init

1 files changed, 14 insertions, 3 deletions

diff --git a/modules/processing.py b/modules/processing.py
index d5172f00..9a033759 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -316,11 +316,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()

 

 

-def process_images(p: StableDiffusionProcessing) -> Processed:

+def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,

+                   aesthetic_imgs=None,aesthetic_slerp=False) -> Processed:

     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""

 

+    aesthetic_lr = float(aesthetic_lr)

+    aesthetic_weight = float(aesthetic_weight)

+    aesthetic_steps = int(aesthetic_steps)

+

     if type(p.prompt) == list:

-        assert(len(p.prompt) > 0)

+        assert (len(p.prompt) > 0)

     else:

         assert p.prompt is not None

 

@@ -394,7 +399,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])

             #c = p.sd_model.get_learned_conditioning(prompts)

             with devices.autocast():

-                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)

+                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):

+                    shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0)

+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],

+                                                            p.steps)

+                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):

+                    shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,

+                                                                          aesthetic_steps, aesthetic_imgs,aesthetic_slerp)

                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)

 

             if len(model_hijack.comments) > 0: