Update app.py

app.py

@@ -24,7 +24,7 @@ os.environ["TRANSFORMERS_CACHE"] = cache_path
 os.environ["HF_HUB_CACHE"] = cache_path
 os.environ["HF_HOME"] = cache_path
 
-torch.set_float32_matmul_precision("high")
+torch.set_float32_matmul_precision("medium")
 
 #torch._inductor.config.conv_1x1_as_mm = True
 #torch._inductor.config.coordinate_descent_tuning = True
@@ -40,6 +40,16 @@ with open('loras.json', 'r') as f:
 #base_model = "stabilityai/stable-diffusion-3.5-large"
 pipe = AutoPipelineForText2Image.from_pretrained("ariG23498/sd-3.5-merged", torch_dtype=torch.bfloat16)
 
+model_id = ("zer0int/LongCLIP-GmP-ViT-L-14")
+config = CLIPConfig.from_pretrained(model_id)
+config.text_config.max_position_embeddings = 248
+clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True)
+clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=248)
+pipe.tokenizer = clip_processor.tokenizer
+pipe.text_encoder = clip_model.text_model
+pipe.tokenizer_max_length = 248
+pipe.text_encoder.dtype = torch.bfloat16
+
 #clipmodel = 'norm'
 #if clipmodel == "long":
 #    model_id = "zer0int/LongCLIP-GmP-ViT-L-14"
@@ -85,9 +95,10 @@ class calculateDuration:
 
 def update_selection(evt: gr.SelectData, width, height):
     selected_lora = loras[evt.index]
-    new_placeholder = f"Type a prompt for {selected_lora['title']}"
+    new_placeholder = f"Prompt with activator word(s): '{selected_lora['trigger_word']}'! "
     lora_repo = selected_lora["repo"]
-    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
+    lora_trigger = selected_lora['trigger_word']
+    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}). Prompt using: '{lora_trigger}'!"
     if "aspect" in selected_lora:
         if selected_lora["aspect"] == "portrait":
             width = 768
@@ -103,7 +114,7 @@ def update_selection(evt: gr.SelectData, width, height):
         height,
     )
 
-@spaces.GPU(duration=70)
+@spaces.GPU(duration=50)
 def infer(prompt, negative_prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress):
     pipe.to("cuda")
     generator = torch.Generator(device="cuda").manual_seed(seed)
@@ -128,7 +139,7 @@ def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_index, randomiz
 
     selected_lora = loras[selected_index]
     lora_path = selected_lora["repo"]
-    trigger_word = selected_lora["trigger_word"]
+    trigger_word = selected_lora['trigger_word']
 
     # Load LoRA weights
     with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
@@ -158,17 +169,17 @@ css = '''
 '''
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
     title = gr.HTML(
-        """<h1><img src="https://huggingface.co/AlekseyCalvin/StabledHSTorY_SD3.5_LoRA_V2_rank256/resolve/main/acs62v.png" alt="LoRA">Stabled LoRAs soon® on S.D.3.5 Merged</h1>""",
+        """<h1><img src="https://huggingface.co/AlekseyCalvin/StabledHSTorY_SD3.5_LoRA_V2_rank256/resolve/main/acs62v.png" alt="LoRA">Stabled LoRAs soon® on S.D.3.5L Merged</h1>""",
         elem_id="title",
     )
     	    # Info blob stating what the app is running
     info_blob = gr.HTML(
-        """<div id="info_blob">SOON®'s curated LoRa Gallery & Art Manufactory.|Now testing HST-triggered historic photo-trained LoRAs for Stable Diffusion 3.5.</div>"""
+        """<div id="info_blob">SOON®'s curated Art Manufactory & Gallery of fine-tuned Low-Rank Adapter (LoRA) models for Stable Diffusion 3.5 Large (S.D.3.5L). Running on a base model variant averaging weights b/w slow S.D.3.5L & its turbo distillation.</div>"""
     )
 
         # Info blob stating what the app is running
     info_blob = gr.HTML(
-        """<div id="info_blob">Prephrase prompts w/: "HST style autochrome photo" </div>"""
+        """<div id="info_blob"> To reinforce/focus a selected adapter style, add its pre-encoded “trigger" word/phrase to your prompt. Corresponding activator info &/or prompt template appears once an adapter square is clicked. Copy/Paste these into prompt box as a starting point.  </div>"""
     )
     selected_index = gr.State(None)
     with gr.Row():