added smooth step to mikey sampler base only

This commit is contained in:
bash-j
2023-08-23 08:14:19 +09:30
parent 21670f732d
commit 6cc28c53e2
+6 -6
View File
@@ -1773,7 +1773,8 @@ class MikeySamplerBaseOnly:
"model_name": (folder_paths.get_filename_list("upscale_models"), ),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"upscale_by": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
"hires_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),}}
"hires_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
'smooth_step': ("INT", {"default": 0, "min": 0, "max": 100})}}
RETURN_TYPES = ('LATENT',)
FUNCTION = 'run'
@@ -1784,11 +1785,10 @@ class MikeySamplerBaseOnly:
if image_complexity > 1:
image_complexity = 1
image_complexity = min([0.55, image_complexity]) * hires_strength
return min([16, 16 - int(round(image_complexity * 16,0))])
return min([31, 31 - int(round(image_complexity * 31,0))])
def run(self, seed, base_model, vae, samples, positive_cond_base, negative_cond_base,
model_name, upscale_by=1.0, hires_strength=1.0,
upscale_method='normal'):
model_name, upscale_by=1.0, hires_strength=1.0, upscale_method='normal', smooth_step=0):
image_scaler = ImageScale()
vaeencoder = VAEEncode()
vaedecoder = VAEDecode()
@@ -1801,7 +1801,7 @@ class MikeySamplerBaseOnly:
sample1 = common_ksampler(base_model, seed, 30, 5, 'dpmpp_3m_sde_gpu', 'exponential', positive_cond_base, negative_cond_base, samples,
start_step=0, last_step=14, force_full_denoise=False)[0]
# step 2 run base model high cfg
sample2 = common_ksampler(base_model, seed+1, 31, 9.5, 'dpmpp_3m_sde_gpu', 'exponential', positive_cond_base, negative_cond_base, sample1,
sample2 = common_ksampler(base_model, seed+1, 31 + smooth_step, 9.5, 'dpmpp_3m_sde_gpu', 'exponential', positive_cond_base, negative_cond_base, sample1,
disable_noise=True, start_step=15, force_full_denoise=True)[0]
# step 3 upscale
pixels = vaedecoder.decode(vae, sample2)[0]
@@ -1816,7 +1816,7 @@ class MikeySamplerBaseOnly:
# encode image
latent = vaeencoder.encode(vae, img)[0]
# step 3 run base model
out = common_ksampler(base_model, seed, 16, 9.5, 'dpmpp_3m_sde_gpu', 'exponential', positive_cond_base, negative_cond_base, latent,
out = common_ksampler(base_model, seed, 31, 9.5, 'dpmpp_3m_sde_gpu', 'exponential', positive_cond_base, negative_cond_base, latent,
start_step=start_step, force_full_denoise=True)
return out