From d597d5912d31c9394bfc5dd6e2c2291a86fc765b Mon Sep 17 00:00:00 2001 From: Seunghoon Lee Date: Sat, 26 Oct 2024 13:40:54 +0900 Subject: [PATCH 01/81] use bitmasking for agent detection --- modules/rocm.py | 40 ++++++++++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 14 deletions(-) diff --git a/modules/rocm.py b/modules/rocm.py index 831932199..ef76a1cfa 100644 --- a/modules/rocm.py +++ b/modules/rocm.py @@ -52,37 +52,49 @@ class MicroArchitecture(Enum): class Agent: name: str + gfx_version: int arch: MicroArchitecture is_apu: bool if sys.platform != "win32": blaslt_supported: bool + @staticmethod + def parse_gfx_version(name: str) -> int: + result = 0 + for i in range(3, len(name)): + if name[i].isdigit(): + result *= 0x10 + result += ord(name[i]) - 48 + continue + if name[i] in "abcdef": + result *= 0x10 + result += ord(name[i]) - 87 + continue + break + return result + def __init__(self, name: str): self.name = name - gfx = name[3:7] - if len(gfx) == 4: + self.gfx_version = Agent.parse_gfx_version(name) + if self.gfx_version > 0x1000: self.arch = MicroArchitecture.RDNA - elif gfx in ("908", "90a", "942",): + elif self.gfx_version in (0x908, 0x90a, 0x942,): self.arch = MicroArchitecture.CDNA else: self.arch = MicroArchitecture.GCN - self.is_apu = gfx.startswith("115") or gfx in ("801", "902", "90c", "1013", "1033", "1035", "1036", "1103",) + self.is_apu = (self.gfx_version & 0xFFF0 == 0x1150) or self.gfx_version in (0x801, 0x902, 0x90c, 0x1013, 0x1033, 0x1035, 0x1036, 0x1103,) if sys.platform != "win32": self.blaslt_supported = os.path.exists(os.path.join(HIPBLASLT_TENSILE_LIBPATH, f"extop_{name}.co")) def get_gfx_version(self) -> Union[str, None]: - if self.name.startswith("gfx12"): + if self.gfx_version >= 0x1200: return "12.0.0" - elif self.name.startswith("gfx11"): + elif self.gfx_version >= 0x1100: return "11.0.0" - elif self.name.startswith("gfx103"): + elif self.gfx_version >= 0x1000: + # gfx1010 users had to override gfx version to 10.3.0 in Linux + # it is unknown whether overriding is needed in ZLUDA return "10.3.0" - elif self.name.startswith("gfx102"): - return "10.2.0" - elif self.name.startswith("gfx101"): - return "10.1.0" - elif self.name.startswith("gfx100"): - return "10.0.0" return None @@ -198,7 +210,7 @@ else: if os.environ.get("FLASH_ATTENTION_USE_TRITON_ROCM", "FALSE") == "TRUE": return "pytest git+https://github.com/ROCm/flash-attention@micmelesse/upstream_pr" default = "git+https://github.com/ROCm/flash-attention" - if agent.arch == MicroArchitecture.RDNA: + if agent.gfx_version >= 0x1100: default = "git+https://github.com/ROCm/flash-attention@howiejay/navi_support" return os.environ.get("FLASH_ATTENTION_PACKAGE", default) From 81bd236cc3d30af18ab85a0f9f8440b15a850b6c Mon Sep 17 00:00:00 2001 From: Seunghoon Lee Date: Sat, 26 Oct 2024 13:59:21 +0900 Subject: [PATCH 02/81] zluda&rocm bf16 test --- installer.py | 1 + modules/devices.py | 19 +++++++++---------- modules/zluda_installer.py | 8 +++++++- 3 files changed, 17 insertions(+), 11 deletions(-) diff --git a/installer.py b/installer.py index ef6ce44ba..aca36056b 100644 --- a/installer.py +++ b/installer.py @@ -549,6 +549,7 @@ def install_rocm_zluda(): log.warning("ZLUDA support: experimental") error = None from modules import zluda_installer + zluda_installer.set_default_agent(device) try: if args.reinstall_zluda: zluda_installer.uninstall() diff --git a/modules/devices.py b/modules/devices.py index 490d2a54d..49864e66b 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -4,6 +4,7 @@ import time import contextlib from functools import wraps import torch +from modules import rocm from modules.errors import log, display, install as install_traceback from installer import install @@ -283,16 +284,14 @@ def test_bf16(): if sys.platform == "darwin" or backend == 'openvino' or backend == 'directml': # override bf16_ok = False return bf16_ok - elif backend == 'zluda': - device_name = torch.cuda.get_device_name(device) - if device_name.startswith("AMD Radeon RX "): # only force AMD - device_name = device_name.replace("AMD Radeon RX ", "").split(" ", maxsplit=1)[0] - if len(device_name) == 4 and device_name[0] in {"5", "6"}: # RDNA 1 and 2 - bf16_ok = False - return bf16_ok - elif backend == 'rocm': - gcn_arch = getattr(torch.cuda.get_device_properties(device), "gcnArchName", "gfx0000")[3:7] - if len(gcn_arch) == 4 and gcn_arch[0:2] == "10": # RDNA 1 and 2 + elif backend == 'rocm' or backend == 'zluda': + gcn_arch = None + if backend == 'rocm': + gcn_arch = rocm.Agent.parse_gfx_version(getattr(torch.cuda.get_device_properties(device), "gcnArchName", "gfx0000")) + else: + from modules.zluda_installer import default_agent + gcn_arch = 0x0 if default_agent is None else default_agent.gfx_version + if gcn_arch < 0x1100: # all cards before RDNA 3 bf16_ok = False return bf16_ok try: diff --git a/modules/zluda_installer.py b/modules/zluda_installer.py index 506652edf..84c130e8d 100644 --- a/modules/zluda_installer.py +++ b/modules/zluda_installer.py @@ -4,7 +4,7 @@ import ctypes import shutil import zipfile import urllib.request -from typing import Optional +from typing import Optional, Union from modules import rocm @@ -15,12 +15,18 @@ DLL_MAPPING = { } HIPSDK_TARGETS = ['rocblas.dll', 'rocsolver.dll', f'hiprtc{"".join([v.zfill(2) for v in rocm.version.split(".")])}.dll'] ZLUDA_TARGETS = ('nvcuda.dll', 'nvml.dll',) +default_agent: Union[rocm.Agent, None] = None def get_path() -> str: return os.path.abspath(os.environ.get('ZLUDA', '.zluda')) +def set_default_agent(agent: rocm.Agent): + global default_agent # pylint: disable=global-statement + default_agent = agent + + def install(zluda_path: os.PathLike) -> None: if os.path.exists(zluda_path): return From a76893bd72b39f04de91e0a3b1f71b46026d2a8b Mon Sep 17 00:00:00 2001 From: Seunghoon Lee Date: Sat, 26 Oct 2024 14:09:50 +0900 Subject: [PATCH 03/81] add cdna check --- modules/devices.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/devices.py b/modules/devices.py index 49864e66b..1aa5532a8 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -285,13 +285,13 @@ def test_bf16(): bf16_ok = False return bf16_ok elif backend == 'rocm' or backend == 'zluda': - gcn_arch = None + agent = None if backend == 'rocm': - gcn_arch = rocm.Agent.parse_gfx_version(getattr(torch.cuda.get_device_properties(device), "gcnArchName", "gfx0000")) + agent = rocm.Agent(getattr(torch.cuda.get_device_properties(device), "gcnArchName", "gfx0000")) else: from modules.zluda_installer import default_agent - gcn_arch = 0x0 if default_agent is None else default_agent.gfx_version - if gcn_arch < 0x1100: # all cards before RDNA 3 + agent = default_agent + if agent is not None and agent.gfx_version < 0x1100 and agent.arch != rocm.MicroArchitecture.CDNA: # all cards before RDNA 3 except for CDNA cards bf16_ok = False return bf16_ok try: From dbb9ba08903413849395fe5f5accc95d6dd7a511 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 26 Oct 2024 07:51:37 -0400 Subject: [PATCH 04/81] cuda memory limits Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 6 +++++- modules/devices.py | 22 ++++++++++++++++++---- modules/shared.py | 3 ++- 3 files changed, 25 insertions(+), 6 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3c2f1e3bb..4441e92f4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,8 +1,12 @@ # Change Log for SD.Next -## Update for 2024-10-25 +## Update for 2024-10-26 Improvements: +- Torch CUDA set device memory limit + in *settings -> compute settings -> torch memory limit* + default=0 meaning no limit, if set torch will limit memory usage to specified fraction + *note*: this is not a hard limit, torch will try to stay under this value - Model selector: - change-in-behavior - when typing, it will auto-load model as soon as exactly one match is found diff --git a/modules/devices.py b/modules/devices.py index 1aa5532a8..56ac50091 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -51,8 +51,8 @@ def has_zluda() -> bool: if not cuda_ok: return False try: - device = torch.device("cuda") - return torch.cuda.get_device_name(device).endswith("[ZLUDA]") + dev = torch.device("cuda") + return torch.cuda.get_device_name(dev).endswith("[ZLUDA]") except Exception: return False @@ -207,7 +207,7 @@ def torch_gc(force=False, fast=False): force = True if oom > previous_oom: previous_oom = oom - log.warning(f'GPU out-of-memory error: {mem}') + log.warning(f'Torch GPU out-of-memory error: {mem}') force = True if force: # actual gc @@ -247,13 +247,26 @@ def set_cuda_sync_mode(mode): return try: import ctypes - log.info(f'Set cuda sync: mode={mode}') + log.info(f'Torch CUDA sync: mode={mode}') torch.cuda.set_device(torch.device(get_optimal_device_name())) ctypes.CDLL('libcudart.so').cudaSetDeviceFlags({'auto': 0, 'spin': 1, 'yield': 2, 'block': 4}[mode]) except Exception: pass +def set_cuda_memory_limit(): + if not cuda_ok or opts.cuda_mem_fraction == 0: + return + from modules.shared import cmd_opts + try: + torch_gc(force=True) + mem = torch.cuda.get_device_properties(device).total_memory + torch.cuda.set_per_process_memory_fraction(float(opts.cuda_mem_fraction), cmd_opts.device_id if cmd_opts.device_id is not None else 0) + log.info(f'Torch CUDA memory limit: fraction={opts.cuda_mem_fraction:.2f} limit={round(opts.cuda_mem_fraction * mem / 1024 / 1024)} total={round(mem / 1024 / 1024)}') + except Exception as e: + log.warning(f'Torch CUDA memory limit: fraction={opts.cuda_mem_fraction:.2f} {e}') + + def test_fp16(): global fp16_ok # pylint: disable=global-statement if fp16_ok is not None: @@ -449,6 +462,7 @@ def set_dtype(): def set_cuda_params(): override_ipex_math() + set_cuda_memory_limit() set_cudnn_params() set_sdpa_params() set_dtype() diff --git a/modules/shared.py b/modules/shared.py index f7be44390..a3a9a5482 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -393,7 +393,7 @@ def get_default_modes(): elif gpu_memory <= 8: cmd_opts.medvram = True default_offload_mode = "model" - log.info(f"Device detect: memory={gpu_memory:.1f} ptimization=medvram") + log.info(f"Device detect: memory={gpu_memory:.1f} optimization=medvram") else: default_offload_mode = "none" log.info(f"Device detect: memory={gpu_memory:.1f} optimization=none") @@ -479,6 +479,7 @@ options_templates.update(options_section(('cuda', "Compute Settings"), { "cudnn_benchmark": OptionInfo(False, "Full-depth cuDNN benchmark feature"), "diffusers_fuse_projections": OptionInfo(False, "Fused projections"), "torch_expandable_segments": OptionInfo(False, "Torch expandable segments"), + "cuda_mem_fraction": OptionInfo(0.0, "Torch memory limit", gr.Slider, {"minimum": 0, "maximum": 2.0, "step": 0.05}), "torch_gc_threshold": OptionInfo(80, "Torch memory threshold for GC", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}), "torch_malloc": OptionInfo("native", "Torch memory allocator", gr.Radio, {"choices": ['native', 'cudaMallocAsync'] }), From 6760632f38f7ff9c2b9e75c154d0dc28af7101f7 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 26 Oct 2024 13:22:29 -0400 Subject: [PATCH 05/81] major model load refactor Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 1 + modules/extras.py | 4 +- modules/face/faceid.py | 7 +- modules/loader.py | 2 + modules/model_flux.py | 6 +- modules/model_quant.py | 2 + modules/model_sd3.py | 6 +- modules/onnx_impl/ui.py | 4 +- modules/processing.py | 14 +- modules/processing_info.py | 7 +- modules/processing_original.py | 6 +- modules/sd_checkpoint.py | 382 +++++++++++++++++++ modules/sd_detect.py | 150 ++++++++ modules/sd_models.py | 662 ++------------------------------- modules/sd_vae.py | 6 +- modules/shared.py | 13 +- modules/token_merge.py | 77 ++++ modules/ui_control.py | 1 + modules/ui_img2img.py | 1 + modules/ui_models.py | 12 +- modules/ui_txt2img.py | 1 + scripts/x_adapter.py | 4 +- scripts/xyz_grid_classes.py | 4 +- 23 files changed, 705 insertions(+), 667 deletions(-) create mode 100644 modules/sd_checkpoint.py create mode 100644 modules/sd_detect.py create mode 100644 modules/token_merge.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 4441e92f4..5381b08b5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -26,6 +26,7 @@ Improvements: - gguf transformer loader (prototype) - OpenVINO: add accuracy option - ZLUDA: guess GPU arch +- Major model load refactor Fixes: - fix send-to-control diff --git a/modules/extras.py b/modules/extras.py index e22360f8a..162491580 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -188,7 +188,7 @@ def run_modelmerger(id_task, **kwargs): # pylint: disable=unused-argument _, extension = os.path.splitext(output_modelname) if os.path.exists(output_modelname) and not kwargs.get("overwrite", False): - return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Model alredy exists: {output_modelname}"] + return [*[gr.Dropdown.update(choices=sd_models.checkpoint_titles()) for _ in range(4)], f"Model alredy exists: {output_modelname}"] if extension.lower() == ".safetensors": safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata) else: @@ -202,7 +202,7 @@ def run_modelmerger(id_task, **kwargs): # pylint: disable=unused-argument created_model.calculate_shorthash() devices.torch_gc(force=True) shared.state.end() - return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Model saved to {output_modelname}"] + return [*[gr.Dropdown.update(choices=sd_models.checkpoint_titles()) for _ in range(4)], f"Model saved to {output_modelname}"] def run_modelconvert(model, checkpoint_formats, precision, conv_type, custom_name, unet_conv, text_encoder_conv, diff --git a/modules/face/faceid.py b/modules/face/faceid.py index 754ce59a3..e2d5efccb 100644 --- a/modules/face/faceid.py +++ b/modules/face/faceid.py @@ -6,9 +6,10 @@ import numpy as np import diffusers import huggingface_hub as hf from PIL import Image -from modules import processing, shared, devices, extra_networks, sd_models, sd_hijack_freeu, script_callbacks, ipadapter +from modules import processing, shared, devices, extra_networks, sd_hijack_freeu, script_callbacks, ipadapter, token_merge from modules.sd_hijack_hypertile import context_hypertile_vae, context_hypertile_unet + FACEID_MODELS = { "FaceID Base": "h94/IP-Adapter-FaceID/ip-adapter-faceid_sd15.bin", "FaceID Plus v1": "h94/IP-Adapter-FaceID/ip-adapter-faceid-plus_sd15.bin", @@ -69,7 +70,7 @@ def face_id( shared.prompt_styles.apply_styles_to_extra(p) if shared.opts.cuda_compile_backend == 'none': - sd_models.apply_token_merging(p.sd_model) + token_merge.apply_token_merging(p.sd_model) sd_hijack_freeu.apply_freeu(p, not shared.native) script_callbacks.before_process_callback(p) @@ -246,7 +247,7 @@ def face_id( if faceid_model is not None and original_load_ip_adapter is not None: faceid_model.