Jason Hoku
31aa5dc692
The previous fix correctly identified that meta.model was empty (printing 'Checkpoint (empty) not found'), but the actual right behavior isn't to error out — it's to fall back to a per-item model. Items always store their own model name, so even if meta.model wasn't recorded for whatever reason (legacy manifest, write race, older session format), the items themselves know. Scan target_items for the first non-empty model name, use that as the session base. Florence2's from_manifest mode would handle per-item model differences on top anyway; this just gives us a valid starting point. The 'checkpoint not found' error message is preserved for the case where the picked model genuinely isn't on disk — that's still an actionable user-facing problem.
633 lines
30 KiB
Python
633 lines
30 KiB
Python
"""
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Dashboard Upscale Runner
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Background thread for upscaling images triggered from the dashboard.
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Loads models via ComfyUI APIs, runs upscale pipeline chains,
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saves results and updates manifests.
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"""
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import os
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import time
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import json
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import threading
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import uuid
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import torch
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import numpy as np
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from PIL import Image as PILImage
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from .model_loader import load_checkpoint, load_vae_by_name
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from .batch_encoding import encode_prompt_with_combinators
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from .image_generation import upscale_image, decode_latent_with_vae, create_image_metadata
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from .manifest_utils import save_manifest
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import comfy.model_management as mm
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# Active upscale jobs (module-level state)
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_upscale_jobs = {}
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def start_upscale_job(session_name, image_ids, upscale_config, all_favorited=False):
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"""
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Start an async upscale job. Returns (job_id, error) immediately.
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The actual work runs in a background thread.
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"""
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import folder_paths
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benchmarks_dir = os.path.realpath(os.path.join(folder_paths.get_output_directory(), "benchmarks"))
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session_dir = os.path.join(benchmarks_dir, session_name)
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manifest_path = os.path.join(session_dir, "manifest.json")
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images_dir = os.path.join(session_dir, "images")
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if not os.path.exists(manifest_path):
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return None, "Manifest not found"
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with open(manifest_path, "r", encoding="utf-8") as f:
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manifest_data = json.load(f)
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items = manifest_data.get("items", [])
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meta = manifest_data.get("meta", {})
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# Resolve image list
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if all_favorited:
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target_items = [item for item in items if item.get("favorited")]
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else:
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id_set = set(image_ids) if image_ids else set()
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target_items = [item for item in items if item.get("id") in id_set]
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if not target_items:
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return None, "No matching images found"
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job_id = f"upscale_{uuid.uuid4().hex[:12]}"
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job = {
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"id": job_id,
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"status": "running",
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"completed": 0,
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"total": len(target_items),
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"current_image": "",
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"error": None,
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"cancel": threading.Event(),
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}
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_upscale_jobs[job_id] = job
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thread = threading.Thread(
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target=_run_upscale_thread,
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args=(job, target_items, upscale_config, meta, manifest_data, manifest_path, images_dir, session_name),
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daemon=True
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)
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thread.start()
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return job_id, None
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def get_upscale_status(job_id):
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"""Get status of an upscale job."""
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job = _upscale_jobs.get(job_id)
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if not job:
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return {"status": "not_found"}
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return {
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"status": job["status"],
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"completed": job["completed"],
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"total": job["total"],
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"current_image": job.get("current_image", ""),
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"error": job.get("error"),
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}
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def cancel_upscale_job(job_id):
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"""Signal an upscale job to stop."""
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job = _upscale_jobs.get(job_id)
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if job and job["status"] == "running":
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job["cancel"].set()
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return True
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return False
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def _run_upscale_thread(job, target_items, upscale_config, meta, manifest_data, manifest_path, images_dir, session_name):
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"""
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Background thread that runs the upscale pipeline on each image.
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Loads the session's model/VAE/CLIP, encodes prompts, runs pipeline chains.
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"""
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import itertools as up_itertools
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import random as up_random
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try:
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pipelines = upscale_config.get("pipelines", [])
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if not pipelines:
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job["status"] = "error"
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job["error"] = "No upscale pipelines configured"
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return
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# Resolve image format from manifest meta (written by orchestrator)
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_raw_fmt = meta.get("image_format", "webp")
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_fmt = (_raw_fmt or "webp").lower().strip()
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if _fmt not in ("webp", "png", "jpg", "jpeg"):
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_fmt = "webp"
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_ext = "jpg" if _fmt in ("jpg", "jpeg") else _fmt
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# Load model, VAE, CLIP for conditioning
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model_name = meta.get("model", "") or ""
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vae_name = meta.get("vae", "")
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# If meta.model is empty/missing (legacy manifests, manifests written before
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# meta.model was recorded), fall back to the first non-empty item.model in
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# the target set. Items always store their own model name.
