new server info panel

Signed-off-by: vladmandic <mandic00@live.com>

eslint.config.mjs

@@ -31,6 +31,7 @@ const jsConfig = defineConfig([
         ...globals.jquery,
         panzoom: 'readonly',
         authFetch: 'readonly',
+        initServerInfo: 'readonly',
         log: 'readonly',
         debug: 'readonly',
         error: 'readonly',

installer.py

@@ -20,6 +20,11 @@ class Dot(dict): # dot notation access to dictionary attributes
     __setattr__ = dict.__setitem__
     __delattr__ = dict.__delitem__
 
+class Torch(dict):
+    def set(self, **kwargs):
+        for k, v in kwargs.items():
+            self[k] = v
+
 version = {
     'app': 'sd.next',
     'updated': 'unknown',
@@ -75,6 +80,8 @@ control_extensions = [ # 3rd party extensions marked as safe for control ui
     'IP Adapters',
     'Remove background',
 ]
+gpu_info = []
+torch_info = Torch()
 
 
 try:
@@ -588,7 +595,7 @@ def install_rocm_zluda():
             if device_id < len(amd_gpus):
                 device = amd_gpus[device_id]
 
-    if sys.platform == "win32" and not args.use_zluda and device is not None and device.therock is not None and not installed("rocm"):
+    if sys.platform == "win32" and (not args.use_zluda) and (device is not None) and (device.therock is not None) and not installed("rocm"):
         check_python(supported_minors=[11, 12, 13], reason='ROCm backend requires a Python version between 3.11 and 3.13')
         install(f"rocm[devel,libraries] --index-url https://rocm.nightlies.amd.com/{device.therock}")
         rocm.refresh()
@@ -750,6 +757,18 @@ def check_cudnn():
                 os.environ['CUDA_PATH'] = cuda_path
 
 
+def get_cuda_arch(capability):
+    major, minor = capability
+    mapping = {9: "Hopper",
+               8: "Ada Lovelace" if minor == 9 else "Ampere",
+               7: "Turing" if minor == 5 else "Volta",
+               6: "Pascal",
+               5: "Maxwell",
+               3: "Kepler"}
+    name = mapping.get(major, "Unknown")
+    return f"{major}.{minor} {name}"
+
+
 # check torch version
 def check_torch():
     log.info('Torch: verifying installation')
@@ -832,6 +851,7 @@ def check_torch():
             log.info(f'Torch backend: type=IPEX version={ipex.__version__}')
         except Exception:
             pass
+        torch_info.set(version=torch.__version__)
         if 'cpu' in torch.__version__:
             if is_cuda_available:
                 if args.use_cuda:
@@ -845,20 +865,44 @@ def check_torch():
                     install(torch_command, 'torch torchvision', quiet=True, reinstall=True, force=True) # foce reinstall
                 else:
                     log.warning(f'Torch: version="{torch.__version__}" CPU version installed and ROCm is available - consider reinstalling')
+            if args.use_openvino:
+                torch_info.set(type='openvino')
+            else:
+                torch_info.set(type='cpu')
+
         if hasattr(torch, "xpu") and torch.xpu.is_available() and allow_ipex:
             if shutil.which('icpx') is not None:
                 log.info(f'{os.popen("icpx --version").read().rstrip()}')
+            torch_info.set(type='xpu', oneapi=torch.xpu.runtime_version(), dpc=torch.xpu.dpcpp_version(), driver=torch.xpu.driver_version())
             for device in range(torch.xpu.device_count()):
-                log.info(f'Torch detected: gpu="{torch.xpu.get_device_name(device)}" vram={round(torch.xpu.get_device_properties(device).total_memory / 1024 / 1024)} units={torch.xpu.get_device_properties(device).max_compute_units}')
+                gpu = {
+                    'gpu': torch.xpu.get_device_name(device),
+                    'vram': round(torch.xpu.get_device_properties(device).total_memory / 1024 / 1024),
+                    'units': torch.xpu.get_device_properties(device).max_compute_units,
+                }
+                log.info(f'Torch detected: {gpu}')
+                gpu_info.append(gpu)
+
         elif torch.cuda.is_available() and (allow_cuda or allow_rocm):
-            if torch.version.cuda and allow_cuda:
-                log.info(f'Torch backend: version="{torch.__version__}" type=CUDA CUDA={torch.version.cuda} cuDNN={torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else "N/A"}')
+            if args.use_zluda:
+                torch_info.set(type="zluda", cuda=torch.version.cuda)
+            elif torch.version.cuda and allow_cuda:
+                torch_info.set(type='cuda', cuda=torch.version.cuda, cudnn=torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else 'N/A')
             elif torch.version.hip and allow_rocm:
-                log.info(f'Torch backend: version="{torch.__version__}" type=ROCm HIP={torch.version.hip}')
+                torch_info.set(type='rocm', hip=torch.version.hip)
             else:
                 log.warning('Unknown Torch backend')
+            log.info(f"Torch backend: {torch_info}")
             for device in [torch.cuda.device(i) for i in range(torch.cuda.device_count())]:
-                log.info(f'Torch detected: gpu="{torch.cuda.get_device_name(device)}" vram={round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} arch={torch.cuda.get_device_capability(device)} cores={torch.cuda.get_device_properties(device).multi_processor_count}')
+                gpu = {
+                    'gpu': torch.cuda.get_device_name(device),
+                    'vram': round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024),
+                    'arch': get_cuda_arch(torch.cuda.get_device_capability(device)),
+                    'cores': torch.cuda.get_device_properties(device).multi_processor_count,
+                }
+                gpu_info.append(gpu)
+                log.info(f'Torch detected: {gpu}')
+
         else:
             try:
                 if args.use_directml and allow_directml:
@@ -867,7 +911,11 @@ def check_torch():
                     log.warning(f'Torch backend: DirectML ({dml_ver})')
                     log.warning('DirectML: end-of-life')
                     for i in range(0, torch_directml.device_count()):
-                        log.info(f'Torch detected GPU: {torch_directml.device_name(i)}')
+                        gpu = {
+                            'gpu': torch_directml.device_name(i),
+                        }
+                        gpu_info.append(gpu)
+                        log.info(f'Torch detected GPU: {gpu}')
             except Exception:
                 log.warning("Torch reports CUDA not available")
     except Exception as e:

javascript/authWrap.js

@@ -24,9 +24,9 @@ async function authFetch(url, options = {}) {
   let res;
   try {
     res = await fetch(url, options);
-    if (!res.ok) error('fetch', { status: res.status, url, user, token });
+    if (!res.ok) error('fetch', { status: res?.status || 503, url, user, token });
   } catch (err) {
-    error('fetch', { status: res.status, url, user, token, error: err });
+    error('fetch', { status: res?.status || 503, url, user, token, error: err });
   }
   return res;
 }

javascript/gpu.js

@@ -30,7 +30,7 @@ async function updateGPU() {
   const gpuEl = document.getElementById('gpu');
   const gpuTable = document.getElementById('gpu-table');
   try {
-    const res = await authFetch(`${window.api}/gpu`);
+    const res = await authFetch(`${window.api}/gpu-smi`);
     if (!res.ok) {
       clearInterval(gpuInterval);
       gpuEl.style.display = 'none';

modules/api/api.py

@@ -39,12 +39,14 @@ class Api:
     def register(self):
         # fetch js/css
         self.add_api_route("/js", server.get_js, methods=["GET"], auth=False)
+
         # server api
         self.add_api_route("/sdapi/v1/motd", server.get_motd, methods=["GET"], response_model=str)
         self.add_api_route("/sdapi/v1/log", server.get_log, methods=["GET"], response_model=list[str])
         self.add_api_route("/sdapi/v1/log", server.post_log, methods=["POST"])
         self.add_api_route("/sdapi/v1/start", self.get_session_start, methods=["GET"])
         self.add_api_route("/sdapi/v1/version", server.get_version, methods=["GET"])
+        self.add_api_route("/sdapi/v1/torch", server.get_torch, methods=["GET"])
         self.add_api_route("/sdapi/v1/status", server.get_status, methods=["GET"], response_model=models.ResStatus)
         self.add_api_route("/sdapi/v1/platform", server.get_platform, methods=["GET"])
         self.add_api_route("/sdapi/v1/progress", server.get_progress, methods=["GET"], response_model=models.ResProgress)
@@ -54,7 +56,8 @@ class Api:
         self.add_api_route("/sdapi/v1/shutdown", server.post_shutdown, methods=["POST"])
         self.add_api_route("/sdapi/v1/memory", server.get_memory, methods=["GET"], response_model=models.ResMemory)
         self.add_api_route("/sdapi/v1/cmd-flags", server.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
-        self.add_api_route("/sdapi/v1/gpu", gpu.get_gpu_status, methods=["GET"], response_model=list[models.ResGPU])
+        self.add_api_route("/sdapi/v1/gpu", gpu.get_gpu, methods=["GET"])
+        self.add_api_route("/sdapi/v1/gpu-smi", gpu.get_gpu_smi, methods=["GET"], response_model=list[models.ResGPU])
 