__class__.load_ip_adapter = original_load_ip_adapter if shared.opts.cuda_compile_backend == 'none': - sd_models.remove_token_merging(p.sd_model) + token_merge.remove_token_merging(p.sd_model) script_callbacks.after_process_callback(p) return processed_images diff --git a/modules/loader.py b/modules/loader.py index a2970abfd..05e5ec394 100644 --- a/modules/loader.py +++ b/modules/loader.py @@ -44,6 +44,8 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__: timer.startup.record("torch") import transformers # pylint: disable=W0611,C0411 +from transformers import logging as transformers_logging # pylint: disable=W0611,C0411 +transformers_logging.set_verbosity_error() timer.startup.record("transformers") import accelerate # pylint: disable=W0611,C0411 diff --git a/modules/model_flux.py b/modules/model_flux.py index 38207f73b..9bbc24f83 100644 --- a/modules/model_flux.py +++ b/modules/model_flux.py @@ -122,10 +122,12 @@ def quant_flux_bnb(checkpoint_info, transformer, text_encoder_2): bnb_4bit_quant_type=shared.opts.bnb_quantization_type, bnb_4bit_compute_dtype=devices.dtype ) - if 'Model' in shared.opts.bnb_quantization and transformer is None: + if ('Model' in shared.opts.bnb_quantization) and (transformer is None): transformer = diffusers.FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=cache_dir, quantization_config=bnb_config, torch_dtype=devices.dtype) shared.log.debug(f'Quantization: module=transformer type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}') - if 'Text Encoder' in shared.opts.bnb_quantization and text_encoder_2 is None: + if ('Text Encoder' in shared.opts.bnb_quantization) and (text_encoder_2 is None): + if repo_id == 'sayakpaul/flux.1-dev-nf4': + repo_id = 'black-forest-labs/FLUX.1-dev' # workaround since sayakpaul model is missing model_index.json text_encoder_2 = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2", cache_dir=cache_dir, quantization_config=bnb_config, torch_dtype=devices.dtype) shared.log.debug(f'Quantization: module=t5 type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}') except Exception as e: diff --git a/modules/model_quant.py b/modules/model_quant.py index d54d6ff6d..1348662de 100644 --- a/modules/model_quant.py +++ b/modules/model_quant.py @@ -23,6 +23,7 @@ def load_bnb(msg='', silent=False): bnb = None if not silent: raise + return None def load_quanto(msg='', silent=False): @@ -42,6 +43,7 @@ def load_quanto(msg='', silent=False): quanto = None if not silent: raise + return None def get_quant(name): diff --git a/modules/model_sd3.py b/modules/model_sd3.py index 639f6e4eb..da99e6c4b 100644 --- a/modules/model_sd3.py +++ b/modules/model_sd3.py @@ -85,6 +85,9 @@ def load_missing(kwargs, fn, cache_dir): if 'text_encoder_3' not in kwargs and 'text_encoder_3' not in keys: kwargs['text_encoder_3'] = transformers.T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_3", variant='fp16', cache_dir=cache_dir, torch_dtype=devices.dtype) shared.log.debug(f'Load model: type=SD3 missing=te3 repo="{repo_id}"') + if 'vae' not in kwargs and 'vae' not in keys: + kwargs['vae'] = diffusers.AutoencoderKL.from_pretrained(repo_id, subfolder='vae', cache_dir=cache_dir, torch_dtype=devices.dtype) + shared.log.debug(f'Load model: type=SD3 missing=vae repo="{repo_id}"') # if 'transformer' not in kwargs and 'transformer' not in keys: # kwargs['transformer'] = diffusers.SD3Transformer2DModel.from_pretrained(default_repo_id, subfolder="transformer", cache_dir=cache_dir, torch_dtype=devices.dtype) return kwargs @@ -120,7 +123,8 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None): kwargs = {} kwargs = load_overrides(kwargs, cache_dir) - kwargs = load_quants(kwargs, repo_id, cache_dir) + if fn is None or not os.path.exists(fn): + kwargs = load_quants(kwargs, repo_id, cache_dir) loader = diffusers.StableDiffusion3Pipeline.from_pretrained if fn is not None and os.path.exists(fn): diff --git a/modules/onnx_impl/ui.py b/modules/onnx_impl/ui.py index f73e477c4..49af8d98b 100644 --- a/modules/onnx_impl/ui.py +++ b/modules/onnx_impl/ui.py @@ -15,7 +15,7 @@ def create_ui(): from modules.ui_common import create_refresh_button from modules.ui_components import DropdownMulti from modules.shared import log, opts, cmd_opts, refresh_checkpoints - from modules.sd_models import checkpoint_tiles, get_closet_checkpoint_match + from modules.sd_models import checkpoint_titles, get_closet_checkpoint_match from modules.paths import sd_configs_path from .execution_providers import ExecutionProvider, install_execution_provider from .utils import check_diffusers_cache @@ -46,7 +46,7 @@ def create_ui(): with gr.TabItem("Manage cache", id="manage_cache"): cache_state_dirname = gr.Textbox(value=None, visible=False) with gr.Row(): - model_dropdown = gr.Dropdown(label="Model", value="Please select model", choices=checkpoint_tiles()) + model_dropdown = gr.Dropdown(label="Model", value="Please select model", choices=checkpoint_titles()) create_refresh_button(model_dropdown, refresh_checkpoints, {}, "onnx_cache_refresh_diffusers_model") with gr.Row(): def remove_cache_onnx_converted(dirname: str): diff --git a/modules/processing.py b/modules/processing.py index 04350ee39..99d0cb351 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -4,7 +4,7 @@ import time from contextlib import nullcontext import numpy as np from PIL import Image, ImageOps -from modules import shared, devices, errors, images, scripts, memstats, lowvram, script_callbacks, extra_networks, detailer, sd_hijack_freeu, sd_models, sd_vae, processing_helpers, timer, face_restoration +from modules import shared, devices, errors, images, scripts, memstats, lowvram, script_callbacks, extra_networks, detailer, sd_hijack_freeu, sd_models, sd_checkpoint, sd_vae, processing_helpers, timer, face_restoration, token_merge from modules.sd_hijack_hypertile import context_hypertile_vae, context_hypertile_unet from modules.processing_class import StableDiffusionProcessing, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, StableDiffusionProcessingControl # pylint: disable=unused-import from modules.processing_info import create_infotext @@ -46,7 +46,8 @@ class Processed: self.width = p.width if hasattr(p, 'width') else (self.images[0].width if len(self.images) > 0 else 0) self.height = p.height if hasattr(p, 'height') else (self.images[0].height if len(self.images) > 0 else 0) self.sampler_name = p.sampler_name or '' - self.cfg_scale = p.cfg_scale or 0 + self.cfg_scale = p.cfg_scale if p.cfg_scale > 1 else None + self.cfg_end = p.cfg_end if p.cfg_end < 0 else None self.image_cfg_scale = p.image_cfg_scale or 0 self.steps = p.steps or 0 self.batch_size = max(1, p.batch_size) @@ -96,6 +97,7 @@ class Processed: "height": self.height, "sampler_name": self.sampler_name, "cfg_scale": self.cfg_scale, + "cfg_end": self.cfg_end, "steps": self.steps, "batch_size": self.batch_size, "detailer": self.detailer, @@ -136,11 +138,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed: processed = None try: # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint - if p.override_settings.get('sd_model_checkpoint', None) is not None and sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None: + if p.override_settings.get('sd_model_checkpoint', None) is not None and sd_checkpoint.