import os import random import einops import torch import numpy as np import comfy.model_management import comfy.utils from comfy.sd import load_checkpoint_guess_config from nodes import VAEDecode, EmptyLatentImage, CLIPTextEncode from comfy.sample import prepare_mask, broadcast_cond, load_additional_models, cleanup_additional_models from modules.samplers_advanced import KSampler, KSamplerWithRefiner from modules.patch import patch_all patch_all() opCLIPTextEncode = CLIPTextEncode() opEmptyLatentImage = EmptyLatentImage() opVAEDecode = VAEDecode() class StableDiffusionModel: def __init__(self, unet, vae, clip, clip_vision): self.unet = unet self.vae = vae self.clip = clip self.clip_vision = clip_vision def to_meta(self): if self.unet is not None: self.unet.model.to('meta') if self.clip is not None: self.clip.cond_stage_model.to('meta') if self.vae is not None: self.vae.first_stage_model.to('meta') @torch.no_grad() def load_model(ckpt_filename): unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename) return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision) @torch.no_grad() def load_lora(model, lora_filename, strength_model=1.0, strength_clip=1.0): if strength_model == 0 and strength_clip == 0: return model lora = comfy.utils.load_torch_file(lora_filename, safe_load=True) unet, clip = comfy.sd.load_lora_for_models(model.unet, model.clip, lora, strength_model, strength_clip) return StableDiffusionModel(unet=unet, clip=clip, vae=model.vae, clip_vision=model.clip_vision) @torch.no_grad() def encode_prompt_condition(clip, prompt): return opCLIPTextEncode.encode(clip=clip, text=prompt)[0] @torch.no_grad() def generate_empty_latent(width=1024, height=1024, batch_size=1): return opEmptyLatentImage.generate(width=width, height=height, batch_size=batch_size)[0] @torch.no_grad() def decode_vae(vae, latent_image): return opVAEDecode.decode(samples=latent_image, vae=vae)[0] def get_previewer(device, latent_format): from latent_preview import TAESD, TAESDPreviewerImpl taesd_decoder_path = os.path.abspath(os.path.realpath(os.path.join("models", "vae_approx", latent_format.taesd_decoder_name))) if not os.path.exists(taesd_decoder_path): print(f"Warning: TAESD previews enabled, but could not find {taesd_decoder_path}") return None taesd = TAESD(None, taesd_decoder_path).to(device) def preview_function(x0, step, total_steps): global cv2_is_top with torch.no_grad(): x_sample = taesd.decoder(torch.nn.functional.avg_pool2d(x0, kernel_size=(2, 2))).detach() * 255.0 x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c') x_sample = x_sample.cpu().numpy().clip(0, 255).astype(np.uint8) return x_sample[0] taesd.preview = preview_function return taesd @torch.no_grad() def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu', scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, callback_function=None): # SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"] # SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral", # "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", # "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"] seed = seed if isinstance(seed, int) else random.randint(1, 2 ** 64) device = comfy.model_management.get_torch_device() latent_image = latent["samples"] if disable_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] previewer = get_previewer(device, model.model.latent_format) pbar = comfy.utils.ProgressBar(steps) def callback(step, x0, x, total_steps): y = None if previewer and step % 3 == 0: y = previewer.preview(x0, step, total_steps) if callback_function is not None: callback_function(step, x0, x, total_steps, y) pbar.update_absolute(step + 1, total_steps, None) sigmas = None disable_pbar = False if noise_mask is not None: noise_mask = prepare_mask(noise_mask, noise.shape, device) comfy.model_management.load_model_gpu(model) real_model = model.model noise = noise.to(device) latent_image = latent_image.to(device) positive_copy = broadcast_cond(positive, noise.shape[0], device) negative_copy = broadcast_cond(negative, noise.shape[0], device) models = load_additional_models(positive, negative, model.model_dtype()) sampler = KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.cpu() cleanup_additional_models(models) out = latent.copy() out["samples"] = samples return out @torch.no_grad() def ksampler_with_refiner(model, positive, negative, refiner, refiner_positive, refiner_negative, latent, seed=None, steps=30, refiner_switch_step=20, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu', scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, callback_function=None): # SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"] # SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral", # "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", # "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"] seed = seed if isinstance(seed, int) else random.randint(1, 2 ** 64) device = comfy.model_management.get_torch_device() latent_image = latent["samples"] if disable_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] previewer = get_previewer(device, model.model.latent_format) pbar = comfy.utils.ProgressBar(steps) def callback(step, x0, x, total_steps): y = None if previewer and step % 3 == 0: y = previewer.preview(x0, step, total_steps) if callback_function is not None: callback_function(step, x0, x, total_steps, y) pbar.update_absolute(step + 1, total_steps, None) sigmas = None disable_pbar = False if noise_mask is not None: noise_mask = prepare_mask(noise_mask, noise.shape, device) comfy.model_management.load_model_gpu(model) noise = noise.to(device) latent_image = latent_image.to(device) positive_copy = broadcast_cond(positive, noise.shape[0], device) negative_copy = broadcast_cond(negative, noise.shape[0], device) refiner_positive_copy = broadcast_cond(refiner_positive, noise.shape[0], device) refiner_negative_copy = broadcast_cond(refiner_negative, noise.shape[0], device) models = load_additional_models(positive, negative, model.model_dtype()) sampler = KSamplerWithRefiner(model=model, refiner_model=refiner, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) samples = sampler.sample(noise, positive_copy, negative_copy, refiner_positive=refiner_positive_copy, refiner_negative=refiner_negative_copy, refiner_switch_step=refiner_switch_step, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback_function=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.cpu() cleanup_additional_models(models) out = latent.copy() out["samples"] = samples return out @torch.no_grad() def image_to_numpy(x): return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x]