Joyai image

README.md

@@ -32,6 +32,8 @@ We believe that a well-developed open-source code framework can lower the thresh
 > DiffSynth-Studio has undergone major version updates, and some old features are no longer maintained. If you need to use old features, please switch to the [last historical version](https://github.com/modelscope/DiffSynth-Studio/tree/afd101f3452c9ecae0c87b79adfa2e22d65ffdc3) before the major version update.
 
 > Currently, the development personnel of this project are limited, with most of the work handled by [Artiprocher](https://github.com/Artiprocher) and [mi804](https://github.com/mi804). Therefore, the progress of new feature development will be relatively slow, and the speed of responding to and resolving issues is limited. We apologize for this and ask developers to understand.
+- **April 14, 2026** JoyAI-Image open-sourced, welcome a new member to the image editing model family! Support includes instruction-guided image editing, low VRAM inference, and training capabilities. For details, please refer to the [documentation](/docs/en/Model_Details/JoyAI-Image.md) and [example code](/examples/joyai_image/).
+
 - **March 19, 2026**: Added support for [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) and [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) models, including training and inference capabilities. [Documentation](/docs/en/Model_Details/Wan.md) and [example code](/examples/mova/) are now available.
 
 - **March 12, 2026**: We have added support for the [LTX-2.3](https://modelscope.cn/models/Lightricks/LTX-2.3) audio-video generation model. The features includes text-to-audio/video, image-to-audio/video, IC-LoRA control, audio-to-video, and audio-video inpainting. We have supported the complete inference and training functionalities. For details, please refer to the [documentation](/docs/en/Model_Details/LTX-2.md) and [code](/examples/ltx2/).
@@ -875,6 +877,82 @@ Example code for Wan is available at: [/examples/wanvideo/](/examples/wanvideo/)
 
 </details>
 
+#### JoyAI-Image: [/docs/en/Model_Details/JoyAI-Image.md](/docs/en/Model_Details/JoyAI-Image.md)
+
+<details>
+
+<summary>Quick Start</summary>
+
+Running the following code will quickly load the [jd-opensource/JoyAI-Image-Edit](https://modelscope.cn/models/jd-opensource/JoyAI-Image-Edit) model and perform inference. VRAM management is enabled, and the framework will automatically control the loading of model parameters based on available VRAM. The model can run with a minimum of 4GB VRAM.
+
+```python
+from diffsynth.pipelines.joyai_image import JoyAIImagePipeline, ModelConfig
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+
+# Download dataset
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="joyai_image/JoyAI-Image-Edit/*"
+)
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cpu",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = JoyAIImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="transformer/transformer.pth", **vram_config),
+        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="JoyAI-Image-Und/model*.safetensors", **vram_config),
+        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="vae/Wan2.1_VAE.pth", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="JoyAI-Image-Und/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+# Use first sample from dataset
+dataset_base_path = "data/diffsynth_example_dataset/joyai_image/JoyAI-Image-Edit"
+prompt = "将裙子改为粉色"
+edit_images = Image.open(f"{dataset_base_path}/edit/image1.jpg").convert("RGB")
+
+output = pipe(
+    prompt=prompt,
+    edit_images=[edit_images],
+    height=1024,
+    width=1024,
+    seed=0,
+    num_inference_steps=30,
+    cfg_scale=5.0,
+)
+
+output.save("output_joyai_edit_low_vram.png")
+```
+
+</details>
+
+<details>
+
+<summary>Examples</summary>
+
+Example code for JoyAI-Image is available at: [/examples/joyai_image/](/examples/joyai_image/)
+
+| Model ID | Inference | Low VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+|-|-|-|-|-|-|-|
+|[jd-opensource/JoyAI-Image-Edit](https://modelscope.cn/models/jd-opensource/JoyAI-Image-Edit)|[code](/examples/joyai_image/model_inference/JoyAI-Image-Edit.py)|[code](/examples/joyai_image/model_inference_low_vram/JoyAI-Image-Edit.py)|[code](/examples/joyai_image/model_training/full/JoyAI-Image-Edit.sh)|[code](/examples/joyai_image/model_training/validate_full/JoyAI-Image-Edit.py)|[code](/examples/joyai_image/model_training/lora/JoyAI-Image-Edit.sh)|[code](/examples/joyai_image/model_training/validate_lora/JoyAI-Image-Edit.py)|
+
+</details>
+
 ## Innovative Achievements
 
 DiffSynth-Studio is not just an engineered model framework, but also an incubator for innovative achievements.

README_zh.md

@@ -33,6 +33,8 @@ DiffSynth 目前包括两个开源项目：
 
 > 目前本项目的开发人员有限，大部分工作由 [Artiprocher](https://github.com/Artiprocher) 和 [mi804](https://github.com/mi804) 负责，因此新功能的开发进展会比较缓慢，issue 的回复和解决速度有限，我们对此感到非常抱歉，请各位开发者理解。
 
+- **2026年4月14日** JoyAI-Image 开源，欢迎加入图像编辑模型家族！支持指令引导的图像编辑推理、低显存推理和训练能力。详情请参考[文档](/docs/zh/Model_Details/JoyAI-Image.md)和[示例代码](/examples/joyai_image/)。
+
 - **2026年3月19日** 新增对 [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) 和 [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) 模型的支持，包括完整的训练和推理功能。[文档](/docs/zh/Model_Details/Wan.md)和[示例代码](/examples/mova/)现已可用。
 
