Update app.py

app.py

@@ -11,39 +11,37 @@ from diffusers import DiffusionPipeline, AutoencoderTiny
 from huggingface_hub import login
 from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images
 
-# Authenticate for gated repo access
-hf_token = os.environ.get("HF_TOKEN")
-if hf_token:
-    login(token=hf_token)
+# Ensure image_preview dir exists
+os.makedirs("image_preview", exist_ok=True)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# ✅ DO NOT CHANGE: Working pipeline using taef1
+# ✅ Load model only once
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
 
 pipe = DiffusionPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-dev",
     torch_dtype=dtype,
-    token=hf_token,
     vae=taef1
 ).to(device)
 
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 pipe.load_lora_weights("ZennyKenny/flux_lora_natalie-diffusion")
 
-# Ensure image_preview dir exists
-os.makedirs("image_preview", exist_ok=True)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
 
 def sanitize_filename(name):
     return re.sub(r"[^a-zA-Z0-9_-]", "_", name)[:80]
 
+
 @spaces.GPU(duration=75)
-def infer(user_token, prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-    login(token=user_token)  # Authenticate for this call only
+def infer(user_token, prompt, seed=42, randomize_seed=False, width=1024, height=1024,
+          guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+    # Authenticate using user's token for this session
+    login(token=user_token)
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -60,14 +58,16 @@ def infer(user_token, prompt, seed=42, randomize_seed=False, width=1024, height=
         generator=generator,
         output_type="pil",
     ):
-        # Save image to /image_preview
+        # Save low-quality JPG
         safe_name = sanitize_filename(prompt)
         img_path = f"image_preview/{safe_name}_{seed}.jpg"
         img.convert("RGB").save(img_path, "JPEG", quality=60)
 
+        # Collect previews
         previews = [f"image_preview/{f}" for f in sorted(os.listdir("image_preview")) if f.endswith(".jpg")]
         return img, seed, previews
 
+
 examples = [
     "a man walking in the forest",
     "a viking ship sailing down a river",
@@ -86,13 +86,13 @@ Generate images in the surreal style of artist [Natalie Kav](https://www.behance
 > This space is designed for prototyping concept art for a forthcoming game called **ХТОНЬ**. All outputs are generated locally in the browser using GPU acceleration.
 """)
 
-            with gr.Row():
-                hf_token_input = gr.Textbox(
-                    label="Your Hugging Face API Token",
-                    placeholder="Paste your token here",
-                    type="password",
-                )
+            hf_token_input = gr.Textbox(
+                label="Your Hugging Face API Token",
+                placeholder="Paste your token here",
+                type="password"
+            )
 
+            with gr.Row():
                 prompt = gr.Text(
                     label="Prompt",
                     show_label=False,
@@ -116,19 +116,17 @@ Generate images in the surreal style of artist [Natalie Kav](https://www.behance
 
             result_example = gr.Image(visible=False)
 
-                gr.Examples(
-                    examples=examples,
-                    fn=infer,
-                    inputs=[hf_token_input, prompt],
-                    outputs=[result_example, seed, gr.Gallery(visible=False)],
-                    cache_examples=False,  # Don't cache examples with real tokens!
-                )
-
+            gr.Examples(
+                examples=examples,
+                fn=infer,
+                inputs=[hf_token_input, prompt],
+                outputs=[result_example, seed, gr.Gallery(visible=False)],
+                cache_examples=False,
+            )
 
         with gr.Column(scale=1, elem_id="right-column"):
             result = gr.Image(label="", show_label=False, elem_id="generated-image")
 
-    gr_state = gr.State([])  # internal list of previews
     with gr.Column():
         gr.Markdown("<h3 style='text-align:center;'>Generated Images Preview</h3>")
         gallery = gr.Gallery(label="", columns=4, height="auto", object_fit="cover")
@@ -140,6 +138,5 @@ Generate images in the surreal style of artist [Natalie Kav](https://www.behance
         outputs=[result, seed, gallery],
     )
 
-
 if __name__ == "__main__":
     natalie_diffusion.launch()