How to diagnose problems in training custom inpaint model

[Marquess98]

What I want to do is to perform image inpainting when the input is a set of multimodal images, using sdxl as the pre trained model. But the results are very poor now, and I cannot determine whether it is a problem with the code, dataset, pre trained model, or training parameters.
The infer code snipped is as follows:

noise_scheduler = DDIMScheduler.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler")
noise_scheduler.set_timesteps(denoise_steps, device=device)

zi = vae.encode(masked_image).latent_dist.sample()
# zi = vae.encode(masked_image).latent_dist.sample()
zi = zi * vae.config.scaling_factor

zd = vae.encode(img2).latent_dist.sample()
zd = zd * vae.config.scaling_factor

zi_m = vae.encode(masked_image).latent_dist.sample()
zi_m = zi_m * vae.config.scaling_factor

noise = torch.randn_like(zi)
denoise_steps = torch.tensor(denoise_steps,dtype=torch.int32,device=device)
timesteps_add, _  = get_timesteps(noise_scheduler, denoise_steps, 1.0, device, denoising_start=None)
start_step = 5

zi_t = noise_scheduler.add_noise(zi, noise, timesteps_add[start_step])  
# mask = mask.unsqueeze(1)
m = F.interpolate(mask.to(zi.dtype), size=(zi.shape[2], zi.shape[3]), 
                    mode='bilinear', align_corners=False)

input_ids = dataset["prompt_ids"].to(device)
input_ids = input_ids.unsqueeze(0)
encoder_hidden_states = text_encoder(input_ids, return_dict=False)[0]

timesteps = noise_scheduler.timesteps
iterable = tqdm(
    enumerate(timesteps),
    total=len(timesteps),
    leave=False,
    desc=" " * 4 + "Diffusion denoising",
)
# iterable = enumerate(timesteps)
start_step = 1
# -----------------------denoise------------------------
for i, t in iterable:
    if i >= start_step:
        unet_input = torch.cat([zi_t, zi_m, zd, m], dim=1)      
        with torch.no_grad():
            noise_pred = unet(unet_input, t, 
                                encoder_hidden_states)[0]
        zi_t = noise_scheduler.step(noise_pred, t, zi_t).prev_sample

# torch.cuda.empty_cache()
decode_rgb = vae.decode(zi_t / vae.config.scaling_factor)
decode_rgb = decode_rgb['sample'].squeeze()

And the results of different start_steps are as follow:[0, 5, 15 respectively]

Another wired thing is the decoder_rgb range is about [-2, 2], Shouldn't its range be [-1, 1] ?
Currently, I think the problem may lie in either the infer code or the scale of dataset（about 5000 sets images so far）. Can someone guide me on how to determine which part of the problem it is?
Any suggestions and ideas will be greatly appreciated !!!!