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rework refiner

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rework refiner
This commit is contained in:
lllyasviel
2023-10-11 23:44:40 -07:00
committed by GitHub
parent 5e6b27a680
commit 132afcc2a2
12 changed files with 284 additions and 40 deletions
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modules/core.py
+18 -12
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@@ -15,6 +15,7 @@ import comfy.utils
import comfy.controlnet
import modules.sample_hijack
import comfy.samplers
import comfy.latent_formats
from comfy.sd import load_checkpoint_guess_config
from nodes import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, VAEEncodeForInpaint, \
@@ -154,17 +155,21 @@ class VAEApprox(torch.nn.Module):
return x
VAE_approx_model = None
VAE_approx_models = {}
@torch.no_grad()
@torch.inference_mode()
def get_previewer():
global VAE_approx_model
def get_previewer(model):
global VAE_approx_models
if VAE_approx_model is None:
from modules.path import vae_approx_path
vae_approx_filename = os.path.join(vae_approx_path, 'xlvaeapp.pth')
from modules.path import vae_approx_path
is_sdxl = isinstance(model.model.latent_format, comfy.latent_formats.SDXL)
vae_approx_filename = os.path.join(vae_approx_path, 'xlvaeapp.pth' if is_sdxl else 'vaeapp_sd15.pth')
if vae_approx_filename in VAE_approx_models:
VAE_approx_model = VAE_approx_models[vae_approx_filename]
else:
sd = torch.load(vae_approx_filename, map_location='cpu')
VAE_approx_model = VAEApprox()
VAE_approx_model.load_state_dict(sd)
@@ -179,6 +184,7 @@ def get_previewer():
VAE_approx_model.current_type = torch.float32
VAE_approx_model.to(comfy.model_management.get_torch_device())
VAE_approx_models[vae_approx_filename] = VAE_approx_model
@torch.no_grad()
@torch.inference_mode()
@@ -198,7 +204,10 @@ def get_previewer():
def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_fooocus_2m_sde_inpaint_seamless',
scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
previewer_start=None, previewer_end=None, noise_multiplier=1.0):
previewer_start=None, previewer_end=None, sigmas=None):
if sigmas is not None:
sigmas = sigmas.clone().to(comfy.model_management.get_torch_device())
latent_image = latent["samples"]
if disable_noise:
@@ -207,14 +216,11 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
if noise_multiplier != 1.0:
noise = noise * noise_multiplier
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
previewer = get_previewer()
previewer = get_previewer(model)
if previewer_start is None:
previewer_start = 0
@@ -240,7 +246,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
denoise=denoise, disable_noise=disable_noise, start_step=start_step,
last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback,
disable_pbar=disable_pbar, seed=seed)
disable_pbar=disable_pbar, seed=seed, sigmas=sigmas)
out = latent.copy()
out["samples"] = samples