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[Fooocus 2.0.60] Fooocus Inpaint or Outpaint (Midjourney Left/Right/Top/Bottom) (#402)

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[Fooocus 2.0.60] Fooocus Inpaint or Outpaint (Midjourney Left/Right/Top/Bottom) (#402)
This commit is contained in:
lllyasviel
2023-09-18 01:16:07 -07:00
committed by GitHub
parent 43e59c1676
commit b61642ecba
11 changed files with 572 additions and 53 deletions
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modules/core.py
+4 -4
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@@ -11,7 +11,7 @@ from comfy.sd import load_checkpoint_guess_config
from nodes import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled
from comfy.sample import prepare_mask, broadcast_cond, load_additional_models, cleanup_additional_models
from comfy.model_base import SDXLRefiner
from modules.samplers_advanced import KSampler, KSamplerWithRefiner
from modules.samplers_advanced import KSamplerBasic, KSamplerWithRefiner
from modules.patch import patch_all
@@ -147,7 +147,7 @@ def get_previewer(device, latent_format):
@torch.no_grad()
@torch.inference_mode()
def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
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):
# SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
@@ -199,7 +199,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
models = load_additional_models(positive, negative, model.model_dtype())
sampler = KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler,
sampler = KSamplerBasic(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,
@@ -220,7 +220,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
@torch.no_grad()
@torch.inference_mode()
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',
seed=None, steps=30, refiner_switch_step=20, 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):
# SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]