Image-to-Image
Diffusers
English
driving
autonomous-driving
diffusion
controllable-generation
scene-editing
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("ZehongZhu/drivinggfm", dtype=torch.bfloat16, device_map="cuda")
prompt = "Turn this cat into a dog"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
image = pipe(image=input_image, prompt=prompt).images[0]Configuration Parsing Warning:In UNKNOWN_FILENAME: "diffusers._class_name" must be a string
DrivingGFM v1.0
Generalist Foundation Model for Unified Driving Visual Perception — DrivingGFM Team, Tongji University
Quick start
pip install diffusers transformers accelerate torch
Recommended entry point, using the DrivinggfmPipeline wrapper shipped within this repository. trust_remote_code=True is required to load the custom pipeline code from the repository.
import torch
from PIL import Image
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"ZehongZhu/drivinggfm",
torch_dtype=torch.float16,
trust_remote_code=True
)
pipe.to("cuda")
out = pipe.drive(
Image.open("scene.png"),
"make it a rainy night road scene",
num_inference_steps=20,
image_guidance_scale=1.5,
guidance_scale=7.5,
seed=2026,
)
out.save("result.png")
License
CreativeML Open RAIL‑M, full text in LICENSE. Commercial use and redistribution are permitted, provided the full license text and attribution are retained, downstream users are bound by the Attachment A use restrictions, and modified files are clearly marked as modified.
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