Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import torch
-from diffusers import StableDiffusionXLPipeline, AutoencoderKL
+from diffusers import StableDiffusionXLPipeline, AutoencoderKL, LCMScheduler
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
 from share_btn import community_icon_html, loading_icon_html, share_js
@@ -11,7 +11,9 @@ import json
 import gc
 import random
 from urllib.parse import quote
-with open("sdxl_loras.json", "r") as file:
+
+lora_list = hf_hub_download(repo_id="multimodalart/LoraTheExplorer", filename="sdxl_loras.json", repo_type="space")
+with open(lora_list, "r") as file:
     data = json.load(file)
     sdxl_loras_raw = [
         {
@@ -62,7 +64,7 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
     vae=vae,
     torch_dtype=torch.float16,
 )
-original_pipe = copy.deepcopy(pipe)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 pipe.to(device)
 
 last_lora = ""
@@ -75,6 +77,7 @@ button.addEventListener('click', function() {
     element.classList.add('selected');
 });
 '''
+
 def update_selection(selected_state: gr.SelectData, sdxl_loras, is_new=False):
     lora_repo = sdxl_loras[selected_state.index]["repo"]
     instance_prompt = sdxl_loras[selected_state.index]["trigger_word"]
@@ -139,29 +142,6 @@ def check_selected(selected_state):
     if not selected_state:
         raise gr.Error("You must select a LoRA")
 
-def merge_incompatible_lora(full_path_lora, lora_scale):
-    for weights_file in [full_path_lora]:
-                if ";" in weights_file:
-                    weights_file, multiplier = weights_file.split(";")
-                    multiplier = float(multiplier)
-                else:
-                    multiplier = lora_scale
-
-                lora_model, weights_sd = lora.create_network_from_weights(
-                    multiplier,
-                    full_path_lora,
-                    pipe.vae,
-                    pipe.text_encoder,
-                    pipe.unet,
-                    for_inference=True,
-                )
-                lora_model.merge_to(
-                    pipe.text_encoder, pipe.unet, weights_sd, torch.float16, "cuda"
-                )
-                del weights_sd
-                del lora_model
-                gc.collect()
-
 def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, sdxl_loras_new, progress=gr.Progress(track_tqdm=True)):
     global last_lora, last_merged, last_fused, pipe
     print("Index when running ", selected_state.index)
@@ -187,11 +167,9 @@ def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, sdxl_lora
     if last_lora != repo_name:
         if(last_fused):
             pipe.unfuse_lora()
-            pipe.unload_lora_weights()
-        #is_compatible = sdxl_loras[selected_state.index]["is_compatible"]
-        pipe.load_lora_weights(loaded_state_dict)#, adapter_name="loaded_lora")
-        #pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm_lora")
-        #pipe.set_adapters(["loaded_lora", "lcm_lora"], adapter_weights=[0.8, 1.0])
+        pipe.load_lora_weights(loaded_state_dict, adapter_name="loaded_lora")
+        pipe.load_lora_weights(lcm_lora_id, weight_name="lcm_sdxl_lora.safetensors", adapter_name="lcm_lora", use_auth_token=True)
+        pipe.set_adapters(["loaded_lora", "lcm_lora"], adapter_weights=[0.8, 1.0])
         pipe.fuse_lora()
         last_fused = True
         is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
@@ -207,11 +185,10 @@ def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, sdxl_lora
     image = pipe(
         prompt=prompt,
         negative_prompt=negative,
-        width=1024,
-        height=1024,
-        num_inference_steps=20,
-        guidance_scale=7.5,
+        num_inference_steps=4,
+        guidance_scale=0.5,
     ).images[0]
+    
     last_lora = repo_name
     gc.collect()
     return image, gr.update(visible=True)