initial support for training textual inversion

1 files changed, 8 insertions, 5 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 7eeb5191..8223423a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -56,7 +56,7 @@ class StableDiffusionProcessing:
         self.prompt: str = prompt

         self.prompt_for_display: str = None

         self.negative_prompt: str = (negative_prompt or "")

-        self.styles: str = styles

+        self.styles: list = styles or []

         self.seed: int = seed

         self.subseed: int = subseed

         self.subseed_strength: float = subseed_strength

@@ -271,7 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),

         "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.eta == p.sampler.default_eta else p.sampler.eta),

+        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),

     }

 

     generation_params.update(p.extra_generation_params)

@@ -295,8 +295,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

     fix_seed(p)

 

-    os.makedirs(p.outpath_samples, exist_ok=True)

-    os.makedirs(p.outpath_grids, exist_ok=True)

+    if p.outpath_samples is not None:

+        os.makedirs(p.outpath_samples, exist_ok=True)

+

+    if p.outpath_grids is not None:

+        os.makedirs(p.outpath_grids, exist_ok=True)

 

     modules.sd_hijack.model_hijack.apply_circular(p.tiling)

 

@@ -323,7 +326,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)

 

     if os.path.exists(cmd_opts.embeddings_dir):

-        model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)

+        model_hijack.embedding_db.load_textual_inversion_embeddings()

 

     infotexts = []

     output_images = []