1 files changed, 25 insertions, 6 deletions
diff --git a/modules/processing.py b/modules/processing.py
index f773a30e..94d2dd62 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -46,6 +46,12 @@ def apply_color_correction(correction, image):
     return image
 
 
+def get_correct_sampler(p):
+    if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
+        return sd_samplers.samplers
+    elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
+        return sd_samplers.samplers_for_img2img
+
 class StableDiffusionProcessing:
     def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
         self.sd_model = sd_model
@@ -123,6 +129,7 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
+        self.clip_skip = opts.CLIP_stop_at_last_layers
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -169,6 +176,7 @@ class Processed:
             "infotexts": self.infotexts,
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
+            "clip_skip": self.clip_skip,
         }
 
         return json.dumps(obj)
@@ -266,14 +274,18 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
+    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
+
     generation_params = {
         "Steps": p.steps,
-        "Sampler": sd_samplers.samplers[p.sampler_index].name,
+        "Sampler": get_correct_sampler(p)[p.sampler_index].name,
         "CFG scale": p.cfg_scale,
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
+        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
@@ -281,6 +293,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+        "Clip skip": None if clip_skip <= 1 else clip_skip,
     }
 
     generation_params.update(p.extra_generation_params)
@@ -312,6 +325,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         os.makedirs(p.outpath_grids, exist_ok=True)
 
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
+    modules.sd_hijack.model_hijack.clear_comments()
 
     comments = {}
 
@@ -341,7 +355,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     infotexts = []
     output_images = []
 
-    with torch.no_grad():
+    with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
             p.init(all_prompts, all_seeds, all_subseeds)
 
@@ -349,6 +363,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             state.job_count = p.n_iter
 
         for n in range(p.n_iter):
+            if state.skipped:
+                state.skipped = False
+            
             if state.interrupted:
                 break
 
@@ -375,9 +392,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
-            if state.interrupted:
+            if state.interrupted or state.skipped:
 
-                # if we are interruped, sample returns just noise
+                # if we are interrupted, sample returns just noise
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
 
@@ -436,7 +453,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
                 text = infotext(n, i)
                 infotexts.append(text)
-                image.info["parameters"] = text
+                if opts.enable_pnginfo:
+                    image.info["parameters"] = text
                 output_images.append(image)
 
             del x_samples_ddim 
@@ -455,7 +473,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if opts.return_grid:
                 text = infotext()
                 infotexts.insert(0, text)
-                grid.info["parameters"] = text
+                if opts.enable_pnginfo:
+                    grid.info["parameters"] = text
                 output_images.insert(0, grid)
                 index_of_first_image = 1