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None: shared.log.warning(f"Override not found: checkpoint={p.override_settings.get('sd_model_checkpoint', None)}") p.override_settings.pop('sd_model_checkpoint', None) sd_models.reload_model_weights() - if p.override_settings.get('sd_model_refiner', None) is not None and sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_refiner')) is None: + if p.override_settings.get('sd_model_refiner', None) is not None and sd_checkpoint.checkpoint_aliases.get(p.override_settings.get('sd_model_refiner')) is None: shared.log.warning(f"Override not found: refiner={p.override_settings.get('sd_model_refiner', None)}") p.override_settings.pop('sd_model_refiner', None) sd_models.reload_model_weights() @@ -162,7 +164,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: shared.prompt_styles.apply_styles_to_extra(p) shared.prompt_styles.extract_comments(p) if shared.opts.cuda_compile_backend == 'none': - sd_models.apply_token_merging(p.sd_model) + token_merge.apply_token_merging(p.sd_model) sd_hijack_freeu.apply_freeu(p, not shared.native) if p.width is not None: @@ -205,7 +207,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: finally: pag.unapply() if shared.opts.cuda_compile_backend == 'none': - sd_models.remove_token_merging(p.sd_model) + token_merge.remove_token_merging(p.sd_model) script_callbacks.after_process_callback(p) diff --git a/modules/processing_info.py b/modules/processing_info.py index 29513167d..e798211b1 100644 --- a/modules/processing_info.py +++ b/modules/processing_info.py @@ -41,11 +41,12 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No # basic "Steps": p.steps, "Seed": all_seeds[index], - "Sampler": p.sampler_name, - "CFG scale": p.cfg_scale, + "Sampler": p.sampler_name if p.sampler_name != 'Default' else None, + "CFG scale": p.cfg_scale if p.cfg_scale > 1.0 else None, + "CFG end": p.cfg_end if p.cfg_end < 1.0 else None, "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None, "Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None, - "Parser": shared.opts.prompt_attention, + "Parser": shared.opts.prompt_attention.split()[0], "Model": None if (not shared.opts.add_model_name_to_info) or (not shared.sd_model.sd_checkpoint_info.model_name) else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''), "Model hash": getattr(p, 'sd_model_hash', None if (not shared.opts.add_model_hash_to_info) or (not shared.sd_model.sd_model_hash) else shared.sd_model.sd_model_hash), "VAE": (None if not shared.opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD', diff --git a/modules/processing_original.py b/modules/processing_original.py index 852eb9a37..649023aae 100644 --- a/modules/processing_original.py +++ b/modules/processing_original.py @@ -1,7 +1,7 @@ import torch import numpy as np from PIL import Image -from modules import shared, devices, processing, images, sd_models, sd_vae, sd_samplers, processing_helpers, prompt_parser +from modules import shared, devices, processing, images, sd_vae, sd_samplers, processing_helpers, prompt_parser, token_merge from modules.sd_hijack_hypertile import hypertile_set @@ -135,10 +135,10 @@ def sample_txt2img(p: processing.StableDiffusionProcessingTxt2Img, conditioning, p.sampler.initialize(p) samples = samples[:, :, p.truncate_y//2:samples.shape[2]-(p.truncate_y+1)//2, p.truncate_x//2:samples.shape[3]-(p.truncate_x+1)//2] noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=p) - sd_models.apply_token_merging(p.sd_model) + token_merge.apply_token_merging(p.sd_model) hypertile_set(p, hr=True) samples = p.sampler.sample_img2img(p, samples, noise, conditioning, unconditional_conditioning, steps=p.hr_second_pass_steps or p.steps, image_conditioning=image_conditioning) - sd_models.apply_token_merging(p.sd_model) + token_merge.apply_token_merging(p.sd_model) else: p.ops.append('upscale') x = None diff --git a/modules/sd_checkpoint.py b/modules/sd_checkpoint.py new file mode 100644 index 000000000..45834a1f0 --- /dev/null +++ b/modules/sd_checkpoint.py @@ -0,0 +1,382 @@ +import os +import re +import time +import json +import collections +from modules import shared, paths, modelloader, hashes, sd_hijack_accelerate + + +checkpoints_list = {} +checkpoint_aliases = {} +checkpoints_loaded = collections.OrderedDict() +model_dir = "Stable-diffusion" +model_path = os.path.abspath(os.path.join(paths.models_path, model_dir)) +sd_metadata_file = os.path.join(paths.data_path, "metadata.json") +sd_metadata = None +sd_metadata_pending = 0 +sd_metadata_timer = 0 + + +class CheckpointInfo: + def __init__(self, filename, sha=None): + self.name = None + self.hash = sha + self.filename = filename + self.type = '' + relname = filename + app_path = os.path.abspath(paths.script_path) + + def rel(fn, path): + try: + return os.path.relpath(fn, path) + except Exception: + return fn + + if relname.startswith('..'): + relname = os.path.abspath(relname) + if relname.startswith(shared.opts.ckpt_dir): + relname = rel(filename, shared.opts.ckpt_dir) + elif relname.startswith(shared.opts.diffusers_dir): + relname = rel(filename, shared.opts.diffusers_dir) + elif relname.startswith(model_path): + relname = rel(filename, model_path) + elif relname.startswith(paths.script_path): + relname = rel(filename, paths.script_path) + elif relname.startswith(app_path): + relname = rel(filename, app_path) + else: + relname = os.path.abspath(relname) + relname, ext = os.path.splitext(relname) + ext = ext.lower()[1:] + + if os.path.isfile(filename): # ckpt or safetensor + self.name = relname + self.filename = filename + self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{relname}") + self.type = ext + if 'nf4' in filename: + self.type = 'transformer' + else: # maybe a diffuser + if self.hash is None: + repo = [r for r in modelloader.diffuser_repos if self.filename == r['name']] + else: + repo = [r for r in modelloader.diffuser_repos if self.hash == r['hash']] + if len(repo) == 0: + self.name = filename + self.filename = filename + self.sha256 = None + self.type = 'unknown' + else: + self.name = os.path.join(os.path.basename(shared.opts.diffusers_dir), repo[0]['name']) + self.filename = repo[0]['path'] + self.sha256 = repo[0]['hash'] + self.type = 'diffusers' + + self.shorthash = self.sha256[0:10] if self.sha256 else None + self.title = self.name if self.shorthash is None else f'{self.name} [{self.shorthash}]' + self.path = self.filename + self.model_name = os.path.basename(self.name) + self.metadata = read_metadata_from_safetensors(filename) + # shared.log.debug(f'Checkpoint: