# JoyAI-Image-Edit-Plus [JoyAI-Image](https://github.com/jd-opensource/JoyAI-Image) is a unified multimodal foundation model for image understanding, text-to-image generation, and instruction-guided image editing. It combines an 8B Multimodal Large Language Model (MLLM) with a 16B Multimodal Diffusion Transformer (MMDiT). JoyAI-Image-Edit-Plus is a multi-image instruction-guided editing model that accepts **multiple reference images** and a text instruction to generate a new image that combines elements from the references according to the instruction. It supports 1–5 reference images per sample. | Model | Description | Download | |:-----:|:-----------:|:--------:| | JoyAI-Image-Edit-Plus | Multi-image instruction-guided editing with element composition from multiple references | [Hugging Face](https://huggingface.co/jdopensource/JoyAI-Image-Edit-Plus-Diffusers) | ```python import torch from PIL import Image from diffusers import JoyImageEditPlusPipeline pipeline = JoyImageEditPlusPipeline.from_pretrained( "jdopensource/JoyAI-Image-Edit-Plus-Diffusers", torch_dtype=torch.bfloat16 ) pipeline.to("cuda") images = [ Image.open("reference_0.png").convert("RGB"), Image.open("reference_1.png").convert("RGB"), ] target_h, target_w = pipeline.image_processor.get_default_height_width(images[-1]) output = pipeline( images=images, prompt="Combine the person from the second image with the scene from the first image.", negative_prompt="low quality, blurry, deformed", height=target_h, width=target_w, num_inference_steps=30, guidance_scale=4.0, generator=torch.Generator("cuda").manual_seed(42), ).images[0] output.save("joyimage_edit_plus_output.png") ``` ## JoyImageEditPlusPipeline[[diffusers.JoyImageEditPlusPipeline]] - **scheduler** ([FlowMatchEulerDiscreteScheduler](/docs/diffusers/main/en/api/schedulers/flow_match_euler_discrete#diffusers.FlowMatchEulerDiscreteScheduler)) -- A scheduler to be used in combination with `transformer` to denoise the encoded image latents. - **vae** ([AutoencoderKLWan](/docs/diffusers/main/en/api/models/autoencoder_kl_wan#diffusers.AutoencoderKLWan)) -- Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. - **text_encoder** (`Qwen3VLForConditionalGeneration`) -- Multimodal text encoder for prompt encoding with inline image understanding. - **tokenizer** (`Qwen2Tokenizer`) -- Tokenizer for text processing. - **transformer** ([JoyImageEditPlusTransformer3DModel](/docs/diffusers/main/en/api/models/transformer_joyimage_edit_plus#diffusers.JoyImageEditPlusTransformer3DModel)) -- Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. - **processor** (`Qwen3VLProcessor`) -- Processor for multimodal inputs (text + images). - **text_token_max_length** (`int`, defaults to `2048`) -- Maximum token length for text encoding. Diffusion pipeline for multi-image instruction-guided editing using JoyImage Edit Plus. Supports multiple reference images with different resolutions. Each reference image is independently VAE-encoded and patchified, then concatenated with the target noise patches for joint denoising. - **images** (`list[Image.Image]` or `list[list[Image.Image]]`, *optional*) -- Reference images for editing. Each image can have a different resolution. If a flat list is provided, it is treated as one sample with multiple references. - **prompt** (`str` or `list[str]`, *optional*) -- The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds` instead. - **height** (`int`, *optional*) -- The height in pixels of the generated image. If `None`, determined from the last reference image. - **width** (`int`, *optional*) -- The width in pixels of the generated image. If `None`, determined from the last reference image. - **num_inference_steps** (`int`, *optional*, defaults to `30`) -- The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. - **timesteps** (`list[int]`, *optional*) -- Custom timesteps to use for the denoising process. If