Model reference · open weights
JoyAI-Image-Edit is an open-weight image model from jdopensource. JoyAI-Image-Edit-Diffusers (BF16) weighs 50.3 GB; the smallest configuration that runs it is L40S 48 GB.
JoyAI-Image-Edit is a 16.3B parameter multimodal foundation model developed by jdopensource for instruction-guided image editing. It supports English and Chinese and is designed to perform precise modifications such as object movement, rotation, and camera control based on spatial understanding. The model is released under the Apache 2.0 license.
Summary of the jdopensource/JoyAI-Image-Edit-Diffusers model card, 2026-10-01
What it is
| Released by | jdopensource |
|---|---|
| Type | Image models |
| Task | Image edit |
| Parameters (lead) | 16.3B |
| Runs with | diffusers |
| Released | 2026-04-10 |
| Popularity | 62k downloads / month |
| Weights | 50.3 GB (JoyAI-Image-Edit-Diffusers (BF16), file size) |
| Licence | Open weights |
What it runs on
Weights 50.3 GB (file size) · its biggest part 32.5 GB · working memory for one 1024×1024 image about 5.0 GB · overhead about 537 MB.
| Card | One 1024×1024 image | Counted memory |
|---|---|---|
| RTX 3060 12 GB … RTX 5090 32 GB | does not fit | |
| L40S 48 GB | tight (encoders offloaded) | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
Estimates, not measurements: the weights are the build's file size; one 1024×1024 image needs about 5 GB of working memory (larger images more); "encoders offloaded" means only the biggest part is on the card at once — diffusers' model offload, or ComfyUI unloading the text encoder. diffusers can also place a pipeline's parts on separate cards (device_map) — not estimated here. Counted memory is 92 % of what CUDA reports for the card.
From the model card
JoyAI-Image-Edit is a multimodal foundation model specialized in instruction-guided image editing. It enables precise and controllable edits by leveraging strong spatial understanding, including scene parsing, relational grounding, and instruction decomposition, allowing complex modifications to be applied accurately to specified regions.
Requirements: Python >= 3.10, CUDA-capable GPU
Note: JoyImageEditPipeline will be included in the next official diffusers release (>0.38.0). Until then, install from source as shown above.
pip install torch transformers torchvision
pip install git+https://github.com/huggingface/diffusers.git
import torch
from PIL import Image
from diffusers import JoyImageEditPipeline
pipeline = JoyImageEditPipeline.from_pretrained("jdopensource/JoyAI-Image-Edit-Diffusers")
pipeline.to(torch.bfloat16)
pipeline.to("cuda")
pipeline.set_progress_bar_config(disable=None)
print("pipeline loaded")
img_path = "./test_images/input.png"
prompt = "Remove the construction structure from the top of the crane."
image = Image.open(img_path).convert("RGB")
inputs = {
"image": image,
"prompt": prompt,
"generator": torch.manual_seed(0),
"num_inference_steps": 40,
"guidance_scale": 4.0,
}
print("run pipeline...")
with torch.inference_mode():
output = pipeline(**inputs)
image = output.images[0]
image.save("joyai_image_edit_output.png")
print("image saved.")
JoyAI-Image supports three spatial editing prompt patterns: Object Move, Object Rotation, and Camera Control. For the most stable behavior, we recommend following the prompt templates below as closely as possible.
Use this pattern when you want to move a target object into a specified region.
Prompt template:
Move the into the red box and finally remove the red box.
Rules:
Example:
Move the board into the red box and finally remove the red box.
Use this pattern when you want to rotate an object to a specific canonical view.
Prompt template:
Rotate the to show the side view.
Supported `` values:
frontrightleftrearfront rightfront leftrear rightrear leftRules:
Examples:
Rotate the dog to show the left side view.
Use this pattern when you want to change only the camera viewpoint while keeping the 3D scene itself unchanged.
Prompt template:
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.
Rules:
{y_rotation} specifies the yaw rotation angle in degrees.
{p_rotation} specifies the pitch rotation angle in degrees.
Camera zoom must be one of:
inoutunchangedThe last line is important: it explicitly tells the model to preserve the 3D scene content and geometry, and only adjust the camera viewpoint.
Examples:
Move the camera.
- Camera rotation: Yaw 0.0°, Pitch -15.0°.
- Camera zoom: unchanged.
- Keep the 3D scene static; only change the viewpoint.
JoyAI-Image is licensed under Apache 2.0.
We are actively hiring Research Scientists, AI Infra Engineers, and Interns to join us in building next-generation generative foundation models and bringing them into real-world applications. If you’re interested, please send your resume to: huanghaoyang.ocean@jd.com
Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. Runs as it is with diffusers — on the machine, in Python.
# on your rented machine: pip install diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
pipe = DiffusionPipeline.from_pretrained("jdopensource/JoyAI-Image-Edit-Diffusers", torch_dtype=torch.bfloat16).to("cuda")
start = load_image("/workspace/in.png")
image = pipe(prompt="the same scene at golden hour", image=start).images[0]
image.save("/workspace/out.png")