# JoyAI-Image-Edit [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). A central principle of JoyAI-Image is the closed-loop collaboration between understanding, generation, and editing. JoyAI-Image-Edit supports general image editing as well as spatial editing capabilities including object move, object rotation, and camera control. | Model | Description | Download | |:-----:|:-----------:|:--------:| | JoyAI-Image-Edit | Instruction-guided image editing with precise and controllable spatial manipulation | [Hugging Face](https://huggingface.co/jdopensource/JoyAI-Image-Edit-Diffusers) | ```python import torch from diffusers import JoyImageEditPipeline from diffusers.utils import load_image pipeline = JoyImageEditPipeline.from_pretrained( "jdopensource/JoyAI-Image-Edit-Diffusers", torch_dtype=torch.bfloat16 ) pipeline.to("cuda") image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg") prompt = "Add wings to the astronaut." output = pipeline( image=image, prompt=prompt, num_inference_steps=40, guidance_scale=4.0, generator=torch.Generator("cuda").manual_seed(0), ).images[0] output.save("joyimage_edit_output.png") ``` ## Spatial editing JoyAI-Image supports three spatial editing prompt patterns: **Object Move**, **Object Rotation**, and **Camera Control**. For best results, follow the prompt templates below as closely as possible. For more information, refer to [SpatialEdit](https://github.com/EasonXiao-888/SpatialEdit). ### Object Move Move a target object into a specified region marked by a red box in the input image. ```text Move the into the red box and finally remove the red box. ``` ### Object Rotation Rotate an object to a specific canonical view. Supported `` values: `front`, `right`, `left`, `rear`, `front right`, `front left`, `rear right`, `rear left`. ```text Rotate the to show the side view. ``` ### Camera Control Change the camera viewpoint while keeping the 3D scene unchanged. ```text Move the camera. - Camera rotation: Yaw {y_rotation}°, Pitch {p_rotation}°. - Camera zoom: in/out/unchanged. - Keep the 3D scene static; only change the viewpoint. ``` ## JoyImageEditPipeline[[diffusers.JoyImageEditPipeline]] Diffusion pipeline for image editing using the JoyImage architecture. The pipeline encodes text and image conditioning via a Qwen3-VL text encoder, denoises latents with a 3-D transformer, and decodes the result with a WAN VAE. Model offloading order: text_encoder -> transformer -> vae. - **prompt** (*str* or *List[str]*) -- The prompt or prompts to guide generation. - **height** (*int*) -- Height of the generated output in pixels. - **width** (*int*) -- Width of the generated output in pixels. - **image** (*PipelineImageInput*, *optional*) -- Reference image used for conditioning. When provided the pipeline operates in image-editing mode with `num_items=2`. - **num_inference_steps** (*int*, *optional*, defaults to 40) -- Number of denoising steps. More steps generally improve quality at the cost of slower inference. - **timesteps** (*List[int]*, *optional*) -- Custom timesteps for the denoising process. When provided, `num_inference_steps` is inferred from the list length. - **sigmas** (*List[float]*, *optional*) -- Custom sigmas for the denoising process. Mutually exclusive with `timesteps`. - **guidance_scale** (*float*, *optional*, defaults to 4.0) -- Classifier-free guidance scale. - **negative_prompt** (*str* or *List[str]*, *optional*) -- Negative prompt(s) used to suppress undesired content. - **num_images_per_prompt** (*int*, *optional*, defaults to 1) -- Number of generated samples per prompt. - **generator** (*torch.Generator* or *List[torch.Generator]*, *optional*) -- RNG generator(s) for deterministic sampling. - **latents** (*torch.Tensor*, *optional*) -- Pre-generated noisy latents for the target slot. Sampled from a Gaussian distribution when not provided. Can be used to seed generation from a specific starting noise tensor. - **prompt_embeds** (*torch.Tensor*, *optional*) -- Pre-computed prompt embeddings. When provided `prompt` can be omitted. - **prompt_embeds_mask** (*torch.Tensor*, *optional*) -- Attention mask for `prompt_embeds`. - **negative_prompt_embeds** (*torch.Tensor*, *optional*) -- Pre-computed negative prompt embeddings. - **negative_prompt_embeds_mask** (*torch.Tensor*, *optional*) -- Attention mask for `negative_prompt_embeds`. - **output_type** (*str*, *optional*, defaults to `"pil"`) -- Output format. Pass `"latent"` to return raw latents. - **return_dict** (*bool*, *optional*, defaults to *True*) -- Whether to return a [JoyImageEditPipelineOutput](/docs/diffusers/main/en/api/pipelines/joyimage_edit#diffusers.JoyImageEditPipelineOutput) or a plain tensor. - **callback_on_step_end** (*Callable*, *PipelineCallback*, *MultiPipelineCallbacks*, *optional*) -- Callback invoked at the end of each denoising step with signature `(self, step: int, timestep: int, callback_kwargs: Dict)`. - **callback_on_step_end_tensor_inputs** (*List[str]*, *optional*, defaults to `["latents"]`) -- Tensor keys included in `callback_kwargs` for `callback_on_step_end`. - **max_sequence_length** (*int*, *optional*, defaults to 4096) -- Maximum sequence length for prompt encoding. - **enable_denormalization** (*bool*, *optional*, defaults to *True*) -- Denormalise latents before VAE decoding.[*~pipelines.joyimage.JoyImageEditPipelineOutput*] or *torch.Tensor*If `return_dict` is `True`, returns a pipeline output object containing the generated image(s). Otherwise returns the image tensor directly. Generate an edited image conditioned on a reference image and a text prompt. Examples: ```python >>> import torch >>> from diffusers import JoyImageEditPipeline >>> from diffusers.utils import load_image >>> model_id = "jdopensource/JoyAI-Image-Edit-Diffusers" >>> pipe = JoyImageEditPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> image = load_image("https://huggingface.co/datasets/diffusers/docs-images/resolve/main/astronaut.jpg") >>> output = pipe( ... image=image, # pass an image for editing; omit for text-to-image generation ... prompt="Add wings to the astronaut.", ... num_inference_steps=40, ... guidance_scale=4.0, ... generator=torch.manual_seed(0), ... ) >>> output.images[0].save("joyimage_edit.png") ``` - ``ValueError`` -- On any invalid combination of arguments.``ValueError`` Validate pipeline inputs before the forward pass. - **latent** -- Normalised latent tensor.Latent tensor in the scale expected by `vae.decode`. Invert `normalize_latents` to recover the original latent scale. - **prompt** -- Prompt string or list of prompt strings. - **device** -- Target device. - **num_images_per_prompt** -- Number of outputs to generate per prompt. - **prompt_embeds** -- Pre-computed prompt embeddings. - **prompt_embeds_mask** -- Attention mask for pre-computed embeddings. - **max_sequence_length** -- Maximum output sequence length. - **template_type** -- Prompt template key (`"image"` or `"multiple_images"`).Tuple of (prompt_embeds, prompt_embeds_mask). Encode a text prompt into embeddings (text-only path). Pre-computed `prompt_embeds` bypass encoding entirely. - **prompt** -- Prompt string(s), optionally containing `<image>\n` tokens. - **device** -- Target device. - **num_images_per_prompt** -- Number of outputs to generate per prompt. - **images** -- Pixel tensors corresponding to the inline image tokens. - **prompt_embeds** -- Pre-computed prompt embeddings. - **prompt_embeds_mask** -- Attention mask for pre-computed embeddings. - **template_type** -- Must be `"multiple_images"`. - **max_sequence_length** -- If set, truncate the output to this length (keeping the last `max_sequence_length` tokens).Tuple of (prompt_embeds, prompt_embeds_mask). Encode prompts that contain inline image tokens via the Qwen processor. `<image>\n` placeholders in each prompt string are replaced by the Qwen vision special tokens before being fed to the multimodal encoder. - **latent** -- Raw latent tensor from `vae.encode`.Normalised latent tensor. Normalise latents using per-channel statistics from the VAE config. Uses (latent - mean) / std when the VAE exposes `latents_mean` and `latents_std`; otherwise falls back to scaling by `scaling_factor`. - **batch_size** -- Number of samples in the batch. - **num_channels_latents** -- Latent channel dimension from the transformer config. - **height** -- Spatial height in pixels. - **width** -- Spatial width in pixels. - **video_length** -- Number of frames (1 for image inference). - **dtype** -- Floating-point dtype for the latent tensor. - **device** -- Target device. - **generator** -- RNG generator(s) for reproducible sampling. - **latents** -- Optional user-provided initial noise for the target slot. When `None` random noise is sampled. - **image** -- Optional list of PIL reference images to VAE-encode as conditioning slots. - **enable_denormalization** -- Whether to normalise encoded reference latents.Tuple of `(latents, image_latents)` where `latents` has shape `(B, 1, C, T, H', W')` and `image_latents` has shape `(B, N_ref, C, T, H', W')` or `None` when no reference images are given.- `ValueError` -- If `generator` is a list whose length differs from `batch_size`.`ValueError` Prepare the initial noisy latent tensor for the denoising loop. ## JoyImageEditPipelineOutput[[diffusers.JoyImageEditPipelineOutput]] Output class for JoyImageEdit generation pipelines.