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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, VAEEncode
from comfy.sample import prepare_mask, broadcast_cond, get_additional_models, cleanup_additional_models
from comfy_extras.nodes_post_processing import ImageScaleToTotalPixels
from modules.samplers_advanced import KSampler, KSamplerWithRefiner
from modules.patch import patch_all
comfy.model_management.DISABLE_SMART_MEMORY = True
patch_all()
opCLIPTextEncode = CLIPTextEncode()
opEmptyLatentImage = EmptyLatentImage()
opVAEDecode = VAEDecode()
opVAEEncode = VAEEncode()
opImageScaleToTotalPixels = ImageScaleToTotalPixels()
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]
@torch.no_grad()
def encode_vae(vae, pixels):
return opVAEEncode.encode(pixels=pixels, vae=vae)[0]
@torch.no_grad()
def upscale(image):
return opImageScaleToTotalPixels.upscale(image=image, upscale_method='bicubic', megapixels=1.0)[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", "sgm_uniform", "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", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
seed = seed if isinstance(seed, int) else random.randint(0, 2**63 - 1)
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)
real_model = None
models = get_additional_models(positive, negative)
comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]))
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)
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", "sgm_uniform", "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", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
seed = seed if isinstance(seed, int) else random.randint(0, 2**63 - 1)
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)
models = get_additional_models(positive, negative)
comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]))
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)
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]
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