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 numpy as np from . import utils class BaseModel(torch.nn.Module): def __init__(self, model_config, v_prediction=False): super().__init__() unet_config = model_config.unet_config self.latent_format = model_config.latent_format self.model_config = model_config self.register_schedule(given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) self.diffusion_model = UNetModel(**unet_config) self.v_prediction = v_prediction if self.v_prediction: self.parameterization = "v" else: self.parameterization = "eps" self.adm_channels = unet_config.get("adm_in_channels", None) if self.adm_channels is None: self.adm_channels = 0 print("v_prediction", v_prediction) print("adm", self.adm_channels) 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 = np.cumprod(alphas, axis=0) 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)) def apply_model(self, x, t, c_concat=None, c_crossattn=None, c_adm=None, control=None, transformer_options={}): if c_concat is not None: xc = torch.cat([x] + c_concat, dim=1) else: xc = x context = torch.cat(c_crossattn, 1) return self.diffusion_model(xc, t, context=context, y=c_adm, control=control, transformer_options=transformer_options) 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 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_state_dict = self.diffusion_model.state_dict() 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) return {**unet_state_dict, **vae_state_dict, **clip_state_dict} class SD21UNCLIP(BaseModel): def __init__(self, model_config, noise_aug_config, v_prediction=True): super().__init__(model_config, v_prediction) 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 not None: adm_inputs = [] weights = [] noise_aug = [] for unclip_cond in unclip_conditioning: adm_cond = unclip_cond["clip_vision_output"].image_embeds weight = unclip_cond["strength"] noise_augment = unclip_cond["noise_augmentation"] noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment) c_adm, noise_level_emb = self.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) #TODO: add a way to control this noise_augment = 0.05 noise_level = round((self.noise_augmentor.max_noise_level - 1) * noise_augment) c_adm, noise_level_emb = self.noise_augmentor(adm_out[:, :self.noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) adm_out = torch.cat((c_adm, noise_level_emb), 1) else: adm_out = torch.zeros((1, self.adm_channels)) return adm_out class SDInpaint(BaseModel): def __init__(self, model_config, v_prediction=False): super().__init__(model_config, v_prediction) self.concat_keys = ("mask", "masked_image") class SDXLRefiner(BaseModel): def __init__(self, model_config, v_prediction=False): super().__init__(model_config, v_prediction) self.embedder = Timestep(256) def encode_adm(self, **kwargs): clip_pooled = kwargs["pooled_output"] 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) print(clip_pooled.shape, width, height, crop_w, crop_h, aesthetic_score) out = [] out.append(self.embedder(torch.Tensor([width]))) out.append(self.embedder(torch.Tensor([height]))) out.append(self.embedder(torch.Tensor([crop_w]))) out.append(self.embedder(torch.Tensor([crop_h]))) out.append(self.embedder(torch.Tensor([aesthetic_score]))) flat = torch.flatten(torch.cat(out))[None, ] return torch.cat((clip_pooled.to(flat.device), flat), dim=1) class SDXL(BaseModel): def __init__(self, model_config, v_prediction=False): super().__init__(model_config, v_prediction) self.embedder = Timestep(256) def encode_adm(self, **kwargs): clip_pooled = kwargs["pooled_output"] 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) print(clip_pooled.shape, width, height, crop_w, crop_h, target_width, target_height) out = [] out.append(self.embedder(torch.Tensor([width]))) out.append(self.embedder(torch.Tensor([height]))) out.append(self.embedder(torch.Tensor([crop_w]))) out.append(self.embedder(torch.Tensor([crop_h]))) out.append(self.embedder(torch.Tensor([target_width]))) out.append(self.embedder(torch.Tensor([target_height]))) flat = torch.flatten(torch.cat(out))[None, ] return torch.cat((clip_pooled.to(flat.device), flat), dim=1)