why train loss is different from the paper ?

[Johnson-yue]

x-flux/train_flux_deepspeed_controlnet.py

Lines 213 to 251 in 4749542

	x_1 = vae.encode(img.to(accelerator.device).to(torch.float32))
	inp = prepare(t5=t5, clip=clip, img=x_1, prompt=prompts)
	x_1 = rearrange(x_1, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
	bs = img.shape[0]
	t = torch.sigmoid(torch.randn((bs,), device=accelerator.device))
	x_0 = torch.randn_like(x_1).to(accelerator.device)
	print(t.shape, x_1.shape, x_0.shape)
	x_t = (1 - t.unsqueeze(1).unsqueeze(2).repeat(1, x_1.shape[1], x_1.shape[2])) * x_1 + t.unsqueeze(1).unsqueeze(2).repeat(1, x_1.shape[1], x_1.shape[2]) * x_0
	bsz = x_1.shape[0]
	guidance_vec = torch.full((x_t.shape[0],), 4, device=x_t.device, dtype=x_t.dtype)
	block_res_samples = controlnet(
	img=x_t.to(weight_dtype),
	img_ids=inp['img_ids'].to(weight_dtype),
	controlnet_cond=control_image.to(weight_dtype),
	txt=inp['txt'].to(weight_dtype),
	txt_ids=inp['txt_ids'].to(weight_dtype),
	y=inp['vec'].to(weight_dtype),
	timesteps=t.to(weight_dtype),
	guidance=guidance_vec.to(weight_dtype),
	)
	# Predict the noise residual and compute loss
	model_pred = dit(
	img=x_t.to(weight_dtype),
	img_ids=inp['img_ids'].to(weight_dtype),
	txt=inp['txt'].to(weight_dtype),
	txt_ids=inp['txt_ids'].to(weight_dtype),
	block_controlnet_hidden_states=[
	sample.to(dtype=weight_dtype) for sample in block_res_samples
	],
	y=inp['vec'].to(weight_dtype),
	timesteps=t.to(weight_dtype),
	guidance=guidance_vec.to(weight_dtype),
	)
	loss = F.mse_loss(model_pred.float(), (x_0 - x_1).float(), reduction="mean")

In paper , x0 is noise, x1 is data

x_t = t * x1 +(1-t) *x0,  it means start from x0 to x1 
loss = || (x1- x0) - pred(x_t) ||

In your code , x0 is noise ,x1 is data, the same as paper

x_t =(1-t)*x1 + t * x0  it is not equal to  x_t = t * x1 +(1-t) *x0
then the loss = || (x0 - x1) -pred(x_t) ||

why are you do this ??