[UniLLaDA] Add UniLLaDA multimodal discrete diffusion pipeline

[ChinChyi]

What does this PR do?

Adds support for LLaDA 2.0-Uni, a unified multimodal discrete diffusion language model that supports text understanding, image understanding, and image generation in a single framework.

Paper: LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model

New Components

• LLaDA2UniImageTransformer2DModel — Image diffusion transformer for decoding VQ tokens to images
• UniLLaDaPipeline — Unified pipeline supporting three modes:
  • Text-to-image generation
  • Image understanding (VQA, captioning)
  • Image editing
• LLaDA2UniFlowMatchEulerScheduler — Flow matching scheduler with Euler ODE integration
• Image tokenizer utilities — SigVQ-based image encoding/decoding

Key Features

• Multimodal capabilities: Single model handles both vision and language tasks
• Discrete diffusion: Block-wise iterative refinement for token generation
• FP8 quantization support: Efficient inference with quantized weights
• Flexible decoding: Supports both quality mode (50 steps) and turbo mode (8 steps)

Usage Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from diffusers import UniLLaDaPipeline, BlockRefinementScheduler
from diffusers.pipelines.unillada.image_tokenizer import ImageTokenizer

model_id = "inclusionAI/LLaDA2.0-Uni"
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
scheduler = BlockRefinementScheduler()
image_tokenizer = ImageTokenizer(model_path=model_id)

pipe = UniLLaDaPipeline(
    transformer=model,
    tokenizer=tokenizer,
    scheduler=scheduler,
    image_tokenizer=image_tokenizer,
)

# Text-to-Image
result = pipe(prompt="A cat sitting on a windowsill at sunset")
result.images[0].save("output.png")

# Image Understanding
from PIL import Image
img = Image.open("photo.jpg")
result = pipe(image=img, question="Describe this image in detail.")
print(result.text)

# Image Editing
result = pipe(image=img, instruction="Change the background to a beach.")
result.images[0].save("edited.png")

Testing

• Added unit tests in tests/pipelines/unillada/test_unillada.py
• Tests cover all three modes (generation, understanding, editing)
• Mock components for CI compatibility

Model Weights

Official weights available at: https://huggingface.co/inclusionAI/LLaDA2.0-Uni

Before submitting

• Did you read the contributor guideline?
• Did you read our philosophy doc?
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?

Who can review?

@yiyixuxu @a-r-r-o-w @DN6