revise noise formulation

revise noise formulation

modules/default_pipeline.py

@@ -270,13 +270,11 @@ refresh_everything(
 
 @torch.no_grad()
 @torch.inference_mode()
-def vae_parse(latent, k=1.0):
+def vae_parse(latent):
     if final_refiner_vae is None:
-        result = latent["samples"]
-    else:
-        result = vae_interpose.parse(latent["samples"])
-    if k != 1.0:
-        result = result * k
+        return latent
+
+    result = vae_interpose.parse(latent["samples"])
     return {'samples': result}
 
 
@@ -433,6 +431,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
         decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
 
     if refiner_swap_method == 'vae':
+        modules.patch.eps_record = 'vae'
+
         if modules.inpaint_worker.current_task is not None:
             modules.inpaint_worker.current_task.unswap()
 
@@ -458,13 +458,9 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             target_model = final_unet
             print('Use base model to refine itself - this may because of developer mode.')
 
-        # Fooocus' vae parameters
-        k_data = 1.025
-        k_noise = 0.25
+        sampled_latent = vae_parse(sampled_latent)
+
         k_sigmas = 1.4
-
-        sampled_latent = vae_parse(sampled_latent, k=k_data)
-
         sigmas = calculate_sigmas(sampler=sampler_name,
                                   scheduler=scheduler_name,
                                   model=target_model.model,
@@ -472,6 +468,9 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
                                   denoise=denoise)[switch:] * k_sigmas
         len_sigmas = len(sigmas) - 1
 
+        assert isinstance(modules.patch.eps_record, torch.Tensor)
+        residual_noise = modules.patch.eps_record / modules.patch.eps_record.std()
+
         if modules.inpaint_worker.current_task is not None:
             modules.inpaint_worker.current_task.swap()
 
@@ -481,7 +480,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=final_clip),
             latent=sampled_latent,
             steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True,
-            seed=image_seed + 1,  # Avoid artifacts
+            seed=image_seed,
             denoise=denoise,
             callback_function=callback,
             cfg=cfg_scale,
@@ -490,7 +489,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             previewer_start=switch,
             previewer_end=steps,
             sigmas=sigmas,
-            extra_noise=k_noise
+            noise=residual_noise
         )
 
         target_model = final_refiner_vae
@@ -499,4 +498,5 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
         decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
 
     images = core.pytorch_to_numpy(decoded_latent)
+    modules.patch.eps_record = None
     return images