From f0a171923cc8240177302f3dccb6177a2c646ab3 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Thu, 11 May 2023 18:37:43 +0200
Subject: Update

---
 environment.yaml       |  8 +++++---
 train_lora.py          |  2 +-
 train_ti.py            | 24 ++++++++++++++++++++++++
 training/functional.py | 24 +++++++++++++++---------
 4 files changed, 45 insertions(+), 13 deletions(-)

diff --git a/environment.yaml b/environment.yaml
index dfbafaf..85033ce 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -6,6 +6,8 @@ channels:
     - defaults
     - conda-forge
 dependencies:
+    - cuda-nvcc=11.8
+    - cuda-cudart-dev=11.8
     - gcc=11.3.0
     - gxx=11.3.0
     - matplotlib=3.6.2
@@ -20,13 +22,13 @@ dependencies:
           - -e git+https://github.com/huggingface/accelerate#egg=accelerate
           - -e git+https://github.com/huggingface/diffusers#egg=diffusers
           - -e git+https://github.com/facebookresearch/dadaptation#egg=dadaptation
+          - --pre --extra-index-url https://download.hidet.org/whl hidet
           - bitsandbytes==0.38.1
-          - hidet==0.2.3
           - lion-pytorch==0.0.7
-          - peft==0.2.0
+          - peft==0.3.0
           - python-slugify>=6.1.2
           - safetensors==0.3.1
           - setuptools==65.6.3
           - test-tube>=0.7.5
           - timm==0.8.17.dev0
-          - transformers==4.28.1
+          - transformers==4.29.0
diff --git a/train_lora.py b/train_lora.py
index 70fbae4..737af58 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -46,7 +46,7 @@ torch.backends.cudnn.benchmark = True
 torch._dynamo.config.log_level = logging.WARNING
 
 hidet.torch.dynamo_config.use_tensor_core(True)
-# hidet.torch.dynamo_config.use_attention(True)
+hidet.torch.dynamo_config.use_attention(True)
 hidet.torch.dynamo_config.search_space(0)
 
 
diff --git a/train_ti.py b/train_ti.py
index 26f7941..6fd974e 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -5,13 +5,16 @@ from functools import partial
 from pathlib import Path
 from typing import Union
 import math
+import warnings
 
 import torch
 import torch.utils.checkpoint
+import hidet
 
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import LoggerType, set_seed
+from diffusers.models.attention_processor import AttnProcessor
 from timm.models import create_model
 import transformers
 
@@ -28,10 +31,18 @@ from training.util import AverageMeter, save_args
 
 logger = get_logger(__name__)
 
+warnings.filterwarnings('ignore')
+
 
 torch.backends.cuda.matmul.allow_tf32 = True
 torch.backends.cudnn.benchmark = True
 
+# torch._dynamo.config.log_level = logging.WARNING
+
+hidet.torch.dynamo_config.use_tensor_core(True)
+hidet.torch.dynamo_config.use_attention(True)
+hidet.torch.dynamo_config.search_space(0)
+
 
 def parse_args():
     parser = argparse.ArgumentParser(
@@ -706,6 +717,19 @@ def main():
     if args.use_xformers:
         vae.set_use_memory_efficient_attention_xformers(True)
         unet.enable_xformers_memory_efficient_attention()
+    elif args.compile_unet:
+        unet.mid_block.attentions[0].transformer_blocks[0].attn1._use_2_0_attn = False
+    
+        proc = AttnProcessor()
+    
+        def fn_recursive_set_proc(module: torch.nn.Module):
+            if hasattr(module, "processor"):
+                module.processor = proc
+    
+            for child in module.children():
+                fn_recursive_set_proc(child)
+    
+        fn_recursive_set_proc(unet)
 
     if args.gradient_checkpointing:
         unet.enable_gradient_checkpointing()
diff --git a/training/functional.py b/training/functional.py
index eae5681..49c21c7 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -27,6 +27,7 @@ from models.convnext.discriminator import ConvNeXtDiscriminator
 from training.util import AverageMeter
 from training.sampler import ScheduleSampler, LossAwareSampler, UniformSampler
 from util.slerp import slerp
+from util.noise import perlin_noise
 
 
 def const(result=None):
@@ -350,28 +351,33 @@ def loss_step(
         device=latents.device,
         generator=generator
     )
+    applied_noise = noise
 
     if offset_noise_strength != 0:
-        offset_noise = torch.randn(
-            (latents.shape[0], latents.shape[1], 1, 1),
+        applied_noise = applied_noise + offset_noise_strength * perlin_noise(
+            latents.shape,
+            res=1,
+            octaves=4,
             dtype=latents.dtype,
             device=latents.device,
             generator=generator
-        ).expand(noise.shape)
-        noise = noise + offset_noise_strength * offset_noise
+        )
 
     if input_pertubation != 0:
-        new_noise = noise + input_pertubation * torch.randn_like(noise)
+        applied_noise = applied_noise + input_pertubation * torch.randn(
+            latents.shape,
+            dtype=latents.dtype,
+            layout=latents.layout,
+            device=latents.device,
+            generator=generator
+        )
 
     # Sample a random timestep for each image
     timesteps, weights = schedule_sampler.sample(bsz, latents.device)
 
     # Add noise to the latents according to the noise magnitude at each timestep
     # (this is the forward diffusion process)
-    if input_pertubation != 0:
-        noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)
-    else:
-        noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+    noisy_latents = noise_scheduler.add_noise(latents, applied_noise, timesteps)
     noisy_latents = noisy_latents.to(dtype=unet.dtype)
 
     # Get the text embedding for conditioning
-- 
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