Merge pull request #14979 from drhead/refiner_cumprod_fix

Protect alphas_cumprod during refiner switchover

modules/processing.py

@@ -915,33 +915,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
-            def rescale_zero_terminal_snr_abar(alphas_cumprod):
-                alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-                # Store old values.
-                alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-                alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-                # Shift so the last timestep is zero.
-                alphas_bar_sqrt -= (alphas_bar_sqrt_T)
-
-                # Scale so the first timestep is back to the old value.
-                alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-                # Convert alphas_bar_sqrt to betas
-                alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-                alphas_bar[-1] = 4.8973451890853435e-08
-                return alphas_bar
-
-            if hasattr(p.sd_model, 'alphas_cumprod') and hasattr(p.sd_model, 'alphas_cumprod_original'):
-                p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod_original.to(shared.device)
-
-                if opts.use_downcasted_alpha_bar:
-                    p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
-                    p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod.half().to(shared.device)
-                if opts.sd_noise_schedule == "Zero Terminal SNR":
-                    p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
-                    p.sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(p.sd_model.alphas_cumprod).to(shared.device)
+            sd_models.apply_alpha_schedule_override(p.sd_model, p)
 
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)

modules/sd_models.py

@@ -15,6 +15,7 @@
 
 from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
 from modules.timer import Timer
+from modules.shared import opts
 import tomesd
 import numpy as np
 
@@ -549,6 +550,36 @@ def repair_config(sd_config):
         karlo_path = os.path.join(paths.models_path, 'karlo')
         sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
 
+def apply_alpha_schedule_override(sd_model, p=None):
+    def rescale_zero_terminal_snr_abar(alphas_cumprod):
+        alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+        # Store old values.
+        alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+        alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+        # Shift so the last timestep is zero.
+        alphas_bar_sqrt -= (alphas_bar_sqrt_T)
+
+        # Scale so the first timestep is back to the old value.
+        alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+        # Convert alphas_bar_sqrt to betas
+        alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+        alphas_bar[-1] = 4.8973451890853435e-08
+        return alphas_bar
+
+    if hasattr(sd_model, 'alphas_cumprod') and hasattr(sd_model, 'alphas_cumprod_original'):
+        sd_model.alphas_cumprod = sd_model.alphas_cumprod_original.to(shared.device)
+
+        if opts.use_downcasted_alpha_bar:
+            if p is not None:
+                p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
+            sd_model.alphas_cumprod = sd_model.alphas_cumprod.half().to(shared.device)
+        if opts.sd_noise_schedule == "Zero Terminal SNR":
+            if p is not None:
+                p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
+            sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(sd_model.alphas_cumprod).to(shared.device)
 
 sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
 sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
@@ -812,6 +843,7 @@ def reload_model_weights(sd_model=None, info=None, forced_reload=False):
 
     sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
     if not forced_reload and sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+        apply_alpha_schedule_override(sd_model)
         return sd_model
 
     if sd_model is not None: