nodes/asagi4-ComfyUI-CADS
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Reformat

Browse Source
This commit is contained in:
asagi4
2023-12-06 13:24:46 +02:00
parent 9752ff3f25
commit e4bcfba98e
1 changed files with 23 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files
__init__.py
+23 -19
View File
@@ -1,35 +1,42 @@
import torch
def randn_like(cond, generator=None):
return torch.randn(cond.size(), generator=generator).to(cond)
class CADS:
generator = None
current_step = 0
@classmethod
def IS_CHANGED(*args, **kwargs):
return id(CADS.generator)
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL",),
"noise_scale": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.25}),
"t1": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.6}),
"t2": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.9}),
"rescale": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.0}),
},
"optional": {
"start_step": ("INT", {"min": -1, "max": 10000, "default": -1}),
"total_steps": ("INT", {"min": -1, "max": 10000, "default": -1}),
}}
return {
"required": {
"model": ("MODEL",),
"noise_scale": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.25}),
"t1": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.6}),
"t2": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.9}),
"rescale": ("FLOAT", {"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.0}),
},
"optional": {
"start_step": ("INT", {"min": -1, "max": 10000, "default": -1}),
"total_steps": ("INT", {"min": -1, "max": 10000, "default": -1}),
},
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "do"
CATEGORY = "utils"
def do(self, model, noise_scale, t1, t2, rescale, start_step=0, total_steps=0):
previous_wrapper = model.model_options.get('model_function_wrapper')
previous_wrapper = model.model_options.get("model_function_wrapper")
im = model.model.model_sampling
CADS.current_step = start_step
@@ -98,15 +105,12 @@ class CADS:
return uncond + (cond - uncond) * cond_scale
m = model.clone()
m.set_model_unet_function_wrapper(apply_cads)
# Alternative implementation. Doesn't seem to do the right thing
#m.set_model_sampler_cfg_function(apply_cads_cfg)
# m.set_model_sampler_cfg_function(apply_cads_cfg)
return (m,)
NODE_CLASS_MAPPINGS = {
'CADS': CADS
}
NODE_CLASS_MAPPINGS = {"CADS": CADS}