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if not model_name:
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for _it in target_items:
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_it_model = (_it.get("model", "") or "").strip()
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if _it_model:
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model_name = _it_model
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print(f"[DashboardUpscale] meta.model is empty; using first item's model: {model_name}")
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break
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# Validate the checkpoint exists before calling load_checkpoint. Without
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# this guard, a missing file ends up at comfy/utils.py:load_torch_file(None,
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# ...) and crashes with the cryptic "'NoneType' object has no attribute
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# 'lower'". Common cause: manifest copied from another machine where the
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# checkpoint isn't installed; or manifest entry has model=None.
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try:
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import folder_paths as _fp_check
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ckpt_path_check = _fp_check.get_full_path("checkpoints", model_name) if model_name else None
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except Exception:
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ckpt_path_check = None
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if not model_name or ckpt_path_check is None:
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job["status"] = "error"
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job["error"] = (
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f"Checkpoint '{model_name or '(empty)'}' not found in "
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f"ComfyUI/models/checkpoints/. The session manifest references it but "
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f"the file is not installed on this machine. Copy the .safetensors "
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f"file over, or regenerate the images on this machine first."
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)
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print(f"[DashboardUpscale] ❌ {job['error']}")
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return
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# VRAM hygiene before load_checkpoint: the previous workflow's model handles
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# may still be Python-referenced and won't be evicted by ComfyUI's LRU on a
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# new load_checkpoint call. soft_empty_cache + gc.collect releases those
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# before we add the upscale session's checkpoint on top. Critical for
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# Florence2 mode on small-VRAM cards (otherwise prior SDXL gen + new SDXL
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# session + Florence2 = OOM during beam search).
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try:
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import gc as _gc_pre
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_gc_pre.collect()
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mm.soft_empty_cache()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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except Exception:
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pass
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print(f"[DashboardUpscale] 🔄 Loading model: {model_name}")
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loaded_model, loaded_clip, loaded_vae = load_checkpoint(
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target_model_name=model_name,
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ckpt_name=model_name, # Same as target since we're loading directly
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use_remote_vae=False,
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)
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if vae_name and vae_name != "None" and vae_name != "Default":
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print(f"[DashboardUpscale] 🔄 Loading VAE: {vae_name}")
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loaded_vae = load_vae_by_name(vae_name)
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patched_model = loaded_model
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patched_clip = loaded_clip
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# Conditioning cache for this job
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conditioning_cache = {"positive": {}, "negative": {}}
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# Get PromptServer for sending events
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PromptServer = None
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try:
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import server as comfy_server
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PromptServer = comfy_server.PromptServer
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# Ensure last_prompt_id exists — KSampler progress callbacks require it
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if PromptServer.instance is not None and not hasattr(PromptServer.instance, 'last_prompt_id'):
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PromptServer.instance.last_prompt_id = f"dashboard_upscale_{job['id']}"
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except Exception:
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pass
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for img_idx, item in enumerate(target_items):
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if job["cancel"].is_set():
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print(f"[DashboardUpscale] 🛑 Cancelled — clearing VRAM...")