 - **2026年3月12日** 我们新增了 [LTX-2.3](https://modelscope.cn/models/Lightricks/LTX-2.3) 音视频生成模型的支持，模型支持的功能包括文生音视频、图生音视频、IC-LoRA控制、音频生视频、音视频局部Inpainting，框架支持完整的推理和训练功能。详细信息请参考 [文档](/docs/zh/Model_Details/LTX-2.md) 和 [示例代码](/examples/ltx2/)。
@@ -876,6 +878,82 @@ Wan 的示例代码位于：[/examples/wanvideo/](/examples/wanvideo/)
 
 </details>
 
+#### JoyAI-Image: [/docs/zh/Model_Details/JoyAI-Image.md](/docs/zh/Model_Details/JoyAI-Image.md)
+
+<details>
+
+<summary>快速开始</summary>
+
+运行以下代码可以快速加载 [jd-opensource/JoyAI-Image-Edit](https://modelscope.cn/models/jd-opensource/JoyAI-Image-Edit) 模型并进行推理。显存管理已启动，框架会自动根据剩余显存控制模型参数的加载，最低 4G 显存即可运行。
+
+```python
+from diffsynth.pipelines.joyai_image import JoyAIImagePipeline, ModelConfig
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+
+# Download dataset
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
+    local_dir="data/diffsynth_example_dataset",
+    allow_file_pattern="joyai_image/JoyAI-Image-Edit/*"
+)
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cpu",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cpu",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+
+pipe = JoyAIImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="transformer/transformer.pth", **vram_config),
+        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="JoyAI-Image-Und/model*.safetensors", **vram_config),
+        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="vae/Wan2.1_VAE.pth", **vram_config),
+    ],
+    processor_config=ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="JoyAI-Image-Und/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+
+# Use first sample from dataset
+dataset_base_path = "data/diffsynth_example_dataset/joyai_image/JoyAI-Image-Edit"
+prompt = "将裙子改为粉色"
+edit_images = Image.open(f"{dataset_base_path}/edit/image1.jpg").convert("RGB")
+
+output = pipe(
+    prompt=prompt,
+    edit_images=[edit_images],
+    height=1024,
+    width=1024,
+    seed=0,
+    num_inference_steps=30,
+    cfg_scale=5.0,
+)
+
+output.save("output_joyai_edit_low_vram.png")
+```
+
+</details>
+
+<details>
+
+<summary>示例代码</summary>
+
+JoyAI-Image 的示例代码位于：[/examples/joyai_image/](/examples/joyai_image/)
+
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[jd-opensource/JoyAI-Image-Edit](https://modelscope.cn/models/jd-opensource/JoyAI-Image-Edit)|[code](/examples/joyai_image/model_inference/JoyAI-Image-Edit.py)|[code](/examples/joyai_image/model_inference_low_vram/JoyAI-Image-Edit.py)|[code](/examples/joyai_image/model_training/full/JoyAI-Image-Edit.sh)|[code](/examples/joyai_image/model_training/validate_full/JoyAI-Image-Edit.py)|[code](/examples/joyai_image/model_training/lora/JoyAI-Image-Edit.sh)|[code](/examples/joyai_image/model_training/validate_lora/JoyAI-Image-Edit.py)|
+
+</details>
+
 ## 创新成果
 
 DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果的孵化器。

diffsynth/configs/model_configs.py

@@ -884,4 +884,22 @@
         "model_class": "diffsynth.models.mova_dual_tower_bridge.DualTowerConditionalBridge",
     },
 ]
-MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series + anima_series + mova_series
+joyai_image_series = [
+    {
+        # Example: ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="transformer/transformer.pth")
+        "model_hash": "56592ddfd7d0249d3aa527d24161a863",
+        "model_name": "joyai_image_dit",
+        "model_class": "diffsynth.models.joyai_image_dit.Transformer3DModel",
+        "extra_kwargs": {"patch_size": [1, 2, 2], "in_channels": 16, "out_channels": 16, "hidden_size": 4096, "heads_num": 32, "text_states_dim": 4096, "mlp_width_ratio": 4.0, "mm_double_blocks_depth": 40, "rope_dim_list": [16, 56, 56], "rope_type": "rope", "dit_modulation_type": "wanx", "theta": 10000},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.joyai_image_dit.JoyAIImageDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="JoyAI-Image-Und/model-*.safetensors")
+        "model_hash": "2d11bf14bba8b4e87477c8199a895403",
+        "model_name": "joyai_image_text_encoder",
+        "model_class": "diffsynth.models.joyai_image_text_encoder.JoyAIImageTextEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.joyai_image_text_encoder.JoyAIImageTextEncoderStateDictConverter",
+    },
+]
+
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series + anima_series + mova_series + joyai_image_series

diffsynth/configs/vram_management_module_maps.py

@@ -267,6 +267,22 @@
         "torch.nn.Conv1d": "diffsynth.core.vram.layers.AutoWrappedModule",
         "torch.nn.ConvTranspose1d": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
+    "diffsynth.models.joyai_image_dit.Transformer3DModel": {
+        "diffsynth.models.joyai_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.joyai_image_dit.ModulateWan": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.joyai_image_text_encoder.JoyAIImageTextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLVisionModel": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
 }
 
 def QwenImageTextEncoder_Module_Map_Updater():

diffsynth/diffusion/flow_match.py

@@ -146,6 +146,18 @@ def set_timesteps_z_image(num_inference_steps=100, denoising_strength=1.0, shift
                 timesteps[timestep_id] = timestep
         return sigmas, timesteps
 
+    @staticmethod
+    def set_timesteps_joyai_image(num_inference_steps=100, denoising_strength=1.0, shift=None):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        shift = 4.0 if shift is None else shift
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+
     @staticmethod
     def set_timesteps_ltx2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None, terminal=0.1, special_case=None):
         num_train_timesteps = 1000