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Running on Zero
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233947a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | """Geometric fallback backend — no GPU and no HF token required.
When neither a CUDA GPU (local Qwen) nor an HF token (serverless Inference
Providers) is available, AngleForge would otherwise have no usable engine.
This backend approximates each camera-angle preset with cheap Pillow
geometric transforms (perspective tilt, rotation, zoom, translation) so the
Space is always functional as a bootstrap. It is **not** AI image editing —
results are geometric approximations of the requested viewpoint.
"""
from __future__ import annotations
from typing import Dict, List
import numpy as np
from PIL import Image
from ..config import ANGLE_PRESETS
from .base import ImageEditBackend
def _find_coeffs(dst: List[tuple], src: List[tuple]) -> List[float]:
matrix = []
for (dx, dy), (sx, sy) in zip(dst, src):
matrix.append([dx, dy, 1, 0, 0, 0, -sx * dx, -sx * dy])
matrix.append([0, 0, 0, dx, dy, 1, -sy * dx, -sy * dy])
a = np.array(matrix, dtype=float)
b = np.array(src, dtype=float).reshape(8)
res, *_ = np.linalg.lstsq(a, b, rcond=None)
return res.tolist()
def _perspective(img: Image.Image, src_quad: List[tuple]) -> Image.Image:
w, h = img.size
dst = [(0, 0), (w, 0), (w, h), (0, h)]
coeffs = _find_coeffs(dst, src_quad)
return img.transform((w, h), Image.PERSPECTIVE, coeffs, resample=Image.BICUBIC)
def _tilt(img: Image.Image, top_inset: float, bottom_inset: float) -> Image.Image:
w, h = img.size
src = [
(w * top_inset, 0),
(w * (1 - top_inset), 0),
(w * (1 - bottom_inset), h),
(w * bottom_inset, h),
]
return _perspective(img, src)
def _zoom(img: Image.Image, factor: float) -> Image.Image:
w, h = img.size
if factor >= 1.0: # crop in, then scale back up
cw, ch = int(w / factor), int(h / factor)
left, top = (w - cw) // 2, (h - ch) // 2
return img.crop((left, top, left + cw, top + ch)).resize((w, h), Image.LANCZOS)
# zoom out: paste shrunk image onto a padded canvas
sw, sh = int(w * factor), int(h * factor)
small = img.resize((sw, sh), Image.LANCZOS)
canvas = Image.new("RGB", (w, h), (20, 20, 20))
canvas.paste(small, ((w - sw) // 2, (h - sh) // 2))
return canvas
def _shift(img: Image.Image, dx_frac: float, dy_frac: float) -> Image.Image:
w, h = img.size
dx, dy = int(w * dx_frac), int(h * dy_frac)
return img.transform(
(w, h), Image.AFFINE, (1, 0, -dx, 0, 1, -dy), resample=Image.BICUBIC
)
def _transform_for_key(img: Image.Image, key: str) -> Image.Image:
if key in ("top_down", "birds_eye"):
return _tilt(img, top_inset=0.0, bottom_inset=0.20 if key == "top_down" else 0.12)
if key == "worms_eye":
return _tilt(img, top_inset=0.16, bottom_inset=0.0)
if key == "rotate_left_45":
return img.rotate(45, resample=Image.BICUBIC, expand=False)
if key == "rotate_right_45":
return img.rotate(-45, resample=Image.BICUBIC, expand=False)
if key == "rotate_left_90":
return img.rotate(90, resample=Image.BICUBIC, expand=False)
if key == "rotate_right_90":
return img.rotate(-90, resample=Image.BICUBIC, expand=False)
if key == "close_up":
return _zoom(img, 1.45)
if key == "wide_angle":
return _zoom(img, 0.7)
if key == "move_left":
return _shift(img, dx_frac=0.15, dy_frac=0.0)
if key == "move_right":
return _shift(img, dx_frac=-0.15, dy_frac=0.0)
if key == "move_forward":
return _zoom(img, 1.2)
if key == "move_down":
return _shift(img, dx_frac=0.0, dy_frac=-0.15)
return img # original / unknown
class GeometricBackend(ImageEditBackend):
"""Token-free, CPU-only viewpoint approximation using Pillow transforms."""
source = "geometric_fallback"
def __init__(self, image_size: int = 512) -> None:
self.image_size = image_size
# Reverse map: bilingual prompt -> preset key.
self._prompt_to_key: Dict[str, str] = {
prompt: key for key, (_label, prompt) in ANGLE_PRESETS.items()
}
def prepare(self) -> None:
return None
def edit(
self,
image: Image.Image,
prompt: str,
seed: int,
num_inference_steps: int,
true_guidance_scale: float,
) -> Image.Image:
img = image.convert("RGB")
if not prompt or not prompt.strip():
return img
key = self._prompt_to_key.get(prompt.strip(), "original")
return _transform_for_key(img, key)
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