         # core api using locking
         self.add_api_route("/sdapi/v1/txt2img", self.generate.post_text2img, methods=["POST"], response_model=models.ResTxt2Img, tags=["Generation"])

modules/api/gpu.py

@@ -5,7 +5,17 @@ from modules.logger import log
 device = None
 
 
-def get_gpu_status():
+def get_gpu():
+    import installer
+    res = {}
+    if len(installer.gpu_info) == 1:
+        return installer.gpu_info[0]
+    for i, item in enumerate(installer.gpu_info):
+        res[i] = item
+    return res
+
+
+def get_gpu_smi():
     """Return real-time GPU metrics (utilization, temperature, memory, clock speeds) via vendor-specific APIs (NVML, ROCm SMI, XPU SMI)."""
     global device # pylint: disable=global-statement
     if device is None:
@@ -37,5 +47,5 @@ class ResGPU(BaseModel):
 
 if __name__ == '__main__':
     from rich import print as rprint
-    for gpu in get_gpu_status():
+    for gpu in get_gpu_smi():
         rprint(gpu)

modules/api/middleware.py

@@ -19,7 +19,7 @@ ignore_endpoints = [
     '/sdapi/v1/version',
     '/sdapi/v1/log',
     '/sdapi/v1/browser',
-    '/sdapi/v1/gpu',
+    '/sdapi/v1/gpu-smi',
     '/sdapi/v1/network/thumb',
     '/sdapi/v1/progress',
 ]

modules/api/server.py

@@ -8,14 +8,6 @@ from modules import shared
 from modules.logger import log
 from modules.api import models, helpers
 
-def _get_version():
-    return installer.get_version()
-
-
-def post_shutdown():
-    log.info('Shutdown request received')
-    import sys
-    sys.exit(0)
 
 def get_js(request: Request):
     file = request.query_params.get("file", None)
@@ -43,32 +35,34 @@ def get_js(request: Request):
         media_type = 'application/octet-stream'
     return FileResponse(file, media_type=media_type)
 
+def get_version():
+    return installer.get_version()
+
 def get_motd():
     import requests
-    motd = ''
-    ver = _get_version()
-    if ver.get('updated', None) is not None:
-        motd = f"version <b>{ver['commit']} {ver['updated']}</b> <span style='color: var(--primary-500)'>{ver['url'].split('/')[-1]}</span><br>" # pylint: disable=use-maxsplit-arg
+    motd = ""
+    ver = get_version()
+    if ver.get("updated", None) is not None:
+        motd = f"version <b>{ver['commit']} {ver['updated']}</b> <span style='color: var(--primary-500)'>{ver['url'].split('/')[-1]}</span><br>"  # pylint: disable=use-maxsplit-arg
     if shared.opts.motd:
         try:
-            res = requests.get('https://vladmandic.github.io/sdnext/motd', timeout=3)
+            res = requests.get("https://vladmandic.github.io/sdnext/motd", timeout=3)
             if res.status_code == 200:
-                msg = (res.text or '').strip()
-                log.info(f'MOTD: {msg if len(msg) > 0 else "N/A"}')
+                msg = (res.text or "").strip()
+                log.info(f"MOTD: {msg if len(msg) > 0 else 'N/A'}")
                 motd += res.text
             else:
-                log.error(f'MOTD: {res.status_code}')
+                log.error(f"MOTD: {res.status_code}")
         except Exception as err:
-            log.error(f'MOTD: {err}')
+            log.error(f"MOTD: {err}")
     return motd
 
-def get_version():
-    return _get_version()
-
 def get_platform():
-    from installer import get_platform as installer_get_platform
     from modules.loader import get_packages as loader_get_packages
-    return { **installer_get_platform(), **loader_get_packages() }
+    return { **installer.get_platform(), **loader_get_packages() }
+
+def get_torch():
+    return dict(installer.torch_info)
 
 def get_log(req: models.ReqGetLog = Depends()):
     lines = log.buffer[:req.lines] if req.lines > 0 else log.buffer.copy()
@@ -85,6 +79,11 @@ def post_log(req: models.ReqPostLog):
         log.error(f'UI: {req.error}')
     return {}
 