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try:
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mm.soft_empty_cache()
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import gc
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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except Exception:
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pass
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job["status"] = "cancelled"
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return
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# Extract filename from manifest item — may be in "filename" or parsed from "file" URL
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filename = item.get("filename", "")
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if not filename and item.get("file"):
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import re
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fname_match = re.search(r'filename=([^&]+)', item["file"])
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if fname_match:
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filename = fname_match.group(1)
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job["current_image"] = filename
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image_path = os.path.join(images_dir, filename)
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if not os.path.exists(image_path):
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print(f"[DashboardUpscale] ⚠️ File not found: {filename}, skipping")
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job["completed"] += 1
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continue
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# Load image and VAE-encode to latent
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try:
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pil_image = PILImage.open(image_path).convert("RGB")
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img_array = np.array(pil_image).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_array).unsqueeze(0)
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encoded = loaded_vae.encode(img_tensor[:, :, :, :3])
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result_latent = {"samples": encoded}
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except Exception as e:
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print(f"[DashboardUpscale] ⚠️ Failed to load/encode {filename}: {e}")
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job["completed"] += 1
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continue
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# Get conditioning
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pos_prompt = item.get("positive", meta.get("positive", ""))
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neg_prompt = item.get("negative", meta.get("negative", ""))
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clip_skip = item.get("clip_skip", 0)
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if pos_prompt not in conditioning_cache["positive"]:
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conditioning_cache["positive"][pos_prompt] = encode_prompt_with_combinators(patched_clip, pos_prompt, clip_skip)
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if neg_prompt not in conditioning_cache["negative"]:
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conditioning_cache["negative"][neg_prompt] = encode_prompt_with_combinators(patched_clip, neg_prompt, clip_skip)
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final_positive = conditioning_cache["positive"][pos_prompt]
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final_negative = conditioning_cache["negative"][neg_prompt]
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# HiRes prompt adjustment
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hires_positive_cond = final_positive
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hires_prompt_active = False
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hires_prompt_behavior_rt = ""
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hires_prompt_text_rt = ""
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if upscale_config.get("hires_prompt_adjust") and upscale_config.get("hires_prompt_text", "").strip():
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hires_prompt_behavior_rt = upscale_config.get("hires_prompt_behavior", "append_end")
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hires_prompt_text_rt = upscale_config["hires_prompt_text"].strip()
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if hires_prompt_behavior_rt == "prepend":
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adjusted = hires_prompt_text_rt + " " + pos_prompt
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elif hires_prompt_behavior_rt == "append_end":
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adjusted = pos_prompt + " " + hires_prompt_text_rt
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elif hires_prompt_behavior_rt == "replace":
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adjusted = hires_prompt_text_rt
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else:
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adjusted = pos_prompt
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if adjusted not in conditioning_cache["positive"]:
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conditioning_cache["positive"][adjusted] = encode_prompt_with_combinators(patched_clip, adjusted, clip_skip)
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hires_positive_cond = conditioning_cache["positive"][adjusted]
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hires_prompt_active = True
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pipe_w = item.get("width", 512)
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pipe_h = item.get("height", 512)
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upscale_combo_idx = 0
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total_upscale_duration = 0
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for pipeline_idx, pipeline in enumerate(pipelines):
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if pipeline.get("active", True) is False:
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continue
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pipeline_name = pipeline.get("name", f"Pipeline {pipeline_idx + 1}")
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pipeline_steps = pipeline.get("steps", [])
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if not pipeline_steps:
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continue
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pipe_latent = result_latent
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pipe_w_current = pipe_w
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pipe_h_current = pipe_h
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# Flatten steps with repeat
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expanded_steps = []
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for step in pipeline_steps:
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if step.get("active", True) is False:
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continue
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repeat = max(1, int(step.get("repeat", 1)))
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for _ in range(repeat):
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expanded_steps.append(step)
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for step_idx, ucfg in enumerate(expanded_steps):
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mode = ucfg.get("mode", "hires_only")
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show_hires = mode in ("hires_only", "model_then_hires")
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show_model = mode in ("model_only", "model_then_hires")
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# --- SeedVR2 upscale mode ---
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if mode == "seedvr2":
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from .image_generation import seedvr2_upscale
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sv_config = ucfg.get("seedvr2", {})
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# Convert source PIL image directly (no latent decoding needed)
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pil_input = PILImage.open(os.path.join(images_dir, filename))
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result_pil, up_w, up_h, sv_duration = seedvr2_upscale(pil_input, sv_config)
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total_upscale_duration += sv_duration
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is_last_step = step_idx == len(expanded_steps) - 1
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if is_last_step:
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# Save the upscaled image
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upscale_id = int(time.time() * 100000) + up_random.randint(0, 1000)
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upscaled_filename = f"img_{upscale_id}_upscaled.{_ext}"
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_up_path = os.path.join(images_dir, upscaled_filename)
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if _fmt == "png":
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result_pil.save(_up_path, format="PNG")
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elif _fmt in ("jpg", "jpeg"):
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result_pil.save(_up_path, format="JPEG", quality=95)
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else:
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result_pil.save(_up_path, format="WEBP", quality=95)
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# Create manifest entry
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upscaled_meta = {
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k: v for k, v in item.items()
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if k not in ("id", "gen_index", "file", "filename", "upscaled", "width", "height", "duration",
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"upscale_source", "upscale_pipeline", "upscale_mode",
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"upscale_ratio", "upscale_denoise", "upscale_model")
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}
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upscaled_meta.update({
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"id": upscale_id,
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"gen_index": len(manifest_data["items"]),
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"file": f"/view?filename={upscaled_filename}&type=output&subfolder=benchmarks/{session_name}/images",
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"filename": upscaled_filename,
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"width": up_w, "height": up_h,
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"duration": round(sv_duration, 2),
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"upscaled": True,
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"upscale_source": "dashboard",
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"upscale_pipeline": pipeline_name,
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"upscale_mode": "seedvr2",
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"upscale_model": sv_config.get("dit_model", ""),
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})
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manifest_data["items"].insert(0, upscaled_meta)
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upscale_combo_idx += 1
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else:
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# For chained steps, the next step gets the PIL result
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# Re-read from saved temp or keep in memory
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pil_input = result_pil
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pipe_w_current = up_w
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pipe_h_current = up_h
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continue # Skip the normal combo loop
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# --- Florence2 Hi-Res Fix mode ---
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if mode == "florence2_hires":
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from .florence2_hires import (
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run_florence2_step, preflight_florence2,
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build_florence2_manifest_entry, build_florence2_no_detection_entry,
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)
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import numpy as _np
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# Preflight once per job (cached)
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if not job.get("_florence2_preflighted"):
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preflight_florence2()
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job["_florence2_preflighted"] = True
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# Merge top-level step sampling fields into a unified config dict.
