configurable hf cache

CHANGELOG.md

@@ -32,6 +32,8 @@
   - Better Lora trigger words support  
   - Auto refresh styles on change  
 - **General**  
+  - Configurable location for HF cache folder  
+    Default is standard `~/.cache/huggingface/hub`  
   - Reworked parser when pasting previously generated images/prompts  
     includes all `txt2img`, `img2img` and `override` params  
   - Reworked **model compile**

installer.py

@@ -795,6 +795,8 @@ def set_environment():
     os.environ.setdefault('TF_ENABLE_ONEDNN_OPTS', '0')
     os.environ.setdefault('USE_TORCH', '1')
     os.environ.setdefault('UVICORN_TIMEOUT_KEEP_ALIVE', '60')
+    os.environ.setdefault('HF_HUB_CACHE', opts.get('hfcache_dir', os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'hub')))
+    log.debug(f'Cache folder: {os.environ.get("HF_HUB_CACHE")}')
     if sys.platform == 'darwin':
         os.environ.setdefault('PYTORCH_ENABLE_MPS_FALLBACK', '1')
 

modules/processing_diffusers.py

@@ -155,7 +155,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
             decoded = full_vae_decode(latents=latents, model=shared.sd_model)
         else:
             decoded = taesd_vae_decode(latents=latents)
-        # decoded = validate_sample(decoded) # TODO validate sample
+        # TODO validate decoded sample diffusers
+        # decoded = validate_sample(decoded)
         imgs = model.image_processor.postprocess(decoded, output_type=output_type)
         shared.state.job = prev_job
         return imgs

modules/sd_models_compile.py

@@ -126,13 +126,13 @@ def compile_torch(sd_model):
 
 def compile_diffusers(sd_model):
     if not (shared.opts.cuda_compile or shared.opts.cuda_compile_vae or shared.opts.cuda_compile_upscaler):
-        return
+        return sd_model
     if not hasattr(sd_model, 'unet') or not hasattr(sd_model.unet, 'config'):
         shared.log.warning('Model compile enabled but model has no Unet')
-        return
+        return sd_model
     if shared.opts.cuda_compile_backend == 'none':
         shared.log.warning('Model compile enabled but no backend specified')
-        return
+        return sd_model
     size = 8*getattr(sd_model.unet.config, 'sample_size', 0)
     shared.log.info(f"Model compile: pipeline={sd_model.__class__.__name__} shape={size} mode={shared.opts.cuda_compile_mode} backend={shared.opts.cuda_compile_backend} fullgraph={shared.opts.cuda_compile_fullgraph} unet={shared.opts.cuda_compile} vae={shared.opts.cuda_compile_vae} upscaler={shared.opts.cuda_compile_upscaler}")
     if shared.opts.cuda_compile_backend == 'stable-fast':

modules/shared.py

@@ -377,6 +377,7 @@ options_templates.update(options_section(('system-paths', "System Paths"), {
     "models_dir": OptionInfo('models', "Base path where all models are stored", folder=True),
     "ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Folder with stable diffusion models", folder=True),
     "diffusers_dir": OptionInfo(os.path.join(paths.models_path, 'Diffusers'), "Folder with Hugggingface models", folder=True),
+    "hfcache_dir": OptionInfo(os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'hub'), "Folder for Hugggingface cache", folder=True),
     "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Folder with VAE files", folder=True),
     "sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
     "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Folder with LoRA network(s)", folder=True),

modules/ui_extra_networks.py

@@ -203,7 +203,7 @@ class ExtraNetworksPage:
                     os.remove(f)
                 elif img.width > 1024 or img.height > 1024 or os.path.getsize(f) > 65536:
                     img = img.convert('RGB')
-                    img.thumbnail((512, 512), Image.HAMMING)
+                    img.thumbnail((512, 512), Image.Resampling.HAMMING)
                     img.save(fn, quality=50)
                     img.close()
                     created += 1
@@ -576,7 +576,7 @@ def create_ui(container, button_parent, tabname, skip_indexing = False):
         fn_delete_img(image)
         if image.width > 512 or image.height > 512:
             image = image.convert('RGB')
-            image.thumbnail((512, 512), Image.HAMMING)
+            image.thumbnail((512, 512), Image.Resampling.HAMMING)
         try:
             image.save(ui.last_item.local_preview, quality=50)
             shared.log.debug(f'Extra network save image: item={ui.last_item.name} filename="{ui.last_item.local_preview}"')

modules/upscaler.py

@@ -7,8 +7,8 @@ import modules.shared
 from modules import modelloader
 
 
-LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
-NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
+LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.Resampling.LANCZOS)
+NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.Resampling.NEAREST)
 models = None
 
 class Upscaler: