Merge branch 'AUTOMATIC1111:master' into master

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