fix sampler eta

modules/processing.py

@@ -1028,7 +1028,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         self.ops.append('txt2img')
         hypertile_set(self)
-        self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model)
+        self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model, self)
         x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
         if not self.enable_hr or shared.state.interrupted or shared.state.skipped:
@@ -1076,7 +1076,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 if self.denoising_strength > 0:
                     self.ops.append('hires')
                     devices.torch_gc() # GC now before running the next img2img to prevent running out of memory
-                    self.sampler = modules.sd_samplers.create_sampler(self.latent_sampler or self.sampler_name, self.sd_model)
+                    self.sampler = modules.sd_samplers.create_sampler(self.latent_sampler or self.sampler_name, self.sd_model, self)
                     samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
                     noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
                     modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
@@ -1130,7 +1130,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         if self.sampler_name == "PLMS":
             self.sampler_name = 'UniPC'
-        self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model)
+        self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model, self)
 
         if self.image_mask is not None:
             self.ops.append('inpaint')

modules/sd_samplers.py

@@ -34,7 +34,7 @@ def find_sampler_config(name):
     return config
 
 
-def create_sampler(name, model):
+def create_sampler(name, model, p=None):
     if name == 'Default' and hasattr(model, 'scheduler'):
         config = {k: v for k, v in model.scheduler.config.items() if not k.startswith('_')}
         shared.log.debug(f'Sampler default {type(model.scheduler).__name__}: {config}')
@@ -46,7 +46,7 @@ def create_sampler(name, model):
     if shared.backend == shared.Backend.ORIGINAL:
         sampler = config.constructor(model)
         sampler.config = config
-        sampler.initialize(p=None)
+        sampler.initialize(p)
         sampler.name = name
         shared.log.debug(f'Sampler: sampler={sampler.name} config={sampler.config.options}')
         return sampler

modules/sd_samplers_compvis.py

@@ -128,26 +128,24 @@ class VanillaStableDiffusionSampler:
         self.update_step(x)
 
     def initialize(self, p):
-        if self.is_ddim:
-            self.eta = p.eta if p.eta is not None else shared.opts.scheduler_eta
-        else:
-            self.eta = 0.0
-        if self.eta != 0.0:
-            p.extra_generation_params["Sampler Eta"] = self.eta
-
-        if self.is_unipc:
-            keys = [
-                ('Solver order', 'schedulers_solver_order'),
-                ('Sampler low order', 'schedulers_use_loworder'),
-                ('UniPC variant', 'uni_pc_variant'),
-                ('UniPC skip type', 'uni_pc_skip_type'),
-            ]
-
-            for name, key in keys:
-                v = getattr(shared.opts, key)
-                if v != shared.opts.get_default(key):
-                    p.extra_generation_params[name] = v
-
+        if p is not None:
+            if self.is_ddim:
+                self.eta = p.eta if p.eta is not None else shared.opts.scheduler_eta
+            else:
+                self.eta = 0.0
+            if self.eta != 0.0:
+                p.extra_generation_params["Sampler Eta"] = self.eta
+            if self.is_unipc:
+                keys = [
+                    ('Solver order', 'schedulers_solver_order'),
+                    ('Sampler low order', 'schedulers_use_loworder'),
+                    ('UniPC variant', 'uni_pc_variant'),
+                    ('UniPC skip type', 'uni_pc_skip_type'),
+                ]
+                for name, key in keys:
+                    v = getattr(shared.opts, key)
+                    if v != shared.opts.get_default(key):
+                        p.extra_generation_params[name] = v
         for fieldname in ['p_sample_ddim', 'p_sample_plms']:
             if hasattr(self.sampler, fieldname):
                 setattr(self.sampler, fieldname, self.p_sample_ddim_hook)