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Update nodes.py

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kijai 2024-02-25 18:49:09 +02:00
parent ef25bfcee0
commit 0cb0f87a6b
1 changed files with 5 additions and 2 deletions
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nodes.py
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@ -3694,7 +3694,7 @@ class Intrinsic_lora_sampling:
CATEGORY = "KJNodes" CATEGORY = "KJNodes"
def onestepsample(self, model, lora_name, clip, vae, image, text, task): def onestepsample(self, model, lora_name, clip, vae, image, text, task):
pbar = comfy.utils.ProgressBar(3)
encoded_latent, = VAEEncode.encode(self, vae, image[:,:,:,:3]) encoded_latent, = VAEEncode.encode(self, vae, image[:,:,:,:3])
sample = encoded_latent["samples"] sample = encoded_latent["samples"]
noise = torch.zeros(sample.size(), dtype=sample.dtype, layout=sample.layout, device="cpu") noise = torch.zeros(sample.size(), dtype=sample.dtype, layout=sample.layout, device="cpu")
@ -3702,6 +3702,7 @@ class Intrinsic_lora_sampling:
prompt = task + "," + text prompt = task + "," + text
print(prompt) print(prompt)
positive, = CLIPTextEncode.encode(self, clip, prompt) positive, = CLIPTextEncode.encode(self, clip, prompt)
pbar.update(1)
negative = positive #negative shouldn't do anything in this scenario negative = positive #negative shouldn't do anything in this scenario
#custom model sampling to pass latent through as it is #custom model sampling to pass latent through as it is
@ -3724,8 +3725,9 @@ class Intrinsic_lora_sampling:
samples = {"samples": comfy.sample.sample(model_clone_with_lora, noise, 1, 1.0, "euler", "simple", positive, negative, sample, samples = {"samples": comfy.sample.sample(model_clone_with_lora, noise, 1, 1.0, "euler", "simple", positive, negative, sample,
denoise=1.0, disable_noise=True, start_step=0, last_step=1, denoise=1.0, disable_noise=True, start_step=0, last_step=1,
force_full_denoise=True, noise_mask=None, callback=None, disable_pbar=True, seed=None)} force_full_denoise=True, noise_mask=None, callback=None, disable_pbar=True, seed=None)}
pbar.update(1)
image_out, = VAEDecode.decode(self, vae, samples) image_out, = VAEDecode.decode(self, vae, samples)
pbar.update(1)
if task == 'depth map': if task == 'depth map':
imax = image_out.max() imax = image_out.max()
imin = image_out.min() imin = image_out.min()
@ -3737,6 +3739,7 @@ class Intrinsic_lora_sampling:
image_out = 1.0 - image_out image_out = 1.0 - image_out
else: else:
image_out = image_out.clamp(-1.,1.) image_out = image_out.clamp(-1.,1.)
return (image_out, ) return (image_out, )
NODE_CLASS_MAPPINGS = { NODE_CLASS_MAPPINGS = {