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Add Intrinsic lora sampling node

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To use these loras: https://github.com/duxiaodan/intrinsic-lora
This commit is contained in:
kijai 2024-02-25 15:40:11 +02:00
parent 2441d098b8
commit 8a50975b14
1 changed files with 63 additions and 17 deletions
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80
nodes.py
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@ -3660,35 +3660,81 @@ class ImageNormalize_Neg1_To_1:
return (images,)
class X0_passlatent(comfy.model_sampling.EPS):
def calculate_denoised(self, sigma, model_output, model_input):
return model_output
def calculate_input(self, sigma, noise):
return noise
class SingleStepSampling:
import comfy.sample
from nodes import CLIPTextEncode, VAEEncode, VAEDecode, LoraLoader
class Intrinsic_lora_sampling:
def __init__(self):
self.loaded_lora = None
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"lora_name": (folder_paths.get_filename_list("loras"), ),
"task": (
[
'depth map',
'surface normals',
'albedo',
'shading',
],
{
"default": 'depth map'
}),
"text": ("STRING", {"multiline": True, "default": "depth map"}),
"clip": ("CLIP", ),
"vae": ("VAE", ),
"image": ("IMAGE",),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "onestepsample"
CATEGORY = "KJNodes"
def patch(self, model):
m = model.clone()
def onestepsample(self, model, lora_name, clip, vae, image, text, task):
encoded_latent, = VAEEncode.encode(self, vae, image[:,:,:,:3])
sample = encoded_latent["samples"]
noise = torch.zeros(sample.size(), dtype=sample.dtype, layout=sample.layout, device="cpu")
prompt = task + "," + text
print(prompt)
positive, = CLIPTextEncode.encode(self, clip, prompt)
negative = positive #negative shouldn't do anything in this scenario
#custom model sampling to pass latent through as it is
class X0_PassThrough(comfy.model_sampling.EPS):
def calculate_denoised(self, sigma, model_output, model_input):
return model_output
def calculate_input(self, sigma, noise):
return noise
sampling_base = comfy.model_sampling.ModelSamplingDiscrete
sampling_type = X0_passlatent
sampling_type = X0_PassThrough
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_clone = model.clone()
model_clone_with_lora = LoraLoader.load_lora(self, model_clone, None, lora_name, 1.0, 0)[0]
model_clone_with_lora.add_object_patch("model_sampling", model_sampling)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
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,
force_full_denoise=True, noise_mask=None, callback=None, disable_pbar=True, seed=None)}
image_out, = VAEDecode.decode(self, vae, samples)
if task == 'depth map':
imax = image_out.max()
imin = image_out.min()
image_out = (image_out-imin)/(imax-imin)
image_out = 0.299 * image_out[..., 0] + 0.587 * image_out[..., 1] + 0.114 * image_out[..., 2]
image_out = image_out.unsqueeze(-1).repeat(1, 1, 1, 3)
else:
image_out = image_out.clamp(-1.,1.)
return (image_out, )
NODE_CLASS_MAPPINGS = {
"INTConstant": INTConstant,
@ -3758,7 +3804,7 @@ NODE_CLASS_MAPPINGS = {
"ScaleBatchPromptSchedule": ScaleBatchPromptSchedule,
"EffnetEncode": EffnetEncode,
"ImageNormalize_Neg1_To_1": ImageNormalize_Neg1_To_1,
"SingleStepSampling": SingleStepSampling,
"Intrinsic_lora_sampling": Intrinsic_lora_sampling,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"INTConstant": "INT Constant",
@ -3827,5 +3873,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"ScaleBatchPromptSchedule": "ScaleBatchPromptSchedule",
"EffnetEncode": "EffnetEncode",
"ImageNormalize_Neg1_To_1": "ImageNormalize_Neg1_To_1",
"SingleStepSampling": "SingleStepSampling",
"Intrinsic_lora_sampling": "Intrinsic_lora_sampling",
}