Remove Junk

This commit is contained in:
Silversith 2023-03-31 09:19:34 +02:00
parent b8a0fb6e83
commit a43b6de556
4 changed files with 2 additions and 804 deletions

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@ -126,117 +126,7 @@ class SaveImageList:
return {"ui": {"images": sorted_results}}
def custom_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, in_seed=None):
latent_image = latent["samples"]
noise_mask = None
device = model_management.get_torch_device()
if in_seed is not None:
seed = in_seed
print(seed)
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout,
generator=torch.manual_seed(seed), device="cpu")
if "noise_mask" in latent:
noise_mask = latent['noise_mask']
noise_mask = torch.nn.functional.interpolate(noise_mask[None, None,], size=(noise.shape[2], noise.shape[3]),
mode="bilinear")
noise_mask = noise_mask.round()
noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
noise_mask = torch.cat([noise_mask] * noise.shape[0])
noise_mask = noise_mask.to(device)
real_model = None
model_management.load_model_gpu(model)
real_model = model.model
noise = noise.to(device)
latent_image = latent_image.to(device)
positive_copy = []
negative_copy = []
control_nets = []
for p in positive:
t = p[0]
if t.shape[0] < noise.shape[0]:
t = torch.cat([t] * noise.shape[0])
t = t.to(device)
if 'control' in p[1]:
control_nets += [p[1]['control']]
positive_copy += [[t] + p[1:]]
for n in negative:
t = n[0]
if t.shape[0] < noise.shape[0]:
t = torch.cat([t] * noise.shape[0])
t = t.to(device)
if 'control' in n[1]:
control_nets += [n[1]['control']]
negative_copy += [[t] + n[1:]]
control_net_models = []
for x in control_nets:
control_net_models += x.get_control_models()
model_management.load_controlnet_gpu(control_net_models)
if sampler_name in comfy.samplers.KSampler.SAMPLERS:
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name,
scheduler=scheduler, denoise=denoise)
else:
# other samplers
pass
samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image,
start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise,
denoise_mask=noise_mask)
samples = samples.cpu()
for c in control_nets:
c.cleanup()
out = latent.copy()
out["samples"] = samples
return (out, seed,)
class CustomKSampler:
@classmethod
def INPUT_TYPES(s):
return {
"required":
{
"model": ("MODEL",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
},
"optional":
{
"in_seed": ()
}
}
RETURN_TYPES = ("LATENT", "seed",)
FUNCTION = "sample"
CATEGORY = "silver_custom"
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0,
in_seed=None):
return custom_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, in_seed=in_seed)
NODE_CLASS_MAPPINGS = {
"Note": Note,
"SaveImageList": SaveImageList,
"CustomKSampler": CustomKSampler,
}

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@ -1,693 +0,0 @@
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"properties": {}
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@ -1052,4 +1052,5 @@ def load_custom_nodes():
load_custom_nodes()
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "silver_custom.py"))