update

README.md

@@ -34,9 +34,9 @@ Fork does differ in few things:
 Only Python library which is not auto-updated is `PyTorch` itself as that is very system specific  
 I'm currently using **PyTorch 2.0-nightly** compiled with **CUDA 11.8** and with **Triton** optimizations:
 
-> pip3 install --pre torch torchvision torchaudio torchtriton --extra-index-url https://download.pytorch.org/whl/nightly/cu118  
+> pip3 install --pre torch torchvision torchaudio torchtriton --extra-index-url https://download.pytorch.org/whl/nightly/cu118 --force 
 > pip show torch  
-> 2.0.0.dev20230111+cu118  
+> 2.0.0.dev20230113+cu118  
 
 
 <br>

TODO.md

@@ -31,7 +31,6 @@ Things I'm looking into...
 
 Stuff to be fixed...
 
-- Model switch memory leak
 - Torch 2.0 model compile
 
 Tech that can be integrated as part of the core workflow...
@@ -62,6 +61,11 @@ Cool stuff that is not integrated anywhere...
 - [Disco Diffusion](https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb)
 - [Video Killed the Radio Star](https://colab.research.google.com/github/dmarx/video-killed-the-radio-star/blob/main/Video_Killed_The_Radio_Star_Defusion.ipynb)
 
+# SDAPI
+
+- Support TLS
+- Support auth
+
 # External
 
 Ideas that can be value-added to core tech...
@@ -69,9 +73,7 @@ Ideas that can be value-added to core tech...
 - Prevalent colors to interrogate
 - Auto-Sort inputs by face recognition
 - Use semantic segmentation to remove background from inputs
-- Auto-filter training inputs based 
 - Auto-filter training inputs based on blur  
   <https://pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/>
 
-if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
-clip_grad_mode
+/home/vlado/dev/sd-extensions/api/losschart.py:48: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.

automatic.sh

@@ -1,5 +1,5 @@
 #/bin/env bash
 
 export PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0.9,max_split_size_mb:512
-python launch.py --api --disable-console-progressbars 
-# python launch.py --api --xformers --disable-console-progressbars --opt-channelslast
+python launch.py --api --xformers --disable-console-progressbars 
+# python launch.py --api  --disable-console-progressbars --opt-channelslast

cli/xformers.sh

@@ -0,0 +1,12 @@
+#/bin/env bash
+echo "Installing xformers"
+
+NVCC_FLAGS="--use_fast_math" 
+FORCE_CUDA="1"
+TORCH_CUDA_ARCH_LIST="8.6"
+pip install ninja -q
+pip uninstall xformers -y 2>/dev/null
+pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
+pip show torch
+pip show xformers
+python -m xformers.info

config.json

@@ -56,7 +56,7 @@
     "training_image_repeats_per_epoch": 1,
     "training_write_csv_every": 10.0,
     "training_xattention_optimizations": false,
-    "sd_model_checkpoint": "zeipher-f222.ckpt [44bf0551]",
+    "sd_model_checkpoint": "sd-v15-runwayml.ckpt [81761151]",
     "sd_checkpoint_cache": 0,
     "sd_vae": "vae-ft-mse-840000-ema-pruned",
     "sd_vae_as_default": false,
@@ -153,5 +153,8 @@
     "always_discard_next_to_last_sigma": false,
     "clip_models_path": "models/CLIP",
     "save_training_settings_to_txt": true,
-    "use_old_hires_fix_width_height": false
+    "use_old_hires_fix_width_height": false,
+    "training_enable_tensorboard": true,
+    "training_tensorboard_save_images": true,
+    "training_tensorboard_flush_every": 60.0
 }

extensions-builtin/info-tab/scripts/info-tab.py

@@ -30,6 +30,10 @@ def get_cuda():
         except Exception as e:
             return { 'error': e }
 
+def get_uptime():
+    s = vars(shared.state)
+    return time.strftime('%c', time.localtime(s.get('server_start', time.time())))
+
 def get_state():
     s = vars(shared.state)
     flags = 'skipped ' if s.get('skipped', False) else ''
@@ -193,6 +197,7 @@ def get_full_data():
     data = {
         'date': datetime.datetime.now().strftime('%c'),
         'timestamp': datetime.datetime.now().strftime('%X'),
+        'uptime': get_uptime(),
         'version': get_version(),
         'model': get_model(),
         'vae': get_vae(),
@@ -247,6 +252,7 @@ def on_ui_tabs():
                 with gr.Box():
                     with gr.Row():
                         with gr.Column():
+                            gr.Textbox(data['uptime'], label = 'Server start time', lines = 1)
                             gr.Textbox(dict2text(data['version']), label = 'Version', lines = len(data['version']))
                         with gr.Column():
                             model = gr.Textbox(dict2text(data['model']), label = 'Model', lines = len(data['model']))

modules/sd_hijack.py

@@ -103,6 +103,15 @@ class StableDiffusionModelHijack:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
+        try:
+            import time
+            t0 = time.time()
+            m = torch.compile(m, mode="max-autotune", fullgraph=True)
+            t1 = time.time()
+            print(f"Model compiled in {round(t1 - t0, 2)} sec")
+        except Exception as err:
+            print(f"Model compile not supported: {err}")
+
         self.optimization_method = apply_optimizations()
 
         self.clip = m.cond_stage_model