diffusers/pr_13881/en/api/pipelines/joyimage_edit.md

JoyAI-Image-Edit

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

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.

Object Move

Move a target object into a specified region marked by a red box in the input image.

Move the <object> into the red box and finally remove the red box.

Object Rotation

Rotate an object to a specific canonical view. Supported <view> values: front, right, left, rear, front right, front left, rear right, rear left.

Rotate the <object> to show the <view> side view.

Camera Control

Change the camera viewpoint while keeping the 3D scene unchanged.

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 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.TensorIf 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:

>>> 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.