JiaxinGe/Diffusers-BAGEL

BAGEL: Diffusers Integration

This repository hosts the BAGEL custom pipeline for 🤗 Diffusers, enabling seamless text-to-image, image editing, and visual understanding tasks with the BAGEL model.

🚀 Quick Start

pipe = DiffusionPipeline.from_pretrained(
    "JiaxinGe/Diffusers-BAGEL",
    custom_pipeline="JiaxinGe/Diffusers-BAGEL",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)
pipe = pipe.to("cuda:0")
# (1) text→image
prompt = "A female cosplayer portraying an ethereal fairy or elf, wearing a flowing dress made of delicate fabrics in soft, mystical colors like emerald green and silver. She has pointed ears, a gentle, enchanting expression, and her outfit is adorned with sparkling jewels and intricate patterns. The background is a magical forest with glowing plants, mystical creatures, and a serene atmosphere."
out = pipe(text=prompt)
print(out)
out['images'][0].save("bagel_text2img.png")

# 2) text→image with “think”
out = pipe(
    text="a car made of small cars",
    think=True
)
print(out['text'])
out['images'][0].save("bagel_text2img_think.png")

# 3) image editing
from PIL import Image
img = Image.open("~/Bagel/test_images/women.jpg")
out = pipe(
    image=img,
    text="She boards a modern subway, quietly reading a folded newspaper…"
)
out['images'][0].save("bagel_img_edit.png")

# 4) image editing + think
img = Image.open("~/Bagel/test_images/octupusy.jpg")
out = pipe(
    image=img,
    text="Could you display the sculpture that takes after this design?",
    think=True
)
print(out['text'])
out['images'][0].save("bagel_img_edit_think.png")

# 4) image understanding
meme = Image.open("~/Bagel/test_images/meme.jpg")
out = pipe(
    image=meme,
    text="Can someone explain what’s funny about this meme?",
    understanding_output=True
)
print(out['text'])

🔧 Inference Hyperparameters

• cfg_text_scale: text guidance strength (typical: 4.0–8.0)
• cfg_image_scale: image guidance strength (1.0–2.0)
• cfg_interval: fraction of steps to apply CFG (e.g. 0.4–1.0)
• num_timesteps: total denoising steps (e.g. 50)
• timestep_shift: offset of denoising schedule
• cfg_renorm_min / cfg_renorm_type: renormalization settings for CFG

For detailed explanations, see the original BAGEL repository.