mirrors/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-09-12 12:37:01 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix model needing to be loaded on GPU to generate the sigmas.

Browse Source
This commit is contained in:
comfyanonymous
2024-04-04 22:08:49 -04:00
parent 1f8d8e6c77
commit 1a0486bb96
3 changed files with 21 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
comfy/samplers.py
View File

@@ -274,8 +274,8 @@ class KSamplerX0Inpaint:
out = out * denoise_mask + self.latent_image * latent_mask
return out
def simple_scheduler(model, steps):
s = model.model_sampling
def simple_scheduler(model_sampling, steps):
s = model_sampling
sigs = []
ss = len(s.sigmas) / steps
for x in range(steps):
@@ -283,8 +283,8 @@ def simple_scheduler(model, steps):
sigs += [0.0]
return torch.FloatTensor(sigs)
def ddim_scheduler(model, steps):
s = model.model_sampling
def ddim_scheduler(model_sampling, steps):
s = model_sampling
sigs = []
ss = max(len(s.sigmas) // steps, 1)
x = 1
@@ -295,8 +295,8 @@ def ddim_scheduler(model, steps):
sigs += [0.0]
return torch.FloatTensor(sigs)
def normal_scheduler(model, steps, sgm=False, floor=False):
s = model.model_sampling
def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
s = model_sampling
start = s.timestep(s.sigma_max)
end = s.timestep(s.sigma_min)
@@ -660,19 +660,19 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
def calculate_sigmas_scheduler(model, scheduler_name, steps):
def calculate_sigmas(model_sampling, scheduler_name, steps):
if scheduler_name == "karras":
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
elif scheduler_name == "exponential":
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
elif scheduler_name == "normal":
sigmas = normal_scheduler(model, steps)
sigmas = normal_scheduler(model_sampling, steps)
elif scheduler_name == "simple":
sigmas = simple_scheduler(model, steps)
sigmas = simple_scheduler(model_sampling, steps)
elif scheduler_name == "ddim_uniform":
sigmas = ddim_scheduler(model, steps)
sigmas = ddim_scheduler(model_sampling, steps)
elif scheduler_name == "sgm_uniform":
sigmas = normal_scheduler(model, steps, sgm=True)
sigmas = normal_scheduler(model_sampling, steps, sgm=True)
else:
logging.error("error invalid scheduler {}".format(scheduler_name))
return sigmas
@@ -714,7 +714,7 @@ class KSampler:
steps += 1
discard_penultimate_sigma = True
sigmas = calculate_sigmas_scheduler(self.model.model, self.scheduler, steps)
sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps)
if discard_penultimate_sigma:
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])