lock defaults (1.05/9/3), single-button UI, ⭐👌 emoji

app.py

@@ -7,36 +7,43 @@ from diffusers import Flux2KleinPipeline
 
 BASE = "black-forest-labs/FLUX.2-klein-4B"
 LORA = "noema-art/impresstation-klein"
-# Default edit instruction for klein (an edit model): trigger words + style + directive.
+# Locked-in look (dialed in: strength 1.05 / steps 9 / guidance 3).
 PROMPT = (
-    "stationthis, stationthis, low poly playstation screenshot style. "
-    "Convert this image into a low-poly PlayStation-era 3D game screenshot in the stationthis style."
+    "stationthis, low poly playstation screenshot style. A low-poly PlayStation 2 era 3D model, "
+    "lightly upscaled like a PS2 emulator running at 2x internal resolution: keep the chunky "
+    "low-polygon geometry and flat-shaded surfaces, with cleaner, moderately sharp edges and "
+    "readable textures — but keep a gentle retro softness and a little texture warmth. Slightly "
+    "crisp, not razor-sharp; clean but not clinical. Extrude any flat 2D elements into solid 3D "
+    "polygonal forms with real depth and foreshortening. A nicely emulated PS2 screenshot — "
+    "clearer than the console, softer than 4K."
 )
+STRENGTH = 1.05
+STEPS = 9
+GUIDANCE = 3.0
 HF_TOKEN = os.environ.get("HF_TOKEN")
 MAX_SEED = 2**31 - 1
 
 dtype = torch.bfloat16
 pipe = Flux2KleinPipeline.from_pretrained(BASE, torch_dtype=dtype, token=HF_TOKEN)
 pipe.load_lora_weights(LORA, adapter_name="stationthis", token=HF_TOKEN)
+pipe.set_adapters("stationthis", adapter_weights=STRENGTH)
 pipe.to("cuda")
 
 
 @spaces.GPU(duration=120)
-def station_this(image, prompt, strength, steps, guidance, seed, randomize, progress=gr.Progress(track_tqdm=True)):
+def station_this(image, progress=gr.Progress(track_tqdm=True)):
     if image is None:
         raise gr.Error("Drop in an image first.")
-    if randomize:
-        seed = random.randint(0, MAX_SEED)
-    pipe.set_adapters("stationthis", adapter_weights=float(strength))
-    g = torch.Generator("cuda").manual_seed(int(seed))
+    seed = random.randint(0, MAX_SEED)
+    g = torch.Generator("cuda").manual_seed(seed)
     out = pipe(
         image=[image.convert("RGB")],
-        prompt=(prompt.strip() or PROMPT),
-        guidance_scale=float(guidance),
-        num_inference_steps=int(steps),
+        prompt=PROMPT,
+        guidance_scale=GUIDANCE,
+        num_inference_steps=STEPS,
         generator=g,
     ).images[0]
-    return out, seed
+    return out
 
 
 CSS = """
@@ -50,26 +57,18 @@ footer { display:none !important; }
 
 with gr.Blocks(css=CSS, title="stationthis") as demo:
     gr.HTML(
-        "<div id='title'><h1>🕹️ stationthis</h1>"
+        "<div id='title'><h1>⭐👌 stationthis</h1>"
         "<p>Drop an image — get it back as a low-poly PlayStation-era still. "
         "Powered by FLUX.2 klein + <b>NOEMA</b>. "
         "<a href='https://noema.art'>noema.art</a></p></div>"
     )
     with gr.Row():
         with gr.Column():
-            inp = gr.Image(label="Your image", type="pil", height=360)
-            with gr.Accordion("Dial it in", open=True):
-                prompt = gr.Textbox(label="Prompt", value=PROMPT, lines=3)
-                strength = gr.Slider(0.0, 1.5, value=1.0, step=0.05, label="LoRA strength (PS2 intensity)")
-                steps = gr.Slider(4, 30, value=8, step=1, label="Steps")
-                guidance = gr.Slider(1.0, 6.0, value=2.5, step=0.1, label="Guidance")
-                seed = gr.Slider(0, MAX_SEED, value=42, step=1, label="Seed")
-                randomize = gr.Checkbox(value=True, label="Randomize seed")
+            inp = gr.Image(label="Your image", type="pil", height=380)
             go = gr.Button("Station this", variant="primary")
         with gr.Column():
-            out = gr.Image(label="result", height=360)
-            used_seed = gr.Number(label="Seed used", interactive=False)
-    go.click(station_this, [inp, prompt, strength, steps, guidance, seed, randomize], [out, used_seed])
+            out = gr.Image(label="result", height=380)
+    go.click(station_this, inp, out)
 
 if __name__ == "__main__":
     demo.launch()