Code simplifications, avoid autocast

4 files changed, 92 insertions, 82 deletions

diff --git a/infer.py b/infer.py
index 420cb83..f566114 100644
--- a/infer.py
+++ b/infer.py
@@ -209,6 +209,7 @@ def create_pipeline(model, embeddings_dir, dtype):
     return pipeline
 
 
+@torch.inference_mode()
 def generate(output_dir, pipeline, args):
     if isinstance(args.prompt, str):
         args.prompt = [args.prompt]
@@ -245,30 +246,29 @@ def generate(output_dir, pipeline, args):
     elif args.scheduler == "kdpm2_a":
         pipeline.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
 
-    with torch.autocast("cuda"), torch.inference_mode():
-        for i in range(args.batch_num):
-            pipeline.set_progress_bar_config(
-                desc=f"Batch {i + 1} of {args.batch_num}",
-                dynamic_ncols=True
-            )
-
-            generator = torch.Generator(device="cuda").manual_seed(args.seed + i)
-            images = pipeline(
-                prompt=args.prompt,
-                negative_prompt=args.negative_prompt,
-                height=args.height,
-                width=args.width,
-                num_images_per_prompt=args.batch_size,
-                num_inference_steps=args.steps,
-                guidance_scale=args.guidance_scale,
-                generator=generator,
-                image=init_image,
-                strength=args.image_noise,
-            ).images
-
-            for j, image in enumerate(images):
-                image.save(output_dir.joinpath(f"{args.seed + i}_{j}.png"))
-                image.save(output_dir.joinpath(f"{args.seed + i}_{j}.jpg"), quality=85)
+    for i in range(args.batch_num):
+        pipeline.set_progress_bar_config(
+            desc=f"Batch {i + 1} of {args.batch_num}",
+            dynamic_ncols=True
+        )
+
+        generator = torch.Generator(device="cuda").manual_seed(args.seed + i)
+        images = pipeline(
+            prompt=args.prompt,
+            negative_prompt=args.negative_prompt,
+            height=args.height,
+            width=args.width,
+            num_images_per_prompt=args.batch_size,
+            num_inference_steps=args.steps,
+            guidance_scale=args.guidance_scale,
+            generator=generator,
+            image=init_image,
+            strength=args.image_noise,
+        ).images
+
+        for j, image in enumerate(images):
+            image.save(output_dir.joinpath(f"{args.seed + i}_{j}.png"))
+            image.save(output_dir.joinpath(f"{args.seed + i}_{j}.jpg"), quality=85)
 
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
diff --git a/train_dreambooth.py b/train_dreambooth.py
index e239833..2c765ec 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -389,6 +389,7 @@ def parse_args():
 class Checkpointer(CheckpointerBase):
     def __init__(
         self,
+        weight_dtype,
         datamodule,
         accelerator,
         vae,
@@ -416,6 +417,7 @@ class Checkpointer(CheckpointerBase):
             sample_batch_size=sample_batch_size
         )
 
+        self.weight_dtype = weight_dtype
         self.accelerator = accelerator
         self.vae = vae
         self.unet = unet
@@ -452,6 +454,12 @@ class Checkpointer(CheckpointerBase):
         unet = self.ema_unet.averaged_model if self.ema_unet is not None else self.accelerator.unwrap_model(self.unet)
         text_encoder = self.accelerator.unwrap_model(self.text_encoder)
 
+        orig_unet_dtype = unet.dtype
+        orig_text_encoder_dtype = text_encoder.dtype
+
+        unet.to(dtype=self.weight_dtype)
+        text_encoder.to(dtype=self.weight_dtype)
+
         pipeline = VlpnStableDiffusion(
             text_encoder=text_encoder,
             vae=self.vae,
@@ -463,6 +471,9 @@ class Checkpointer(CheckpointerBase):
 
         super().save_samples(pipeline, step, num_inference_steps, guidance_scale, eta)
 
+        unet.to(dtype=orig_unet_dtype)
+        text_encoder.to(dtype=orig_text_encoder_dtype)
+
         del unet
         del text_encoder
         del pipeline
@@ -798,6 +809,7 @@ def main():
     max_acc_val = 0.0
 
     checkpointer = Checkpointer(
+        weight_dtype=weight_dtype,
         datamodule=datamodule,
         accelerator=accelerator,
         vae=vae,
diff --git a/train_ti.py b/train_ti.py
index 5f37d54..a228795 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -361,6 +361,7 @@ def parse_args():
 class Checkpointer(CheckpointerBase):
     def __init__(
         self,
+        weight_dtype,
         datamodule,
         accelerator,
         vae,
@@ -387,6 +388,7 @@ class Checkpointer(CheckpointerBase):
             sample_batch_size=sample_batch_size
         )
 
+        self.weight_dtype = weight_dtype
         self.accelerator = accelerator
         self.vae = vae
         self.unet = unet
@@ -417,8 +419,9 @@ class Checkpointer(CheckpointerBase):
     @torch.no_grad()
     def save_samples(self, step, num_inference_steps, guidance_scale=7.5, eta=0.0):
         text_encoder = self.accelerator.unwrap_model(self.text_encoder)
+        orig_dtype = text_encoder.dtype
+        text_encoder.to(dtype=self.weight_dtype)
 
