 IPAdapter injector applied. KSampler model input re-routed through {len(chain_items)} IPAdapter(s).")

chain_injectors/style_injector.py

@@ -0,0 +1,71 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    flux_guidance_name = chain_definition.get('flux_guidance_node')
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+
+    target_node_id = None
+    target_input_name = None
+
+    if flux_guidance_name and flux_guidance_name in assembler.node_map:
+        flux_guidance_id = assembler.node_map[flux_guidance_name]
+        if 'conditioning' in assembler.workflow[flux_guidance_id]['inputs']:
+            target_node_id = flux_guidance_id
+            target_input_name = 'conditioning'
+
+    if not target_node_id:
+        if ksampler_name in assembler.node_map:
+            ksampler_id = assembler.node_map[ksampler_name]
+            if 'positive' in assembler.workflow[ksampler_id]['inputs']:
+                target_node_id = ksampler_id
+                target_input_name = 'positive'
+        else:
+            return
+
+    if not target_node_id:
+        return
+
+    current_conditioning = assembler.workflow[target_node_id]['inputs'][target_input_name]
+
+    style_model_loader_id = assembler._get_unique_id()
+    style_model_loader_node = assembler._get_node_template("StyleModelLoader")
+    style_model_loader_node['inputs']['style_model_name'] = "flux1-redux-dev.safetensors"
+    assembler.workflow[style_model_loader_id] = style_model_loader_node
+
+    clip_vision_loader_id = assembler._get_unique_id()
+    clip_vision_loader_node = assembler._get_node_template("CLIPVisionLoader")
+    clip_vision_loader_node['inputs']['clip_name'] = "sigclip_vision_patch14_384.safetensors"
+    assembler.workflow[clip_vision_loader_id] = clip_vision_loader_node
+
+    for item_data in chain_items:
+        image = item_data.get('image')
+        strength = item_data.get('strength', 1.0)
+        if not image or strength is None:
+            continue
+
+        load_image_id = assembler._get_unique_id()
+        clip_vision_encode_id = assembler._get_unique_id()
+        style_apply_id = assembler._get_unique_id()
+
+        load_image_node = assembler._get_node_template("LoadImage")
+        clip_vision_encode_node = assembler._get_node_template("CLIPVisionEncode")
+        style_apply_node = assembler._get_node_template("StyleModelApply")
+
+        load_image_node['inputs']['image'] = image
+        clip_vision_encode_node['inputs']['crop'] = "center"
+        clip_vision_encode_node['inputs']['clip_vision'] = [clip_vision_loader_id, 0]
+        clip_vision_encode_node['inputs']['image'] = [load_image_id, 0]
+
+        style_apply_node['inputs']['strength'] = strength
+        style_apply_node['inputs']['strength_type'] = "multiply"
+        style_apply_node['inputs']['conditioning'] = current_conditioning
+        style_apply_node['inputs']['style_model'] = [style_model_loader_id, 0]
+        style_apply_node['inputs']['clip_vision_output'] = [clip_vision_encode_id, 0]
+
+        assembler.workflow[load_image_id] = load_image_node
+        assembler.workflow[clip_vision_encode_id] = clip_vision_encode_node
+        assembler.workflow[style_apply_id] = style_apply_node
+        current_conditioning = [style_apply_id, 0]
+
+    assembler.workflow[target_node_id]['inputs'][target_input_name] = current_conditioning

chain_injectors/vae_injector.py

@@ -0,0 +1,30 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    vae_name = chain_items[0] if isinstance(chain_items, list) else chain_items
+    if not vae_name or vae_name == "None":
+        return
+
+    targets = chain_definition.get('targets', [])
+    if not targets:
+        return
+
+    vae_loader_id = assembler._get_unique_id()
+    vae_loader_node = assembler._get_node_template("VAELoader")
+    vae_loader_node['inputs']['vae_name'] = vae_name
+    assembler.workflow[vae_loader_id] = vae_loader_node
+
+    injected_count = 0
+    for target_str in targets:
+        try:
+            node_name, input_name = target_str.split(':')
+            if node_name in assembler.node_map:
+                node_id = assembler.node_map[node_name]
+                assembler.workflow[node_id]['inputs'][input_name] = [vae_loader_id, 0]
+                injected_count += 1
+        except ValueError:
+            print(f"Warning: Invalid VAE injector target format '{target_str}'. Expected 'node_name:input_name'.")
+
+    if injected_count > 0:
+        print(f"VAE injector applied. Rerouted {injected_count} connection(s) to new VAELoader ({vae_name}).")

comfy_integration/nodes.py

@@ -0,0 +1,44 @@
+import asyncio
+import execution
+import server
+from nodes import (
+    init_extra_nodes, CheckpointLoaderSimple, EmptyLatentImage, KSampler,
+    VAEDecode, SaveImage, NODE_CLASS_MAPPINGS, LoadImage, VAEEncode,
+    VAEEncodeForInpaint, ImagePadForOutpaint, LatentUpscaleBy, RepeatLatentBatch
+)
+
+
+def import_custom_nodes() -> None:
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+
+    loop.run_until_complete(init_extra_nodes())
+
+import_custom_nodes()
+
+CLIPTextEncode = NODE_CLASS_MAPPINGS['CLIPTextEncode']
+CLIPTextEncodeSDXL = NODE_CLASS_MAPPINGS['CLIPTextEncodeSDXL']
+LoraLoader = NODE_CLASS_MAPPINGS['LoraLoader']
+CLIPSetLastLayer = NODE_CLASS_MAPPINGS['CLIPSetLastLayer']
+
+if 'EmptyHunyuanImageLatent' in NODE_CLASS_MAPPINGS:
+    EmptyHunyuanImageLatent = NODE_CLASS_MAPPINGS['EmptyHunyuanImageLatent']
+else:
+    print("⚠️ Warning: 'EmptyHunyuanImageLatent' not found in NODE_CLASS_MAPPINGS. HunyuanImage txt2img may fail if this node is required.")
+
+try:
+    KSamplerNode = NODE_CLASS_MAPPINGS['KSampler']
+    SAMPLER_CHOICES = KSamplerNode.INPUT_TYPES()["required"]["sampler_name"][0]
+    SCHEDULER_CHOICES = KSamplerNode.INPUT_TYPES()["required"]["scheduler"][0]
+except Exception:
+    print("⚠️ Could not dynamically get sampler/scheduler choices, using fallback list.")
+    SAMPLER_CHOICES = ['euler', 'dpmpp_2m_sde_gpu']
+    SCHEDULER_CHOICES = ['normal', 'karras']
+
+checkpointloadersimple = CheckpointLoaderSimple()
+loraloader = LoraLoader()
+
+
+print("✅ ComfyUI custom nodes and class mappings are ready.")

comfy_integration/setup.py

@@ -0,0 +1,161 @@
+"""Install the pinned ComfyUI runtime without overwriting application code."""
+
+from __future__ import annotations
+
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import yaml
+
+from core.settings import CATEGORY_TO_DIR_MAP, INPUT_DIR, OUTPUT_DIR
+
+
+APP_DIR = Path(__file__).resolve().parents[1]
+LOCK_FILE = APP_DIR / "vendor.lock.yaml"
+VENDOR_DIR = APP_DIR / "_vendor"
+CUSTOM_NODES_DIR = APP_DIR / "custom_nodes"
+
+
+def _bounded_env_int(name: str, default: int, minimum: int, maximum: int) -> int:
+    try:
+        value = int(os.getenv(name, str(default)))
+    except ValueError:
+        value = default
+    return max(minimum, min(maximum, value))
+
+
+GIT_TIMEOUT_SECONDS = _bounded_env_int(
+    "IMAGEGEN_GIT_TIMEOUT_SECONDS", 180, 30, 900
+)
+GIT_NETWORK_ATTEMPTS = _bounded_env_int("IMAGEGEN_GIT_ATTEMPTS", 2, 1, 5)
+
+
+def _run_git(*args: str, cwd: Path | None = None) -> str:
+    attempts = GIT_NETWORK_ATTEMPTS if args and args[0] == "fetch" else 1
+    for attempt in range(1, attempts + 1):
+        try:
+            completed = subprocess.run(
+                ["git", *args],
+                cwd=str(cwd) if cwd else None,
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+                timeout=GIT_TIMEOUT_SECONDS,
+            )
+            return completed.stdout.strip()
+        except subprocess.TimeoutExpired as exc:
+            if attempt == attempts:
+                raise RuntimeError(
+                    f"Git 操作超过 {GIT_TIMEOUT_SECONDS} 秒：git {args[0]}。"
+                    "可稍后重启，或设置 COMFYUI_PATH 使用本地 checkout。"
+                ) from exc
+            print(f"⚠️ Git {args[0]} 超时，正在重试（{attempt}/{attempts}）…")
+        except subprocess.CalledProcessError:
+            if attempt == attempts:
+                raise
+            print(f"⚠️ Git {args[0]} 失败，正在重试（{attempt}/{attempts}）…")
+    raise RuntimeError("Unreachable git retry state")
+
+
+def _ensure_pinned_repo(name: str, url: str, revision: str, destination: Path) -> None:
+    if destination.exists() and not (destination / ".git").is_dir():
+        raise RuntimeError(
+            f"{name} 目录已存在但不是 Git 仓库：{destination}。"
+            "请移走该目录后重新启动。"
+        )
+
+    if not destination.exists():
+        destination.parent.mkdir(parents=True, exist_ok=True)
+        print(f"--- [Vendor] Cloning pinned {name} ---")
+        partial = destination.with_name(f"{destination.name}.partial")
+        last_error = None
+        for attempt in range(1, GIT_NETWORK_ATTEMPTS + 1):
+            if partial.exists():
+                shutil.rmtree(partial)
+            try:
+                _run_git(
+                    "clone", "--filter=blob:none", "--no-checkout", url, str(partial)
+                )
+                partial.replace(destination)
+                last_error = None
+                break
+            except (subprocess.CalledProcessError, RuntimeError) as exc:
+                last_error = exc
+                if attempt < GIT_NETWORK_ATTEMPTS:
+                    print(f"⚠️ {name} clone 失败，正在重试（{attempt}/{GIT_NETWORK_ATTEMPTS}）…")
+        if last_error is not None:
+            raise RuntimeError(
+                f"无法拉取 {name}。可稍后重启；本地离线运行可设置 "
+                "COMFYUI_PATH，并按需设置 IMAGEGEN_SKIP_CUSTOM_NODES=1。"
+            ) from last_error
+
+    current = ""
+    try:
+        current = _run_git("rev-parse", "HEAD", cwd=destination)
+    except (subprocess.CalledProcessError, RuntimeError):
+        pass
+
+    if current != revision:
+        print(f"--- [Vendor] Checking out {name} @ {revision[:12]} ---")
+        _run_git("fetch", "--depth", "1", "origin", revision, cwd=destination)
+        _run_git("checkout", "--detach", "--force", "FETCH_HEAD", cwd=destination)
+
+    actual = _run_git("rev-parse", "HEAD", cwd=destination)
+    if actual != revision:
+        raise RuntimeError(
+            f"{name} 版本不匹配：期望 {revision}，实际 {actual}。"
+        )
+    print(f"✅ {name} ready @ {actual[:12]}")
+
+
+def _load_lock() -> dict:
+    with LOCK_FILE.open("r", encoding="utf-8") as handle:
+        data = yaml.safe_load(handle) or {}
+    if "comfyui" not in data:
+        raise RuntimeError(f"Missing comfyui entry in {LOCK_FILE}")
+    return data
+
+
+def initialize_comfyui() -> Path:
+    """Prepare pinned sources and make ComfyUI importable.
+
+    Set ``COMFYUI_PATH`` to use an existing local checkout. This is the
+    recommended offline/local-development route.
+    """
+
+    lock = _load_lock()
+    configured_path = os.getenv("COMFYUI_PATH", "").strip()
+
+    if configured_path:
+        comfyui_path = Path(configured_path).expanduser().resolve()
+        if not (comfyui_path / "nodes.py").is_file():
+            raise RuntimeError(f"COMFYUI_PATH is not a ComfyUI checkout: {comfyui_path}")
+    else:
+        comfyui_path = VENDOR_DIR / "ComfyUI"
+        comfy = lock["comfyui"]
+        _ensure_pinned_repo("ComfyUI", comfy["url"], comfy["revision"], comfyui_path)
+
+    CUSTOM_NODES_DIR.mkdir(parents=True, exist_ok=True)
+    if os.getenv("IMAGEGEN_SKIP_CUSTOM_NODES", "0").lower() not in {"1", "true", "yes"}:
+        for name, spec in (lock.get("custom_nodes") or {}).items():
+            _ensure_pinned_repo(name, spec["url"], spec["revision"], CUSTOM_NODES_DIR / name)
+
+    # ComfyUI contains a top-level `utils` package. The application uses the
+    # collision-free `imagegen_utils` package, so ComfyUI can safely come first.
+    comfyui_str = str(comfyui_path)
+    if comfyui_str not in sys.path:
+        sys.path.insert(0, comfyui_str)
+
+    for relative_path in CATEGORY_TO_DIR_MAP.values():
+        (APP_DIR / relative_path).mkdir(parents=True, exist_ok=True)
+    (APP_DIR / INPUT_DIR).mkdir(parents=True, exist_ok=True)
+    (APP_DIR / OUTPUT_DIR).mkdir(parents=True, exist_ok=True)
+
+    import comfy.model_management  # noqa: F401
+
+    print(f"✅ ComfyUI initialized from isolated path: {comfyui_path}")
+    return comfyui_path

core/execution_plan.py

@@ -0,0 +1,450 @@
+"""Build small sequential generation plans for PK and multi-image workflows.
+
+The in-process ComfyUI runtime is intentionally single-model-at-a-time.  This
+module therefore expands comparisons into bounded sequential runs instead of
+trying to keep several checkpoints resident on one ZeroGPU allocation.
+"""
+
+from __future__ import annotations
+
+import random
+from collections.abc import Callable, Iterable, Sequence
+from dataclasses import dataclass
+from itertools import pairwise
+from pathlib import Path
+from typing import Any
+
+from PIL import Image
+
+from core.model_capabilities import supports_chain_for_model
+from core.runtime_config import CONFIG
+from core.settings import (
+    ARCHITECTURES_CONFIG,
+    FEATURES_CONFIG,
+    MODEL_DEFAULTS_CONFIG,
+    MODEL_MAP_CHECKPOINT,
+    MODEL_TYPE_MAP,
+)
+
+MODE_SINGLE = "single"
+MODE_MODEL_PK = "model_pk"
+MODE_MULTI_INDEPENDENT = "multi_independent"
+MODE_MULTI_MODEL_GRID = "multi_model_grid"
+MODE_MULTI_REFERENCE = "multi_reference"
+
+RUN_MODE_CHOICES = [
+    ("普通生成", MODE_SINGLE),
+    ("模型 PK：同一输入对比多个模型", MODE_MODEL_PK),
+    ("多图独立：每张图分别处理", MODE_MULTI_INDEPENDENT),
+    ("多图 × 多模型：组合对比", MODE_MULTI_MODEL_GRID),
+    ("多图融合：多张参考图生成一组结果", MODE_MULTI_REFERENCE),
+]
+
+INDEPENDENT_IMAGE_TASK_KEYS = {
+    "img2img": "img2img_image",
+    "outpaint": "outpaint_image",
+    "hires_fix": "hires_image",
+}
+
+COMPARISON_CHAIN_INPUT_KEYS = (
+    "lora_data",
+    "controlnet_data",
+    "anima_controlnet_lllite_data",
+    "diffsynth_controlnet_data",
+    "krea2_controlnet_data",
+    "ipadapter_data",
+    "sd3_ipadapter_chain",
+    "flux1_ipadapter_data",
+    "style_data",
+    "embedding_data",
+    "conditioning_data",
+    "reference_latent_data",
+    "hidream_o1_reference_data",
+    "joyai_reference_data",
+    "krea2_identity_edit_data",
+    "krea2_reference_edit_data",
+    "qwen_image_edit_data",
+    "boogu_edit_data",
+    "reference_image_data",
+)
+
+# (chain name, pipeline input key, maximum images supported by its injector)
+REFERENCE_CHAIN_SPECS = {
+    "qwen_image_edit": ("qwen_image_edit_data", 3),
+    "joyai_image": ("joyai_reference_data", 2),
+    "boogu_image_edit": ("boogu_edit_data", 10),
+    "reference_image": ("reference_image_data", 10),
+    "reference_latent": ("reference_latent_data", 10),
+    "hidream_o1_reference": ("hidream_o1_reference_data", 10),
+    "krea2_identity_edit": ("krea2_identity_edit_data", 2),
+    "krea2_style_reference": ("krea2_reference_edit_data", 3),
+}
+
+
+class ExecutionPlanError(ValueError):
+    pass
+
+
+@dataclass(frozen=True)
+class PlannedGeneration:
+    inputs: dict[str, Any]
+    caption: str
+
+
+def _unique(values: Iterable[str]) -> list[str]:
+    return list(dict.fromkeys(value for value in values if value))
+
+
+def _caption(label: str, values: dict[str, Any]) -> str:
+    seed = values.get("seed", "-")
+    steps = values.get("num_inference_steps", "-")
+    cfg = values.get("guidance_scale", "-")
+    return f"{label} · Seed {seed} · {steps} 步 · CFG {cfg}"
+
+
+def _workflow_type(model_name: str) -> str:
+    architecture = MODEL_TYPE_MAP.get(model_name, "SDXL")
+    architecture_info = ARCHITECTURES_CONFIG.get("architectures", {}).get(
+        architecture, {}
+    )
+    return architecture_info.get(
+        "model_type", architecture.lower().replace(" ", "").replace(".", "")
+    )
+
+
+def _model_defaults(model_name: str) -> dict[str, Any]:
+    workflow_type = _workflow_type(model_name)
+    defaults = {
+        "steps": 25,
+        "cfg": 7.0,
+        "sampler_name": "euler",
+        "scheduler": "simple",
+    }
+    defaults.update(MODEL_DEFAULTS_CONFIG.get("Default", {}))
+    type_key = next(
+        (
+            key
+            for key in MODEL_DEFAULTS_CONFIG
+            if key.lower().replace(" ", "-").replace(".", "")
+            == workflow_type.lower()
+        ),
+        None,
+    )
+    if type_key:
+        section = MODEL_DEFAULTS_CONFIG.get(type_key, {})
+        defaults.update(section.get("_defaults", {}))
+        defaults.update(section.get(model_name, {}))
+    return defaults
+
+
+def _for_model(
+    base_inputs: dict[str, Any], model_name: str, use_model_defaults: bool
+) -> dict[str, Any]:
+    # Pipeline processing replaces and occasionally mutates chain containers.
+    # Copy those containers while sharing immutable/PIL payloads.
+    values = {
+        key: list(value)
+        if isinstance(value, list)
+        else dict(value)
+        if isinstance(value, dict)
+        else value
+        for key, value in base_inputs.items()
+    }
+    values["model_display_name"] = model_name
+    if use_model_defaults:
+        defaults = _model_defaults(model_name)
+        values.update(
+            {
+                "num_inference_steps": defaults.get("steps", 20),
+                "guidance_scale": defaults.get("cfg", 1.0),
+                "sampler": defaults.get("sampler_name", "euler"),
+                "scheduler": defaults.get("scheduler", "simple"),
+            }
+        )
+    return values
+
+
+def _make_fair_comparison(values: dict[str, Any]) -> None:
+    """Keep V1 comparisons to capabilities shared by every base checkpoint."""
+
+    for key in COMPARISON_CHAIN_INPUT_KEYS:
+        values[key] = []
+    values["pid_settings"] = "OFF"
+    values["vae_source"] = None
+    values["vae_id"] = None
+    values["vae_file"] = None
+
+
+def load_uploaded_images(uploaded_files: Sequence[Any] | None) -> list[Image.Image]:
+    """Materialize Gradio File values as detached PIL images."""
+
+    images: list[Image.Image] = []
+    total_megapixels = 0.0
+    for item in uploaded_files or []:
+        raw_path = getattr(item, "name", item)
+        if not raw_path:
+            continue
+        path = Path(str(raw_path))
+        if not path.is_file():
+            raise ExecutionPlanError(f"找不到上传图片：{path.name}")
+        try:
+            with Image.open(path) as source:
+                source.load()
+                megapixels = (source.width * source.height) / 1_000_000
+                if megapixels > CONFIG.max_input_megapixels:
+                    raise ExecutionPlanError(
+                        f"图片“{path.name}”为 {megapixels:.1f} MP，超过单图上限 "
+                        f"{CONFIG.max_input_megapixels:g} MP。"
+                    )
+                total_megapixels += megapixels
+                if total_megapixels > CONFIG.max_reference_megapixels:
+                    raise ExecutionPlanError(
+                        f"上传图片累计为 {total_megapixels:.1f} MP，超过上限 "
+                        f"{CONFIG.max_reference_megapixels:g} MP；请缩小图片或减少数量。"
+                    )
+                images.append(source.convert("RGB").copy())
+        except ExecutionPlanError:
+            raise
+        except Exception as exc:
+            raise ExecutionPlanError(f"无法读取图片“{path.name}”：{exc}") from exc
+
+    if len(images) > CONFIG.max_multi_images:
+        raise ExecutionPlanError(
+            f"一次最多上传 {CONFIG.max_multi_images} 张图片；当前为 {len(images)} 张。"
+        )
+    return images
+
+
+def _pick_reference_chain(model_name: str, role: str) -> tuple[str, str, int]:
+    workflow_type = _workflow_type(model_name)
+    enabled = set(
+        FEATURES_CONFIG.get(workflow_type, {}).get("enabled_chains", [])
+    )
+
+    if role == "identity":
+        order = ["krea2_identity_edit"]
+    elif role == "style":
+        # The generic FLUX style injector has a different image/weight schema;
+        # keep this high-level path limited to the validated Krea reference chain.
+        order = ["krea2_style_reference"]
+    else:
+        order = [
+            "qwen_image_edit",
+            "joyai_image",
+            "boogu_image_edit",
+            "reference_image",
+            "reference_latent",
+            "hidream_o1_reference",
+            "krea2_identity_edit",
+            "krea2_style_reference",
+        ]
+
+    for chain_name in order:
+        if (
+            chain_name in enabled
+            and chain_name in REFERENCE_CHAIN_SPECS
+            and supports_chain_for_model(model_name, chain_name)
+        ):
+            input_key, maximum = REFERENCE_CHAIN_SPECS[chain_name]
+            return chain_name, input_key, maximum
+
+    if role in {"identity", "style"}:
+        raise ExecutionPlanError(
+            f"模型“{model_name}”不支持所选的{('身份' if role == 'identity' else '风格')}参考方式。"
+        )
+    raise ExecutionPlanError(
+        f"模型“{model_name}”没有可自动使用的多图参考链；请换用编辑/多模态模型。"
+    )
+
+
+def build_execution_plan(
+    base_inputs: dict[str, Any],
+    mode: str = MODE_SINGLE,
+    extra_models: Sequence[str] | None = None,
+    images: Sequence[Image.Image] | None = None,
+    reference_role: str = "auto",
+    use_model_defaults: bool = True,
+) -> list[PlannedGeneration]:
+    """Expand one UI submission into a bounded list of sequential runs."""
+
+    if mode not in {choice[1] for choice in RUN_MODE_CHOICES}:
+        raise ExecutionPlanError(f"未知运行模式：{mode}")
+
+    base_model = str(base_inputs.get("model_display_name") or "")
+    if base_model not in MODEL_MAP_CHECKPOINT:
+        raise ExecutionPlanError("请先选择有效模型。")
+
+    comparison_mode = mode in {MODE_MODEL_PK, MODE_MULTI_MODEL_GRID}
+    models = _unique(
+        [base_model, *(extra_models or [])] if comparison_mode else [base_model]
+    )
+    unknown_models = [name for name in models if name not in MODEL_MAP_CHECKPOINT]
+    if unknown_models:
+        raise ExecutionPlanError(f"未知模型：{', '.join(unknown_models)}")
+    if len(models) > CONFIG.max_pk_models:
+        raise ExecutionPlanError(
+            f"模型 PK 最多 {CONFIG.max_pk_models} 个模型；当前为 {len(models)} 个。"
+        )
+
+    if mode in {MODE_MODEL_PK, MODE_MULTI_MODEL_GRID} and len(models) < 2:
+        raise ExecutionPlanError("模型 PK 至少需要再选择 1 个对比模型。")
+
+    source_images = list(images or [])
+    shared_seed = base_inputs.get("seed", -1)
+    try:
+        shared_seed = int(shared_seed)
+    except (TypeError, ValueError):
+        shared_seed = -1
+    if shared_seed < 0 and mode != MODE_SINGLE:
+        shared_seed = random.randint(0, 2**32 - 1)
+
+    plan: list[PlannedGeneration] = []
+    if mode == MODE_SINGLE:
+        plan.append(PlannedGeneration(dict(base_inputs), base_model))
+
+    elif mode == MODE_MODEL_PK:
+        for model_name in models:
+            values = _for_model(base_inputs, model_name, use_model_defaults)
+            _make_fair_comparison(values)
+            values["seed"] = shared_seed
+            plan.append(
+                PlannedGeneration(values, _caption(f"模型 PK · {model_name}", values))
+            )
+
+    elif mode in {MODE_MULTI_INDEPENDENT, MODE_MULTI_MODEL_GRID}:
+        task_type = str(base_inputs.get("task_type") or "")
+        task_input_key = INDEPENDENT_IMAGE_TASK_KEYS.get(task_type)
+        if not task_input_key:
+            raise ExecutionPlanError(
+                "多图独立处理仅支持图生图、扩图和高清修复；局部重绘需要逐张绘制蒙版。"
+            )
+        if not source_images:
+            raise ExecutionPlanError("请上传至少 1 张批量输入图片。")
+        target_models = models if mode == MODE_MULTI_MODEL_GRID else [base_model]
+        # Keep one checkpoint active for all its inputs before switching.  This
+        # avoids needless reloads while preserving Gallery captions by source.
+        for model_name in target_models:
+            for image_index, image in enumerate(source_images, start=1):
+                values = _for_model(
+                    base_inputs,
+                    model_name,
+                    use_model_defaults if mode == MODE_MULTI_MODEL_GRID else False,
+                )
+                if mode == MODE_MULTI_MODEL_GRID:
+                    _make_fair_comparison(values)
+                values["seed"] = shared_seed
+                values[task_input_key] = image
+                caption = _caption(f"输入 {image_index} · {model_name}", values)
+                plan.append(PlannedGeneration(values, caption))
+
+    elif mode == MODE_MULTI_REFERENCE:
+        if str(base_inputs.get("task_type")) != "txt2img":
+            raise ExecutionPlanError("多图融合请把任务切换为“文生图”；参考图会直接进入编辑模型。")
+        if not source_images:
+            raise ExecutionPlanError("多图融合需要上传至少 1 张参考图。")
+        chain_name, input_key, maximum = _pick_reference_chain(
+            base_model, reference_role
+        )
+        if len(source_images) > maximum:
+            raise ExecutionPlanError(
+                f"当前模型的 {chain_name} 最多支持 {maximum} 张参考图。"
+            )
+        values = _for_model(base_inputs, base_model, False)
+        existing = [value for value in values.get(input_key, []) if value is not None]
+        values[input_key] = [*existing, *source_images][:maximum]
+        values["seed"] = shared_seed
+        plan.append(
+            PlannedGeneration(
+                values,
+                _caption(
+                    f"多图融合 · {base_model} · {len(source_images)} 张参考图",
+                    values,
+                ),
+            )
+        )
+
+    if len(plan) > CONFIG.max_plan_jobs:
+        raise ExecutionPlanError(
+            f"本次会产生 {len(plan)} 个任务，超过上限 {CONFIG.max_plan_jobs}；请减少图片或模型。"
+        )
+    batch_size = max(1, int(base_inputs.get("batch_size") or 1))
+    estimated_outputs = len(plan) * batch_size
+    if estimated_outputs > CONFIG.max_plan_outputs:
+        raise ExecutionPlanError(
+            f"预计输出 {estimated_outputs} 张，超过上限 {CONFIG.max_plan_outputs}；"
+            "请减少模型、输入图片或单次生成数量。"
+        )
+    # Release Comfy's global model state only at an actual model boundary.  The
+    # final model remains warm for a likely follow-up generation.
+    for current, following in pairwise(plan):
+        if (
+            current.inputs.get("model_display_name")
+            != following.inputs.get("model_display_name")
+        ):
+            current.inputs["_release_models_after_run"] = True
+    return plan
+
+
+class _PlanProgress:
+    def __init__(self, parent: Any, index: int, total: int, caption: str):
+        self.parent = parent
+        self.index = index
+        self.total = total
+        self.caption = caption
+
+    def __call__(self, value: float = 0.0, desc: str | None = None):
+        if not self.parent:
+            return None
+        try:
+            fraction = max(0.0, min(1.0, float(value)))
+        except (TypeError, ValueError):
+            fraction = 0.0
+        overall = (self.index + fraction) / self.total
+        detail = f"[{self.index + 1}/{self.total}] {self.caption}"
+        if desc:
+            detail += f" · {desc}"
+        return self.parent(overall, desc=detail)
+
+
+def execute_generation_plan(
+    plan: Sequence[PlannedGeneration],
+    generate: Callable[[dict[str, Any], Any], Any],
+    progress: Any = None,
+    cancel_event: Any = None,
+) -> tuple[list[Any], str]:
+    """Run the plan sequentially and retain partial successes."""
+
+    gallery: list[Any] = []
+    summary: list[str] = []
+    total = max(1, len(plan))
+    for index, item in enumerate(plan):
+        if cancel_event is not None and cancel_event.is_set():
+            if gallery:
+                summary.append("- ⏹️ 已取消：后续组合未执行，已保留成功结果。")
+                break
+            raise ExecutionPlanError("任务已取消，后续组合未执行。")
+        try:
+            result = generate(
+                item.inputs,
+                _PlanProgress(progress, index, total, item.caption),
+            )
+            paths = result if isinstance(result, list) else ([result] if result else [])
+            for output_index, path in enumerate(paths, start=1):
+                caption = item.caption
+                if len(paths) > 1:
+                    caption += f" · 结果 {output_index}"
+                gallery.append((path, caption))
+            summary.append(f"- ✅ {item.caption}：{len(paths)} 张")
+        except Exception as exc:
+            if cancel_event is not None and cancel_event.is_set():
+                if gallery:
+                    summary.append("- ⏹️ 已取消：后续组合未执行，已保留成功结果。")
+                    break
+                raise
+            summary.append(f"- ❌ {item.caption}：{exc}")
+
+    if not gallery:
+        raise ExecutionPlanError("本次任务没有成功生成图片。\n" + "\n".join(summary))
+    if progress:
+        progress(1.0, desc="全部组合执行完成。")
+    return gallery, "### 本次执行\n" + "\n".join(summary)

