model_base.py

import torch
from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel
from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
from comfy.ldm.modules.diffusionmodules.openaimodel import Timestep
import comfy.model_management
import comfy.conds
import numpy as np
from enum import Enum
from . import utils

class ModelType(Enum):
    EPS = 1
    V_PREDICTION = 2


#NOTE: all this sampling stuff will be moved
class EPS:
    def calculate_input(self, sigma, noise):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
        return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5

    def calculate_denoised(self, sigma, model_output, model_input):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        return model_input - model_output * sigma


class V_PREDICTION(EPS):
    def calculate_denoised(self, sigma, model_output, model_input):
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5


class ModelSamplingDiscrete(torch.nn.Module):
    def __init__(self, model_config=None):
        super().__init__()
        beta_schedule = "linear"
        if model_config is not None:
            beta_schedule = model_config.beta_schedule
        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
        self.sigma_data = 1.0

    def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
        if given_betas is not None:
            betas = given_betas
        else:
            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
        alphas = 1. - betas
        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
        # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])

        timesteps, = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end

        # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
        # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
        # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))

        sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5

        self.register_buffer('sigmas', sigmas)
        self.register_buffer('log_sigmas', sigmas.log())

    @property
    def sigma_min(self):
        return self.sigmas[0]

    @property
    def sigma_max(self):
        return self.sigmas[-1]

    def timestep(self, sigma):
        log_sigma = sigma.log()
        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
        return dists.abs().argmin(dim=0).view(sigma.shape)

    def sigma(self, timestep):
        t = torch.clamp(timestep.float(), min=0, max=(len(self.sigmas) - 1))
        low_idx = t.floor().long()
        high_idx = t.ceil().long()
        w = t.frac()
        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
        return log_sigma.exp()

    def percent_to_sigma(self, percent):
        return self.sigma(torch.tensor(percent * 999.0))

def model_sampling(model_config, model_type):
    if model_type == ModelType.EPS:
        c = EPS
    elif model_type == ModelType.V_PREDICTION:
        c = V_PREDICTION

    s = ModelSamplingDiscrete

    class ModelSampling(s, c):
        pass

    return ModelSampling(model_config)



class BaseModel(torch.nn.Module):
    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
        super().__init__()

        unet_config = model_config.unet_config
        self.latent_format = model_config.latent_format
        self.model_config = model_config

        if not unet_config.get("disable_unet_model_creation", False):
            self.diffusion_model = UNetModel(**unet_config, device=device)
        self.model_type = model_type
        self.model_sampling = model_sampling(model_config, model_type)

        self.adm_channels = unet_config.get("adm_in_channels", None)
        if self.adm_channels is None:
            self.adm_channels = 0
        self.inpaint_model = False
        print("model_type", model_type.name)
        print("adm", self.adm_channels)

    def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
        sigma = t
        xc = self.model_sampling.calculate_input(sigma, x)
        if c_concat is not None:
            xc = torch.cat([xc] + [c_concat], dim=1)

        context = c_crossattn
        dtype = self.get_dtype()
        xc = xc.to(dtype)
        t = self.model_sampling.timestep(t).float()
        context = context.to(dtype)
        extra_conds = {}
        for o in kwargs:
            extra_conds[o] = kwargs[o].to(dtype)
        model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
        return self.model_sampling.calculate_denoised(sigma, model_output, x)

    def get_dtype(self):
        return self.diffusion_model.dtype

    def is_adm(self):
        return self.adm_channels > 0

    def encode_adm(self, **kwargs):
        return None

    def extra_conds(self, **kwargs):
        out = {}
        if self.inpaint_model:
            concat_keys = ("mask", "masked_image")
            cond_concat = []
            denoise_mask = kwargs.get("denoise_mask", None)
            latent_image = kwargs.get("latent_image", None)
            noise = kwargs.get("noise", None)
            device = kwargs["device"]

            def blank_inpaint_image_like(latent_image):
                blank_image = torch.ones_like(latent_image)
                # these are the values for "zero" in pixel space translated to latent space
                blank_image[:,0] *= 0.8223
                blank_image[:,1] *= -0.6876
                blank_image[:,2] *= 0.6364
                blank_image[:,3] *= 0.1380
                return blank_image

            for ck in concat_keys:
                if denoise_mask is not None:
                    if ck == "mask":
                        cond_concat.append(denoise_mask[:,:1].to(device))
                    elif ck == "masked_image":
                        cond_concat.append(latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
                else:
                    if ck == "mask":
                        cond_concat.append(torch.ones_like(noise)[:,:1])
                    elif ck == "masked_image":
                        cond_concat.append(blank_inpaint_image_like(noise))
            data = torch.cat(cond_concat, dim=1)
            out['c_concat'] = comfy.conds.CONDNoiseShape(data)
        adm = self.encode_adm(**kwargs)
        if adm is not None:
            out['y'] = comfy.conds.CONDRegular(adm)
        return out