-        # Save a sample image
         pipeline = VlpnStableDiffusion(
             text_encoder=text_encoder,
             vae=self.vae,
@@ -430,6 +433,8 @@ class Checkpointer(CheckpointerBase):
 
         super().save_samples(pipeline, step, num_inference_steps, guidance_scale, eta)
 
+        text_encoder.to(dtype=orig_dtype)
+
         del text_encoder
         del pipeline
 
@@ -739,6 +744,7 @@ def main():
     max_acc_val = 0.0
 
     checkpointer = Checkpointer(
+        weight_dtype=weight_dtype,
         datamodule=datamodule,
         accelerator=accelerator,
         vae=vae,
diff --git a/training/util.py b/training/util.py
index 5c056a6..a0c15cd 100644
--- a/training/util.py
+++ b/training/util.py
@@ -60,7 +60,7 @@ class CheckpointerBase:
         self.sample_batches = sample_batches
         self.sample_batch_size = sample_batch_size
 
-    @torch.no_grad()
+    @torch.inference_mode()
     def save_samples(self, pipeline, step, num_inference_steps, guidance_scale=7.5, eta=0.0):
         samples_path = Path(self.output_dir).joinpath("samples")
 
@@ -68,65 +68,57 @@ class CheckpointerBase:
         val_data = self.datamodule.val_dataloader()
 
         generator = torch.Generator(device=pipeline.device).manual_seed(self.seed)
-        stable_latents = torch.randn(
-            (self.sample_batch_size, pipeline.unet.in_channels, self.sample_image_size // 8, self.sample_image_size // 8),
-            device=pipeline.device,
-            generator=generator,
-        )
 
         grid_cols = min(self.sample_batch_size, 4)
         grid_rows = (self.sample_batches * self.sample_batch_size) // grid_cols
 
-        with torch.autocast("cuda"), torch.inference_mode():
-            for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]:
-                all_samples = []
-                file_path = samples_path.joinpath(pool, f"step_{step}.jpg")
-                file_path.parent.mkdir(parents=True, exist_ok=True)
-
-                data_enum = enumerate(data)
-
-                batches = [
-                    batch
-                    for j, batch in data_enum
-                    if j * data.batch_size < self.sample_batch_size * self.sample_batches
-                ]
-                prompts = [
-                    prompt
-                    for batch in batches
-                    for prompt in batch["prompts"]
-                ]
-                nprompts = [
-                    prompt
-                    for batch in batches
-                    for prompt in batch["nprompts"]
-                ]
-
-                for i in range(self.sample_batches):
-                    prompt = prompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size]
-                    nprompt = nprompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size]
-
-                    samples = pipeline(
-                        prompt=prompt,
-                        negative_prompt=nprompt,
-                        height=self.sample_image_size,
-                        width=self.sample_image_size,
-                        image=latents[:len(prompt)] if latents is not None else None,
-                        generator=generator if latents is not None else None,
-                        guidance_scale=guidance_scale,
-                        eta=eta,
-                        num_inference_steps=num_inference_steps,
-                        output_type='pil'
-                    ).images
-
-                    all_samples += samples
-
-                    del samples
-
-                image_grid = make_grid(all_samples, grid_rows, grid_cols)
-                image_grid.save(file_path, quality=85)
-
-                del all_samples
-                del image_grid
+        for pool, data, gen in [("stable", val_data, generator), ("val", val_data, None), ("train", train_data, None)]:
+            all_samples = []
+            file_path = samples_path.joinpath(pool, f"step_{step}.jpg")
+            file_path.parent.mkdir(parents=True, exist_ok=True)
+
+            data_enum = enumerate(data)
+
+            batches = [
+                batch
+                for j, batch in data_enum
+                if j * data.batch_size < self.sample_batch_size * self.sample_batches
+            ]
+            prompts = [
+                prompt
+                for batch in batches
+                for prompt in batch["prompts"]
+            ]
+            nprompts = [
+                prompt
+                for batch in batches
+                for prompt in batch["nprompts"]
+            ]
+
+            for i in range(self.sample_batches):
+                prompt = prompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size]
+                nprompt = nprompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size]
+
+                samples = pipeline(
+                    prompt=prompt,
+                    negative_prompt=nprompt,
+                    height=self.sample_image_size,
+                    width=self.sample_image_size,
+                    generator=gen,
+                    guidance_scale=guidance_scale,
+                    eta=eta,
+                    num_inference_steps=num_inference_steps,
+                    output_type='pil'
+                ).images
+
+                all_samples += samples
+
+                del samples
+
+            image_grid = make_grid(all_samples, grid_rows, grid_cols)
+            image_grid.save(file_path, quality=85)
+
+            del all_samples
+            del image_grid
 
         del generator
-        del stable_latents