i

modules/core.py

@@ -83,7 +83,7 @@ def close_all_preview():
 
 
 @torch.no_grad()
-def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=9.0, sampler_name='euler_ancestral',
+def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='euler_ancestral',
              scheduler='normal', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
              force_full_denoise=False):
     seed = seed if isinstance(seed, int) else random.randint(1, 2 ** 64)
@@ -147,7 +147,7 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=9.0, sa
 
 @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=9.0, sampler_name='euler_ancestral',
+                          seed=None, steps=30, refiner_switch_step=20, cfg=7.0, sampler_name='euler_ancestral',
                           scheduler='normal', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
                           force_full_denoise=False):
     seed = seed if isinstance(seed, int) else random.randint(1, 2 ** 64)

modules/default_pipeline.py

@@ -14,7 +14,7 @@ del xl_base.vae
 
 
 @torch.no_grad()
-def process(positive_prompt, negative_prompt, width=1280, height=960, batch_size=1):
+def process(positive_prompt, negative_prompt, width=1024, height=1024, batch_size=1):
     positive_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=positive_prompt)
     negative_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=negative_prompt)