Update reforge

ldm_patched/k_diffusion/sampling.py

@@ -69,10 +69,11 @@ def get_sigmas_ays(n, sigma_min, sigma_max, is_sdxl=False, device='cpu'):
         return interped_ys
 
     if is_sdxl:
-        sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
+        sigmas = [sigma_max, sigma_max/2.314, sigma_max/3.875, sigma_max/6.701, sigma_max/10.89, sigma_max/16.954, sigma_max/26.333, sigma_max/38.46, sigma_max/62.457, sigma_max/129.336, 0.029]
     else:
         # Default to SD 1.5 sigmas.
-        sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
+        sigmas = [sigma_max, sigma_max/2.257, sigma_max/3.785, sigma_max/5.418, sigma_max/7.749, sigma_max/10.469, sigma_max/15.176, sigma_max/22.415, sigma_max/36.629, sigma_max/96.151, 0.029]
+
 
     if n != len(sigmas):
         sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
@@ -91,9 +92,10 @@ def get_sigmas_ays_gits(n, sigma_min, sigma_max, is_sdxl=False, device='cpu'):
         return interped_ys
 
     if is_sdxl:
-        sigmas = [14.615, 4.734, 2.567, 1.529, 0.987, 0.652, 0.418, 0.268, 0.179, 0.127, 0.029]
+        sigmas = [sigma_max, sigma_max/3.087, sigma_max/5.693, sigma_max/9.558, sigma_max/14.807, sigma_max/22.415, sigma_max/34.964, sigma_max/54.533, sigma_max/81.648, sigma_max/115.078, 0.029]
+
     else:
-        sigmas = [14.615, 4.617, 2.507, 1.236, 0.702, 0.402, 0.240, 0.156, 0.104, 0.094, 0.029]
+        sigmas = [sigma_max, sigma_max/3.165, sigma_max/5.829, sigma_max/11.824, sigma_max/20.819, sigma_max/36.355, sigma_max/60.895, sigma_max/93.685, sigma_max/140.528, sigma_max/155.478, 0.029]
 
     if n != len(sigmas):
         sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
@@ -116,10 +118,10 @@ def get_sigmas_ays_32steps(n, sigma_min, sigma_max, is_sdxl=False, device='cpu')
         return interped_ys
     
     if is_sdxl:
-        sigmas = [14.61500000000000000, 11.14916180000000000, 8.505221270000000000, 6.488271510000000000, 5.437074020000000000, 4.603986190000000000, 3.898547040000000000, 3.274074570000000000, 2.743965270000000000, 2.299686590000000000, 1.954485140000000000, 1.671087150000000000, 1.428781520000000000, 1.231810090000000000, 1.067896490000000000, 0.925794430000000000, 0.802908860000000000, 0.696601210000000000, 0.604369030000000000, 0.528525520000000000, 0.467733440000000000, 0.413933790000000000, 0.362581860000000000, 0.310085170000000000, 0.265189250000000000, 0.223264610000000000, 0.176538770000000000, 0.139591920000000000, 0.105873810000000000, 0.055193690000000000, 0.028773340000000000, 0.015000000000000000]
+        sigmas = [sigma_max, sigma_max/1.310860875657935, sigma_max/1.718356235075352, sigma_max/2.252525958180810, sigma_max/2.688026675053433, sigma_max/3.174423075322040, sigma_max/3.748832539417044, sigma_max/4.463856789920335, sigma_max/5.326233593328242, sigma_max/6.355213820679800, sigma_max/7.477672611007930, sigma_max/8.745803592589411, sigma_max/10.228995682978878, sigma_max/11.864653584709637, sigma_max/13.685783347784952, sigma_max/15.786441921021279, sigma_max/18.202564111697559, sigma_max/20.980440157432400, sigma_max/24.182245076323649, sigma_max/27.652401723193991, sigma_max/31.246429590323925, sigma_max/35.307579021272943, sigma_max/40.308138967569972, sigma_max/47.132212095147923, sigma_max/55.111585405517003, sigma_max/65.460441760115945, sigma_max/82.786347724072168, sigma_max/104.698036963744033, sigma_max/138.041693219503482, sigma_max/264.794761864988552, sigma_max/507.935470821253285, 0.015000000000000000]
     else:
-        sigmas = [14.61500000000000000, 11.23951352000000000, 8.643630810000000000, 6.647294240000000000, 5.572508620000000000, 4.716485460000000000, 3.991960650000000000, 3.519560900000000000, 3.134904660000000000, 2.792287880000000000, 2.487736280000000000, 2.216638650000000000, 1.975083510000000000, 1.779317200000000000, 1.614753350000000000, 1.465409530000000000, 1.314849000000000000, 1.166424970000000000, 1.034755470000000000, 0.915737440000000000, 0.807481690000000000, 0.712023610000000000, 0.621739000000000000, 0.530652020000000000, 0.452909600000000000, 0.374914550000000000, 0.274618190000000000, 0.201152900000000000, 0.141058730000000000, 0.066828810000000000, 0.031661210000000000, 0.015000000000000000]
-    
+        sigmas = [sigma_max, sigma_max/1.300323183382763, sigma_max/1.690840379611262, sigma_max/2.198638945761486, sigma_max/2.622696705671493, sigma_max/3.098705619671305, sigma_max/3.661108232617473, sigma_max/4.152506637972936, sigma_max/4.662023756728857, sigma_max/5.234059175875519, sigma_max/5.874818853387466, sigma_max/6.593316416277412, sigma_max/7.399687115002039, sigma_max/8.213824943635682, sigma_max/9.050917900247738, sigma_max/9.973321246245751, sigma_max/11.115344803852001, sigma_max/12.529738625194212, sigma_max/14.124109921351757, sigma_max/15.959814856974724, sigma_max/18.099481611774999, sigma_max/20.526004748634670, sigma_max/23.506648288108032, sigma_max/27.541589307433523, sigma_max/32.269132736422456, sigma_max/38.982216080970984, sigma_max/53.219344283057142, sigma_max/72.656173487928834, sigma_max/103.609326413189740, sigma_max/218.693105563304210, sigma_max/461.605857767280530, 0.015000000000000000]
+        
     if n != len(sigmas):
         sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
     else:

