Update Reforge

ldm_patched/k_diffusion/sampling.py

@@ -1054,6 +1054,51 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
         # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)
     return x
 
+@torch.no_grad()
+def sample_dpmpp_sde_classic(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+    """DPM-Solver++ (stochastic)."""
+    # Older and faster DPM++ SDE version.
+    eta = modules.shared.opts.dpmpp_sde_og_eta
+    s_noise = modules.shared.opts.dpmpp_sde_og_s_noise
+    r = modules.shared.opts.dpmpp_sde_og_r
+    
+    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+    seed = extra_args.get("seed", None)
+    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) 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]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+    
+    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})
+        if sigmas[i + 1] == 0:
+            # Euler method
+            d = to_d(x, sigmas[i], denoised)
+            dt = sigmas[i + 1] - sigmas[i]
+            x = x + d * dt
+        else:
+            # DPM-Solver++
+            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+            h = t_next - t
+            s = t + h * r
+            fac = 1 / (2 * r)
+            # Step 1
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
+            s_ = t_fn(sd)
+            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
+            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+            # Step 2
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
+            t_next_ = t_fn(sd)
+            denoised_d = (1 - fac) * denoised + fac * denoised_2
+            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
+            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
+    return x
+
 @torch.no_grad()
 def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
     """DPM-Solver++ (stochastic)."""

modules/sd_models.py

@@ -1149,7 +1149,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, forced_relo
         vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
         sd_vae.load_vae(sd_model, vae_file, vae_source)
         timer.record("load VAE")
-        validate_and_fix_vae(sd_model)
+        # validate_and_fix_vae(sd_model) # comment it for now, go back if any issue happens.
         timer.record("load textual inversion embeddings")
 
         script_callbacks.model_loaded_callback(sd_model)

modules_forge/forge_alter_samplers.py

@@ -79,6 +79,7 @@ class AlterSampler(sd_samplers_kdiffusion.KDiffusionSampler):
             'SEEDS_3': k_diffusion_sampling.sample_seeds_3,
             'SA-Solver': k_diffusion_sampling.sample_sa_solver,
             'SA-Solver-Pece': k_diffusion_sampling.sample_sa_solver_pece,
+            'dpmpp_sde_classic': k_diffusion_sampling.sample_dpmpp_sde_classic,
         }
         
         sampler_function = sampler_functions.get(sampler_name)
@@ -214,6 +215,7 @@ def build_constructor(sampler_name):
     return constructor
 
 samplers_data_alter = [
+    sd_samplers_common.SamplerData('DPM++ SDE Classic', build_constructor(sampler_name='dpmpp_sde_classic'), ['dpmpp_sde_classic'], {}),
     sd_samplers_common.SamplerData('ER SDE', build_constructor(sampler_name='ER SDE'), ['ER SDE'], {}),
     sd_samplers_common.SamplerData('Kohaku_LoNyu_Yog', build_constructor(sampler_name='Kohaku_LoNyu_Yog'), ['Kohaku_LoNyu_Yog'], {}),
     sd_samplers_common.SamplerData('Euler CFG++', build_constructor(sampler_name='euler_cfg_pp'), ['euler_cfg_pp'], {}),