Remove 3m from PR #1213 because of some small issues.

comfy/k_diffusion/sampling.py

@@ -638,32 +638,6 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
         h_last = h
     return x
 
-@torch.no_grad()
-def sample_dpmpp_3m(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """DPM-Solver++(3M) without SDE-specific parts."""
-
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-
-        # Update x using the DPM-Solver++(3M) update rule
-        t, s = -sigmas[i].log(), -sigmas[i + 1].log()
-        h = s - t
-        h_eta = h * (eta + 1)
-
-        x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
-
-        if eta:
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
-
-    return x
-
 @torch.no_grad()
 def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """DPM-Solver++(3M) SDE."""

comfy/samplers.py

@@ -539,7 +539,7 @@ class KSampler:
     SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
     SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
                 "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
-                "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
+                "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
 
     def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
         self.model = model