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wip: update ldm_patched

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currently issues with calculate_sigmas call
This commit is contained in:
Manuel Schmid
2024-06-02 20:52:52 +02:00
parent ab01104d42
commit 3881fa0806
84 changed files with 5380 additions and 1923 deletions
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ldm_patched/contrib/external_model_advanced.py
+46 -9
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@@ -1,8 +1,7 @@
# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import ldm_patched.utils.path_utils
import ldm_patched.utils.path_utils as folder_paths
import ldm_patched.modules.sd
import ldm_patched.modules.model_sampling
import ldm_patched.modules.latent_formats
import torch
class LCM(ldm_patched.modules.model_sampling.EPS):
@@ -19,6 +18,10 @@ class LCM(ldm_patched.modules.model_sampling.EPS):
return c_out * x0 + c_skip * model_input
class X0(ldm_patched.modules.model_sampling.EPS):
def calculate_denoised(self, sigma, model_output, model_input):
return model_output
class ModelSamplingDiscreteDistilled(ldm_patched.modules.model_sampling.ModelSamplingDiscrete):
original_timesteps = 50
@@ -70,7 +73,7 @@ class ModelSamplingDiscrete:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["eps", "v_prediction", "lcm", "tcd"]),
"sampling": (["eps", "v_prediction", "lcm", "x0"],),
"zsnr": ("BOOLEAN", {"default": False}),
}}
@@ -90,9 +93,8 @@ class ModelSamplingDiscrete:
elif sampling == "lcm":
sampling_type = LCM
sampling_base = ModelSamplingDiscreteDistilled
elif sampling == "tcd":
sampling_type = ldm_patched.modules.model_sampling.EPS
sampling_base = ModelSamplingDiscreteDistilled
elif sampling == "x0":
sampling_type = X0
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
@@ -104,11 +106,37 @@ class ModelSamplingDiscrete:
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class ModelSamplingStableCascade:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "advanced/model"
def patch(self, model, shift):
m = model.clone()
sampling_base = ldm_patched.modules.model_sampling.StableCascadeSampling
sampling_type = ldm_patched.modules.model_sampling.EPS
class ModelSamplingAdvanced(sampling_base, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift)
m.add_object_patch("model_sampling", model_sampling)
return (m, )
class ModelSamplingContinuousEDM:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["v_prediction", "eps"],),
"sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
"sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
}}
@@ -121,17 +149,25 @@ class ModelSamplingContinuousEDM:
def patch(self, model, sampling, sigma_max, sigma_min):
m = model.clone()
latent_format = None
sigma_data = 1.0
if sampling == "eps":
sampling_type = ldm_patched.modules.model_sampling.EPS
elif sampling == "v_prediction":
sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION
elif sampling == "edm_playground_v2.5":
sampling_type = ldm_patched.modules.model_sampling.EDM
sigma_data = 0.5
latent_format = ldm_patched.modules.latent_formats.SDXL_Playground_2_5()
class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type):
pass
model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_sigma_range(sigma_min, sigma_max)
model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
m.add_object_patch("model_sampling", model_sampling)
if latent_format is not None:
m.add_object_patch("latent_format", latent_format)
return (m, )
class RescaleCFG:
@@ -176,5 +212,6 @@ class RescaleCFG:
NODE_CLASS_MAPPINGS = {
"ModelSamplingDiscrete": ModelSamplingDiscrete,
"ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
"ModelSamplingStableCascade": ModelSamplingStableCascade,
"RescaleCFG": RescaleCFG,
}