auto-git:

[change] generate_equirect.py

generate_equirect.py

@@ -358,6 +358,7 @@ def generate(
                     ).to(device)
                     base_pipe.unet = unet
                     gen_pipe = base_pipe
+                    del base_pipe, unet
                 else:
                     gen_pipe = StableDiffusionXLPipeline.from_pretrained(
                         model_path,
@@ -440,8 +441,12 @@ def generate(
                     "Try one of: euler, euler_a, heun, ddim, dpmsolver, dpmsolver-sde."
                 )
         gen_pipe.enable_attention_slicing()
-        if is_sdxl and vae is not None and hasattr(gen_pipe, "enable_vae_tiling"):
+        if hasattr(gen_pipe, "enable_vae_tiling"):
             gen_pipe.enable_vae_tiling()
+        if hasattr(gen_pipe, "enable_vae_slicing"):
+            gen_pipe.enable_vae_slicing()
+        if "pipe_kwargs" in locals():
+            del pipe_kwargs
 
         def progress_cb(phase: str, current: int, total: int):
             payload = {
@@ -463,7 +468,19 @@ def generate(
             height=height,
             callback_steps=1,
             callback=lambda step, timestep, kwargs: progress_cb("gen", step + 1, steps),
-        ).images[0]
+            output_type="latent",
+        )
+        latents = latent_output.images.detach().cpu()
+        decoder_vae = gen_pipe.vae
+        vae = None
+        del latent_output, gen_pipe
+        clear_torch_cache(device)
+
+        progress_cb("decode", 0, 1)
+        image = decode_latents_to_image(decoder_vae, latents, device)
+        progress_cb("decode", 1, 1)
+        del latents, decoder_vae
+        clear_torch_cache(device)
 
         gen_path = os.path.join(tempdir, f"base_{width}x{height}.png")
         image.save(gen_path)