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# hires_denoise on the step may be "0.45, 0.65" — Florence2 uses just one
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# value per pass; take the first.
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_hd = str(ucfg.get("hires_denoise", "0.45")).split(",")[0].strip() or "0.45"
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f2_config = {
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**(ucfg.get("florence2", {}) or {}),
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"hires_denoise": float(_hd),
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"hires_steps": int(ucfg.get("hires_steps", 15) or 15),
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"cfg": float(ucfg.get("cfg", 1.5) or 1.5),
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"sampler": ucfg.get("sampler", "euler"),
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"scheduler": ucfg.get("scheduler", "simple"),
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}
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# Source image: load fresh from disk as tensor (1, H, W, 3)
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src_pil = PILImage.open(os.path.join(images_dir, filename)).convert("RGB")
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src_arr = _np.array(src_pil).astype(_np.float32) / 255.0
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src_tensor = torch.from_numpy(src_arr).unsqueeze(0)
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# Per-job checkpoint+lora cache (lazy init)
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if "_florence2_ckpt_cache" not in job:
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job["_florence2_ckpt_cache"] = {}
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step_result = run_florence2_step(
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source_image=src_tensor,
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item=item,
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step_config=f2_config,
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fallback_handles=(patched_model, patched_clip, loaded_vae),
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ckpt_cache=job["_florence2_ckpt_cache"],
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conditioning_cache=conditioning_cache,
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positive_prompt=pos_prompt,
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negative_prompt=neg_prompt,
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clip_skip=clip_skip,
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# Pass the session's already-loaded model name so Florence2 can
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# short-circuit duplicate loads when item.model matches it.
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session_model_name=meta.get("model", ""),
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)
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is_last_step = step_idx == len(expanded_steps) - 1
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if step_result.get("status") == "no_detection":
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sentinel_id = int(time.time() * 100000) + up_random.randint(0, 1000)
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entry = build_florence2_no_detection_entry(
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step_result, item,
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sentinel_id=sentinel_id,
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current_index=len(manifest_data["items"]),
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)
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manifest_data["items"].insert(0, entry)
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upscale_combo_idx += 1
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total_upscale_duration += float(step_result.get("duration", 0))
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continue
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total_upscale_duration += float(step_result.get("duration", 0))
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if is_last_step:
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upscale_id = int(time.time() * 100000) + up_random.randint(0, 1000)
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upscaled_filename = f"img_{upscale_id}_upscaled.{_ext}"
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_up_path = os.path.join(images_dir, upscaled_filename)
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result_pil = step_result["image_pil"]
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if _fmt == "png":
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result_pil.save(_up_path, format="PNG")
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elif _fmt in ("jpg", "jpeg"):
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result_pil.save(_up_path, format="JPEG", quality=95)
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else:
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result_pil.save(_up_path, format="WEBP", quality=95)
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entry = build_florence2_manifest_entry(
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step_result, item,
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session_name=session_name,
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|
pipeline_name=pipeline_name,
|
|
upscale_id=upscale_id,
|
|
upscaled_filename=upscaled_filename,
|
|
current_index=len(manifest_data["items"]),
|
|
hires_denoise=f2_config["hires_denoise"],
|
|
)
|
|
manifest_data["items"].insert(0, entry)
|
|
upscale_combo_idx += 1
|
|
else:
|
|
pipe_w_current = step_result["image_width"]
|
|
pipe_h_current = step_result["image_height"]
|
|
continue # Skip the normal combo loop
|
|
|
|
raw_ratios = str(ucfg.get("upscale_ratios", "1.5"))
|
|
ratios = [float(r.strip()) for r in raw_ratios.split(",") if r.strip()] or [1.5]
|
|
raw_denoise = str(ucfg.get("hires_denoise", "0.3"))
|
|
denoises = [float(d.strip()) for d in raw_denoise.split(",") if d.strip()] or [0.3]
|
|
models = ucfg.get("upscale_models", []) or [""]
|
|
|
|
if show_hires and show_model:
|
|
combos = list(up_itertools.product(models, ratios, denoises))
|
|
elif show_hires:
|
|
combos = list(up_itertools.product([""], ratios, denoises))
|
|
elif show_model:
|
|
combos = list(up_itertools.product(models, [1.0], [0.0]))
|
|
else:
|
|
combos = []
|
|
|
|
for combo in combos:
|
|
up_model, up_ratio, up_denoise = combo
|
|
single_config = {
|
|
"mode": mode, "upscale_model": up_model,
|
|
"upscale_ratio": up_ratio, "hires_denoise": up_denoise,
|
|
"hires_steps": ucfg.get("hires_steps", 0),
|
|
"tiled_vae": ucfg.get("tiled_vae", False),
|
|
"tile_size": ucfg.get("tile_size", 512),
|
|
"upscale_size": ucfg.get("upscale_size", "2.0"),
|
|
"resize_method": ucfg.get("resize_method", "bilinear"),
|
|
"hires_tiled_sampling": ucfg.get("hires_tiled_sampling", False),
|
|
"hires_tile_width": ucfg.get("hires_tile_width", 512),
|
|
"hires_tile_height": ucfg.get("hires_tile_height", 512),
|
|
"hires_mask_blur": ucfg.get("hires_mask_blur", 8),
|
|
"hires_tile_padding": ucfg.get("hires_tile_padding", 32),
|
|
"hires_force_uniform_tiles": ucfg.get("hires_force_uniform_tiles", False)
|
|
}
|
|
|
|
up_positive = hires_positive_cond if (show_hires and hires_prompt_active) else final_positive
|
|
upscale_result, upscale_duration = upscale_image(
|
|
pipe_latent, loaded_vae, patched_model, single_config,
|
|
item, up_positive, final_negative, pipe_w_current, pipe_h_current
|
|
)
|
|
|
|
is_last_step = step_idx == len(expanded_steps) - 1
|
|
is_last_combo = combo == combos[-1]
|
|
is_final_output = is_last_step and is_last_combo
|
|
|
|
if isinstance(upscale_result, dict) and "samples" in upscale_result:
|
|
upscaled_pil = decode_latent_with_vae(loaded_vae, upscale_result["samples"])
|
|
up_w, up_h = upscaled_pil.size
|
|
if is_final_output:
|
|
upscale_id = int(time.time() * 100000) + up_random.randint(0, 1000)
|
|
upscaled_filename = f"img_{upscale_id}_upscaled.{_ext}"
|
|
_up_path = os.path.join(images_dir, upscaled_filename)
|
|
if _fmt == "png":
|
|
upscaled_pil.save(_up_path, format="PNG")
|
|