not defined, equal spacing is used. - **sigmas** (`list[float]`, *optional*) -- Custom sigmas to use for the denoising process. - **guidance_scale** (`float`, *optional*, defaults to `4.0`) -- Classifier-free guidance scale. Higher values encourage the model to generate images more aligned with the `prompt` at the expense of lower image quality. - **negative_prompt** (`str` or `list[str]`, *optional*) -- The prompt or prompts not to guide the image generation. If not defined, a blank prompt is used for classifier-free guidance. - **generator** (`torch.Generator` or `list[torch.Generator]`, *optional*) -- One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. - **latents** (`torch.Tensor`, *optional*) -- Pre-generated noisy latents to be used as inputs for image generation. - **prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated text embeddings. Can be used to easily tweak text inputs. - **prompt_embeds_mask** (`torch.Tensor`, *optional*) -- Attention mask for pre-generated text embeddings. - **negative_prompt_embeds** (`torch.Tensor`, *optional*) -- Pre-generated negative text embeddings. - **negative_prompt_embeds_mask** (`torch.Tensor`, *optional*) -- Attention mask for pre-generated negative text embeddings. - **output_type** (`str`, *optional*, defaults to `"pil"`) -- The output format of the generated image. Choose between `"pil"` (`PIL.Image.Image`), `"np"` (`np.ndarray`), `"pt"` (`torch.Tensor`), or `"latent"` for raw latent output. - **return_dict** (`bool`, *optional*, defaults to `True`) -- Whether or not to return a [JoyImageEditPlusPipelineOutput](/docs/diffusers/main/en/api/pipelines/joyimage_edit_plus#diffusers.JoyImageEditPlusPipelineOutput) instead of a plain tuple. - **callback_on_step_end** (`Callable`, *optional*) -- A function called at the end of each denoising step with arguments: the pipeline, step index, timestep, and a dict of callback tensor inputs. - **callback_on_step_end_tensor_inputs** (`list[str]`, *optional*, defaults to `["latents"]`) -- The list of tensor inputs for the `callback_on_step_end` function. - **max_sequence_length** (`int`, *optional*, defaults to `4096`) -- Maximum sequence length for the text encoder.[JoyImageEditPlusPipelineOutput](/docs/diffusers/main/en/api/pipelines/joyimage_edit_plus#diffusers.JoyImageEditPlusPipelineOutput) or `tuple`If `return_dict` is `True`, [JoyImageEditPlusPipelineOutput](/docs/diffusers/main/en/api/pipelines/joyimage_edit_plus#diffusers.JoyImageEditPlusPipelineOutput) is returned, otherwise a `tuple` is returned where the first element is a list of generated images. Function invoked when calling the pipeline for generation. Examples: ```python >>> import torch >>> from diffusers import JoyImageEditPlusPipeline >>> from diffusers.utils import load_image >>> model_id = "jdopensource/JoyAI-Image-Edit-Plus-Diffusers" >>> pipe = JoyImageEditPlusPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> images = [ ... load_image("dog.png"), ... load_image("person.png"), ... ] >>> output = pipe( ... images=images, ... prompt="Let the person lovingly play with the dog.", ... height=1024, ... width=1024, ... num_inference_steps=30, ... guidance_scale=4.0, ... generator=torch.manual_seed(42), ... ) >>> output.images[0].save("output.png") ``` Encode prompts with inline tokens via the Qwen3-VL processor. - **latents** -- Optional pre-computed noise for the target slot. Shape `(B, C, 1, H', W')` where `H'` and `W'` are the latent-space dimensions. When `None`, random noise is sampled.padded_latents[B, max_patches, C, pt, ph, pw] target_mask: [B, max_patches] (True for target patches) shape_list: per-sample list of (t, h, w) tuples for each component Prepare 6D padded latent tensor with target noise + reference image latents. ## JoyImageEditPlusPipelineOutput[[diffusers.JoyImageEditPlusPipelineOutput]] Output class for JoyImage Edit Plus multi-image editing pipelines.