mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 01:04:32 +02:00
cleanup lora detect
This commit is contained in:
Vladimir Mandic
@@ -65,8 +65,14 @@ class NetworkOnDisk:
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if arch.startswith("flux"):
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return 'f1'
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if "v1-5" in str(self.metadata.get('ss_sd_model_name', "")):
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return 'sd1'
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if str(self.metadata.get('ss_v2', "")) == "True":
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return 'sd2'
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if 'flux' in self.name.lower():
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return 'f1'
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if 'xl' in self.name.lower():
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return 'xl'
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return ''
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+3
-2
@@ -240,7 +240,8 @@ def pip(arg: str, ignore: bool = False, quiet: bool = False, uv = True):
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log.info(f'Install: package="{arg.replace("install", "").replace("--upgrade", "").replace("--no-deps", "").replace("--force", "").replace(" ", " ").strip()}" mode={"uv" if uv else "pip"}')
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env_args = os.environ.get("PIP_EXTRA_ARGS", "")
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all_args = f'{pip_log}{arg} {env_args}'.strip()
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log.debug(f'Running: {pipCmd}="{all_args}"')
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if not quiet:
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log.debug(f'Running: {pipCmd}="{all_args}"')
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result = subprocess.run(f'"{sys.executable}" -m {pipCmd} {all_args}', shell=True, check=False, env=os.environ, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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txt = result.stdout.decode(encoding="utf8", errors="ignore")
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if len(result.stderr) > 0:
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@@ -430,7 +431,7 @@ def check_python(supported_minors=[9, 10, 11, 12], reason=None):
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# check diffusers version
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def check_diffusers():
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sha = '14a1b86fc7de53ff1dbf803f616cbb16ad530e45'
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sha = 'aa73072f1f7014635e3de916cbcf47858f4c37a0'
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pkg = pkg_resources.working_set.by_key.get('diffusers', None)
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minor = int(pkg.version.split('.')[1] if pkg is not None else 0)
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cur = opts.get('diffusers_version', '') if minor > 0 else ''
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@@ -59,7 +59,7 @@ def full_vae_decode(latents, model):
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model.vae = model.vae.to(dtype=torch.float32)
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latents = latents.to(torch.float32)
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else:
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latents = latents.to(model.vae.device)
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latents = latents.to(devices.device)
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if getattr(model.vae, "post_quant_conv", None) is not None:
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latents = latents.to(next(iter(model.vae.post_quant_conv.parameters())).dtype)
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+47
-47
@@ -216,6 +216,10 @@ if cmd_opts.use_openvino: # override for openvino
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backend = Backend.DIFFUSERS
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from modules.intel.openvino import get_device_list as get_openvino_device_list # pylint: disable=ungrouped-imports
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native = backend == Backend.DIFFUSERS
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cpu_memory = round(psutil.virtual_memory().total / 1024 / 1024 / 1024, 2)
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mem_stat = memory_stats()
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gpu_memory = mem_stat['gpu']['total'] if "gpu" in mem_stat else 0
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class OptionInfo:
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def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, folder=None, submit=None, comment_before='', comment_after=''):
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@@ -361,52 +365,48 @@ def temp_disable_extensions():
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cmd_opts.controlnet_loglevel = 'WARNING'
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return disabled
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gpu_memory = 0
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offload_mode_default = "none"
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cpu_memory = round(psutil.virtual_memory().total / 1024 / 1024 / 1024, 2)
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mem_stat = memory_stats()
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if "gpu" in mem_stat:
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gpu_memory = mem_stat['gpu']['total']
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def get_default_modes():
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default_offload_mode = "none"
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if not (cmd_opts.lowvram or cmd_opts.medvram):
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if "gpu" in mem_stat:
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if gpu_memory <= 4:
