Update app_quant_latent.py

app_quant_latent.py

@@ -687,7 +687,6 @@ def generate_image_all_latents(prompt, height, width, steps, seed, guidance_scal
         LOGS.append(f"❌ Standard pipeline failed: {e2}")
         yield placeholder, latent_gallery, LOGS
 
-# this is astable vesopn tha can gen final and a noise to latent
 @spaces.GPU
 def generate_image(prompt, height, width, steps, seed, guidance_scale=0.0):
     LOGS = []
@@ -698,6 +697,85 @@ def generate_image(prompt, height, width, steps, seed, guidance_scale=0.0):
     latent_gallery = []
     final_gallery = []
 
+    last_latents = []  # store last 5 preview latents
+
+    # --- Generate latent previews ---
+    try:
+        latents = safe_get_latents(pipe, height, width, generator, device, LOGS)
+        latents = latents.float()
+
+        num_previews = min(10, steps)
+        preview_steps = torch.linspace(0, 1, num_previews)
+
+        for alpha in preview_steps:
+            try:
+                with torch.no_grad():
+                    preview_latent = latents * alpha + latents * 0  # simple progression
+                    preview_latent = preview_latent.to(pipe.vae.device).to(pipe.vae.dtype)
+
+                    decoded = pipe.vae.decode(preview_latent, return_dict=False)[0]
+                    decoded = (decoded / 2 + 0.5).clamp(0, 1)
+                    decoded = decoded.cpu().permute(0, 2, 3, 1).float().numpy()
+                    decoded = (decoded * 255).round().astype("uint8")
+                    latent_img = Image.fromarray(decoded[0])
+
+            except Exception as e:
+                LOGS.append(f"⚠️ Latent preview decode failed: {e}")
+                latent_img = placeholder
+
+            latent_gallery.append(latent_img)
+
+            # Keep last 5 latents
+            last_latents.append(preview_latent.cpu().clone())
+            if len(last_latents) > 5:
+                last_latents.pop(0)
+
+            yield None, latent_gallery, LOGS
+
+        # Optionally: save only last 5 latents
+        # latent_dict = {"latents": last_latents, "prompt": prompt, "seed": seed}
+        # hf_url = upload_latents_to_hf(latent_dict, filename=f"latents_last5_{seed}.pt")
+        # LOGS.append(f"🔹 Last 5 latents uploaded: {hf_url}")
+
+    except Exception as e:
+        LOGS.append(f"⚠️ Latent generation failed: {e}")
+        latent_gallery.append(placeholder)
+        yield None, latent_gallery, LOGS
+
+    # --- Final image: standard pipeline ---
+    try:
+        output = pipe(
+            prompt=prompt,
+            height=height,
+            width=width,
+            num_inference_steps=steps,
+            guidance_scale=guidance_scale,
+            generator=generator,
+        )
+        final_img = output.images[0]
+        final_gallery.append(final_img)
+        latent_gallery.append(final_img)  # fallback preview
+        LOGS.append("✅ Standard pipeline succeeded.")
+        yield final_img, latent_gallery, LOGS
+
+    except Exception as e2:
+        LOGS.append(f"❌ Standard pipeline failed: {e2}")
+        final_gallery.append(placeholder)
+        latent_gallery.append(placeholder)
+        yield placeholder, latent_gallery, LOGS
+
+
+# this is astable vesopn tha can gen final and a noise to latent
+@spaces.GPU
+def generate_image_verygood_realnoise(prompt, height, width, steps, seed, guidance_scale=0.0):
+    LOGS = []
+    device = "cuda"
+    generator = torch.Generator(device).manual_seed(int(seed))
+
+    placeholder = Image.new("RGB", (width, height), color=(255, 255, 255))
+    latent_gallery = []
+    final_gallery = []
+
     # --- Generate latent previews ---
     try:
         latents = safe_get_latents(pipe, height, width, generator, device, LOGS)