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# HANDLER v6 — SDXL txt2img + inpaint, supports image_url/mask_url, guards UNet channels
import os, io, json, base64, requests
from typing import Any, Dict
from PIL import Image
import torch
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLInpaintPipeline
from huggingface_hub import snapshot_download
MODEL_ID = os.getenv("MODEL_ID", "andro-flock/LUSTIFY-SDXL-NSFW-checkpoint-v2-0-INPAINTING")
class EndpointHandler:
def __init__(self, path: str = "."):
print("HANDLER v6: init start")
token = os.getenv("HF_TOKEN")
local_dir = snapshot_download(MODEL_ID, token=token)
print(f"HANDLER v6: snapshot at {local_dir}")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.pipe_txt2img = None
self.pipe_inpaint = None
last_err = None
for dtype in (torch.float16, torch.bfloat16, torch.float32):
try:
# Try to load txt2img
try:
p = StableDiffusionXLPipeline.from_pretrained(
local_dir, torch_dtype=dtype, use_safetensors=True
).to(self.device)
# Keep txt2img ONLY if UNet is 4-ch (proper base)
if getattr(p.unet.config, "in_channels", 4) == 4:
self.pipe_txt2img = p
print(f"HANDLER v6: txt2img OK ({dtype}, in_ch=4)")
else:
print("HANDLER v6: txt2img UNet in_ch != 4; disabling txt2img for this repo")
try:
p.to("cpu"); del p
except Exception:
pass
self.pipe_txt2img = None
except Exception as e:
self.pipe_txt2img = None
print(f"HANDLER v6: txt2img failed on {dtype}: {e}")
# Load inpaint (required)
self.pipe_inpaint = StableDiffusionXLInpaintPipeline.from_pretrained(
local_dir, torch_dtype=dtype, use_safetensors=True
).to(self.device)
print(f"HANDLER v6: inpaint OK ({dtype}, in_ch={getattr(self.pipe_inpaint.unet.config, 'in_channels', 'NA')})")
break
except Exception as e:
last_err = e
self.pipe_txt2img = None
self.pipe_inpaint = None
print(f"HANDLER v6: inpaint failed on {dtype}: {e}")
if self.pipe_inpaint is None:
raise RuntimeError(f"Failed to load pipelines: {last_err}")
try:
self.pipe_inpaint.enable_attention_slicing()
if self.pipe_txt2img:
self.pipe_txt2img.enable_attention_slicing()
except Exception:
pass
print("HANDLER v6: ready")
# ---------- helpers ----------
def _unwrap(self, data: Dict[str, Any]) -> Dict[str, Any]:
if "inputs" in data:
inner = data["inputs"]
if isinstance(inner, str):
try:
return json.loads(inner)
except Exception:
return {}
if isinstance(inner, dict):
return inner
return data
def _fetch_url_bytes(self, url: str) -> bytes:
r = requests.get(url, timeout=60)
r.raise_for_status()
return r.content
def _to_pil(self, payload: Any, mode: str) -> Image.Image:
# Accept: bytes, base64, or data URL, or HTTP(S) URL
if isinstance(payload, str):
if payload.startswith("http://") or payload.startswith("https://"):
payload = self._fetch_url_bytes(payload)
else:
if payload.startswith("data:"):
payload = payload.split(",", 1)[1]
payload = base64.b64decode(payload)
return Image.open(io.BytesIO(payload)).convert(mode)
def _int(self, data, key, default):
try:
return int(data.get(key, default))
except Exception:
return default
def _float(self, data, key, default):
try:
return float(data.get(key, default))
except Exception:
return default
# ---------- main entry ----------
def __call__(self, data: Dict[str, Any]):
data = self._unwrap(data)
prompt = data.get("prompt", "")
negative_prompt = data.get("negative_prompt", None)
steps = self._int(data, "num_inference_steps", 30)
guidance = self._float(data, "guidance_scale", 7.0)
seed = data.get("seed", None)
generator = None
if seed is not None:
try:
generator = torch.Generator(device=self.device).manual_seed(int(seed))
except Exception:
generator = None
# Normalize keys for images/masks
# Accept: image / init_image / image_url ; mask / mask_url
init_img_payload = None
if "image" in data:
init_img_payload = data["image"]
elif "init_image" in data:
init_img_payload = data["init_image"]
elif "image_url" in data:
init_img_payload = data["image_url"]
mask_payload = data.get("mask") or data.get("mask_url")
# --------- choose mode ---------
if init_img_payload is None:
# txt2img mode (only if we truly have a 4-ch UNet)
width = self._int(data, "width", 1024)
height = self._int(data, "height", 1024)
width = max(64, (width // 8) * 8)
height = max(64, (height // 8) * 8)
if self.pipe_txt2img is not None:
image = self.pipe_txt2img(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
num_inference_steps=steps,
guidance_scale=guidance,
generator=generator,
).images[0]
else:
# Fallback: blank-canvas inpaint (works with 9-ch UNet)
canvas = Image.new("RGB", (width, height), (255, 255, 255))
mask = Image.new("L", (width, height), 255)
image = self.pipe_inpaint(
prompt=prompt,
image=canvas,
mask_image=mask,
negative_prompt=negative_prompt,
num_inference_steps=steps,
guidance_scale=guidance,
generator=generator,
).images[0]
else:
# inpaint mode
init_img = self._to_pil(init_img_payload, "RGB")
if mask_payload is not None:
mask_img = self._to_pil(mask_payload, "L").resize(init_img.size, Image.NEAREST)
else:
mask_img = Image.new("L", init_img.size, 255) # edit-all default
strength = self._float(data, "strength", 0.85)
image = self.pipe_inpaint(
prompt=prompt,
image=init_img,
mask_image=mask_img,
negative_prompt=negative_prompt,
num_inference_steps=steps,
guidance_scale=guidance,
strength=strength,
generator=generator,
).images[0]
# Return PNG as base64
buf = io.BytesIO()
image.save(buf, format="PNG")
out_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
return {"image_base64": out_b64}
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