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cmd_opts.lowvram = True
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default_offload_mode = "sequential"
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log.info(f"GPU detect: memory={gpu_memory} optimization=lowvram")
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elif gpu_memory <= 8:
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cmd_opts.medvram = True
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default_offload_mode = "model"
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log.info(f"GPU detect: memory={gpu_memory} ptimization=medvram")
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else:
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default_offload_mode = "none"
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log.info(f"GPU detect: memory={gpu_memory} optimization=none")
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elif cmd_opts.medvram:
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default_offload_mode = "model"
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elif cmd_opts.lowvram:
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default_offload_mode = "sequential"
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if not (cmd_opts.lowvram or cmd_opts.medvram):
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if "gpu" in mem_stat:
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if gpu_memory <= 4:
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cmd_opts.lowvram = True
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offload_mode_default = "sequential"
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log.info(f"VRAM: Detected={gpu_memory} GB Optimization=lowvram")
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elif gpu_memory <= 8:
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cmd_opts.medvram = True
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offload_mode_default = "model"
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log.info(f"VRAM: Detected={gpu_memory} GB Optimization=medvram")
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else:
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offload_mode_default = "none"
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log.info(f"VRAM: Detected={gpu_memory} GB Optimization=none")
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elif cmd_opts.medvram:
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offload_mode_default = "model"
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elif cmd_opts.lowvram:
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offload_mode_default = "sequential"
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if devices.backend == "directml": # Force BMM for DirectML instead of SDP
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default_cross_attention = "Dynamic Attention BMM" if native else "Sub-quadratic"
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elif devices.backend == "cpu":
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default_cross_attention = "Scaled-Dot-Product" if native else "Doggettx's"
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elif devices.backend == "mps":
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default_cross_attention = "Scaled-Dot-Product" if native else "Doggettx's"
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else: # cuda, rocm, ipex, openvino
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default_cross_attention ="Scaled-Dot-Product"
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if devices.backend == "rocm":
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default_sdp_options = ['Memory attention', 'Math attention']
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#elif devices.backend == "zluda":
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# sdp_options_default = ['Math attention']
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else:
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default_sdp_options = ['Flash attention', 'Memory attention', 'Math attention']
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if (cmd_opts.lowvram or cmd_opts.medvram) and 'Flash attention' not in default_sdp_options:
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default_sdp_options.append('Dynamic attention')
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if devices.backend == "directml": # Force BMM for DirectML instead of SDP
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cross_attention_optimization_default = "Dynamic Attention BMM" if native else "Sub-quadratic"
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elif devices.backend == "cpu":
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cross_attention_optimization_default = "Scaled-Dot-Product" if native else "Doggettx's"
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elif devices.backend == "mps":
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cross_attention_optimization_default = "Scaled-Dot-Product" if native else "Doggettx's"
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else: # cuda, rocm, ipex, openvino
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cross_attention_optimization_default ="Scaled-Dot-Product"
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return default_offload_mode, default_cross_attention, default_sdp_options
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if devices.backend == "rocm":
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sdp_options_default = ['Memory attention', 'Math attention']
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#elif devices.backend == "zluda":
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# sdp_options_default = ['Math attention']
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else:
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sdp_options_default = ['Flash attention', 'Memory attention', 'Math attention']
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if (cmd_opts.lowvram or cmd_opts.medvram) and 'Flash attention' not in sdp_options_default:
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sdp_options_default.append('Dynamic attention')
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startup_offload_mode, startup_cross_attention, startup_sdp_options = get_default_modes()
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options_templates.update(options_section(('sd', "Execution & Models"), {
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"sd_backend": OptionInfo(default_backend, "Execution backend", gr.Radio, {"choices": ["diffusers", "original"] }),
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@@ -446,8 +446,8 @@ options_templates.update(options_section(('cuda', "Compute Settings"), {
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"rollback_vae": OptionInfo(False, "Attempt VAE roll back for NaN values"),
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"cross_attention_sep": OptionInfo("<h2>Cross Attention</h2>", "", gr.HTML),
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"cross_attention_optimization": OptionInfo(cross_attention_optimization_default, "Attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention(native) }),
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"sdp_options": OptionInfo(sdp_options_default, "SDP options", gr.CheckboxGroup, {"choices": ['Flash attention', 'Memory attention', 'Math attention', 'Dynamic attention'] }),
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"cross_attention_optimization": OptionInfo(startup_cross_attention, "Attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention(native) }),
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"sdp_options": OptionInfo(startup_sdp_options, "SDP options", gr.CheckboxGroup, {"choices": ['Flash attention', 'Memory attention', 'Math attention', 'Dynamic attention'] }),
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"xformers_options": OptionInfo(['Flash attention'], "xFormers options", gr.CheckboxGroup, {"choices": ['Flash attention'] }),
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"dynamic_attention_slice_rate": OptionInfo(4, "Dynamic Attention slicing rate in GB", gr.Slider, {"minimum": 0.1, "maximum": gpu_memory, "step": 0.1, "visible": native}),
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"sub_quad_sep": OptionInfo("<h3>Sub-quadratic options</h3>", "", gr.HTML, {"visible": not native}),
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@@ -552,9 +552,9 @@ options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
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"diffusers_move_refiner": OptionInfo(False, "Move refiner model to CPU when not in use"),
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"diffusers_extract_ema": OptionInfo(False, "Use model EMA weights when possible"),
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"diffusers_generator_device": OptionInfo("GPU", "Generator device", gr.Radio, {"choices": ["GPU", "CPU", "Unset"]}),
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"diffusers_offload_mode": OptionInfo(offload_mode_default, "Model offload mode", gr.Radio, {"choices": ['none', 'balanced', 'model', 'sequential']}),
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"diffusers_offload_max_gpu_memory": OptionInfo(round(gpu_memory * 0.75, 2), "Max GPU memory for balanced offload mode in GB", gr.Slider, {"minimum": 0, "maximum": gpu_memory, "step": 0.01,}),
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"diffusers_offload_max_cpu_memory": OptionInfo(round(cpu_memory * 0.75, 2), "Max CPU memory for balanced offload mode in GB", gr.Slider, {"minimum": 0, "maximum": cpu_memory, "step": 0.01,}),
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"diffusers_offload_mode": OptionInfo(startup_offload_mode, "Model offload mode", gr.Radio, {"choices": ['none', 'balanced', 'model', 'sequential']}),
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"diffusers_offload_max_gpu_memory": OptionInfo(round(gpu_memory * 0.75, 1), "Max GPU memory for balanced offload mode in GB", gr.Slider, {"minimum": 0, "maximum": gpu_memory, "step": 0.01,}),
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"diffusers_offload_max_cpu_memory": OptionInfo(round(cpu_memory * 0.75, 1), "Max CPU memory for balanced offload mode in GB", gr.Slider, {"minimum": 0, "maximum": cpu_memory, "step": 0.01,}),
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"diffusers_vae_upcast": OptionInfo("default", "VAE upcasting", gr.Radio, {"choices": ['default', 'true', 'false']}),
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"diffusers_vae_slicing": OptionInfo(True, "VAE slicing"),
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"diffusers_vae_tiling": OptionInfo(cmd_opts.lowvram or cmd_opts.medvram, "VAE tiling"),
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@@ -866,7 +866,7 @@ options_templates.update(options_section(('extra_networks', "Networks"), {
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"lora_maybe_diffusers": OptionInfo(False, "LoRA force loading of specific models using Diffusers"),
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"lora_fuse_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA use merge when using alternative method"),
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"lora_apply_tags": OptionInfo(0, "LoRA auto-apply tags", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
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"lora_in_memory_limit": OptionInfo(1 if not cmd_opts.use_openvino else 0, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
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"lora_in_memory_limit": OptionInfo(0, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
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"lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
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"hypernetwork_enabled": OptionInfo(False, "Enable Hypernetwork support"),
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"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
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