WEBUI插件支持

sd-webui-hunyuan-dit/LICENSE

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+MIT License
+
+Copyright (c) 2024 sethgggg
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

sd-webui-hunyuan-dit/README.md

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+# Hunyuan extension for sd-webui
+
+The extension helps you to use [Hunyuan DiT Model](https://github.com/Tencent/HunyuanDiT) in [Stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui):
+
+### Features
+
+- Core
+  - [x] [Txt2Img]
+  - [x] [Img2Img]
+  - [] [LORA]
+  - [] [ControlNet]
+  - [] [HiresUpscaler]
+- Advanced
+  - [] [MultiDiffusion]
+  - [] [Adetailer]
+
+### Installation
+
+1. You can install this extension via the webui extension downloader by copying the git repository ```https://github.com/sethgggg/sd-webui-hunyuan-dit.git```.
+
+![install](examples/20240709-000053.jpg)
+
+2. Download the HunyuanDiT model from [Huggingface](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers) to local storage, the default storage location is in ```models/hunyuan``` of webui folder. You can change the default storage location via the settings card of the webui.
+
+![folder](examples/20240708-235015.jpg)
+
+![settings](examples/20240708-235001.jpg)
+
+3. You have to place the transformer model in ```models/Stable-Diffusion```, which is the main storage location of checkpoints. If you have fine-tuned a new model, you can also place the transformer model in the same folder and then you could select the model here.
+
+4. Find the HunyuanDiT card and enable them, if you want to use stable diffusion models, remember to disable the HunyuanDiT model.
+
+![enable](examples/20240708-235013.jpg)
+
+5. This project is use the diffusers as inference backend, thus we support the following samplers:
+
+| Sampler Name          | Sampler Instance in diffusers                                                         |
+|-------------------------|------------------------------------------------------------------------------|
+| Euler a                 | EulerAncestralDiscreteScheduler()                                            |
+| Euler                   | EulerDiscreteScheduler()                                                     |
+| LMS                     | LMSDiscreteScheduler()                                                       |
+| Heun                    | HeunDiscreteScheduler()                                                      |
+| DPM2                    | KDPM2DiscreteScheduler()                                                     |
+| DPM2 a                  | KDPM2AncestralDiscreteScheduler()                                            |
+| DPM++ SDE               | DPMSolverSinglestepScheduler()                                               |
+| DPM++ 2M                | DPMSolverMultistepScheduler()                                                |
+| DPM++ 2S a              | DPMSolverSinglestepScheduler()                                               |
+| LMS Karras              | LMSDiscreteScheduler(use_karras_sigmas=True)                                 |
+| DPM2 Karras             | KDPM2DiscreteScheduler(use_karras_sigmas=True)                               |
+| DPM2 a Karras           | KDPM2AncestralDiscreteScheduler(use_karras_sigmas=True)                      |
+| DPM++ SDE Karras        | DPMSolverSinglestepScheduler(use_karras_sigmas=True)                         |
+| DPM++ 2M Karras         | DPMSolverMultistepScheduler(use_karras_sigmas=True)                          |
+| DPM++ 2S a Karras       | DPMSolverSinglestepScheduler(use_karras_sigmas=True)                         |
+| DDIM                    | DDIMScheduler()                                                              |
+| UniPC                   | UniPCMultistepScheduler()                                                    |
+| DPM++ 2M SDE Karras     | DPMSolverMultistepScheduler(use_karras_sigmas=True, algorithm_type="sde-dpmsolver++") |
+| DPM++ 2M SDE            | DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++")                |
+| LCM                     | LCMScheduler()                                                               |
+
+### Examples
+
+⚪ Txt2img: generating images, you can use the webui style prompts to generate
+
+![txt2img](examples/20240708-235005.jpg)
+
+⚪ Img2img: given a image, you can use the Hunyuan DiT model to generate more images.
+
+![img2img](examples/20240708-234944.jpg)

sd-webui-hunyuan-dit/hunyuan_utils/diffusers_learned_conditioning.py

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+from modules import prompt_parser, shared
+
+def get_learned_conditioning_hunyuan(batch: prompt_parser.SdConditioning | list[str]):
+    clip_l_conds, clip_l_attention = shared.clip_l_model(batch)
+    t5_conds, t5_attention = shared.mt5_model(batch)
+    return {"crossattn":clip_l_conds, "mask":clip_l_attention, "crossattn_2":t5_conds, "mask_2":t5_attention}