From b9a04c32b8efe5f0a9bc5d369cbf3c5bcc12d00d Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sun, 27 Nov 2022 16:57:29 +0100
Subject: Update

---
 infer.py | 52 +++++++++++++++++++++++-----------------------------
 1 file changed, 23 insertions(+), 29 deletions(-)

(limited to 'infer.py')

diff --git a/infer.py b/infer.py
index 2bf9cb3..ab5f247 100644
--- a/infer.py
+++ b/infer.py
@@ -20,7 +20,6 @@ torch.backends.cuda.matmul.allow_tf32 = True
 
 default_args = {
     "model": None,
-    "scheduler": "dpmpp",
     "precision": "fp32",
     "ti_embeddings_dir": "embeddings_ti",
     "output_dir": "output/inference",
@@ -29,6 +28,7 @@ default_args = {
 
 
 default_cmds = {
+    "scheduler": "dpmpp",
     "prompt": None,
     "negative_prompt": None,
     "image": None,
@@ -61,11 +61,6 @@ def create_args_parser():
         "--model",
         type=str,
     )
-    parser.add_argument(
-        "--scheduler",
-        type=str,
-        choices=["plms", "ddim", "klms", "dpmpp", "euler_a"],
-    )
     parser.add_argument(
         "--precision",
         type=str,
@@ -91,6 +86,11 @@ def create_cmd_parser():
     parser = argparse.ArgumentParser(
         description="Simple example of a training script."
     )
+    parser.add_argument(
+        "--scheduler",
+        type=str,
+        choices=["plms", "ddim", "klms", "dpmpp", "euler_a"],
+    )
     parser.add_argument(
         "--prompt",
         type=str,
@@ -199,37 +199,17 @@ def load_embeddings_ti(tokenizer, text_encoder, embeddings_dir):
             print(f"Loaded {placeholder_token}")
 
 
-def create_pipeline(model, scheduler, ti_embeddings_dir, dtype):
+def create_pipeline(model, ti_embeddings_dir, dtype):
     print("Loading Stable Diffusion pipeline...")
 
     tokenizer = CLIPTokenizer.from_pretrained(model, subfolder='tokenizer', torch_dtype=dtype)
     text_encoder = CLIPTextModel.from_pretrained(model, subfolder='text_encoder', torch_dtype=dtype)
     vae = AutoencoderKL.from_pretrained(model, subfolder='vae', torch_dtype=dtype)
     unet = UNet2DConditionModel.from_pretrained(model, subfolder='unet', torch_dtype=dtype)
+    scheduler = DDIMScheduler.from_pretrained(model, subfolder='scheduler', torch_dtype=dtype)
 
     load_embeddings_ti(tokenizer, text_encoder, ti_embeddings_dir)
 
-    if scheduler == "plms":
-        scheduler = PNDMScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-        )
-    elif scheduler == "klms":
-        scheduler = LMSDiscreteScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-        )
-    elif scheduler == "ddim":
-        scheduler = DDIMScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False
-        )
-    elif scheduler == "dpmpp":
-        scheduler = DPMSolverMultistepScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-        )
-    else:
-        scheduler = EulerAncestralDiscreteScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
-        )
-
     pipeline = VlpnStableDiffusion(
         text_encoder=text_encoder,
         vae=vae,
@@ -264,6 +244,17 @@ def generate(output_dir, pipeline, args):
     else:
         init_image = None
 
+    if args.scheduler == "plms":
+        pipeline.scheduler = PNDMScheduler.from_config(pipeline.scheduler.config)
+    elif args.scheduler == "klms":
+        pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
+    elif args.scheduler == "ddim":
+        pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
+    elif args.scheduler == "dpmpp":
+        pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+    elif args.scheduler == "euler_a":
+        pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
+
     with torch.autocast("cuda"), torch.inference_mode():
         for i in range(args.batch_num):
             pipeline.set_progress_bar_config(
@@ -331,6 +322,9 @@ class CmdParse(cmd.Cmd):
             generate(self.output_dir, self.pipeline, args)
         except KeyboardInterrupt:
             print('Generation cancelled.')
+        except Exception as e:
+            print(e)
+            return
 
     def do_exit(self, line):
         return True
@@ -345,7 +339,7 @@ def main():
     output_dir = Path(args.output_dir)
     dtype = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}[args.precision]
 
-    pipeline = create_pipeline(args.model, args.scheduler, args.ti_embeddings_dir, dtype)
+    pipeline = create_pipeline(args.model, args.ti_embeddings_dir, dtype)
     cmd_parser = create_cmd_parser()
     cmd_prompt = CmdParse(output_dir, pipeline, cmd_parser)
     cmd_prompt.cmdloop()
-- 
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