Update app.py

app.py

@@ -1,15 +1,15 @@
-# app.py — SDXL + LoRA from Hugging Face (PEFT backend)
+# app.py — SDXL + LoRA from Hub (PEFT) with version-safe adapter weighting
 import torch, gradio as gr
 from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
 
 # --- Config ---
 BASE_MODEL = "stabilityai/stable-diffusion-xl-base-1.0"
-LORA_REPO  = "freshcodestech/LingoSpace"       # your HF LoRA repo
-LORA_FILE  = "LingoSpace-10.safetensors"       # exact filename in that repo
-ADAPTER    = "lingospace"                      # local nickname; can be any safe string
-LORA_W     = 0.8                               # LoRA strength (0.6–1.0 is typical)
+LORA_REPO  = "freshcodestech/LingoSpace"      # your HF LoRA repo
+LORA_FILE  = "LingoSpace-10.safetensors"      # exact filename in that repo
+ADAPTER    = "lingospace"                     # local nickname; any safe string
+LORA_W     = 0.8                              # 0.6–1.0 typical
 
-# Device / dtype (GPU preferred)
+# Device / dtype
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype  = torch.float16 if device == "cuda" else torch.float32
 
@@ -24,8 +24,7 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 pipe = pipe.to(device)
 
-# --- Load LoRA from the Hub via PEFT backend ---
-# NOTE: This requires `peft` to be installed (see requirements.txt below).
+# --- Load LoRA from Hub via PEFT backend ---
 pipe.load_lora_weights(
     LORA_REPO,
     weight_name=LORA_FILE,
@@ -33,26 +32,44 @@ pipe.load_lora_weights(
     use_peft_backend=True
 )
 
-# Preferred scaling API (if available)
+# Try all known set_adapters signatures; else fall back to per-call scaling
+USE_RUNTIME_SCALE = False
 if hasattr(pipe, "set_adapters"):
-    pipe.set_adapters(ADAPTER, weight=LORA_W)
+    try:
+        # Newer diffusers: keyword
+        pipe.set_adapters(ADAPTER, adapter_weights=LORA_W)
+    except TypeError:
+        try:
+            # Older diffusers: 2 positional lists
+            pipe.set_adapters([ADAPTER], [LORA_W])
+        except Exception:
+            USE_RUNTIME_SCALE = True
+else:
+    USE_RUNTIME_SCALE = True
 
-# Memory helpers (helpful on T4 small)
+# Memory helpers (T4)
 if device == "cuda":
     if hasattr(pipe, "enable_xformers_memory_efficient_attention"):
         pipe.enable_xformers_memory_efficient_attention()
     pipe.enable_attention_slicing()
     pipe.enable_vae_tiling()
 
-# --- Inference ---
+# Defaults (smaller on CPU)
+DEF_STEPS = 30 if device == "cuda" else 15
+DEF_H = 1024 if device == "cuda" else 768
+DEF_W = 1024 if device == "cuda" else 768
+
 def infer(prompt, steps, cfg, h, w, lora_strength):
-    # Fallback strength control for older diffusers if set_adapters doesn’t exist:
+    # If we couldn’t set adapter weight globally, pass strength per call
     kwargs = {}
-    if not hasattr(pipe, "set_adapters"):
+    if USE_RUNTIME_SCALE:
         kwargs["cross_attention_kwargs"] = {"scale": float(lora_strength)}
     else:
-        # If adapter API exists, update weight dynamically
-        pipe.set_adapters(ADAPTER, weight=float(lora_strength))
+        # If API exists, update weight dynamically per request
+        try:
+            pipe.set_adapters(ADAPTER, adapter_weights=float(lora_strength))
+        except TypeError:
+            pipe.set_adapters([ADAPTER], [float(lora_strength)])
 
     out = pipe(
         prompt,
@@ -64,12 +81,11 @@ def infer(prompt, steps, cfg, h, w, lora_strength):
     )
     return out.images[0]
 
-DEFAULT_PROMPT = "lingospace, cartoon, 2d illustration, flat colors, clean lineart, a raccoon in a compact utility space suit, holding a steaming bowl of rice, stars and soft nebula, cozy composition"
-
-# Use smaller defaults on CPU for speed
-DEF_STEPS = 30 if device == "cuda" else 15
-DEF_H = 1024 if device == "cuda" else 768
-DEF_W = 1024 if device == "cuda" else 768
+DEFAULT_PROMPT = (
+    "lingospace, cartoon, 2d illustration, flat colors, clean lineart, "
+    "a raccoon in a compact utility space suit, holding a steaming bowl of rice, "
+    "stars and soft nebula, cozy composition"
+)
 
 demo = gr.Interface(
     fn=infer,
@@ -83,7 +99,7 @@ demo = gr.Interface(
     ],
     outputs=gr.Image(label="Output"),
     title="SDXL + LoRA (freshcodestech/LingoSpace)",
-    description=f"Device: {device.upper()} | Base: {BASE_MODEL} | LoRA: {LORA_REPO}/{LORA_FILE}"
+    description=f"Device: {device.upper()} • Base: {BASE_MODEL} • LoRA: {LORA_REPO}/{LORA_FILE}"
 )
 
 if __name__ == "__main__":