core/generation_logic.py

@@ -0,0 +1,10 @@
+from typing import Any, Dict
+import gradio as gr
+
+from core.pipelines.sd_image_pipeline import SdImagePipeline
+
+sd_image_pipeline = SdImagePipeline()
+
+
+def generate_image_wrapper(ui_inputs: dict, progress=gr.Progress(track_tqdm=True)):
+    return sd_image_pipeline.run(ui_inputs=ui_inputs, progress=progress)

core/model_capabilities.py

@@ -0,0 +1,22 @@
+"""Small checkpoint-level capability guards layered over architecture YAML."""
+
+from __future__ import annotations
+
+_EDIT_ONLY_CHAINS = {
+    "qwen_image_edit",
+    "boogu_image_edit",
+    "reference_image",  # Mage-Flow edit checkpoints
+}
+
+
+def supports_chain_for_model(model_name: str, chain_name: str) -> bool:
+    """Return whether a concrete checkpoint is known to support a chain.
+
+    Most capabilities apply to every checkpoint of an architecture.  Qwen,
+    Boogu, and Mage-Flow register generation and edit checkpoints together, so
+    their reference injectors require the concrete Edit variant.
+    """
+
+    if chain_name in _EDIT_ONLY_CHAINS:
+        return "edit" in str(model_name).casefold()
+    return True

core/model_manager.py

@@ -0,0 +1,63 @@
+import gc
+from typing import List
+import gradio as gr
+from imagegen_utils.app_utils import _ensure_model_downloaded
+from core.settings import ALL_MODEL_MAP
+
+class ModelManager:
+    _instance = None
+
+    def __new__(cls, *args, **kwargs):
+        if not cls._instance:
+            cls._instance = super(ModelManager, cls).__new__(cls, *args, **kwargs)
+        return cls._instance
+
+    def __init__(self):
+        if hasattr(self, 'initialized'):
+            return
+        self.initialized = True
+        print("✅ ModelManager initialized.")
+
+    def ensure_models_downloaded(self, required_models: List[str], progress):
+        print(f"--- [ModelManager] Ensuring models are downloaded: {required_models} ---")
+        for i, display_name in enumerate(required_models):
+            if progress and hasattr(progress, '__call__'):
+                progress(i / max(len(required_models), 1), desc=f"Checking file: {display_name}")
+            try:
+                _ensure_model_downloaded(display_name, progress)
+            except Exception as e:
+                raise gr.Error(f"模型“{display_name}”下载失败：{e}")
+        print(f"--- [ModelManager] ✅ All required models are present on disk. ---")
+
+model_manager = ModelManager()
+
+
+def release_loaded_models() -> bool:
+    """Best-effort release of ComfyUI model state while GPU access is active."""
+
+    released = False
+    try:
+        from comfy import model_management
+
+        unload = getattr(model_management, "unload_all_models", None)
+        if callable(unload):
+            unload()
+            released = True
+
+        cleanup = getattr(model_management, "cleanup_models", None)
+        if callable(cleanup):
+            cleanup()
+
+        gc.collect()
+        empty_cache = getattr(model_management, "soft_empty_cache", None)
+        if callable(empty_cache):
+            try:
+                empty_cache(force=True)
+            except TypeError:
+                empty_cache()
+        print("✅ Released ComfyUI model state after a model switch/error.")
+    except Exception as exc:
+        # Cleanup must never hide the original generation result or exception.
+        gc.collect()
+        print(f"Warning: Could not fully release ComfyUI model state: {exc}")
+    return released

core/pipelines/base_pipeline.py

@@ -0,0 +1,55 @@
+from abc import ABC, abstractmethod
+from typing import List, Any, Dict
+import gradio as gr
+import spaces
+import tempfile
+import imageio
+import numpy as np
+import sys
+import os
+
+class BasePipeline(ABC):
+    def __init__(self):
+        from core.model_manager import model_manager
+        self.model_manager = model_manager
+
+    @abstractmethod
+    def get_required_models(self, **kwargs) -> List[str]:
+        pass
+
+    @abstractmethod
+    def run(self, *args, progress: gr.Progress, **kwargs) -> Any:
+        pass
+
+    def _ensure_models_downloaded(self, progress: gr.Progress, **kwargs):
+        """Ensures model files are downloaded before requesting GPU."""
+        required_models = self.get_required_models(**kwargs)
+        self.model_manager.ensure_models_downloaded(required_models, progress=progress)
+
+    def _execute_gpu_logic(self, gpu_function: callable, duration: int, default_duration: int, task_name: str, *args, **kwargs):
+        final_duration = default_duration
+        try:
+            if duration is not None and int(duration) > 0:
+                final_duration = int(duration)
+        except (ValueError, TypeError):
+            print(f"Invalid ZeroGPU duration input for {task_name}. Using default {default_duration}s.")
+            pass
+
+        print(f"Requesting ZeroGPU for {task_name} with duration: {final_duration} seconds.")
+        gpu_runner = spaces.GPU(duration=final_duration)(gpu_function)
+
+        return gpu_runner(*args, **kwargs)
+
+    def _encode_video_from_frames(self, frames_tensor_cpu: 'torch.Tensor', fps: int, progress: gr.Progress) -> str:
+        progress(0.9, desc="Encoding video on CPU...")
+        frames_np = (frames_tensor_cpu.numpy() * 255.0).astype(np.uint8)
+
+        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_video_file:
+            video_path = temp_video_file.name
+            writer = imageio.get_writer(video_path, fps=fps, codec='libx264', quality=8)
+            for frame in frames_np:
+                writer.append_data(frame)
+            writer.close()
+
+        progress(1.0, desc="Done!")
+        return video_path

core/pipelines/pipeline_input_processor.py

@@ -0,0 +1,580 @@
+import os
+import uuid
+import numpy as np
+import gradio as gr
+from PIL import Image, ImageChops
+from typing import Dict, Any, List
+
+from core.settings import INPUT_DIR, MULTIPLIERS_MAP, LORA_DIR, EMBEDDING_DIR, VAE_DIR
+from core.runtime_config import CONFIG
+from imagegen_utils.app_utils import (
+    sanitize_filename,
+    get_lora_path,
+    get_embedding_path,
+    ensure_controlnet_model_downloaded,
+    ensure_ipadapter_models_downloaded,
+    _ensure_model_downloaded,
+    ensure_sd3_ipadapter_models_downloaded,
+    get_vae_path,
+)
+
+
+def _temp_png(stem: str) -> str:
+    return os.path.join(INPUT_DIR, f"{stem}_{uuid.uuid4().hex}.png")
+
+
+REFERENCE_IMAGE_LIMITS = {
+    "controlnet_data": 5,
+    "anima_controlnet_lllite_data": 5,
+    "diffsynth_controlnet_data": 5,
+    "krea2_controlnet_data": 5,
+    "ipadapter_data": 5,
+    "flux1_ipadapter_data": 5,
+    "sd3_ipadapter_chain": 5,
+    "style_data": 5,
+    "reference_latent_data": 10,
+    "hidream_o1_reference_data": 10,
+    "joyai_reference_data": 2,
+    "krea2_identity_edit_data": 2,
+    "krea2_reference_edit_data": 3,
+    "qwen_image_edit_data": 3,
+    "boogu_edit_data": 10,
+    "reference_image_data": 10,
+}
+
+
+def _pil_images(value: Any):
+    if isinstance(value, Image.Image):
+        yield value
+    elif isinstance(value, dict):
+        for child in value.values():
+            yield from _pil_images(child)
+    elif isinstance(value, (list, tuple)):
+        for child in value:
+            yield from _pil_images(child)
+
+
+def _validate_reference_image_budget(ui_inputs: Dict[str, Any]) -> None:
+    """Bound decoded reference images before saving or entering a workflow."""
+
+    all_images = []
+    for input_key, chain_limit in REFERENCE_IMAGE_LIMITS.items():
+        images = list(_pil_images(ui_inputs.get(input_key)))
+        if len(images) > chain_limit:
+            raise gr.Error(
+                f"扩展“{input_key}”最多支持 {chain_limit} 张图片；当前为 {len(images)} 张。"
+            )
+        all_images.extend(images)
+
+    if len(all_images) > CONFIG.max_reference_images:
+        raise gr.Error(
+            f"一次任务最多使用 {CONFIG.max_reference_images} 张参考/控制图；"
+            f"当前合计 {len(all_images)} 张。"
+        )
+
+    total_megapixels = 0.0
+    for index, image in enumerate(all_images, start=1):
+        megapixels = (image.width * image.height) / 1_000_000
+        if megapixels > CONFIG.max_input_megapixels:
+            raise gr.Error(
+                f"参考图 {index} 为 {megapixels:.1f}MP，超过单图上限 "
+                f"{CONFIG.max_input_megapixels:g}MP。"
+            )
+        total_megapixels += megapixels
+    if total_megapixels > CONFIG.max_reference_megapixels:
+        raise gr.Error(
+            f"参考/控制图累计为 {total_megapixels:.1f}MP，超过上限 "
+            f"{CONFIG.max_reference_megapixels:g}MP；请缩小图片或减少数量。"
+        )
+
+def process_pipeline_inputs(ui_inputs: Dict[str, Any], progress: gr.Progress, workflow_model_type: str) -> Dict[str, Any]:
+    task_type = ui_inputs['task_type']
+    temp_files_to_clean = []
+    _validate_reference_image_budget(ui_inputs)
+
+    multiplier = MULTIPLIERS_MAP.get(workflow_model_type, 8)
+    img_w, img_h = 0, 0
+    if task_type == 'txt2img':
+        img_w = int(ui_inputs.get('width', 0))
+        img_h = int(ui_inputs.get('height', 0))
+    elif task_type == 'img2img':
+        input_image_pil = ui_inputs.get('img2img_image')
+        if input_image_pil:
+            img_w, img_h = input_image_pil.width, input_image_pil.height
+    elif task_type == 'inpaint':
+        inpaint_img = ui_inputs.get('inpaint_image')
+        inpaint_dict = ui_inputs.get('inpaint_image_dict')
+        if inpaint_img:
+            img_w, img_h = inpaint_img.width, inpaint_img.height
+        elif inpaint_dict and inpaint_dict.get('background'):
+            img_w, img_h = inpaint_dict['background'].width, inpaint_dict['background'].height
+    elif task_type == 'outpaint':
+        input_image_pil = ui_inputs.get('outpaint_image')
+        if input_image_pil:
+            img_w, img_h = input_image_pil.width, input_image_pil.height
+    elif task_type == 'hires_fix':
+        input_image_pil = ui_inputs.get('hires_image')
+        if input_image_pil:
+            img_w, img_h = input_image_pil.width, input_image_pil.height
+
+    if task_type == "txt2img" and (img_w <= 0 or img_h <= 0):
+        raise gr.Error("文生图的宽度和高度必须为正整数。")
+
+    if img_w > 0 and img_h > 0:
+        input_megapixels = (img_w * img_h) / 1_000_000
+        if input_megapixels > CONFIG.max_input_megapixels:
+            scale = (CONFIG.max_input_megapixels / input_megapixels) ** 0.5
+            suggested_w = max(multiplier, int(img_w * scale) // multiplier * multiplier)
+            suggested_h = max(multiplier, int(img_h * scale) // multiplier * multiplier)
+            raise gr.Error(
+                f"输入图片为 {input_megapixels:.1f}MP，超过当前上限 "
+                f"{CONFIG.max_input_megapixels:g}MP；建议缩小到约 "
+                f"{suggested_w}×{suggested_h}。"
+            )
+
+        projected_w, projected_h = img_w, img_h
+        if task_type == "hires_fix":
+            upscale = float(ui_inputs.get("hires_scale_by") or 1.5)
+            projected_w, projected_h = int(img_w * upscale), int(img_h * upscale)
+        elif task_type == "outpaint":
+            projected_w = img_w + int(ui_inputs.get("left") or 0) + int(ui_inputs.get("right") or 0)
+            projected_h = img_h + int(ui_inputs.get("top") or 0) + int(ui_inputs.get("bottom") or 0)
+        projected_megapixels = (projected_w * projected_h) / 1_000_000
+        if projected_megapixels > CONFIG.max_output_megapixels:
+            raise gr.Error(
+                f"预计输出为 {projected_w}×{projected_h}（{projected_megapixels:.1f}MP），"
+                f"超过当前上限 {CONFIG.max_output_megapixels:g}MP；请降低放大倍数或扩边尺寸。"
+            )
+
+        if (img_w % multiplier != 0) or (img_h % multiplier != 0):
+            suggested_w = max(multiplier, round(img_w / multiplier) * multiplier)
+            suggested_h = max(multiplier, round(img_h / multiplier) * multiplier)
+            warning_msg = (
+                f"当前模型要求宽高均为 {multiplier} 的倍数；"
+                f"收到 {img_w}×{img_h}，可调整为约 {suggested_w}×{suggested_h}。"
+            )
+            raise gr.Error(warning_msg)
+
+    lora_data = ui_inputs.get('lora_data', [])
+    active_loras_for_gpu, active_loras_for_meta = [], []
+    if lora_data:
+        sources, ids, scales, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4]
+        for i, (source, lora_id, scale, _) in enumerate(zip(sources, ids, scales, files)):
+            if scale > 0 and lora_id and lora_id.strip():
+                lora_filename = None
+                if source == "File":
+                    lora_filename = sanitize_filename(lora_id)
+                    local_path = os.path.join(LORA_DIR, lora_filename)
+                    if not os.path.exists(local_path):
+                        raise gr.Error(f"已上传的 LoRA“{lora_id}”已不存在，请重新上传。")
+                elif source in ("Civitai", "Hugging Face"):
+                    local_path, status = get_lora_path(source, lora_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+                    if local_path: lora_filename = os.path.basename(local_path)
+                    else: raise gr.Error(f"LoRA“{lora_id}”准备失败：{status}")
+
+                if lora_filename:
+                    active_loras_for_gpu.append({"lora_name": lora_filename, "strength_model": scale, "strength_clip": scale})
+                    active_loras_for_meta.append(f"{source} {lora_id}:{scale}")
+
+    ui_inputs['denoise'] = 1.0
+    if task_type == 'img2img': ui_inputs['denoise'] = ui_inputs.get('img2img_denoise', 0.7)
+    elif task_type == 'hires_fix': ui_inputs['denoise'] = ui_inputs.get('hires_denoise', 0.55)
+    elif task_type == 'inpaint': ui_inputs['denoise'] = ui_inputs.get('inpaint_denoise', 1.0)
+
+    if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+
+    if task_type == 'img2img':
+        input_image_pil = ui_inputs.get('img2img_image')
+        if not input_image_pil:
+            raise gr.Error("图生图需要先上传源图片。")
+        temp_file_path = _temp_png("temp_input")
+        input_image_pil.save(temp_file_path, "PNG")
+        ui_inputs['input_image'] = os.path.basename(temp_file_path)
+        temp_files_to_clean.append(temp_file_path)
+        ui_inputs['width'] = input_image_pil.width
+        ui_inputs['height'] = input_image_pil.height
+
+    elif task_type == 'inpaint':
+        inpaint_img = ui_inputs.get('inpaint_image')
+        inpaint_dict = ui_inputs.get('inpaint_image_dict')
+
+        if inpaint_img:
+            temp_file_path = _temp_png("temp_inpaint")
+            inpaint_img.save(temp_file_path, "PNG")
+            ui_inputs['input_image'] = os.path.basename(temp_file_path)
+            temp_files_to_clean.append(temp_file_path)
+            ui_inputs['width'] = inpaint_img.width
+            ui_inputs['height'] = inpaint_img.height
+        elif inpaint_dict and inpaint_dict.get('background') and inpaint_dict.get('layers'):
+            background_img = inpaint_dict['background'].convert("RGBA")
+            composite_mask_pil = Image.new('L', background_img.size, 0)
+            for layer in inpaint_dict['layers']:
+                if layer:
+                    layer_alpha = layer.split()[-1]
+                    composite_mask_pil = ImageChops.lighter(composite_mask_pil, layer_alpha)
+
+            inverted_mask_alpha = Image.fromarray(255 - np.array(composite_mask_pil), mode='L')
+            r, g, b, _ = background_img.split()
+            composite_image_with_mask = Image.merge('RGBA', [r, g, b, inverted_mask_alpha])
+
+            temp_file_path = _temp_png("temp_inpaint_composite")
+            composite_image_with_mask.save(temp_file_path, "PNG")
+
+            ui_inputs['input_image'] = os.path.basename(temp_file_path)
+            temp_files_to_clean.append(temp_file_path)
+            ui_inputs.pop('inpaint_mask', None)
+            ui_inputs['width'] = background_img.width
+            ui_inputs['height'] = background_img.height
+        else:
+            raise gr.Error("局部重绘需要输入图片和有效蒙版。")
+
+    elif task_type == 'outpaint':
+        input_image_pil = ui_inputs.get('outpaint_image')
+        if not input_image_pil:
+            raise gr.Error("扩图需要先上传源图片。")
+        temp_file_path = _temp_png("temp_input")
+        input_image_pil.save(temp_file_path, "PNG")
+        ui_inputs['input_image'] = os.path.basename(temp_file_path)
+        temp_files_to_clean.append(temp_file_path)
+
+        ui_inputs['megapixels'] = 0.25
+        ui_inputs['grow_mask_by'] = ui_inputs.get('feathering', 10)
+        ui_inputs['width'] = input_image_pil.width + int(ui_inputs.get('left') or 0) + int(ui_inputs.get('right') or 0)
+        ui_inputs['height'] = input_image_pil.height + int(ui_inputs.get('top') or 0) + int(ui_inputs.get('bottom') or 0)
+
+    elif task_type == 'hires_fix':
+        input_image_pil = ui_inputs.get('hires_image')
+        if not input_image_pil:
+            raise gr.Error("高清修复需要先上传源图片。")
+        temp_file_path = _temp_png("temp_input")
+        input_image_pil.save(temp_file_path, "PNG")
+        ui_inputs['input_image'] = os.path.basename(temp_file_path)
+        temp_files_to_clean.append(temp_file_path)
+        hires_scale = float(ui_inputs.get('hires_scale_by') or 1.5)
+        ui_inputs['width'] = int(input_image_pil.width * hires_scale)
+        ui_inputs['height'] = int(input_image_pil.height * hires_scale)
+
+    embedding_data = ui_inputs.get('embedding_data', [])
+    embedding_filenames = []
+    if embedding_data:
+        emb_sources, emb_ids, emb_files = embedding_data[0::3], embedding_data[1::3], embedding_data[2::3]
+        for i, (source, emb_id, _) in enumerate(zip(emb_sources, emb_ids, emb_files)):
+            if emb_id and emb_id.strip():
+                emb_filename = None
+                if source == "File":
+                    emb_filename = sanitize_filename(emb_id)
+                    local_path = os.path.join(EMBEDDING_DIR, emb_filename)
+                    if not os.path.exists(local_path):
+                        raise gr.Error(f"已上传的 Embedding“{emb_id}”已不存在，请重新上传。")
+                elif source in ("Civitai", "Hugging Face"):
+                    local_path, status = get_embedding_path(source, emb_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+                    if local_path: emb_filename = os.path.basename(local_path)
+                    else: raise gr.Error(f"Embedding“{emb_id}”准备失败：{status}")
+
+                if emb_filename:
+                    embedding_filenames.append(emb_filename)
+
+    controlnet_data = ui_inputs.get('controlnet_data', [])
+    active_controlnets = []
+    if controlnet_data:
+        (cn_images, _, _, cn_strengths, cn_filepaths) = [controlnet_data[i::5] for i in range(5)]
+        for i in range(len(cn_images)):
+            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                cn_temp_path = _temp_png(f"temp_cn_{i}")
+                cn_images[i].save(cn_temp_path, "PNG")
+                temp_files_to_clean.append(cn_temp_path)
+                active_controlnets.append({
+                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                    "start_percent": 0.0, "end_percent": 1.0, "control_net_name": cn_filepaths[i]
+                })
+
+    anima_controlnet_lllite_data = ui_inputs.get('anima_controlnet_lllite_data', [])
+    active_anima_controlnets = []
+    if anima_controlnet_lllite_data:
+        (cn_images, _, _, cn_strengths, cn_filepaths, cn_starts, cn_ends) = [anima_controlnet_lllite_data[i::7] for i in range(7)]
+        for i in range(len(cn_images)):
+            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                _ensure_model_downloaded(cn_filepaths[i], progress)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                cn_temp_path = _temp_png(f"temp_anima_cn_{i}")
+                cn_images[i].save(cn_temp_path, "PNG")
+                temp_files_to_clean.append(cn_temp_path)
+                active_anima_controlnets.append({
+                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                    "start_percent": cn_starts[i], "end_percent": cn_ends[i], "control_net_name": cn_filepaths[i]
+                })
+
+    diffsynth_controlnet_data = ui_inputs.get('diffsynth_controlnet_data', [])
+    active_diffsynth_controlnets = []
+    if diffsynth_controlnet_data:
+        (cn_images, _, _, cn_strengths, cn_filepaths) = [diffsynth_controlnet_data[i::5] for i in range(5)]
+        for i in range(len(cn_images)):
+            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                cn_temp_path = _temp_png(f"temp_diffsynth_cn_{i}")
+                cn_images[i].save(cn_temp_path, "PNG")
+                temp_files_to_clean.append(cn_temp_path)
+                active_diffsynth_controlnets.append({
+                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                    "control_net_name": cn_filepaths[i]
+                })
+
+    krea2_controlnet_data = ui_inputs.get('krea2_controlnet_data', [])
+    active_krea2_controlnets = []
+    if krea2_controlnet_data:
+        (cn_images, _, _, cn_strengths, cn_filepaths) = [krea2_controlnet_data[i::5] for i in range(5)]
+        for i in range(len(cn_images)):
+            if cn_images[i] and cn_strengths[i] > 0 and cn_filepaths[i] and cn_filepaths[i] != "None":
+                ensure_controlnet_model_downloaded(cn_filepaths[i], progress)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                cn_temp_path = _temp_png(f"temp_krea2_cn_{i}")
+                cn_images[i].save(cn_temp_path, "PNG")
+                temp_files_to_clean.append(cn_temp_path)
+                active_krea2_controlnets.append({
+                    "image": os.path.basename(cn_temp_path), "strength": cn_strengths[i],
+                    "control_net_name": cn_filepaths[i]
+                })
+
+    ipadapter_data = ui_inputs.get('ipadapter_data', [])
+    active_ipadapters = []
+    if ipadapter_data:
+        num_ipa_units = (len(ipadapter_data) - 5) // 3
+        final_preset, final_weight, final_lora_strength, final_embeds_scaling, final_combine_method = ipadapter_data[-5:]
+        ipa_images, ipa_weights, ipa_lora_strengths = [ipadapter_data[i*num_ipa_units:(i+1)*num_ipa_units] for i in range(3)]
+        all_presets_to_download = set()
+        for i in range(num_ipa_units):
+            if ipa_images[i] and ipa_weights[i] > 0 and final_preset:
+                all_presets_to_download.add(final_preset)
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                ipa_temp_path = _temp_png(f"temp_ipa_{i}")
+                ipa_images[i].save(ipa_temp_path, "PNG")
+                temp_files_to_clean.append(ipa_temp_path)
+                active_ipadapters.append({
+                    "image": os.path.basename(ipa_temp_path), "preset": final_preset,
+                    "weight": ipa_weights[i], "lora_strength": ipa_lora_strengths[i]
+                })
+        if active_ipadapters and final_preset:
+            all_presets_to_download.add(final_preset)
+        for preset in all_presets_to_download:
+            ensure_ipadapter_models_downloaded(preset, progress)
+
+        model_type_key = 'sd15' if workflow_model_type == 'sd15' else 'sdxl'
+        if active_ipadapters:
+            active_ipadapters.append({
+                'is_final_settings': True, 'model_type': model_type_key, 'final_preset': final_preset,
+                'final_weight': final_weight, 'final_lora_strength': final_lora_strength,
+                'final_embeds_scaling': final_embeds_scaling, 'final_combine_method': final_combine_method
+            })
+
+    flux1_ipadapter_data = ui_inputs.get('flux1_ipadapter_data', [])
+    active_flux1_ipadapters = []
+    if flux1_ipadapter_data:
+        num_units = len(flux1_ipadapter_data) // 4
+        f_images = flux1_ipadapter_data[0*num_units : 1*num_units]
+        f_weights = flux1_ipadapter_data[1*num_units : 2*num_units]
+        f_starts = flux1_ipadapter_data[2*num_units : 3*num_units]
+        f_ends = flux1_ipadapter_data[3*num_units : 4*num_units]
+        for i in range(len(f_images)):
+            if f_images[i] and f_weights[i] > 0:
+                for filename in ["ip-adapter.bin"]:
+                    _ensure_model_downloaded(filename, progress)
+
+                from huggingface_hub import snapshot_download
+                progress(0.5, desc="Caching HF SigLIP model...")
+                snapshot_download(
+                    repo_id="google/siglip-so400m-patch14-384",
+                    allow_patterns=["*.json", "*.safetensors", "*.txt"],
+                    ignore_patterns=["*.msgpack", "*.h5", "*.bin"]
+                )
+
+                temp_path = _temp_png(f"temp_fipa_{i}")
+                f_images[i].save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_flux1_ipadapters.append({
+                    "image": os.path.basename(temp_path),
+                    "weight": f_weights[i], "start_percent": f_starts[i], "end_percent": f_ends[i]
+                })
+
+    sd3_ipadapter_data = ui_inputs.get('sd3_ipadapter_chain', [])
+    active_sd3_ipadapters = []
+    if sd3_ipadapter_data:
+        num_units = len(sd3_ipadapter_data) // 4
+        s_images = sd3_ipadapter_data[0*num_units : 1*num_units]
+        s_weights = sd3_ipadapter_data[1*num_units : 2*num_units]
+        s_starts = sd3_ipadapter_data[2*num_units : 3*num_units]
+        s_ends = sd3_ipadapter_data[3*num_units : 4*num_units]
+        sd3_ipa_downloaded = False
+        for i in range(len(s_images)):
+            if s_images[i] and s_weights[i] > 0:
+                if not sd3_ipa_downloaded:
+                    ensure_sd3_ipadapter_models_downloaded(progress)
+                    sd3_ipa_downloaded = True
+                temp_path = _temp_png(f"temp_s3ipa_{i}")
+                s_images[i].save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_sd3_ipadapters.append({
+                    "image": os.path.basename(temp_path),
+                    "weight": s_weights[i], "start_percent": s_starts[i], "end_percent": s_ends[i]
+                })
+
+    style_data = ui_inputs.get('style_data', [])
+    active_styles = []
+    if style_data:
+        num_units = len(style_data) // 2
+        st_images = style_data[0*num_units : 1*num_units]
+        st_strengths = style_data[1*num_units : 2*num_units]
+        style_models_downloaded = False
+        for i in range(len(st_images)):
+            if st_images[i] and st_strengths[i] > 0:
+                if not style_models_downloaded:
+                    _ensure_model_downloaded("sigclip_vision_patch14_384.safetensors", progress)
+                    _ensure_model_downloaded("flux1-redux-dev.safetensors", progress)
+                    style_models_downloaded = True
+                temp_path = _temp_png(f"temp_style_{i}")
+                st_images[i].save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_styles.append({
+                    "image": os.path.basename(temp_path), "strength": st_strengths[i]
+                })
+
+    reference_latent_data = ui_inputs.get('reference_latent_data', [])
+    active_reference_latents = []
+    if reference_latent_data:
+        for img in reference_latent_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = _temp_png("temp_ref")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_reference_latents.append(os.path.basename(temp_path))
+
+    hidream_o1_reference_data = ui_inputs.get('hidream_o1_reference_data', [])
+    active_hidream_o1_reference = []
+    if hidream_o1_reference_data:
+        for img in hidream_o1_reference_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = _temp_png("temp_ho1_ref")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_hidream_o1_reference.append(os.path.basename(temp_path))
+
+    joyai_reference_data = ui_inputs.get('joyai_reference_data', [])
+    active_joyai_reference = []
+    if joyai_reference_data:
+        for img in joyai_reference_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = _temp_png("temp_joyai_ref")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_joyai_reference.append(os.path.basename(temp_path))
+
+    krea2_identity_edit_data = ui_inputs.get('krea2_identity_edit_data', [])
+    active_krea2_identity_edit = []
+    if krea2_identity_edit_data:
+        for img in krea2_identity_edit_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = _temp_png("temp_krea2_id_ref")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_krea2_identity_edit.append(os.path.basename(temp_path))
+
+    krea2_reference_edit_data = ui_inputs.get('krea2_reference_edit_data', [])
+    active_krea2_reference_edit = []
+    if krea2_reference_edit_data:
+        for img in krea2_reference_edit_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = _temp_png("temp_krea2_reference_ref")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_krea2_reference_edit.append(os.path.basename(temp_path))
+
+    qwen_image_edit_data = ui_inputs.get('qwen_image_edit_data', [])
+    active_qwen_image_edit = []
+    if qwen_image_edit_data:
+        for img in qwen_image_edit_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = _temp_png("temp_qwen_edit_ref")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_qwen_image_edit.append(os.path.basename(temp_path))
+
+    boogu_edit_data = ui_inputs.get('boogu_edit_data', [])
+    active_boogu_edit = []
+    if boogu_edit_data:
+        for img in boogu_edit_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = _temp_png("temp_boogu_edit_ref")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_boogu_edit.append(os.path.basename(temp_path))
+
+    reference_image_data = ui_inputs.get('reference_image_data', [])
+    active_reference_images = []
+    if reference_image_data:
+        for img in reference_image_data:
+            if img:
+                if not os.path.exists(INPUT_DIR): os.makedirs(INPUT_DIR)
+                temp_path = _temp_png("temp_ref_img")
+                img.save(temp_path, "PNG")
+                temp_files_to_clean.append(temp_path)
+                active_reference_images.append(os.path.basename(temp_path))
+
+    vae_source = ui_inputs.get('vae_source')
+    vae_id = ui_inputs.get('vae_id')
+    vae_name_override = None
+    if vae_source and vae_source != "None":
+        if vae_source == "File":
+            vae_name_override = sanitize_filename(vae_id)
+            local_path = os.path.join(VAE_DIR, vae_name_override)
+            if not os.path.exists(local_path):
+                raise gr.Error(f"已上传的 VAE“{vae_id}”已不存在，请重新上传。")
+        elif vae_source in ("Civitai", "Hugging Face") and vae_id and vae_id.strip():
+            local_path, status = get_vae_path(vae_source, vae_id, os.environ.get("CIVITAI_API_KEY", ""), progress)
+            if local_path: vae_name_override = os.path.basename(local_path)
+            else: raise gr.Error(f"VAE“{vae_id}”准备失败：{status}")
+    if vae_name_override:
+        ui_inputs['vae_name'] = vae_name_override
+
+    conditioning_data = ui_inputs.get('conditioning_data', [])
+    active_conditioning = []
+    if conditioning_data:
+        num_units = len(conditioning_data) // 6
+        prompts, widths, heights, xs, ys, strengths = [conditioning_data[i*num_units : (i+1)*num_units] for i in range(6)]
+        for i in range(num_units):
+            if prompts[i] and prompts[i].strip():
+                active_conditioning.append({
+                    "prompt": prompts[i], "width": int(widths[i]), "height": int(heights[i]),
+                    "x": int(xs[i]), "y": int(ys[i]), "strength": float(strengths[i])
+                })
+
+    return {
+        "active_loras_for_gpu": active_loras_for_gpu,
+        "active_loras_for_meta": active_loras_for_meta,
+        "active_controlnets": active_controlnets,
+        "active_anima_controlnets": active_anima_controlnets,
+        "active_diffsynth_controlnets": active_diffsynth_controlnets,
+        "active_krea2_controlnets": active_krea2_controlnets,
+        "active_ipadapters": active_ipadapters,
+        "active_flux1_ipadapters": active_flux1_ipadapters,
+        "active_sd3_ipadapters": active_sd3_ipadapters,
+        "active_styles": active_styles,
+        "active_reference_latents": active_reference_latents,
+        "active_hidream_o1_reference": active_hidream_o1_reference,
+        "active_joyai_reference": active_joyai_reference,
+        "active_krea2_identity_edit": active_krea2_identity_edit,
+        "active_krea2_reference_edit": active_krea2_reference_edit,
+        "active_qwen_image_edit": active_qwen_image_edit,
+        "active_boogu_edit": active_boogu_edit,
+        "active_reference_images": active_reference_images,
+        "active_conditioning": active_conditioning,
+        "temp_files_to_clean": temp_files_to_clean
+    }

