- Added function to convert bytes to a human readable string.

- Added basic implementation of "enable_minimal_memory_usage".

scripts/sd_utils.py

@@ -144,8 +144,14 @@ def set_page_title(title):
                             title.text = '{title}'
                             </script>" />
                             """)
-
+    
-
+def human_readable_size(size, decimal_places=3):
+    """Return a human readable size from bytes."""
+    for unit in ['B','KB','MB','GB','TB']:
+        if size < 1024.0:
+            break
+        size /= 1024.0
+    return f"{size:.{decimal_places}f}{unit}"
 
 @retry(tries=5)
 def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=False, RealESRGAN_model="RealESRGAN_x4plus",
@@ -1130,6 +1136,15 @@ def draw_prompt_matrix(im, width, height, all_prompts):
 
     return result
 
+#
+def enable_minimal_memory_usage(model):
+    """Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs"""
+    model.unet.to(torch.float16).to(torch.device("cuda"))
+    model.enable_attention_slicing(1)
+
+    torch.cuda.empty_cache()
+    torch_gc()
+    
 def check_prompt_length(prompt, comments):
     """this function tests if prompt is too long, and if so, adds a message to comments"""
 
@@ -1149,6 +1164,25 @@ def check_prompt_length(prompt, comments):
 
     comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
 
+#
+def custom_models_available():
+    #
+    # Allow for custom models to be used instead of the default one,
+    # an example would be Waifu-Diffusion or any other fine tune of stable diffusion
+    st.session_state["custom_models"]:sorted = []
+
+    for root, dirs, files in os.walk(os.path.join("models", "custom")):
+        for file in files:
+            if os.path.splitext(file)[1] == '.ckpt':						
+                st.session_state["custom_models"].append(os.path.splitext(file)[0])
+
+
+    if len(st.session_state["custom_models"]) > 0:
+        st.session_state["CustomModel_available"] = True
+        st.session_state["custom_models"].append("Stable Diffusion v1.4")
+    else:
+        st.session_state["CustomModel_available"] = False	
+
 def save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale, 
                 normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
                 save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images, model_name):