elif _fmt in ("jpg", "jpeg"):
|
|
upscaled_pil.save(_up_path, format="JPEG", quality=95)
|
|
else:
|
|
upscaled_pil.save(_up_path, format="WEBP", quality=95)
|
|
elif isinstance(upscale_result, PILImage.Image):
|
|
up_w, up_h = upscale_result.size
|
|
if is_final_output:
|
|
upscale_id = int(time.time() * 100000) + up_random.randint(0, 1000)
|
|
upscaled_filename = f"img_{upscale_id}_upscaled.{_ext}"
|
|
_up_path = os.path.join(images_dir, upscaled_filename)
|
|
if _fmt == "png":
|
|
upscale_result.save(_up_path, format="PNG")
|
|
elif _fmt in ("jpg", "jpeg"):
|
|
upscale_result.save(_up_path, format="JPEG", quality=95)
|
|
else:
|
|
upscale_result.save(_up_path, format="WEBP", quality=95)
|
|
else:
|
|
continue
|
|
|
|
if not is_final_output:
|
|
if isinstance(upscale_result, dict) and "samples" in upscale_result:
|
|
pipe_latent = upscale_result
|
|
pipe_w_current = up_w
|
|
pipe_h_current = up_h
|
|
continue
|
|
|
|
# Create manifest entry — copy source item fields, override with upscale info
|
|
upscaled_meta = {
|
|
k: v for k, v in item.items()
|
|
if k not in ("id", "gen_index", "file", "filename", "upscaled", "width", "height", "duration",
|
|
"upscale_source", "upscale_pipeline", "upscale_mode",
|
|
"upscale_ratio", "upscale_denoise", "upscale_model")
|
|
}
|
|
# gen_index: place upscaled images at the end (newest) since they were just created
|
|
# Use current manifest length so each new upscale gets the next sequential position
|
|
upscale_gen_index = len(manifest_data["items"])
|
|
|
|
upscaled_meta.update({
|
|
"id": upscale_id,
|
|
"gen_index": upscale_gen_index,
|
|
"file": f"/view?filename={upscaled_filename}&type=output&subfolder=benchmarks/{session_name}/images",
|
|
"filename": upscaled_filename,
|
|
"width": up_w,
|
|
"height": up_h,
|
|
"duration": round(upscale_duration, 2),
|
|
"upscaled": True,
|
|
"upscale_source": "dashboard",
|
|
"upscale_pipeline": pipeline_name,
|
|
"upscale_mode": mode,
|
|
"upscale_ratio": up_ratio,
|
|
"upscale_denoise": up_denoise,
|
|
})
|
|
if up_model:
|
|
upscaled_meta["upscale_model"] = up_model
|
|
if hires_prompt_active:
|
|
upscaled_meta["hires_prompt_behavior"] = hires_prompt_behavior_rt
|
|
upscaled_meta["hires_prompt_text"] = hires_prompt_text_rt
|
|
|
|
manifest_data["items"].insert(0, upscaled_meta)
|
|
upscale_combo_idx += 1
|
|
|
|
if upscale_combo_idx > 0:
|
|
save_manifest(manifest_path, manifest_data)
|
|
# Send all new items to dashboard (may be multiple from different pipelines/combos)
|
|
if PromptServer is not None:
|
|
try:
|
|
new_items = manifest_data["items"][-upscale_combo_idx:]
|
|
PromptServer.instance.send_sync("ultimate_grid.update_data", {
|
|
"session_name": session_name,
|
|
"new_items": new_items
|
|
})
|
|
except Exception:
|
|
pass
|
|
|
|
job["completed"] = img_idx + 1
|
|
|
|
# Send progress
|
|
if PromptServer is not None:
|
|
try:
|
|
PromptServer.instance.send_sync("ultimate_grid.progress", {
|
|
"session_name": session_name,
|
|
"current_job": img_idx + 1,
|
|
"total_jobs": len(target_items),
|
|
"progress_pct": int(((img_idx + 1) / len(target_items)) * 100),
|
|
"eta_str": "",
|
|
"avg_duration": 0,
|
|
"last_duration": 0,
|
|
"phase": "upscaling",
|
|
})
|
|
except Exception:
|
|
pass
|
|
|
|
print(f"[DashboardUpscale] 🔄 Upscaled {img_idx+1}/{len(target_items)}: {filename}")
|
|
|
|
# Done — clear VRAM and RAM
|
|
print(f"[DashboardUpscale] 🧹 Clearing VRAM and RAM...")
|
|
try:
|
|
mm.soft_empty_cache()
|
|
import gc
|
|
gc.collect()
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
except Exception as cleanup_err:
|
|
print(f"[DashboardUpscale] ⚠️ Cleanup warning: {cleanup_err}")
|
|
|
|
job["status"] = "complete"
|
|
print(f"[DashboardUpscale] ✅ Complete: {job['completed']}/{job['total']} images upscaled")
|
|
|
|
except Exception as e:
|
|
import traceback
|
|
traceback.print_exc()
|
|
job["status"] = "error"
|
|
job["error"] = str(e)
|
|
print(f"[DashboardUpscale] ❌ Error: {e}")
|
|
finally:
|
|
# Always attempt cleanup on any exit path
|
|
try:
|
|
mm.soft_empty_cache()
|
|
import gc
|
|
gc.collect()
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
except Exception:
|
|
pass
|