noise.py

import torch

import os
import sys

sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "CFUI"))

import CFUI.model_management
import CFUI.sample
import CFUI.sampler_helpers

MAX_RESOLUTION=8192

def prepare_mask(mask, shape):
    mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
    mask = mask.expand((-1,shape[1],-1,-1))
    if mask.shape[0] < shape[0]:
        mask = mask.repeat((shape[0] -1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]]
    return mask

class NoisyLatentImage:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "source":(["CPU", "GPU"], ),
            "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
            "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
            "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
            "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
            }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "create_noisy_latents"

    CATEGORY = "latent/noise"
        
    def create_noisy_latents(self, source, seed, width, height, batch_size):
        torch.manual_seed(seed)
        if source == "CPU":
            device = "cpu"
        else:
            device = CFUI.model_management.get_torch_device()
        noise = torch.randn((batch_size,  4, height // 8, width // 8), dtype=torch.float32, device=device).cpu()
        return ({"samples":noise}, )

class DuplicateBatchIndex:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "latents":("LATENT",),
            "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}),
            "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
            }}
    
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "duplicate_index"

    CATEGORY = "latent"
        
    def duplicate_index(self, latents, batch_index, batch_size):
        s = latents.copy()
        batch_index = min(s["samples"].shape[0] - 1, batch_index)
        target = s["samples"][batch_index:batch_index + 1].clone()
        target = target.repeat((batch_size,1,1,1))
        s["samples"] = target
        return (s,)

# from  https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475
def slerp(val, low, high):
    dims = low.shape

    #flatten to batches
    low = low.reshape(dims[0], -1)
    high = high.reshape(dims[0], -1)

    low_norm = low/torch.norm(low, dim=1, keepdim=True)
    high_norm = high/torch.norm(high, dim=1, keepdim=True)

    # in case we divide by zero
    low_norm[low_norm != low_norm] = 0.0
    high_norm[high_norm != high_norm] = 0.0

    omega = torch.acos((low_norm*high_norm).sum(1))
    so = torch.sin(omega)
    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
    return res.reshape(dims)

class LatentSlerp:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "latents1":("LATENT",),
                "factor": ("FLOAT", {"default": .5, "min": 0.0, "max": 1.0, "step": 0.01}),
            },
            "optional" :{
                "latents2":("LATENT",),
                "mask": ("MASK", ),
            }}
    
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "slerp_latents"

    CATEGORY = "latent"
        
    def slerp_latents(self, latents1, factor, latents2=None, mask=None):
        s = latents1.copy()
        if latents2 is None:
            return (s,)
        if latents1["samples"].shape != latents2["samples"].shape:
            print("warning, shapes in LatentSlerp not the same, ignoring")
            return (s,)
        slerped = slerp(factor, latents1["samples"].clone(), latents2["samples"].clone())
        if mask is not None:
            mask = prepare_mask(mask, slerped.shape)
            slerped = mask * slerped + (1-mask) * latents1["samples"]
        s["samples"] = slerped
        return (s,)

class GetSigma:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "model": ("MODEL",),
            "sampler_name": (CFUI.samplers.KSampler.SAMPLERS, ),
            "scheduler": (CFUI.samplers.KSampler.SCHEDULERS, ),
            "steps": ("INT", {"default": 10000, "min": 0, "max": 10000}),
            "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
            "end_at_step": ("INT", {"default": 10000, "min": 1, "max": 10000}),
            }}
    
    RETURN_TYPES = ("FLOAT",)
    FUNCTION = "calc_sigma"

    CATEGORY = "latent/noise"
        
    def calc_sigma(self, model, sampler_name, scheduler, steps, start_at_step, end_at_step):
        device = CFUI.model_management.get_torch_device()
        end_at_step = min(steps, end_at_step)
        start_at_step = min(start_at_step, end_at_step)
        CFUI.model_management.load_model_gpu(model)
        sampler = CFUI.samplers.KSampler(model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=1.0, model_options=model.model_options)
        sigmas = sampler.sigmas
        sigma = sigmas[start_at_step] - sigmas[end_at_step]
        sigma /= model.model.latent_format.scale_factor
        return (sigma.cpu().numpy(),)

class InjectNoise:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "latents":("LATENT",),
            