    def load_model_weights(self, sd, unet_prefix=""):
        to_load = {}
        keys = list(sd.keys())
        for k in keys:
            if k.startswith(unet_prefix):
                to_load[k[len(unet_prefix):]] = sd.pop(k)

        m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
        if len(m) > 0:
            print("unet missing:", m)

        if len(u) > 0:
            print("unet unexpected:", u)
        del to_load
        return self

    def process_latent_in(self, latent):
        return self.latent_format.process_in(latent)

    def process_latent_out(self, latent):
        return self.latent_format.process_out(latent)

    def state_dict_for_saving(self, clip_state_dict, vae_state_dict):
        clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict)
        unet_sd = self.diffusion_model.state_dict()
        unet_state_dict = {}
        for k in unet_sd:
            unet_state_dict[k] = comfy.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k)

        unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
        vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict)
        if self.get_dtype() == torch.float16:
            clip_state_dict = utils.convert_sd_to(clip_state_dict, torch.float16)
            vae_state_dict = utils.convert_sd_to(vae_state_dict, torch.float16)

        if self.model_type == ModelType.V_PREDICTION:
            unet_state_dict["v_pred"] = torch.tensor([])

        return {**unet_state_dict, **vae_state_dict, **clip_state_dict}

    def set_inpaint(self):
        self.inpaint_model = True

def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0):
    adm_inputs = []
    weights = []
    noise_aug = []
    for unclip_cond in unclip_conditioning:
        for adm_cond in unclip_cond["clip_vision_output"].image_embeds:
            weight = unclip_cond["strength"]
            noise_augment = unclip_cond["noise_augmentation"]
            noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
            c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device))
            adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
            weights.append(weight)
            noise_aug.append(noise_augment)
            adm_inputs.append(adm_out)

    if len(noise_aug) > 1:
        adm_out = torch.stack(adm_inputs).sum(0)
        noise_augment = noise_augment_merge
        noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
        c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))
        adm_out = torch.cat((c_adm, noise_level_emb), 1)

    return adm_out

class SD21UNCLIP(BaseModel):
    def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None):
        super().__init__(model_config, model_type, device=device)
        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config)

    def encode_adm(self, **kwargs):
        unclip_conditioning = kwargs.get("unclip_conditioning", None)
        device = kwargs["device"]
        if unclip_conditioning is None:
            return torch.zeros((1, self.adm_channels))
        else:
            return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05))

def sdxl_pooled(args, noise_augmentor):
    if "unclip_conditioning" in args:
        return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor)[:,:1280]
    else:
        return args["pooled_output"]

class SDXLRefiner(BaseModel):
    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
        super().__init__(model_config, model_type, device=device)
        self.embedder = Timestep(256)
        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})

    def encode_adm(self, **kwargs):
        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
        width = kwargs.get("width", 768)
        height = kwargs.get("height", 768)
        crop_w = kwargs.get("crop_w", 0)
        crop_h = kwargs.get("crop_h", 0)

        if kwargs.get("prompt_type", "") == "negative":
            aesthetic_score = kwargs.get("aesthetic_score", 2.5)
        else:
            aesthetic_score = kwargs.get("aesthetic_score", 6)

        out = []
        out.append(self.embedder(torch.Tensor([height])))
        out.append(self.embedder(torch.Tensor([width])))
        out.append(self.embedder(torch.Tensor([crop_h])))
        out.append(self.embedder(torch.Tensor([crop_w])))
        out.append(self.embedder(torch.Tensor([aesthetic_score])))
        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)

class SDXL(BaseModel):
    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
        super().__init__(model_config, model_type, device=device)
        self.embedder = Timestep(256)
        self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})

    def encode_adm(self, **kwargs):
        clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
        width = kwargs.get("width", 768)
        height = kwargs.get("height", 768)
        crop_w = kwargs.get("crop_w", 0)
        crop_h = kwargs.get("crop_h", 0)
        target_width = kwargs.get("target_width", width)
        target_height = kwargs.get("target_height", height)

        out = []
        out.append(self.embedder(torch.Tensor([height])))
        out.append(self.embedder(torch.Tensor([width])))
        out.append(self.embedder(torch.Tensor([crop_h])))
        out.append(self.embedder(torch.Tensor([crop_w])))
        out.append(self.embedder(torch.Tensor([target_height])))
        out.append(self.embedder(torch.Tensor([target_width])))
        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
        return torch.cat((clip_pooled.to(flat.device), flat), dim=1)