From 3396ca881ed3f3521617cd9024eea56975191d32 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Thu, 5 Jan 2023 13:26:32 +0100
Subject: Update

---
 infer.py | 40 +++++++++++++++++++++++++++++-----------
 1 file changed, 29 insertions(+), 11 deletions(-)

(limited to 'infer.py')

diff --git a/infer.py b/infer.py
index 507d0cf..9c27db4 100644
--- a/infer.py
+++ b/infer.py
@@ -25,6 +25,7 @@ from diffusers import (
 )
 from transformers import CLIPTextModel
 
+from data.keywords import prompt_to_keywords, keywords_to_prompt
 from models.clip.embeddings import patch_managed_embeddings
 from models.clip.tokenizer import MultiCLIPTokenizer
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
@@ -49,6 +50,7 @@ default_cmds = {
     "scheduler": "dpmsm",
     "prompt": None,
     "negative_prompt": None,
+    "shuffle": True,
     "image": None,
     "image_noise": .7,
     "width": 768,
@@ -125,6 +127,10 @@ def create_cmd_parser():
         type=str,
         nargs="*",
     )
+    parser.add_argument(
+        "--shuffle",
+        type=bool,
+    )
     parser.add_argument(
         "--image",
         type=str,
@@ -197,7 +203,7 @@ def load_embeddings(pipeline, embeddings_dir):
         pipeline.text_encoder.text_model.embeddings,
         Path(embeddings_dir)
     )
-    print(f"Added {len(added_tokens)} tokens from embeddings dir: {zip(added_tokens, added_ids)}")
+    print(f"Added {len(added_tokens)} tokens from embeddings dir: {list(zip(added_tokens, added_ids))}")
 
 
 def create_pipeline(model, dtype):
@@ -228,20 +234,35 @@ def create_pipeline(model, dtype):
 
 
 @torch.inference_mode()
-def generate(output_dir, pipeline, args):
+def generate(output_dir: Path, pipeline, args):
     if isinstance(args.prompt, str):
         args.prompt = [args.prompt]
 
+    if args.shuffle:
+        args.prompt *= args.batch_size
+        args.batch_size = 1
+        args.prompt = [keywords_to_prompt(prompt_to_keywords(prompt), shuffle=True) for prompt in args.prompt]
+
     now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    use_subdirs = len(args.prompt) != 1
-    if use_subdirs:
+    image_dir = []
+
+    if len(args.prompt) != 1:
         if len(args.project) != 0:
             output_dir = output_dir.joinpath(f"{now}_{slugify(args.project)}")
         else:
             output_dir = output_dir.joinpath(now)
+
+        for prompt in args.prompt:
+            dir = output_dir.joinpath(slugify(prompt)[:100])
+            dir.mkdir(parents=True, exist_ok=True)
+            image_dir.append(dir)
+
+            with open(dir.joinpath('prompt.txt'), 'w') as f:
+                f.write(prompt)
     else:
         output_dir = output_dir.joinpath(f"{now}_{slugify(args.prompt[0])[:100]}")
-    output_dir.mkdir(parents=True, exist_ok=True)
+        output_dir.mkdir(parents=True, exist_ok=True)
+        image_dir.append(output_dir)
 
     args.seed = args.seed or torch.random.seed()
 
@@ -293,12 +314,9 @@ def generate(output_dir, pipeline, args):
         ).images
 
         for j, image in enumerate(images):
-            image_dir = output_dir
-            if use_subdirs:
-                image_dir = image_dir.joinpath(slugify(args.prompt[j % len(args.prompt)])[:100])
-            image_dir.mkdir(parents=True, exist_ok=True)
-            image.save(image_dir.joinpath(f"{seed}_{j // len(args.prompt)}.png"))
-            image.save(image_dir.joinpath(f"{seed}_{j // len(args.prompt)}.jpg"), quality=85)
+            dir = image_dir[j % len(args.prompt)]
+            image.save(dir.joinpath(f"{seed}_{j // len(args.prompt)}.png"))
+            image.save(dir.joinpath(f"{seed}_{j // len(args.prompt)}.jpg"), quality=85)
 
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
-- 
cgit v1.2.3-54-g00ecf