            "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 200.0, "step": 0.01}),
            },
            "optional":{
                "noise":  ("LATENT",),
                "mask": ("MASK", ),
            }}
    
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "inject_noise"

    CATEGORY = "latent/noise"
        
    def inject_noise(self, latents, strength, noise=None, mask=None):
        s = latents.copy()
        if noise is None:
            return (s,)
        if latents["samples"].shape != noise["samples"].shape:
            print("warning, shapes in InjectNoise not the same, ignoring")
            return (s,)
        noised = s["samples"].clone() + noise["samples"].clone() * strength
        if mask is not None:
            mask = prepare_mask(mask, noised.shape)
            noised = mask * noised + (1-mask) * latents["samples"]
        s["samples"] = noised
        return (s,)
    
class Unsampler:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"model": ("MODEL",),
                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                    "end_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                    "cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
                    "sampler_name": (CFUI.samplers.KSampler.SAMPLERS, ),
                    "scheduler": (CFUI.samplers.KSampler.SCHEDULERS, ),
                    "normalize": (["disable", "enable"], ),
                    "positive": ("CONDITIONING", ),
                    "negative": ("CONDITIONING", ),
                    "latent_image": ("LATENT", ),
                    }}
    
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "unsampler"

    CATEGORY = "sampling"
        
    def unsampler(self, model, cfg, sampler_name, steps, end_at_step, scheduler, normalize, positive, negative, latent_image):
        normalize = normalize == "enable"
        device = CFUI.model_management.get_torch_device()
        latent = latent_image
        latent_image = latent["samples"]

        end_at_step = min(end_at_step, steps-1)
        end_at_step = steps - end_at_step
        
        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
        noise_mask = None
        if "noise_mask" in latent:
            noise_mask = CFUI.sampler_helpers.prepare_mask(latent["noise_mask"], noise.shape, device)

        noise = noise.to(device)
        latent_image = latent_image.to(device)

        conds0 = \
            {"positive": CFUI.sampler_helpers.convert_cond(positive),
             "negative": CFUI.sampler_helpers.convert_cond(negative)}

        conds = {}
        for k in conds0:
            conds[k] = list(map(lambda a: a.copy(), conds0[k]))

        models, inference_memory = CFUI.sampler_helpers.get_additional_models(conds, model.model_dtype())
        
        CFUI.model_management.load_models_gpu([model] + models, model.memory_required(noise.shape) + inference_memory)

        sampler = CFUI.samplers.KSampler(model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=1.0, model_options=model.model_options)

        sigmas = sampler.sigmas.flip(0) + 0.0001

        pbar = CFUI.utils.ProgressBar(steps)
        def callback(step, x0, x, total_steps):
            pbar.update_absolute(step + 1, total_steps)

        samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, force_full_denoise=False, denoise_mask=noise_mask, sigmas=sigmas, start_step=0, last_step=end_at_step, callback=callback)
        if normalize:
            #technically doesn't normalize because unsampling is not guaranteed to end at a std given by the schedule
            samples -= samples.mean()
            samples /= samples.std()
        samples = samples.cpu()
        
        CFUI.sampler_helpers.cleanup_additional_models(models)

        out = latent.copy()
        out["samples"] = samples
        return (out, )
    
NODE_CLASS_MAPPINGS = {
    "BNK_NoisyLatentImage": NoisyLatentImage,
    #"BNK_DuplicateBatchIndex": DuplicateBatchIndex,
    "BNK_SlerpLatent": LatentSlerp,
    "BNK_GetSigma": GetSigma,
    "BNK_InjectNoise": InjectNoise,
    "BNK_Unsampler": Unsampler,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "BNK_NoisyLatentImage": "Noisy Latent Image",
    #"BNK_DuplicateBatchIndex": "Duplicate Batch Index",
    "BNK_SlerpLatent": "Slerp Latents",
    "BNK_GetSigma": "Get Sigma",
    "BNK_InjectNoise": "Inject Noise",
    "BNK_Unsampler": "Unsampler",
}