core/pipelines/sd_image_pipeline.py

@@ -0,0 +1,364 @@
+import os
+import random
+import shutil
+import torch
+import uuid
+import gradio as gr
+from PIL import Image
+from typing import List, Dict, Any
+
+from .base_pipeline import BasePipeline
+from core.settings import *
+from core.model_capabilities import supports_chain_for_model
+from imagegen_utils.app_utils import sanitize_prompt
+from core.workflow_assembler import WorkflowAssembler
+from core.runtime_config import CONFIG, estimate_gpu_duration
+from core.task_scheduler import TaskCancelledError, generation_guard
+from .workflow_executor import WorkflowExecutor
+from .pipeline_input_processor import process_pipeline_inputs
+
+class SdImagePipeline(BasePipeline):
+    CHAIN_INPUT_KEYS = {
+        "lora": ("lora_data",),
+        "controlnet": ("controlnet_data",),
+        "anima_controlnet_lllite": ("anima_controlnet_lllite_data",),
+        "diffsynth_controlnet": ("diffsynth_controlnet_data",),
+        "krea2_controlnet": ("krea2_controlnet_data",),
+        "ipadapter": ("ipadapter_data",),
+        "flux1_ipadapter": ("flux1_ipadapter_data",),
+        "sd3_ipadapter": ("sd3_ipadapter_chain",),
+        "style": ("style_data",),
+        "embedding": ("embedding_data",),
+        "conditioning": ("conditioning_data",),
+        "reference_latent": ("reference_latent_data",),
+        "hidream_o1_reference": ("hidream_o1_reference_data",),
+        "joyai_image": ("joyai_reference_data",),
+        "krea2_identity_edit": ("krea2_identity_edit_data",),
+        "krea2_style_reference": ("krea2_reference_edit_data",),
+        "qwen_image_edit": ("qwen_image_edit_data",),
+        "boogu_image_edit": ("boogu_edit_data",),
+        "reference_image": ("reference_image_data",),
+    }
+    NATIVE_REFERENCE_CHAINS = {
+        "reference_latent",
+        "hidream_o1_reference",
+        "joyai_image",
+        "krea2_identity_edit",
+        "krea2_style_reference",
+        "qwen_image_edit",
+        "boogu_image_edit",
+        "reference_image",
+    }
+
+    def get_required_models(self, model_display_name: str, **kwargs) -> List[str]:
+        model_info = ALL_MODEL_MAP.get(model_display_name)
+        if not model_info:
+            return [model_display_name]
+
+        path_or_components = model_info[1]
+        if isinstance(path_or_components, dict):
+            return [v for v in path_or_components.values() if v and v != "pixel_space"]
+        else:
+            return [model_display_name]
+
+    def _gpu_logic(self, ui_inputs: Dict, loras_string: str, workflow: Dict[str, Any], assembler: WorkflowAssembler, progress=gr.Progress(track_tqdm=True)):
+        model_display_name = ui_inputs['model_display_name']
+        succeeded = False
+        try:
+            progress(0.4, desc="正在执行工作流…")
+
+            initial_objects = {}
+            decoded_images_tensor = WorkflowExecutor.execute_workflow(
+                workflow, initial_objects=initial_objects
+            )
+
+            output_images = []
+            raw_seed = ui_inputs.get('seed')
+            start_seed = int(raw_seed) if (raw_seed is not None and raw_seed != -1) else random.randint(0, 2**64 - 1)
+            image_count = int(decoded_images_tensor.shape[0])
+            for i in range(image_count):
+                img_tensor = decoded_images_tensor[i]
+                pil_image = Image.fromarray((img_tensor.cpu().numpy() * 255.0).astype("uint8"))
+
+                width_for_meta = ui_inputs.get('width', 'N/A')
+                height_for_meta = ui_inputs.get('height', 'N/A')
+
+                params_string = f"{ui_inputs['positive_prompt']}\nNegative prompt: {ui_inputs['negative_prompt']}\n"
+                params_string += f"Steps: {ui_inputs['num_inference_steps']}, Sampler: {ui_inputs['sampler']}, Scheduler: {ui_inputs['scheduler']}, CFG scale: {ui_inputs['guidance_scale']}, Seed: {start_seed}, Size: {width_for_meta}x{height_for_meta}, Base Model: {model_display_name}"
+                if image_count > 1:
+                    params_string += f", Batch index: {i + 1}/{image_count}"
+                if ui_inputs['task_type'] != 'txt2img': params_string += f", Denoise: {ui_inputs['denoise']}"
+                if ui_inputs.get('clip_skip') and ui_inputs['clip_skip'] != 1: params_string += f", Clip skip: {abs(ui_inputs['clip_skip'])}"
+                if loras_string: params_string += f", {loras_string}"
+
+                pil_image.info = {'parameters': params_string.strip()}
+                output_images.append(pil_image)
+
+            succeeded = True
+            return output_images
+        finally:
+            if not succeeded or ui_inputs.get("_release_models_after_run"):
+                from core.model_manager import release_loaded_models
+
+                release_loaded_models()
+
+    @generation_guard
+    def run(self, ui_inputs: Dict, progress):
+        progress(0, desc="正在准备模型…")
+
+        task_type = ui_inputs['task_type']
+        model_display_name = ui_inputs['model_display_name']
+        model_type = MODEL_TYPE_MAP.get(model_display_name, 'sdxl')
+
+        architectures_dict = ARCHITECTURES_CONFIG.get('architectures', {})
+        workflow_model_type = architectures_dict.get(model_type, {}).get("model_type", model_type.lower().replace(" ", "").replace(".", ""))
+
+        enabled_chains = set(
+            FEATURES_CONFIG.get(workflow_model_type, {}).get("enabled_chains", [])
+        )
+        if task_type != "txt2img":
+            has_native_references = any(
+                any(ui_inputs.get(input_key) or [])
+                for chain_name in self.NATIVE_REFERENCE_CHAINS
+                for input_key in self.CHAIN_INPUT_KEYS[chain_name]
+            )
+            if has_native_references:
+                raise gr.Error(
+                    "原生多图参考编辑目前只支持“文生图 / 多图融合”。"
+                    "普通逐张重绘请使用图生图；不要同时叠加源图 latent 与参考图链。"
+                )
+        for chain_name, input_keys in self.CHAIN_INPUT_KEYS.items():
+            if (
+                chain_name not in enabled_chains
+                or not supports_chain_for_model(model_display_name, chain_name)
+            ):
+                for input_key in input_keys:
+                    ui_inputs[input_key] = []
+        if "pid" not in enabled_chains:
+            ui_inputs["pid_settings"] = "OFF"
+        if "vae" not in enabled_chains:
+            ui_inputs["vae_source"] = None
+            ui_inputs["vae_id"] = None
+            ui_inputs["vae_file"] = None
+
+        try:
+            batch_size = int(ui_inputs.get("batch_size") or 1)
+        except (TypeError, ValueError):
+            batch_size = 1
+        if batch_size < 1 or batch_size > CONFIG.max_batch_size:
+            raise gr.Error(f"单次生成数量需为 1–{CONFIG.max_batch_size}。")
+        ui_inputs["batch_size"] = batch_size
+
+        ui_inputs['positive_prompt'] = sanitize_prompt(ui_inputs.get('positive_prompt', ''))
+        ui_inputs['negative_prompt'] = sanitize_prompt(ui_inputs.get('negative_prompt', ''))
+
+        if 'clip_skip' in ui_inputs and ui_inputs['clip_skip'] is not None:
+             ui_inputs['clip_skip'] = -int(ui_inputs['clip_skip'])
+        else:
+             ui_inputs['clip_skip'] = -1
+
+        required_models = self.get_required_models(model_display_name=model_display_name)
+
+        is_pid_enabled = (ui_inputs.get('pid_settings', 'OFF') == 'ON' and task_type == 'txt2img')
+        if is_pid_enabled:
+            import yaml
+            pid_config_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'yaml', 'pid.yaml')
+            pid_unet_name = "pid_flux1_1024_to_4096_4step_mxfp8.safetensors"
+            try:
+                with open(pid_config_path, 'r', encoding='utf-8') as f:
+                    pid_config = yaml.safe_load(f) or {}
+                pid_items = pid_config.get("PiD", [])
+                for item in pid_items:
+                    archs = item.get("architectures", [])
+                    if workflow_model_type in archs:
+                        pid_unet_name = item.get("filepath")
+                        break
+            except Exception as e:
+                print(f"Error loading PiD config for download: {e}")
+
+            if pid_unet_name not in required_models:
+                required_models.append(pid_unet_name)
+            if "gemma_2_2b_it_elm_fp8_scaled.safetensors" not in required_models:
+                required_models.append("gemma_2_2b_it_elm_fp8_scaled.safetensors")
+
+        self.model_manager.ensure_models_downloaded(required_models, progress=progress)
+        cancel_event = ui_inputs.get("_cancel_event")
+        if cancel_event is not None and cancel_event.is_set():
+            raise TaskCancelledError("任务已在模型准备完成后取消，未进入 GPU 生成。")
+
+        temp_files_to_clean = []
+        try:
+            processed = process_pipeline_inputs(ui_inputs, progress, workflow_model_type)
+            temp_files_to_clean.extend(processed["temp_files_to_clean"])
+
+            active_loras_for_gpu = processed["active_loras_for_gpu"]
+            active_loras_for_meta = processed["active_loras_for_meta"]
+            active_controlnets = processed["active_controlnets"]
+            active_anima_controlnets = processed["active_anima_controlnets"]
+            active_diffsynth_controlnets = processed["active_diffsynth_controlnets"]
+            active_krea2_controlnets = processed.get("active_krea2_controlnets", [])
+            active_ipadapters = processed["active_ipadapters"]
+            active_flux1_ipadapters = processed["active_flux1_ipadapters"]
+            active_sd3_ipadapters = processed["active_sd3_ipadapters"]
+            active_styles = processed["active_styles"]
+            active_reference_latents = processed["active_reference_latents"]
+            active_hidream_o1_reference = processed["active_hidream_o1_reference"]
+            active_joyai_reference = processed.get("active_joyai_reference", [])
+            active_krea2_identity_edit = processed.get("active_krea2_identity_edit", [])
+            active_krea2_reference_edit = processed.get("active_krea2_reference_edit", [])
+            active_qwen_image_edit = processed.get("active_qwen_image_edit", [])
+            active_boogu_edit = processed.get("active_boogu_edit", [])
+            active_reference_images = processed.get("active_reference_images", [])
+            active_conditioning = processed["active_conditioning"]
+
+            loras_string = f"LoRAs: [{', '.join(active_loras_for_meta)}]" if active_loras_for_meta else ""
+
+            progress(0.8, desc="正在组装工作流…")
+
+            seed_val = ui_inputs.get('seed')
+            if seed_val is None or seed_val == -1:
+                ui_inputs['seed'] = random.randint(0, 2**32 - 1)
+
+            model_info = ALL_MODEL_MAP[model_display_name]
+            path_or_components = model_info[1]
+            latent_type = model_info[3] if len(model_info) > 3 and model_info[3] else 'latent'
+            latent_generator_template = "EmptyLatentImage"
+            if latent_type == 'sd3_latent':
+                latent_generator_template = "EmptySD3LatentImage"
+            elif latent_type == 'chroma_radiance_latent':
+                latent_generator_template = "EmptyChromaRadianceLatentImage"
+            elif latent_type == 'hunyuan_latent':
+                latent_generator_template = "EmptyHunyuanImageLatent"
+
+            dynamic_values = {
+                'task_type': ui_inputs['task_type'],
+                'model_type': workflow_model_type,
+                'latent_type': latent_type,
+                'latent_generator_template': latent_generator_template
+            }
+
+            recipe_path = os.path.join(os.path.dirname(__file__), "workflow_recipes", "unified_recipe.yaml")
+            assembler = WorkflowAssembler(recipe_path, dynamic_values=dynamic_values)
+
+            hidream_o1_smoothing_data = []
+            if workflow_model_type == 'hidream-o1' and model_display_name == "HiDream-O1-Image":
+                hidream_o1_smoothing_data.append({})
+
+            workflow_inputs = {
+                **ui_inputs,
+                "positive_prompt": ui_inputs['positive_prompt'], "negative_prompt": ui_inputs['negative_prompt'],
+                "seed": ui_inputs['seed'], "steps": ui_inputs['num_inference_steps'], "cfg": ui_inputs['guidance_scale'],
+                "sampler_name": ui_inputs['sampler'], "scheduler": ui_inputs['scheduler'],
+                "batch_size": ui_inputs['batch_size'],
+                "clip_skip": ui_inputs['clip_skip'],
+                "denoise": ui_inputs['denoise'],
+                "vae_name": ui_inputs.get('vae_name'),
+                "guidance": ui_inputs.get('guidance', 3.5),
+                "lora_chain": active_loras_for_gpu,
+                "controlnet_chain": active_controlnets if not active_anima_controlnets else [],
+                "anima_controlnet_lllite_chain": active_anima_controlnets,
+                "diffsynth_controlnet_chain": active_diffsynth_controlnets,
+                "krea2_controlnet_chain": active_krea2_controlnets,
+                "ipadapter_chain": active_ipadapters,
+                "flux1_ipadapter_chain": active_flux1_ipadapters,
+                "sd3_ipadapter_chain": active_sd3_ipadapters,
+                "style_chain": active_styles,
+                "conditioning_chain": active_conditioning,
+                "reference_latent_chain": active_reference_latents,
+                "hidream_o1_reference_chain": active_hidream_o1_reference,
+                "joyai_image_chain": active_joyai_reference,
+                "krea2_identity_edit_chain": active_krea2_identity_edit,
+                "krea2_style_reference_chain": active_krea2_reference_edit,
+                "qwen_image_edit_chain": active_qwen_image_edit,
+                "boogu_image_edit_chain": active_boogu_edit,
+                "reference_image_chain": active_reference_images,
+                "vae_chain": [ui_inputs.get('vae_name')] if ui_inputs.get('vae_name') else [],
+                "hidream_o1_smoothing_chain": hidream_o1_smoothing_data,
+                "pid_chain": [ui_inputs.get('pid_settings', 'OFF')] if is_pid_enabled else [],
+                "scheduler_width": ui_inputs.get('width', 1024),
+                "scheduler_height": ui_inputs.get('height', 1024),
+            }
+
+            if isinstance(path_or_components, dict):
+                workflow_inputs.update({
+                    'unet_name': path_or_components.get('unet'),
+                    'unet_uncond_name': path_or_components.get('unet_uncond'),
+                    'vae_name': ui_inputs.get('vae_name') or path_or_components.get('vae'),
+                    'clip_name': path_or_components.get('clip'),
+                    'clip1_name': path_or_components.get('clip1'),
+                    'clip2_name': path_or_components.get('clip2'),
+                    'clip3_name': path_or_components.get('clip3'),
+                    'clip4_name': path_or_components.get('clip4'),
+                    'lora_name': path_or_components.get('lora'),
+                })
+            else:
+                workflow_inputs['model_name'] = path_or_components
+
+            if task_type == 'txt2img':
+                workflow_inputs['width'] = ui_inputs['width']
+                workflow_inputs['height'] = ui_inputs['height']
+
+            workflow = assembler.assemble(workflow_inputs)
+
+            if cancel_event is not None and cancel_event.is_set():
+                raise TaskCancelledError("任务已在进入 GPU 前取消。")
+
+            gpu_duration = estimate_gpu_duration(ui_inputs)
+            progress(1.0, desc=f"模型已就绪，正在申请 GPU（预计上限 {gpu_duration} 秒）…")
+
+            results = self._execute_gpu_logic(
+                self._gpu_logic,
+                duration=gpu_duration,
+                default_duration=60,
+                task_name=f"ImageGen ({task_type})",
+                ui_inputs=ui_inputs,
+                loras_string=loras_string,
+                workflow=workflow,
+                assembler=assembler,
+                progress=progress
+            )
+
+            import json
+            import glob
+            from PIL import PngImagePlugin
+
+            prompt_json = json.dumps(workflow)
+
+            out_dir = os.path.abspath(OUTPUT_DIR)
+            os.makedirs(out_dir, exist_ok=True)
+
+            try:
+                existing_files = glob.glob(os.path.join(out_dir, "gen_*.png"))
+                existing_files.sort(key=os.path.getmtime)
+                keep_existing = max(0, CONFIG.output_retention - len(results))
+                while len(existing_files) > keep_existing:
+                    os.remove(existing_files.pop(0))
+            except Exception as e:
+                print(f"Warning: Failed to cleanup output dir: {e}")
+
+            final_results = []
+            for img in results:
+                if not isinstance(img, Image.Image):
+                    final_results.append(img)
+                    continue
+
+                metadata = PngImagePlugin.PngInfo()
+                params_string = img.info.get("parameters", "")
+                if params_string:
+                    metadata.add_text("parameters", params_string)
+                metadata.add_text("prompt", prompt_json)
+
+                filename = f"gen_{uuid.uuid4().hex}.png"
+                filepath = os.path.join(out_dir, filename)
+                img.save(filepath, "PNG", pnginfo=metadata)
+                final_results.append(filepath)
+
+            results = final_results
+
+        finally:
+            for temp_file in temp_files_to_clean:
+                if temp_file and os.path.exists(temp_file):
+                    os.remove(temp_file)
+                    print(f"✅ Cleaned up temp file: {temp_file}")
+
+        return results

core/pipelines/workflow_executor.py

@@ -0,0 +1,140 @@
+import torch
+from collections import defaultdict, deque
+from typing import Dict, Any, List
+from comfy_integration.nodes import NODE_CLASS_MAPPINGS
+from imagegen_utils.app_utils import get_value_at_index
+
+class WorkflowExecutor:
+    @staticmethod
+    def topological_sort(workflow: Dict[str, Any]) -> List[str]:
+        graph = defaultdict(list)
+        in_degree = {node_id: 0 for node_id in workflow}
+
+        for node_id, node_info in workflow.items():
+            for input_value in node_info.get('inputs', {}).values():
+                if isinstance(input_value, list) and len(input_value) == 2 and isinstance(input_value[0], str):
+                    source_node_id = input_value[0]
+                    if source_node_id in workflow:
+                        graph[source_node_id].append(node_id)
+                        in_degree[node_id] += 1
+
+        queue = deque([node_id for node_id, degree in in_degree.items() if degree == 0])
+
+        sorted_nodes = []
+        while queue:
+            current_node_id = queue.popleft()
+            sorted_nodes.append(current_node_id)
+
+            for neighbor_node_id in graph[current_node_id]:
+                in_degree[neighbor_node_id] -= 1
+                if in_degree[neighbor_node_id] == 0:
+                    queue.append(neighbor_node_id)
+
+        if len(sorted_nodes) != len(workflow):
+            raise RuntimeError("Workflow contains a cycle and cannot be executed.")
+
+        return sorted_nodes
+
+    @staticmethod
+    def execute_workflow(workflow: Dict[str, Any], initial_objects: Dict[str, Any]):
+        with torch.no_grad():
+            computed_outputs = initial_objects
+
+            try:
+                sorted_node_ids = WorkflowExecutor.topological_sort(workflow)
+
+                final_node_id = None
+                for node_id in reversed(sorted_node_ids):
+                    if workflow[node_id].get('class_type') == 'SaveImage':
+                        final_node_id = node_id
+                        break
+
+                if final_node_id:
+                    required_nodes = set()
+                    nodes_to_visit = [final_node_id]
+                    while nodes_to_visit:
+                        curr_id = nodes_to_visit.pop()
+                        if curr_id in required_nodes:
+                            continue
+                        required_nodes.add(curr_id)
+                        curr_info = workflow.get(curr_id, {})
+                        for input_val in curr_info.get('inputs', {}).values():
+                            if isinstance(input_val, list) and len(input_val) == 2 and isinstance(input_val[0], str):
+                                src_id = input_val[0]
+                                if src_id in workflow and src_id not in required_nodes:
+                                    nodes_to_visit.append(src_id)
+
+                    sorted_node_ids = [nid for nid in sorted_node_ids if nid in required_nodes]
+
+                print(f"--- [Workflow Executor] Execution order: {sorted_node_ids}")
+            except RuntimeError as e:
+                print("--- [Workflow Executor] ERROR: Failed to sort workflow. Dumping graph details. ---")
+                for node_id, node_info in workflow.items():
+                    print(f"  Node {node_id} ({node_info['class_type']}):")
+                    for input_name, input_value in node_info['inputs'].items():
+                         if isinstance(input_value, list) and len(input_value) == 2 and isinstance(input_value[0], str):
+                             print(f"    - {input_name} <- [{input_value[0]}, {input_value[1]}]")
+                raise e
+
+            for node_id in sorted_node_ids:
+                if node_id in computed_outputs:
+                    continue
+
+                node_info = workflow[node_id]
+                class_type = node_info['class_type']
+
+                # The pipeline writes the final PNG itself so it can attach the
+                # canonical parameters and full workflow metadata. Executing
+                # ComfyUI's SaveImage here would create an untracked duplicate.
+                if class_type == 'SaveImage':
+                    continue
+
+                is_loader_with_filename = 'Loader' in class_type and any(key.endswith('_name') for key in node_info['inputs'])
+                if node_id in initial_objects and is_loader_with_filename:
+                    continue
+
+                node_class = NODE_CLASS_MAPPINGS.get(class_type)
+                if node_class is None:
+                     raise RuntimeError(f"Could not find node class '{class_type}'. Is it imported in comfy_integration/nodes.py?")
+
+                node_instance = node_class()
+
+                kwargs = {}
+                for param_name, param_value in node_info['inputs'].items():
+                    if isinstance(param_value, list) and len(param_value) == 2 and isinstance(param_value[0], str):
+                        source_node_id, output_index = param_value
+                        if source_node_id not in computed_outputs:
+                            raise RuntimeError(f"Workflow integrity error: Output of node {source_node_id} needed for {node_id} but not yet computed.")
+
+                        source_output_tuple = computed_outputs[source_node_id]
+                        actual_value = get_value_at_index(source_output_tuple, output_index)
+                    else:
+                        actual_value = param_value
+
+                    if '.' in param_name:
+                        parent_key, child_key = param_name.split('.', 1)
+                        if parent_key not in kwargs or not isinstance(kwargs[parent_key], dict):
+                            kwargs[parent_key] = {}
+                        kwargs[parent_key][child_key] = actual_value
+                    else:
+                        kwargs[param_name] = actual_value
+
+                function_name = getattr(node_class, 'FUNCTION')
+                execution_method = getattr(node_instance, function_name)
+
+                result = execution_method(**kwargs)
+                computed_outputs[node_id] = result
+
+            final_node_id = None
+            for node_id in reversed(sorted_node_ids):
+                 if workflow[node_id]['class_type'] == 'SaveImage':
+                     final_node_id = node_id
+                     break
+
+            if not final_node_id:
+                raise RuntimeError("Workflow does not contain a 'SaveImage' node as the output.")
+
+            save_image_inputs = workflow[final_node_id]['inputs']
+            image_source_node_id, image_source_index = save_image_inputs['images']
+
+            return get_value_at_index(computed_outputs[image_source_node_id], image_source_index)

core/pipelines/workflow_recipes/_partials/_base_sampler.yaml

@@ -0,0 +1,28 @@
+nodes:
+  ksampler:
+    class_type: KSampler
+    title: "KSampler"
+    params:
+      denoise: 1.0
+  vae_decode:
+    class_type: VAEDecode
+    title: "VAE Decode"
+  save_image:
+    class_type: SaveImage
+    title: "Save Image"
+    params: {}
+
+connections:
+  - from: "ksampler:0"
+    to: "vae_decode:samples"
+  - from: "vae_decode:0"
+    to: "save_image:images"
+
+ui_map:
+  seed: "ksampler:seed"
+  steps: "ksampler:steps"
+  cfg: "ksampler:cfg"
+  sampler_name: "ksampler:sampler_name"
+  scheduler: "ksampler:scheduler"
+  denoise: "ksampler:denoise"
+  filename_prefix: "save_image:filename_prefix"

core/pipelines/workflow_recipes/_partials/conditioning/anima.yaml

@@ -0,0 +1,70 @@
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "stable_diffusion"
+      device: "default"
+
+connections:
+  - from: "unet_loader:0"
+    to: "ksampler:model"
+  - from: "clip_loader:0"
+    to: "pos_prompt:clip"
+  - from: "clip_loader:0"
+    to: "neg_prompt:clip"
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    output_map:
+      "unet_loader:0": "model"
+      "clip_loader:0": "clip"
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_anima_controlnet_lllite_chains:
+  anima_controlnet_lllite_chain:
+    ksampler_node: "ksampler"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/_partials/conditioning/auraflow.yaml

@@ -0,0 +1,56 @@
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  ckpt_loader:
+    class_type: CheckpointLoaderSimple
+    title: "Load Checkpoint"
+
+connections:
+  - from: "ckpt_loader:0"
+    to: "ksampler:model"
+  - from: "ckpt_loader:1"
+    to: "pos_prompt:clip"
+  - from: "ckpt_loader:1"
+    to: "neg_prompt:clip"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+  - from: "ckpt_loader:2"
+    to: "vae_decode:vae"
+  - from: "ckpt_loader:2"
+    to: "vae_encode:vae"
+
+dynamic_vae_chains:
+  vae_chain:
+    targets:
+      - "vae_decode:vae"
+      - "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    start: "ckpt_loader"
+    output_map:
+      "0": "model"
+      "1": "clip"
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "ckpt_loader:1"
+
+ui_map:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  model_name: "ckpt_loader:ckpt_name"

core/pipelines/workflow_recipes/_partials/conditioning/boogu-image.yaml

@@ -0,0 +1,67 @@
+nodes:
+  boogu_prompt:
+    class_type: TextEncodeBooguEdit
+    title: "Text Encode Boogu Edit"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "boogu"
+      device: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+
+connections:
+  - from: "unet_loader:0"
+    to: "ksampler:model"
+  - from: "clip_loader:0"
+    to: "boogu_prompt:clip"
+  - from: "boogu_prompt:0"
+    to: "ksampler:positive"
+  - from: "boogu_prompt:1"
+    to: "ksampler:negative"
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    output_map:
+      "unet_loader:0": "model"
+      "clip_loader:0": "clip"
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["boogu_prompt:clip"]
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+dynamic_boogu_image_edit_chains:
+  boogu_image_edit_chain:
+    ksampler_node: "ksampler"
+    boogu_prompt_node: "boogu_prompt"
+    vae_loader_node: "vae_loader"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  positive_prompt: "boogu_prompt:prompt"
+  negative_prompt: "boogu_prompt:negative_prompt"
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"
+  vae_name: "vae_loader:vae_name"

core/pipelines/workflow_recipes/_partials/conditioning/chroma1-radiance.yaml

@@ -0,0 +1,67 @@
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+    params:
+      vae_name: "pixel_space"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "chroma"
+      device: "default"
+  t5_tokenizer:
+    class_type: T5TokenizerOptions
+    title: "T5TokenizerOptions"
+    params:
+      min_padding: 0
+      min_length: 3
+  model_sampler:
+    class_type: ModelSamplingAuraFlow
+    params:
+      shift: 3.0
+
+connections:
+  - from: "unet_loader:0"
+    to: "model_sampler:model"
+  - from: "model_sampler:0"
+    to: "ksampler:model"
+
+  - from: "clip_loader:0"
+    to: "t5_tokenizer:clip"
+  - from: "t5_tokenizer:0"
+    to: "pos_prompt:clip"
+  - from: "t5_tokenizer:0"
+    to: "neg_prompt:clip"
+
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "t5_tokenizer:0"
+
+ui_map:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/_partials/conditioning/chroma1.yaml

@@ -0,0 +1,73 @@
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "chroma"
+      device: "default"
+  t5_tokenizer:
+    class_type: T5TokenizerOptions
+    title: "T5TokenizerOptions"
+    params:
+      min_padding: 1
+      min_length: 0
+  fresca:
+    class_type: FreSca
+    title: "FreSca"
+    params:
+      scale_low: 1.0
+      scale_high: 2.5
+      freq_cutoff: 30
+
+connections:
+  - from: "unet_loader:0"
+    to: "fresca:model"
+  - from: "fresca:0"
+    to: "ksampler:model"
+
+  - from: "clip_loader:0"
+    to: "t5_tokenizer:clip"
+  - from: "t5_tokenizer:0"
+    to: "pos_prompt:clip"
+  - from: "t5_tokenizer:0"
+    to: "neg_prompt:clip"
+
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "t5_tokenizer:0"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/_partials/conditioning/cosmos-predict2.yaml

@@ -0,0 +1,55 @@
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "cosmos"
+      device: "default"
+
+connections:
+  - from: "unet_loader:0"
+    to: "ksampler:model"
+  - from: "clip_loader:0"
+    to: "pos_prompt:clip"
+  - from: "clip_loader:0"
+    to: "neg_prompt:clip"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+dynamic_vae_chains:
+  vae_chain:
+    targets:
+      - "vae_decode:vae"
+      - "vae_encode:vae"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+ui_map:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip_name: "clip_loader:clip_name"

core/pipelines/workflow_recipes/_partials/conditioning/ernie-image.yaml

@@ -0,0 +1,66 @@
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "flux2"
+      device: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+
+connections:
+  - from: "unet_loader:0"
+    to: "ksampler:model"
+  - from: "clip_loader:0"
+    to: "pos_prompt:clip"
+  - from: "clip_loader:0"
+    to: "neg_prompt:clip"
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    output_map:
+      "unet_loader:0": "model"
+      "clip_loader:0": "clip"
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["ksampler:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"
+  vae_name: "vae_loader:vae_name"

core/pipelines/workflow_recipes/_partials/conditioning/flux1.yaml

@@ -0,0 +1,76 @@
+nodes:
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load FLUX UNET"
+    params:
+      weight_dtype: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load FLUX VAE"
+  clip_loader:
+    class_type: DualCLIPLoader
+    title: "Load FLUX Dual CLIP"
+    params:
+      type: "flux"
+      device: "default"
+  flux_guidance:
+    class_type: FluxGuidance
+    title: "FluxGuidance"
+
+connections:
+  - from: "unet_loader:0"
+    to: "ksampler:model"
+  - from: "clip_loader:0"
+    to: "pos_prompt:clip"
+  - from: "clip_loader:0"
+    to: "neg_prompt:clip"
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+  - from: "pos_prompt:0"
+    to: "flux_guidance:conditioning"
+  - from: "flux_guidance:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+
+dynamic_controlnet_chains:
+  controlnet_chain:
+    template: "ControlNetApplyAdvanced"
+    ksampler_node: "ksampler"
+    vae_source: "vae_loader:0"
+
+dynamic_flux1_ipadapter_chains:
+  flux1_ipadapter_chain:
+    ksampler_node: "ksampler"
+
+dynamic_style_chains:
+  style_chain:
+    flux_guidance_node: "flux_guidance"
+    ksampler_node: "ksampler"
+
+dynamic_conditioning_chains:
+  conditioning_chain:
+    flux_guidance_node: "flux_guidance"
+    ksampler_node: "ksampler"
+    clip_source: "clip_loader:0"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+  unet_name: "unet_loader:unet_name"
+  vae_name: "vae_loader:vae_name"
+  clip1_name: "clip_loader:clip_name1"
+  clip2_name: "clip_loader:clip_name2"
+  guidance: "flux_guidance:guidance"

core/pipelines/workflow_recipes/_partials/conditioning/flux2-kv.yaml

@@ -0,0 +1,108 @@
+nodes:
+  unet_loader:
+    class_type: UNETLoader
+    title: "Load Diffusion Model"
+    params:
+      weight_dtype: "default"
+  clip_loader:
+    class_type: CLIPLoader
+    title: "Load CLIP"
+    params:
+      type: "flux2"
+      device: "default"
+  vae_loader:
+    class_type: VAELoader
+    title: "Load VAE"
+
+  flux_kv_cache:
+    class_type: FluxKVCache
+    title: "Flux KV Cache"
+
+  pos_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Positive)"
+  neg_prompt:
+    class_type: CLIPTextEncode
+    title: "CLIP Text Encode (Negative)"
+
+  ksampler:
+    class_type: KSampler
+    title: "KSampler"
+    params:
+      denoise: 1.0
+
+  vae_decode:
+    class_type: VAEDecode
+    title: "VAE Decode"
+
+  save_image:
+    class_type: SaveImage
+    title: "Save Image"
+
+connections:
+  - from: "unet_loader:0"
+    to: "flux_kv_cache:model"
+  - from: "flux_kv_cache:0"
+    to: "ksampler:model"
+
+  - from: "clip_loader:0"
+    to: "pos_prompt:clip"
+  - from: "clip_loader:0"
+    to: "neg_prompt:clip"
+
+  - from: "vae_loader:0"
+    to: "vae_decode:vae"
+  - from: "vae_loader:0"
+    to: "vae_encode:vae"
+
+  - from: "pos_prompt:0"
+    to: "ksampler:positive"
+  - from: "neg_prompt:0"
+    to: "ksampler:negative"
+
+  - from: "latent_source:0"
+    to: "ksampler:latent_image"
+
+  - from: "ksampler:0"
+    to: "vae_decode:samples"
+  - from: "vae_decode:0"
+    to: "save_image:images"
+
+dynamic_lora_chains:
+  lora_chain:
+    template: "LoraLoader"
+    output_map:
+      "unet_loader:0": "model"
+      "clip_loader:0": "clip"
+    input_map:
+      "model": "model"
+      "clip": "clip"
+    end_input_map:
+      "model": ["flux_kv_cache:model"]
+      "clip": ["pos_prompt:clip", "neg_prompt:clip"]
+
+dynamic_reference_latent_chains:
+  reference_latent_chain:
+    ksampler_node: "ksampler"
+    vae_node: "vae_loader"
+
+dynamic_pid_chains:
+  pid_chain:
+    ksampler_node: "ksampler"
+
+ui_map:
+  unet_name: "unet_loader:unet_name"
+  clip_name: "clip_loader:clip_name"
+  vae_name: "vae_loader:vae_name"
+
+  positive_prompt: "pos_prompt:text"
+  negative_prompt: "neg_prompt:text"
+
+  seed: "ksampler:seed"
+  steps: "ksampler:steps"
+  cfg: "ksampler:cfg"
+  sampler_name: "ksampler:sampler_name"
+  scheduler: "ksampler:scheduler"
+  denoise: "ksampler:denoise"
+
+  filename_prefix: "save_image:filename_prefix"