update

app.py

@@ -18,22 +18,29 @@ nav {
   display: inline;
 }
 """
-def infer(prompt, checkpoint="black-fores-labs/FLUX.1-schnell", seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, checkpoint="black-forest-labs/FLUX.1-schnell", seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
     global pipe
     global selected
     # if the new checkpoint is different from the selected one, re-instantiate the pipe
     if selected != checkpoint:
         if checkpoint == "sayakpaul/FLUX.1-merged":
             transformer = FluxTransformer2DModel.from_pretrained("sayakpaul/FLUX.1-merged", torch_dtype=dtype)
-            pipe = FluxPipeline.from_pretrained("cocktailpeanut/xulf-d", transformer=transformer, torch_dtype=torch.bfloat16)
+            pipe = FluxPipeline.from_pretrained("cocktailpeanut/xulf-d", transformer=transformer, torch_dtype=dtype)
         else:
-            pipe = FluxPipeline.from_pretrained(checkpoint, torch_dtype=torch.bfloat16)
+            #transformer = FluxTransformer2DModel.from_single_file("https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-schnell-fp8.safetensors")
+            #pipe = FluxPipeline.from_pretrained(checkpoint, transformer=transformer, torch_dtype=torch.bfloat16)
+            pipe = FluxPipeline.from_pretrained(checkpoint, torch_dtype=dtype)
 
-        #pipe.enable_model_cpu_offload()
         pipe.to(device)
         pipe.enable_attention_slicing()
+        pipe.vae.enable_slicing()
+        pipe.vae.enable_tiling()
+
+        if device == "cuda":
+            #pipe.enable_model_cpu_offload()
+            pipe.enable_sequential_cpu_offload()
+          
         selected = checkpoint
-    devicetorch.empty_cache(torch)
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
@@ -45,6 +52,7 @@ def infer(prompt, checkpoint="black-fores-labs/FLUX.1-schnell", seed=42, randomi
             generator = generator,
             guidance_scale=0.0
     ).images[0] 
+    devicetorch.empty_cache(torch)
     return image, seed
 def update_slider(checkpoint, num_inference_steps):
     if checkpoint == "sayakpaul/FLUX.1-merged":
@@ -53,7 +61,7 @@ def update_slider(checkpoint, num_inference_steps):
         return 4
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.HTML("<nav><img id='logo' src='file/icon.webp'/></nav>")
+        gr.HTML("<nav><img id='logo' src='file/icon.png'/></nav>")
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",

pinokio.js

@@ -3,7 +3,7 @@ module.exports = {
   version: "2.0",
   title: "flux-webui",
   description: "Minimal Flux Web UI powered by Gradio & Diffusers",
-  icon: "icon.webp",
+  icon: "icon.png",
   menu: async (kernel, info) => {
     let installed = info.exists("env")
     let running = {