type={self.type} name={self.name} filename={self.filename} hash={self.shorthash} title={self.title}') + + def register(self): + checkpoints_list[self.title] = self + for i in [self.name, self.filename, self.shorthash, self.title]: + if i is not None: + checkpoint_aliases[i] = self + + def calculate_shorthash(self): + self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}") + if self.sha256 is None: + return None + self.shorthash = self.sha256[0:10] + if self.title in checkpoints_list: + checkpoints_list.pop(self.title) + self.title = f'{self.name} [{self.shorthash}]' + self.register() + return self.shorthash + + +def setup_model(): + list_models() + sd_hijack_accelerate.hijack_hfhub() + # sd_hijack_accelerate.hijack_torch_conv() + if not shared.native: + enable_midas_autodownload() + + +def checkpoint_titles(use_short=False): # pylint: disable=unused-argument + def convert(name): + return int(name) if name.isdigit() else name.lower() + def alphanumeric_key(key): + return [convert(c) for c in re.split('([0-9]+)', key)] + return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key) + + +def list_models(): + t0 = time.time() + global checkpoints_list # pylint: disable=global-statement + checkpoints_list.clear() + checkpoint_aliases.clear() + ext_filter = [".safetensors"] if shared.opts.sd_disable_ckpt or shared.native else [".ckpt", ".safetensors"] + model_list = list(modelloader.load_models(model_path=model_path, model_url=None, command_path=shared.opts.ckpt_dir, ext_filter=ext_filter, download_name=None, ext_blacklist=[".vae.ckpt", ".vae.safetensors"])) + for filename in sorted(model_list, key=str.lower): + checkpoint_info = CheckpointInfo(filename) + if checkpoint_info.name is not None: + checkpoint_info.register() + if shared.native: + for repo in modelloader.load_diffusers_models(clear=True): + checkpoint_info = CheckpointInfo(repo['name'], sha=repo['hash']) + if checkpoint_info.name is not None: + checkpoint_info.register() + if shared.cmd_opts.ckpt is not None: + if not os.path.exists(shared.cmd_opts.ckpt) and not shared.native: + if shared.cmd_opts.ckpt.lower() != "none": + shared.log.warning(f'Load model: path="{shared.cmd_opts.ckpt}" not found') + else: + checkpoint_info = CheckpointInfo(shared.cmd_opts.ckpt) + if checkpoint_info.name is not None: + checkpoint_info.register() + shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title + elif shared.cmd_opts.ckpt != shared.default_sd_model_file and shared.cmd_opts.ckpt is not None: + shared.log.warning(f'Load model: path="{shared.cmd_opts.ckpt}" not found') + shared.log.info(f'Available Models: path="{shared.opts.ckpt_dir}" items={len(checkpoints_list)} time={time.time()-t0:.2f}') + checkpoints_list = dict(sorted(checkpoints_list.items(), key=lambda cp: cp[1].filename)) + +def update_model_hashes(): + txt = [] + lst = [ckpt for ckpt in checkpoints_list.values() if ckpt.hash is None] + # shared.log.info(f'Models list: short hash missing for {len(lst)} out of {len(checkpoints_list)} models') + for ckpt in lst: + ckpt.hash = model_hash(ckpt.filename) + # txt.append(f'Calculated short hash: {ckpt.title} {ckpt.hash}') + # txt.append(f'Updated short hashes for {len(lst)} out of {len(checkpoints_list)} models') + lst = [ckpt for ckpt in checkpoints_list.values() if ckpt.sha256 is None or ckpt.shorthash is None] + shared.log.info(f'Models list: hash missing={len(lst)} total={len(checkpoints_list)}') + for ckpt in lst: + ckpt.sha256 = hashes.sha256(ckpt.filename, f"checkpoint/{ckpt.name}") + ckpt.shorthash = ckpt.sha256[0:10] if ckpt.sha256 is not None else None + if ckpt.sha256 is not None: + txt.append(f'Hash: {ckpt.title} {ckpt.shorthash}') + txt.append(f'Updated hashes for {len(lst)} out of {len(checkpoints_list)} models') + txt = '
'.join(txt) + return txt + + +def get_closet_checkpoint_match(s: str): + if s.startswith('https://huggingface.co/'): + s = s.replace('https://huggingface.co/', '') + if s.startswith('huggingface/'): + model_name = s.replace('huggingface/', '') + checkpoint_info = CheckpointInfo(model_name) # create a virutal model info + checkpoint_info.type = 'huggingface' + return checkpoint_info + + # alias search + checkpoint_info = checkpoint_aliases.get(s, None) + if checkpoint_info is not None: + return checkpoint_info + + # models search + found = sorted([info for info in checkpoints_list.values() if os.path.basename(info.title).lower().startswith(s.lower())], key=lambda x: len(x.title)) + if found and len(found) == 1: + return found[0] + + # reference search + """ + found = sorted([info for info in shared.reference_models.values() if os.path.basename(info['path']).lower().startswith(s.lower())], key=lambda x: len(x['path'])) + if found and len(found) == 1: + checkpoint_info = CheckpointInfo(found[0]['path']) # create a virutal model info + checkpoint_info.type = 'huggingface' + return checkpoint_info + """ + + # huggingface search + if shared.opts.sd_checkpoint_autodownload and s.count('/') == 1: + modelloader.hf_login() + found = modelloader.find_diffuser(s, full=True) + shared.log.info(f'HF search: model="{s}" results={found}') + if found is not None and len(found) == 1 and found[0] == s: + checkpoint_info = CheckpointInfo(s) + checkpoint_info.type = 'huggingface' + return checkpoint_info + + # civitai search + if shared.opts.sd_checkpoint_autodownload and s.startswith("https://civitai.com/api/download/models"): + fn = modelloader.download_civit_model_thread(model_name=None, model_url=s, model_path='', model_type='Model', token=None) + if fn is not None: + checkpoint_info = CheckpointInfo(fn) + return checkpoint_info + + return None + + +def model_hash(filename): + """old hash that only looks at a small part of the file and is prone to collisions""" + try: + with open(filename, "rb") as file: + import hashlib + # t0 = time.time() + m = hashlib.sha256() + file.seek(0x100000) + m.update(file.read(0x10000)) + shorthash = m.hexdigest()[0:8] + # t1 = time.time() + # shared.log.debug(f'Calculating short hash: {filename} hash={shorthash} time={(t1-t0):.2f}') + return shorthash + except FileNotFoundError: + return 'NOFILE' + except Exception: + return 'NOHASH' + + +def select_checkpoint(op='model'): + if op == 'dict': + model_checkpoint = shared.opts.sd_model_dict + elif op == 'refiner': + model_checkpoint = shared.opts.data.get('sd_model_refiner', None) + else: + model_checkpoint = shared.opts.sd_model_checkpoint + if model_checkpoint is None or model_checkpoint == 'None': + return None + checkpoint_info = get_closet_checkpoint_match(model_checkpoint) + if checkpoint_info is not None: + shared.log.info(f'Load {op}: select="{checkpoint_info.title if checkpoint_info is not None else None}"') + return checkpoint_info + if len(checkpoints_list) == 0: + shared.log.warning("Cannot generate without a checkpoint") + shared.log.info("Set system paths to use existing folders") + shared.log.info(" or use --models-dir to specify base folder with all models") + shared.log.info(" or use --ckpt-dir to specify folder with sd models") + shared.log.info(" or use --ckpt to force using specific model") + return None + # checkpoint_info = next(iter(checkpoints_list.values())) + if