ldm_patched/modules/sd.py

@@ -542,6 +542,16 @@ class VAE:
 
         self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
         logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
+
+        if shared.opts.reflective_padding_vae_sd == "Enabled":
+            for module in self.first_stage_model.modules():
+                from torch import nn
+                logging.info(self)
+                if isinstance(module, nn.Conv2d):
+                    pad_h, pad_w = module.padding if isinstance(module.padding, tuple) else (module.padding, module.padding)
+                    if pad_h > 0 or pad_w > 0:
+                        module.padding_mode = "reflect"
+            logging.info("Setting reflective padding")
     def throw_exception_if_invalid(self):
         if self.first_stage_model is None:
             raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")

modules/sd_schedulers.py

@@ -108,10 +108,11 @@ def get_align_your_steps_sigmas(n, sigma_min, sigma_max, device):
         return interped_ys
 
     if shared.sd_model.is_sdxl:
-        sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
+        sigmas = sigmas = [sigma_max, sigma_max/2.314, sigma_max/3.875, sigma_max/6.701, sigma_max/10.89, sigma_max/16.954, sigma_max/26.333, sigma_max/38.46, sigma_max/62.457, sigma_max/129.336, 0.029]
     else:
         # Default to SD 1.5 sigmas.
-        sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
+        sigmas = [sigma_max, sigma_max/2.257, sigma_max/3.785, sigma_max/5.418, sigma_max/7.749, sigma_max/10.469, sigma_max/15.176, sigma_max/22.415, sigma_max/36.629, sigma_max/96.151, 0.029]
+
 
     if n != len(sigmas):
         sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
@@ -219,9 +220,10 @@ def get_align_your_steps_sigmas_GITS(n, sigma_min, sigma_max, device):
         return interped_ys
 
     if shared.sd_model.is_sdxl:
-        sigmas = [14.615, 4.734, 2.567, 1.529, 0.987, 0.652, 0.418, 0.268, 0.179, 0.127, 0.029]
+        sigmas = [sigma_max, sigma_max/3.087, sigma_max/5.693, sigma_max/9.558, sigma_max/14.807, sigma_max/22.415, sigma_max/34.964, sigma_max/54.533, sigma_max/81.648, sigma_max/115.078, 0.029]
+
     else:
-        sigmas = [14.615, 4.617, 2.507, 1.236, 0.702, 0.402, 0.240, 0.156, 0.104, 0.094, 0.029]
+        sigmas = [sigma_max, sigma_max/3.165, sigma_max/5.829, sigma_max/11.824, sigma_max/20.819, sigma_max/36.355, sigma_max/60.895, sigma_max/93.685, sigma_max/140.528, sigma_max/155.478, 0.029]
 
     if n != len(sigmas):
         sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
@@ -245,9 +247,10 @@ def ays_11_sigmas(n, sigma_min, sigma_max, device='cpu'):
         return interped_ys
 
     if shared.sd_model.is_sdxl:
-        sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
+        sigmas = [sigma_max, sigma_max/2.314, sigma_max/3.875, sigma_max/6.701, sigma_max/10.89, sigma_max/16.954, sigma_max/26.333, sigma_max/38.46, sigma_max/62.457, sigma_max/129.336, 0.029]
     else:
-        sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
+        sigmas = [sigma_max, sigma_max/2.257, sigma_max/3.785, sigma_max/5.418, sigma_max/7.749, sigma_max/10.469, sigma_max/15.176, sigma_max/22.415, sigma_max/36.629, sigma_max/96.151, 0.029]
+
 
     if n != len(sigmas):
         sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
@@ -268,9 +271,10 @@ def ays_32_sigmas(n, sigma_min, sigma_max, device='cpu'):
         interped_ys = np.exp(new_ys)[::-1].copy()
         return interped_ys
     if shared.sd_model.is_sdxl:
-        sigmas = [14.61500000000000000, 11.14916180000000000, 8.505221270000000000, 6.488271510000000000, 5.437074020000000000, 4.603986190000000000, 3.898547040000000000, 3.274074570000000000, 2.743965270000000000, 2.299686590000000000, 1.954485140000000000, 1.671087150000000000, 1.428781520000000000, 1.231810090000000000, 1.067896490000000000, 0.925794430000000000, 0.802908860000000000, 0.696601210000000000, 0.604369030000000000, 0.528525520000000000, 0.467733440000000000, 0.413933790000000000, 0.362581860000000000, 0.310085170000000000, 0.265189250000000000, 0.223264610000000000, 0.176538770000000000, 0.139591920000000000, 0.105873810000000000, 0.055193690000000000, 0.028773340000000000, 0.015000000000000000]
+        sigmas = [sigma_max, sigma_max/1.310860875657935, sigma_max/1.718356235075352, sigma_max/2.252525958180810, sigma_max/2.688026675053433, sigma_max/3.174423075322040, sigma_max/3.748832539417044, sigma_max/4.463856789920335, sigma_max/5.326233593328242, sigma_max/6.355213820679800, sigma_max/7.477672611007930, sigma_max/8.745803592589411, sigma_max/10.228995682978878, sigma_max/11.864653584709637, sigma_max/13.685783347784952, sigma_max/15.786441921021279, sigma_max/18.202564111697559, sigma_max/20.980440157432400, sigma_max/24.182245076323649, sigma_max/27.652401723193991, sigma_max/31.246429590323925, sigma_max/35.307579021272943, sigma_max/40.308138967569972, sigma_max/47.132212095147923, sigma_max/55.111585405517003, sigma_max/65.460441760115945, sigma_max/82.786347724072168, sigma_max/104.698036963744033, sigma_max/138.041693219503482, sigma_max/264.794761864988552, sigma_max/507.935470821253285, 0.015000000000000000]
     else:
-        sigmas = [14.61500000000000000, 11.23951352000000000, 8.643630810000000000, 6.647294240000000000, 5.572508620000000000, 4.716485460000000000, 3.991960650000000000, 3.519560900000000000, 3.134904660000000000, 2.792287880000000000, 2.487736280000000000, 2.216638650000000000, 1.975083510000000000, 1.779317200000000000, 1.614753350000000000, 1.465409530000000000, 1.314849000000000000, 1.166424970000000000, 1.034755470000000000, 0.915737440000000000, 0.807481690000000000, 0.712023610000000000, 0.621739000000000000, 0.530652020000000000, 0.452909600000000000, 0.374914550000000000, 0.274618190000000000, 0.201152900000000000, 0.141058730000000000, 0.066828810000000000, 0.031661210000000000, 0.015000000000000000]
+        sigmas = [sigma_max, sigma_max/1.300323183382763, sigma_max/1.690840379611262, sigma_max/2.198638945761486, sigma_max/2.622696705671493, sigma_max/3.098705619671305, sigma_max/3.661108232617473, sigma_max/4.152506637972936, sigma_max/4.662023756728857, sigma_max/5.234059175875519, sigma_max/5.874818853387466, sigma_max/6.593316416277412, sigma_max/7.399687115002039, sigma_max/8.213824943635682, sigma_max/9.050917900247738, sigma_max/9.973321246245751, sigma_max/11.115344803852001, sigma_max/12.529738625194212, sigma_max/14.124109921351757, sigma_max/15.959814856974724, sigma_max/18.099481611774999, sigma_max/20.526004748634670, sigma_max/23.506648288108032, sigma_max/27.541589307433523, sigma_max/32.269132736422456, sigma_max/38.982216080970984, sigma_max/53.219344283057142, sigma_max/72.656173487928834, sigma_max/103.609326413189740, sigma_max/218.693105563304210, sigma_max/461.605857767280530, 0.015000000000000000]
+           
     if n != len(sigmas):
         sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
     else:

modules/shared_options.py

@@ -205,6 +205,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
     "tiling": OptionInfo(False, "Tiling", infotext='Tiling').info("produce a tileable picture"),
     "hires_fix_refiner_pass": OptionInfo("second pass", "Hires fix: which pass to enable refiner for", gr.Radio, {"choices": ["first pass", "second pass", "both passes"]}, infotext="Hires refiner"),
     "cond_stage_model_device_compatibility_check": OptionInfo(False, "Perform device compatibility check for conditional stage model. Enables broader hardware compatibility by falling back to CPU if GPU doesn't support required data types. May improve stability on some systems, but can significantly slow down model loading and potentially impact generation speed.", gr.Checkbox, {"interactive": True}),
+    "reflective_padding_vae_sd": OptionInfo("Disabled", "Enables or disables reflective vae padding, for models/VAEs like MS-LC-EQ-D-VR VAE", gr.Radio, {"choices": ["Disabled", "Enabled"]}, infotext="reflective vae padding"),
 }))
 
 options_templates.update(options_section(('sdxl', "Stable Diffusion XL", "sd"), {