Add ZImageImg2ImgPipeline

[CalamitousFelicitousness]

What does this PR do?

This PR adds img2img pipeline for Z-Image. The summary of changes are below

• Updated the pipeline structure to include ZImageImg2ImgPipeline alongside ZImagePipeline.
• Implemented the ZImageImg2ImgPipeline class
• Mapped the new ZImageImg2ImgPipeline for image generation tasks.
• Added unit tests for ZImageImg2ImgPipeline
• Updated dummy objects to include ZImageImg2ImgPipeline for testing

Closes issue #12752

Tested using a simple script:

Testing script

#!/usr/bin/env python
"""Test script for ZImage img2img support (without LoRA)."""

import sys
sys.path.insert(0, '/home/ohiom/diffusers/src')

import torch
from PIL import Image
from diffusers import ZImageImg2ImgPipeline

# Paths
MODEL_PATH = "database/models/huggingface/models--Tongyi-MAI--Z-Image-Turbo/snapshots/78771b7e11b922c868dd766476bda1f4fc6bfc96"
INPUT_IMAGE_PATH = "aline_1024.jpg"  # Use existing image as input

print("Loading ZImageImg2ImgPipeline...")
pipe = ZImageImg2ImgPipeline.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
    local_files_only=True,
)
pipe.to("cuda")
print("Pipeline loaded.")

# Load input image
print(f"\nLoading input image from {INPUT_IMAGE_PATH}...")
input_image = Image.open(INPUT_IMAGE_PATH).convert("RGB")
print(f"Input image size: {input_image.size}")

# Generate an image
prompt = "a woman sitting under a tree, oil painting style, impressionist, vibrant colors"
strength = 0.6  # 0.0 = no change, 1.0 = full transformation

print(f"\nGenerating image with prompt: {prompt}")
print(f"Strength: {strength}")

image = pipe(
    prompt=prompt,
    image=input_image,
    strength=strength,
    num_inference_steps=8,
    guidance_scale=3.0,
    generator=torch.Generator(device="cuda").manual_seed(42),
).images[0]

output_path = "test_zimage_img2img_output.png"
image.save(output_path)
print(f"\nImage saved to {output_path}")

Prompt: a woman sitting in a dark room, oil painting style, impressionist, vibrant colors

LoRA functionality depends on my other PR #12750, so they will have to be merged sequentially. I did not think there was much point in leaving it out.

Before submitting

• Did you read the contributor guideline?
• Did you read our philosophy doc (important for complex PRs)?
• Was this discussed/approved via a GitHub issue or the forum? Please add a link to it if that's the case.
• Did you make sure to update the documentation with your changes? Here are the
  documentation guidelines, and
  here are tips on formatting docstrings.
• Did you write any new necessary tests?

Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@sayakpaul @asomoza

[CalamitousFelicitousness]

For some reason the VAE Tiling couldn't meet the 0.2 diff threshold, my test has upped that to 0.3, whether further investigation is warranted I am not sure.