model_checkpoint is not None: + if model_checkpoint != 'model.safetensors' and model_checkpoint != 'stabilityai/stable-diffusion-xl-base-1.0': + shared.log.info(f'Load {op}: search="{model_checkpoint}" not found') + else: + shared.log.info("Selecting first available checkpoint") + # shared.log.warning(f"Loading fallback checkpoint: {checkpoint_info.title}") + # shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title + else: + shared.log.info(f'Load {op}: select="{checkpoint_info.title if checkpoint_info is not None else None}"') + return checkpoint_info + + +def read_metadata_from_safetensors(filename): + global sd_metadata # pylint: disable=global-statement + if sd_metadata is None: + sd_metadata = shared.readfile(sd_metadata_file, lock=True) if os.path.isfile(sd_metadata_file) else {} + res = sd_metadata.get(filename, None) + if res is not None: + return res + if not filename.endswith(".safetensors"): + return {} + if shared.cmd_opts.no_metadata: + return {} + res = {} + # try: + t0 = time.time() + with open(filename, mode="rb") as file: + try: + metadata_len = file.read(8) + metadata_len = int.from_bytes(metadata_len, "little") + json_start = file.read(2) + if metadata_len <= 2 or json_start not in (b'{"', b"{'"): + shared.log.error(f'Model metadata invalid: file="{filename}"') + json_data = json_start + file.read(metadata_len-2) + json_obj = json.loads(json_data) + for k, v in json_obj.get("__metadata__", {}).items(): + if v.startswith("data:"): + v = 'data' + if k == 'format' and v == 'pt': + continue + large = True if len(v) > 2048 else False + if large and k == 'ss_datasets': + continue + if large and k == 'workflow': + continue + if large and k == 'prompt': + continue + if large and k == 'ss_bucket_info': + continue + if v[0:1] == '{': + try: + v = json.loads(v) + if large and k == 'ss_tag_frequency': + v = { i: len(j) for i, j in v.items() } + if large and k == 'sd_merge_models': + scrub_dict(v, ['sd_merge_recipe']) + except Exception: + pass + res[k] = v + except Exception as e: + shared.log.error(f'Model metadata: file="{filename}" {e}') + sd_metadata[filename] = res + global sd_metadata_pending # pylint: disable=global-statement + sd_metadata_pending += 1 + t1 = time.time() + global sd_metadata_timer # pylint: disable=global-statement + sd_metadata_timer += (t1 - t0) + # except Exception as e: + # shared.log.error(f"Error reading metadata from: {filename} {e}") + return res + + +def enable_midas_autodownload(): + """ + Gives the ldm.modules.midas.api.load_model function automatic downloading. + + When the 512-depth-ema model, and other future models like it, is loaded, + it calls midas.api.load_model to load the associated midas depth model. + This function applies a wrapper to download the model to the correct + location automatically. + """ + from urllib import request + import ldm.modules.midas.api + midas_path = os.path.join(paths.models_path, 'midas') + for k, v in ldm.modules.midas.api.ISL_PATHS.items(): + file_name = os.path.basename(v) + ldm.modules.midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name) + midas_urls = { + "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", + "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt", + "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt", + "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt", + } + ldm.modules.midas.api.load_model_inner = ldm.modules.midas.api.load_model + + def load_model_wrapper(model_type): + path = ldm.modules.midas.api.ISL_PATHS[model_type] + if not os.path.exists(path): + if not os.path.exists(midas_path): + os.mkdir(midas_path) + shared.log.info(f"Downloading midas model weights for {model_type} to {path}") + request.urlretrieve(midas_urls[model_type], path) + shared.log.info(f"{model_type} downloaded") + return ldm.modules.midas.api.load_model_inner(model_type) + + ldm.modules.midas.api.load_model = load_model_wrapper + + +def scrub_dict(dict_obj, keys): + for key in list(dict_obj.keys()): + if not isinstance(dict_obj, dict): + continue + if key in keys: + dict_obj.pop(key, None) + elif isinstance(dict_obj[key], dict): + scrub_dict(dict_obj[key], keys) + elif isinstance(dict_obj[key], list): + for item in dict_obj[key]: + scrub_dict(item, keys) + + +def write_metadata(): + global sd_metadata_pending # pylint: disable=global-statement + if sd_metadata_pending == 0: + shared.log.debug(f'Model metadata: file="{sd_metadata_file}" no changes') + return + shared.writefile(sd_metadata, sd_metadata_file) + shared.log.info(f'Model metadata saved: file="{sd_metadata_file}" items={sd_metadata_pending} time={sd_metadata_timer:.2f}') + sd_metadata_pending = 0 diff --git a/modules/sd_detect.py b/modules/sd_detect.py new file mode 100644 index 000000000..7144a7be7 --- /dev/null +++ b/modules/sd_detect.py @@ -0,0 +1,150 @@ +import os +import torch +import diffusers +from modules import shared, shared_items, devices, errors + + +debug_load = os.environ.get('SD_LOAD_DEBUG', None) + + +def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False): + guess = shared.opts.diffusers_pipeline + warn = shared.log.warning if warning else lambda *args, **kwargs: None + size = 0 + pipeline = None + if guess == 'Autodetect': + try: + guess = 'Stable Diffusion XL' if 'XL' in f.upper() else 'Stable Diffusion' + # guess by size + if os.path.isfile(f) and f.endswith('.safetensors'): + size = round(os.path.getsize(f) / 1024 / 1024) + if (size > 0 and size < 128): + warn(f'Model size smaller than expected: {f} size={size} MB') + elif (size >= 316 and size <= 324) or (size >= 156 and size <= 164): # 320 or 160 + warn(f'Model detected as VAE model, but attempting to load as model: {op}={f} size={size} MB') + guess = 'VAE' + elif (size >= 4970 and size <= 4976): # 4973 + guess = 'Stable Diffusion 2' # SD v2 but could be eps or v-prediction + # elif size < 0: # unknown + # guess = 'Stable Diffusion 2B' + elif (size >= 5791 and size <= 5799): # 5795 + if op == 'model': + warn(f'Model detected as SD-XL refiner model, but attempting to load a base model: {op}={f} size={size} MB') + guess = 'Stable Diffusion XL Refiner' + elif (size >= 6611 and size <= 7220): # 6617, HassakuXL is 6776, monkrenRealisticINT_v10 is 7217 + guess = 'Stable Diffusion XL' + elif (size >= 3361 and size <= 3369): # 3368 + guess = 'Stable Diffusion Upscale' + elif (size >= 4891 and size <= 4899): # 4897 + guess = 'Stable Diffusion XL Inpaint' + elif (size >= 9791 and size <= 9799): # 9794 + guess = 'Stable Diffusion XL Instruct' + elif (size > 3138 and size < 3142): #3140 + guess = 'Stable Diffusion XL' + elif (size > 5692 and size < 5698) or (size > 4134 and size < 4138) or (size > 10362 and size < 10366) or (size > 15028 and size < 15228): + guess = 'Stable Diffusion 3' + elif (size > 18414 and size < 18420): # sd35-large aio + guess = 'Stable Diffusion 3' + elif (size > 20000 and size < 40000): + guess = 'FLUX' + # guess by name + """ + if 'LCM_' in f.upper() or 'LCM-' in f.upper() or '_LCM' in f.upper() or '-LCM' in f.upper(): + if shared.backend == shared.Backend.ORIGINAL: + warn(f'Model detected as LCM model, but attempting