+def post_shutdown():
+    log.info("Shutdown request received")
+    import sys
+    sys.exit(0)
+
 def get_cmd_flags():
     return vars(shared.cmd_opts)
 

modules/attention.py

@@ -2,7 +2,7 @@ from functools import wraps
 import torch
 from modules import rocm
 from modules.errors import log
-from installer import install, installed
+from installer import install, installed, torch_info
 
 
 def set_dynamic_attention():
@@ -10,6 +10,7 @@ def set_dynamic_attention():
         sdpa_pre_dyanmic_atten = torch.nn.functional.scaled_dot_product_attention
         from modules.sd_hijack_dynamic_atten import dynamic_scaled_dot_product_attention
         torch.nn.functional.scaled_dot_product_attention = dynamic_scaled_dot_product_attention
+        torch_info.set(attention='dynamic')
         return sdpa_pre_dyanmic_atten
     except Exception as err:
         log.error(f'Torch attention: type="dynamic attention" {err}')
@@ -20,6 +21,7 @@ def set_triton_flash_attention(backend: str):
     try:
         if backend in {"rocm", "zluda"}: # flash_attn_triton_amd only works with AMD
             from modules.flash_attn_triton_amd import interface_fa
+
             sdpa_pre_triton_flash_atten = torch.nn.functional.scaled_dot_product_attention
             @wraps(sdpa_pre_triton_flash_atten)
             def sdpa_triton_flash_atten(query: torch.FloatTensor, key: torch.FloatTensor, value: torch.FloatTensor, attn_mask: torch.Tensor | None = None, dropout_p: float = 0.0, is_causal: bool = False, scale: float | None = None, enable_gqa: bool = False, **kwargs) -> torch.FloatTensor:
@@ -42,6 +44,7 @@ def set_triton_flash_attention(backend: str):
                         kwargs["enable_gqa"] = enable_gqa
                     return sdpa_pre_triton_flash_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
             torch.nn.functional.scaled_dot_product_attention = sdpa_triton_flash_atten
+            torch_info.set(attention='triton')
             log.debug('Torch attention: type="Triton Flash attention"')
     except Exception as err:
         log.error(f'Torch attention: type="Triton Flash attention" {err}')
@@ -78,6 +81,7 @@ def set_flex_attention():
             return flex_attention(query, key, value, score_mod=score_mod, block_mask=block_mask, scale=scale, enable_gqa=enable_gqa)
 
         torch.nn.functional.scaled_dot_product_attention = sdpa_flex_atten
+        torch_info.set(attention="flex")
         log.debug('Torch attention: type="Flex attention"')
     except Exception as err:
         log.error(f'Torch attention: type="Flex attention" {err}')
@@ -93,6 +97,7 @@ def set_ck_flash_attention(backend: str, device: torch.device):
         else:
             install('flash-attn')
         from flash_attn import flash_attn_func
+
         sdpa_pre_flash_atten = torch.nn.functional.scaled_dot_product_attention
         @wraps(sdpa_pre_flash_atten)
         def sdpa_flash_atten(query: torch.FloatTensor, key: torch.FloatTensor, value: torch.FloatTensor, attn_mask: torch.Tensor | None = None, dropout_p: float = 0.0, is_causal: bool = False, scale: float | None = None, enable_gqa: bool = False, **kwargs) -> torch.FloatTensor:
@@ -120,6 +125,7 @@ def set_ck_flash_attention(backend: str, device: torch.device):
                     kwargs["enable_gqa"] = enable_gqa
                 return sdpa_pre_flash_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
         torch.nn.functional.scaled_dot_product_attention = sdpa_flash_atten
+        torch_info.set(attention="flash")
         log.debug('Torch attention: type="Flash attention"')
     except Exception as err:
         log.error(f'Torch attention: type="Flash attention" {err}')
@@ -174,6 +180,7 @@ def set_sage_attention(backend: str, device: torch.device):
                     kwargs["enable_gqa"] = enable_gqa
                 return sdpa_pre_sage_atten(query=query, key=key, value=value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, scale=scale, **kwargs)
         torch.nn.functional.scaled_dot_product_attention = sdpa_sage_atten
+        torch_info.set(attention="sage")
         log.debug(f'Torch attention: type="Sage attention" backend={"cuda" if use_cuda_backend else "auto"}')
     except Exception as err:
         log.error(f'Torch attention: type="Sage attention" {err}')
@@ -208,19 +215,22 @@ def set_diffusers_attention(pipe, quiet:bool=False):
 
