diff --git a/.gitignore b/.gitignore
index 5d381cc..892e16a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,3 +160,4 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+FLUX.1-schnell
diff --git a/flux-schnell-local.py b/flux-schnell-local.py
new file mode 100644
index 0000000..d40b2ef
--- /dev/null
+++ b/flux-schnell-local.py
@@ -0,0 +1,52 @@
+from diffusers import FluxPipeline
+import torch
+from pathlib import Path
+import re
+from datetime import datetime
+
+def slugify(text):
+    # remove non-word characters and foreign characters
+    text = re.sub(r"[^\w\s]", "", text)
+    text = re.sub(r"\s+", "-", text)
+    return text
+
+prompt = "the town center of a small futuristic settlement with a fountain and gardens.the buildings are shaped like dodecahedrons, similar to small geodesic domes with a main road leading out to grassy plains sparsely dotted with very broad tall trees"
+height, width = 720, 1280
+
+ckpt_id = "./FLUX.1-schnell"
+
+DIR_NAME="/home/tonydero/remdirs/immich/"
+dirpath = Path(DIR_NAME)
+# create parent dir if doesn't exist
+# dirpath.mkdir(parents=True, exist_ok=True)
+
+# denoising
+pipe = FluxPipeline.from_pretrained(
+    ckpt_id,
+    torch_dtype=torch.bfloat16,
+    use_safetensors=True,
+)
+pipe.vae.enable_tiling()
+pipe.vae.enable_slicing()
+pipe.enable_sequential_cpu_offload() # offloads modules to CPU on a submodule level (rather than model level)
+
+output = pipe(
+    prompt,
+    height=height,
+    width=width,
+    num_images_per_prompt=8,
+    num_inference_steps=4,
+    max_sequence_length=128,
+    guidance_scale=0.0,
+)
+# print('Max mem allocated (GB) while denoising:', torch.cuda.max_memory_allocated() / (1024 ** 3))
+
+# import matplotlib.pyplot as plt
+# plt.imshow(image)
+# image.save("./whitehenge.png")
+# plt.show()
+for idx, image in enumerate(output.images):
+    timestamp = datetime.now().strftime("%Y%m%d%-H%M%S")
+    image_name = f'{slugify(prompt[:64])}-{idx}-{timestamp}.png'
+    image_path = dirpath / image_name
+    image.save(image_path)