Update app_quant_latent1.py

app_quant_latent1.py

@@ -373,88 +373,11 @@ def generate_image(prompt, height, width, steps, seed, guidance_scale=0.0):
     placeholder = Image.new("RGB", (width, height), color=(255, 255, 255))
     print(prompt)
 
-    try:
-        generator = torch.Generator(device).manual_seed(int(seed))
-
-        # -------------------------------
-        # Try advanced latent extraction
-        # -------------------------------
-        try:
-            latents, latent_err = safe_generate_with_latents(
-               transformer=transformer,
-               vae=vae,
-               text_encoder=text_encoder,
-               tokenizer=tokenizer,
-               scheduler=scheduler,
-               pipe=pipe,
-               prompt=prompt,
-               height=height,
-               width=width,
-               steps=steps,
-               guidance_scale=guidance_scale,
-               negative_prompt="",
-               num_images_per_prompt=1,
-               generator=generator,
-               cfg_normalization=False,
-               cfg_truncation=1.0,
-               max_sequence_length=4096,
-               )
-
-            if latent_err is None:
-                 log("✅ Latent generator succeeded.")
-        try:
-          # Decode latents to image
-          shift_factor = getattr(vae.config, "shift_factor", 0.0) or 0.0
-          dec = (latents.to(vae.dtype) / vae.config.scaling_factor) + shift_factor
-          image = vae.decode(dec, return_dict=False)[0]
-
-          image = (image / 2 + 0.5).clamp(0, 1)
-          image = image.cpu().permute(0, 2, 3, 1).numpy()
-          image = (image * 255).round().astype("uint8")
-          from PIL import Image
-          image = Image.fromarray(image[0])
-
-          log("🟢 Final image decoded from latent generator.")
-          return image, latents, LOGS
-
-          
-        except Exception as decode_error:
-        log(f"⚠️ Latent decode failed: {decode_error}")
-        log("🔁 Falling back to standard pipeline...")
-
-            image = output.images[0]
-            gallery = [image]
-            LOGS.append("✅ Advanced latent pipeline succeeded.")
-
-        except Exception as e:
-            LOGS.append(f"⚠️ Latent mode failed: {e}")
-            LOGS.append("🔁 Switching to standard pipeline...")
-            image = placeholder
-            gallery = [image]
-        # ========================================================== # 🟩 STANDARD PIPELINE FALLBACK (Never fails) # ==========================================================
-        try:
-                output = pipe(
-                    prompt=prompt,
-                    height=height,
-                    width=width,
-                    num_inference_steps=steps,
-                    guidance_scale=guidance_scale,
-                    generator=generator,
-                )
-                image = output.images[0]
-                gallery = [image]
-                LOGS.append("✅ Standard pipeline succeeded.")
-
-        except Exception as e2:
-                LOGS.append(f"❌ Standard pipeline failed: {e2}")
-                image = placeholder
-                gallery = [image]
-
-                return image, gallery, LOGS
-
-    except Exception as e:
-        LOGS.append(f"❌ Total failure: {e}")
-        return placeholder, [placeholder], LOGS
+    latents, latent_err = safe_generate_with_latents( transformer=transformer, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler,
+                                                     pipe=pipe, prompt=prompt, height=height, width=width, steps=steps, guidance_scale=guidance_scale, negative_prompt="", num_images_per_prompt=1, generator=generator, cfg_normalization=False, cfg_truncation=1.0, max_sequence_length=4096, ) 
+    if latent_err is None: log("✅ Latent generator succeeded.") 
+        try: # Decode latents to image shift_factor = getattr(vae.config, "shift_factor", 0.0) or 0.0 dec = (latents.to(vae.dtype) / vae.config.scaling_factor) +
+            shift_factor image = vae.decode(dec, return_dict=False)[0] image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() image = (image * 255).round().astype("uint8") from PIL import Image image = Image.fromarray(image[0]) log("🟢 Final image decoded from latent generator.") return image, latents, LOGS except Exception as decode_error: log(f"⚠️ Latent decode failed: {decode_error}") log("🔁 Falling back to standard pipeline...") else: log(f"⚠️ Latent generator failed: {latent_err}") log("🔁 Switching to standard pipeline...") # ========================================================== # 🟩 STANDARD PIPELINE FALLBACK (Never fails) # ========================================================== try: output = pipe( prompt=prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=guidance_scale, generator=generator, ) image = output.images[0] log("🟢 Standard pipeline succeeded.") return image, None, LOGS except Exception as e: log(f"❌ Standard pipeline failed: {e}") return None, None, LOGS