     log.quiet(quiet, f'Setting model: attention="{shared.opts.cross_attention_optimization}"')
     if shared.opts.cross_attention_optimization == "Disabled":
-        pass # do nothing
-    elif shared.opts.cross_attention_optimization == "Scaled-Dot-Product": # The default set by Diffusers
+        torch_info.set(attention="disabled")
+    elif shared.opts.cross_attention_optimization == "Scaled-Dot-Product":  # The default set by Diffusers
+        torch_info.set(attention="sdpa")
         # set_attn(pipe, p.AttnProcessor2_0(), name="Scaled-Dot-Product")
-        pass
     elif shared.opts.cross_attention_optimization == "xFormers":
         if hasattr(pipe, 'enable_xformers_memory_efficient_attention'):
+            torch_info.set(attention="xformers")
             pipe.enable_xformers_memory_efficient_attention()
         else:
             log.warning(f"Attention: xFormers is not compatible with {pipe.__class__.__name__}")
     elif shared.opts.cross_attention_optimization == "Batch matrix-matrix":
+        torch_info.set(attention="bmm")
         set_attn(pipe, p.AttnProcessor(), name="Batch matrix-matrix")
     elif shared.opts.cross_attention_optimization == "Dynamic Attention BMM":
         from modules.sd_hijack_dynamic_atten import DynamicAttnProcessorBMM
+        torch_info.set(attention="dynamic_bmm")
         set_attn(pipe, DynamicAttnProcessorBMM(), name="Dynamic Attention BMM")
 
     if shared.opts.attention_slicing != "Default" and hasattr(pipe, "enable_attention_slicing") and hasattr(pipe, "disable_attention_slicing"):

modules/devices.py

@@ -4,8 +4,10 @@ import time
 import contextlib
 import importlib.metadata
 import torch
+from installer import torch_info
+from modules.logger import log
 from modules import rocm, attention
-from modules.errors import log, display, install as install_traceback
+from modules.errors import display, install as install_traceback
 
 
 debug = os.environ.get('SD_DEVICE_DEBUG', None) is not None
@@ -398,17 +400,35 @@ def test_triton(early: bool = False):
             test_triton_func(torch.randn(16, device=device), torch.randn(16, device=device), torch.randn(16, device=device))
             triton_ok = True
         else:
+            torch_info.set(triton=False)
             triton_ok = False
     except Exception as e:
+        torch_info.set(triton=False)
         triton_ok = False
         line = str(e).splitlines()[0]
         log.warning(f"Triton test fail: {line}")
         if debug:
             from modules import errors
             errors.display(e, 'Triton')
+    triton_version = False
+    if triton_ok:
+        if triton_version is None:
+            try:
+                import torch._inductor.triton as torch_triton
+
+                triton_version = torch_triton.__version__
+            except Exception:
+                pass
+        if triton_version is None:
+            try:
+                import triton
+                triton_version = triton.__version__
+            except Exception:
+                pass
+        torch_info.set(triton=triton_version)
     t1 = time.time()
     fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
-    log.debug(f'Triton: pass={triton_ok} fn={fn} time={t1-t0:.2f}')
+    log.debug(f'Triton: pass={triton_ok} version={triton_version} fn={fn} time={t1-t0:.2f}')
     if not triton_ok and opts is not None:
         opts.sdnq_dequantize_compile = False
     return triton_ok
@@ -469,6 +489,7 @@ def set_sdpa_params():
             torch.backends.cuda.enable_math_sdp('Math' in opts.sdp_options or 'Math attention' in opts.sdp_options)
             if hasattr(torch.backends.cuda, "allow_fp16_bf16_reduction_math_sdp"): # only valid for torch >= 2.5
                 torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
+            torch_info.set(attention="sdpa")
             log.debug(f'Torch attention: type="sdpa" kernels={opts.sdp_options} overrides={opts.sdp_overrides}')
         except Exception as err:
             log.warning(f'Torch attention: type="sdpa" {err}')
@@ -559,6 +580,10 @@ def set_dtype():
         inference_context = contextlib.nullcontext
     else:
         inference_context = torch.no_grad
+    if dtype == dtype_vae:
+        torch_info.set(dtype=str(dtype))
+    else:
+        torch_info.set(dtype=str(dtype), vae=str(dtype_vae))
 
 
 def set_cuda_params():