1127 update to latest

dllm/assets/logo.py

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+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import os
+
+# ---------- Configuration (smaller size) ----------
+W, H = 480, 210  # lower resolution
+TOTAL_DURATION = 3.0
+FPS = 15  # lower fps
+TEXT = "dLLM"
+# TEXT_COLOR = (235, 235, 235)
+TEXT_COLOR = (0, 0, 0)
+OUTPUT = "logo.gif"
+LAST_FRAME_PNG = "logo.png"
+
+DIFFUSION_PORTION = 0.3  # fewer diffusion frames
+SEED = 8
+
+
+# ---------- Auto font size ----------
+def load_font_auto_size(text, w, h, target_width_ratio=0.95, target_height_ratio=0.95):
+    lo, hi = 10, 2000
+    best_font, best_size = None, lo
+    while lo <= hi:
+        mid = (lo + hi) // 2
+        try:
+            font = ImageFont.truetype(
+                "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", size=mid
+            )
+        except:
+            font = ImageFont.load_default()
+        dummy = Image.new("L", (w, h), 0)
+        d = ImageDraw.Draw(dummy)
+        bbox = d.textbbox((0, 0), text, font=font)
+        tw, th = bbox[2] - bbox[0], bbox[3] - bbox[1]
+
+        width_ok = tw <= w * target_width_ratio
+        height_ok = th <= h * target_height_ratio
+
+        if width_ok and height_ok:
+            best_font, best_size = font, mid
+            lo = mid + 1
+        else:
+            hi = mid - 1
+
+    return best_font if best_font is not None else font
+
+
+# ---------- Text rendering ----------
+def render_text_mask(w, h, text, font):
+    img = Image.new("L", (w, h), 0)
+    d = ImageDraw.Draw(img)
+    bbox = d.textbbox((0, 0), text, font=font)
+    tw, th = bbox[2] - bbox[0], bbox[3] - bbox[1]
+    x = (w - tw) // 2 - bbox[0]
+    y = (h - th) // 2 - bbox[1]
+    d.text((x, y), text, font=font, fill=255)
+    return np.asarray(img, np.float32) / 255.0
+
+
+# ---------- Initialization ----------
+font = load_font_auto_size(TEXT, W, H)
+mask = render_text_mask(W, H, TEXT, font)
+
+num_frames = int(TOTAL_DURATION * FPS)
+diffusion_frames = max(1, int(num_frames * DIFFUSION_PORTION))
+hold_ms = int((TOTAL_DURATION - diffusion_frames / FPS) * 1000)
+
+rng = np.random.default_rng(SEED)
+frames = []
+
+# ---------- Diffusion stage ----------
+for i in range(diffusion_frames):
+    t = i / max(1, diffusion_frames - 1)
+    progress = t**0.9
+    noise_sigma = (1.0 - progress) ** 2.2
+
+    noise = rng.standard_normal((H, W, 1)).astype(np.float32)
+    noise_img = 1.0 - noise_sigma * 0.5 * np.abs(noise)
+    np.clip(noise_img, 0.0, 1.0, out=noise_img)
+
+    alpha = progress**2.0
+    alpha_map = (mask * alpha).astype(np.float32)[..., None]
+
+    text_rgb = np.zeros((H, W, 3), dtype=np.float32)
+    for c in range(3):
+        text_rgb[..., c] = (mask > 0).astype(np.float32) * (TEXT_COLOR[c] / 255.0)
+
+    frame = (1.0 - alpha_map) * noise_img + alpha_map * text_rgb
+    frame = (np.clip(frame, 0.0, 1.0) * 255).astype(np.uint8)
+    frames.append(Image.fromarray(frame, mode="RGB"))
+
+# ---------- Last frame ----------
+final_frame = frames[-1]
+
+# ---------- Save last frame as PNG ----------
+final_frame.save(LAST_FRAME_PNG)
+print(f"🖼️  Last frame saved as: {LAST_FRAME_PNG}")
+
+# ---------- Quantization (reduce size) ----------
+pal_frames = [f.convert("P", palette=Image.ADAPTIVE, colors=64) for f in frames]
+pal_final = final_frame.convert("P", palette=Image.ADAPTIVE, colors=64)
+
+# ---------- Save GIF ----------
+normal_ms = int(1000 / FPS)
+durations = [normal_ms] * len(pal_frames) + [hold_ms]
+
+pal_frames[0].save(
+    OUTPUT,
+    save_all=True,
+    append_images=pal_frames[1:] + [pal_final],
+    duration=durations,
+    loop=0,
+    optimize=True,
+)
+
+print(f"✅ GIF saved: {OUTPUT}")
+print(
+    f"Frames (diffusion only): {len(pal_frames)} at {FPS} FPS, final hold {hold_ms} ms, resolution {W}x{H}"
+)