[Scribble] How to catch the color in input image?

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Hi, Thanks for sharing this library for using Image Generation.
There is one questions I want to ask.

I want to catch the color in input image.
For example.. In this image,
I want to depict character's various colors in output image. (yellow, red, blue, green, etc..)
But, output image is not depicted the color.

I use this code in generating output image.
```Python
with torch.no_grad():
img = resize_image(HWC3(input_image), image_resolution)
H, W, C = img.shape

detected_map = np.zeros_like(img, dtype=np.uint8)
detected_map[np.min(img, axis=2) < 127] = 255

control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()

if seed == -1:
seed = random.randint(0, 999999999)
seed_everything(seed)

if config.save_memory:
model.low_vram_shift(is_diffusing=False)

cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([extra_prompt + prompt] * num_samples)]}
un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([negative_prompt] * num_samples)]}
shape = (4, H // 8, W // 8)

if config.save_memory:
model.low_vram_shift(is_diffusing=True)

model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
shape, cond, verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond)

if config.save_memory:
model.low_vram_shift(is_diffusing=False)

x_samples = model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)

results = [x_samples[i] for i in range(num_samples)]
```
How to catch the color in input image and depict in output image?
I think that I will fix this question by modifying the code that detects the boundary. right?
I'll be waiting for your good opinions.

Thank you.

Start with the boundary-detection block that builds detected_map, then trace how control is passed through the ControlNet conditioning path. Compare the input colors with generated outputs and determine whether changing the boundary map can preserve color information. Done would require a documented, reproducible approach that depicts the requested colors in the output.

Written by the indexing model from the issue text.