to load using backend=original: {op}={f} size={size} MB') + guess = 'Latent Consistency Model' + """ + if 'instaflow' in f.lower(): + guess = 'InstaFlow' + if 'segmoe' in f.lower(): + guess = 'SegMoE' + if 'hunyuandit' in f.lower(): + guess = 'HunyuanDiT' + if 'pixart-xl' in f.lower(): + guess = 'PixArt-Alpha' + if 'stable-diffusion-3' in f.lower(): + guess = 'Stable Diffusion 3' + if 'stable-cascade' in f.lower() or 'stablecascade' in f.lower() or 'wuerstchen3' in f.lower() or ('sotediffusion' in f.lower() and "v2" in f.lower()): + if devices.dtype == torch.float16: + warn('Stable Cascade does not support Float16') + guess = 'Stable Cascade' + if 'pixart-sigma' in f.lower(): + guess = 'PixArt-Sigma' + if 'lumina-next' in f.lower(): + guess = 'Lumina-Next' + if 'kolors' in f.lower(): + guess = 'Kolors' + if 'auraflow' in f.lower(): + guess = 'AuraFlow' + if 'cogview' in f.lower(): + guess = 'CogView' + if 'meissonic' in f.lower(): + guess = 'Meissonic' + pipeline = 'custom' + if 'omnigen' in f.lower(): + guess = 'OmniGen' + pipeline = 'custom' + if 'flux' in f.lower(): + guess = 'FLUX' + if size > 11000 and size < 20000: + warn(f'Model detected as FLUX UNET model, but attempting to load a base model: {op}={f} size={size} MB') + # switch for specific variant + if guess == 'Stable Diffusion' and 'inpaint' in f.lower(): + guess = 'Stable Diffusion Inpaint' + elif guess == 'Stable Diffusion' and 'instruct' in f.lower(): + guess = 'Stable Diffusion Instruct' + if guess == 'Stable Diffusion XL' and 'inpaint' in f.lower(): + guess = 'Stable Diffusion XL Inpaint' + elif guess == 'Stable Diffusion XL' and 'instruct' in f.lower(): + guess = 'Stable Diffusion XL Instruct' + # get actual pipeline + pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline + if not quiet: + shared.log.info(f'Autodetect {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}" size={size}MB') + except Exception as e: + shared.log.error(f'Autodetect {op}: file="{f}" {e}') + if debug_load: + errors.display(e, f'Load {op}: {f}') + return None, None + else: + try: + size = round(os.path.getsize(f) / 1024 / 1024) + pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline + if not quiet: + shared.log.info(f'Load {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}" size={size}MB') + except Exception as e: + shared.log.error(f'Load {op}: detect="{guess}" file="{f}" {e}') + + if pipeline is None: + shared.log.warning(f'Load {op}: detect="{guess}" file="{f}" size={size} not recognized') + pipeline = diffusers.StableDiffusionPipeline + return pipeline, guess + + +def get_load_config(model_file, model_type, config_type='yaml'): + if config_type == 'yaml': + yaml = os.path.splitext(model_file)[0] + '.yaml' + if os.path.exists(yaml): + return yaml + if model_type == 'Stable Diffusion': + return 'configs/v1-inference.yaml' + if model_type == 'Stable Diffusion XL': + return 'configs/sd_xl_base.yaml' + if model_type == 'Stable Diffusion XL Refiner': + return 'configs/sd_xl_refiner.yaml' + if model_type == 'Stable Diffusion 2': + return None # dont know if its eps or v so let diffusers sort it out + # return 'configs/v2-inference-512-base.yaml' + # return 'configs/v2-inference-768-v.yaml' + elif config_type == 'json': + if not shared.opts.diffuser_cache_config: + return None + if model_type == 'Stable Diffusion': + return 'configs/sd15' + if model_type == 'Stable Diffusion XL': + return 'configs/sdxl' + if model_type == 'Stable Diffusion 3': + return 'configs/sd3' + if model_type == 'FLUX': + return 'configs/flux' + return None diff --git a/modules/sd_models.py b/modules/sd_models.py index bc293f5fc..9662a005d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -1,16 +1,11 @@ -import re import io import sys import json -import time import copy import inspect import logging import contextlib -import collections import os.path -from os import mkdir -from urllib import request from enum import Enum import diffusers import diffusers.loaders.single_file_utils @@ -18,20 +13,16 @@ from rich import progress # pylint: disable=redefined-builtin import torch import safetensors.torch from omegaconf import OmegaConf -from transformers import logging as transformers_logging from ldm.util import instantiate_from_config -from modules import paths, shared, shared_items, shared_state, modelloader, devices, script_callbacks, sd_vae, sd_unet, errors, hashes, sd_models_config, sd_models_compile, sd_hijack_accelerate +from modules import paths, shared, shared_state, modelloader, devices, script_callbacks, sd_vae, sd_unet, errors, sd_models_config, sd_models_compile, sd_hijack_accelerate, sd_detect from modules.timer import Timer from modules.memstats import memory_stats from modules.modeldata import model_data +from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoints_list, checkpoint_titles, get_closet_checkpoint_match, update_model_hashes, setup_model, write_metadata, read_metadata_from_safetensors # pylint: disable=unused-import -transformers_logging.set_verbosity_error() model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(paths.models_path, model_dir)) -checkpoints_list = {} -checkpoint_aliases = {} -checkpoints_loaded = collections.OrderedDict() sd_metadata_file = os.path.join(paths.data_path, "metadata.json") sd_metadata = None sd_metadata_pending = 0 @@ -42,368 +33,11 @@ debug_process = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is diffusers_version = int(diffusers.__version__.split('.')[1]) -class CheckpointInfo: - def __init__(self, filename, sha=None): - self.name = None - self.hash = sha - self.filename = filename - self.type = '' - relname = filename - app_path = os.path.abspath(paths.script_path) - - def rel(fn, path): - try: - return os.path.relpath(fn, path) - except Exception: - return fn - - if relname.startswith('..'): - relname = os.path.abspath(relname) - if relname.startswith(shared.opts.ckpt_dir): - relname = rel(filename, shared.opts.ckpt_dir) - elif relname.startswith(shared.opts.diffusers_dir): - relname = rel(filename, shared.opts.diffusers_dir) - elif relname.startswith(model_path): - relname = rel(filename, model_path) - elif relname.startswith(paths.script_path): - relname = rel(filename, paths.script_path) - elif relname.startswith(app_path): - relname = rel(filename, app_path) - else: - relname = os.path.abspath(relname) - relname, ext = os.path.splitext(relname) - ext = ext.lower()[1:] - - if os.path.isfile(filename): # ckpt or safetensor - self.name = relname - self.filename = filename - self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{relname}") - self.type = ext - if 'nf4' in filename: - self.type = 'transformer' - else: # maybe a diffuser - if self.hash is None: - repo = [r for r in modelloader.diffuser_repos if self.filename == r['name']] - else: - repo = [r for r in modelloader.diffuser_repos if self.hash == r['hash']] - if len(repo) == 0: - self.name = filename - self.filename = filename - self.sha256 = None - self.type = 'unknown' - else: - self.name = os.path.join(os.path.basename(shared.opts.diffusers_dir), repo[0]['name']) - self.filename = repo[0]['path'] - self.sha256 = repo[0]['hash'] - self.type = 'diffusers' - - self.shorthash = self.sha256[0:10] if self.sha256 else None - self.title = self.name if self.shorthash is None else f'{self.name} [{self.shorthash}]' - self.path = self.filename - self.model_name = os.path.basename(self.name) - self.metadata = read_metadata_from_safetensors(filename) - # shared.log.debug(f'Checkpoint: type={self.type} name={self.name} filename={self.filename} hash={self.shorthash} title={self.title}') - - def register(self): - checkpoints_list[self.title] = self - for i in [self.name, self.filename, self.shorthash, self.title]: - if i is not None: - checkpoint_aliases[i] = self - - def calculate_shorthash(self): - self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}") - if self.sha256 is None: - return None - self.shorthash = self.sha256[0:10] - if self.title in checkpoints_list: - checkpoints_list.pop(self.title) - self.title = f'{self.name} [{self.shorthash}]' - self.register() - return self.shorthash - - class NoWatermark: def apply_watermark(self, img): return img -def setup_model(): - list_models() - sd_hijack_accelerate.hijack_hfhub() - # sd_hijack_accelerate.hijack_torch_conv() - if not shared.native: - enable_midas_autodownload() - - -def checkpoint_tiles(use_short=False): # pylint: disable=unused-argument - def convert(name): - return int(name) if name.isdigit() else name.lower() - def alphanumeric_key(key): - return [convert(c) for c in re.split('([0-9]+)', key)] - return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key) - - -def list_models(): - t0 = time.time() - global checkpoints_list # pylint: disable=global-statement - checkpoints_list.clear() - checkpoint_aliases.clear() - ext_filter = [".safetensors"] if shared.opts.sd_disable_ckpt or shared.native else [".ckpt", ".safetensors"] - model_list = list(modelloader.load_models(model_path=model_path, model_url=None, command_path=shared.opts.ckpt_dir, ext_filter=ext_filter, download_name=None, ext_blacklist=[".vae.ckpt", ".vae.safetensors"])) - for filename in sorted(model_list, key=str.lower): - checkpoint_info = CheckpointInfo(filename) - if checkpoint_info.name is not None: - checkpoint_info.register() - if shared.native: - for repo in modelloader.load_diffusers_models(clear=True): - checkpoint_info = CheckpointInfo(repo['name'], sha=repo['hash']) - if checkpoint_info.name is not None: - checkpoint_info.register() - if shared.cmd_opts.ckpt is not None: - if not os.path.exists(shared.cmd_opts.ckpt) and not shared.native: - if shared.cmd_opts.ckpt.lower() != "none": - shared.log.warning(f'Load model: path="{shared.cmd_opts.ckpt}" not found') - else: - checkpoint_info = CheckpointInfo(shared.cmd_opts.ckpt) - if checkpoint_info.name is not None: - checkpoint_info.register() - shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title - elif shared.cmd_opts.ckpt != shared.default_sd_model_file and shared.cmd_opts.ckpt is not None: - shared.log.warning(f'Load model: path="{shared.cmd_opts.ckpt}" not found') - shared.log.info(f'Available Models: path="{shared.opts.ckpt_dir}" items={len(checkpoints_list)} time={time.time()-t0:.2f}') - checkpoints_list = dict(sorted(checkpoints_list.items(), key=lambda cp: cp[1].filename)) - - -def update_model_hashes(): - txt = [] - lst = [ckpt for ckpt in checkpoints_list.values() if ckpt.hash is None] - # shared.log.info(f'Models list: short hash missing for {len(lst)} out of {len(checkpoints_list)} models') - for ckpt in lst: - ckpt.hash = model_hash(ckpt.filename) - # txt.append(f'Calculated short hash: {ckpt.title} {ckpt.hash}') - # txt.append(f'Updated short hashes for {len(lst)} out of {len(checkpoints_list)} models') - lst = [ckpt for ckpt in checkpoints_list.values() if ckpt.sha256 is None or ckpt.shorthash is None] - shared.log.info(f'Models list: hash missing={len(lst)} total={len(checkpoints_list)}') - for ckpt in lst: - ckpt.sha256 = hashes.sha256(ckpt.filename, f"checkpoint/{ckpt.name}") - ckpt.shorthash = ckpt.sha256[0:10] if ckpt.sha256 is not None else None - if ckpt.sha256 is not None: - txt.append(f'Hash: {ckpt.title} {ckpt.shorthash}') - txt.append(f'Updated hashes for {len(lst)} out of {len(checkpoints_list)} models') - txt = '
'.join(txt) - return txt - - -def get_closet_checkpoint_match(s: str): - if s.startswith('https://huggingface.co/'): - s = s.replace('https://huggingface.co/', '') - if s.startswith('huggingface/'): - model_name = s.replace('huggingface/', '') - checkpoint_info = CheckpointInfo(model_name) # create a virutal model info - checkpoint_info.type = 'huggingface' - return checkpoint_info - - # alias search - checkpoint_info = checkpoint_aliases.get(s, None) - if checkpoint_info is not None: - return checkpoint_info - - # models search - found = sorted([info for info in checkpoints_list.values() if os.path.basename(info.title).lower().startswith(s.lower())], key=lambda x: len(x.title)) - if found and len(found) == 1: - return found[0] - - # reference search - """ - found = sorted([info for info in shared.reference_models.values() if os.path.basename(info['path']).lower().startswith(s.lower())], key=lambda x: len(x['path'])) - if found and len(found) == 1: - checkpoint_info = CheckpointInfo(found[0]['path']) # create a virutal model info - checkpoint_info.type = 'huggingface' - return checkpoint_info - """ - - # huggingface search - if shared.opts.sd_checkpoint_autodownload and s.count('/') == 1: - modelloader.hf_login() - found = modelloader.find_diffuser(s, full=True) - shared.log.info(f'HF search: model="{s}" results={found}') - if found is not None and len(found) == 1 and found[0] == s: - checkpoint_info = CheckpointInfo(s) - checkpoint_info.type = 'huggingface' - return checkpoint_info - - # civitai search - if shared.opts.sd_checkpoint_autodownload and s.startswith("https://civitai.com/api/download/models"): - fn = modelloader.download_civit_model_thread(model_name=None, model_url=s, model_path='', model_type='Model', token=None) - if fn is not None: - checkpoint_info = CheckpointInfo(fn) - return checkpoint_info - - return None - - -def model_hash(filename): - """old hash that only looks at a small part of the file and is prone to collisions""" - try: - with open(filename, "rb") as file: - import hashlib - # t0 = time.time() - m = hashlib.sha256() - file.seek(0x100000) - m.update(file.read(0x10000)) - shorthash = m.hexdigest()[0:8] - # t1 = time.time() - # shared.log.debug(f'Calculating short hash: {filename} hash={shorthash} time={(t1-t0):.2f}') - return shorthash - except FileNotFoundError: - return 'NOFILE' - except Exception: - return 'NOHASH' - - -def select_checkpoint(op='model'): - if op == 'dict': - model_checkpoint = shared.opts.sd_model_dict - elif op == 'refiner': - model_checkpoint = shared.opts.data.get('sd_model_refiner', None) - else: - model_checkpoint = shared.opts.sd_model_checkpoint - if model_checkpoint is None or model_checkpoint == 'None': - return None - checkpoint_info = get_closet_checkpoint_match(model_checkpoint) - if checkpoint_info is not None: - shared.log.info(f'Load {op}: select="{checkpoint_info.title if checkpoint_info is not None else None}"') - return checkpoint_info - if len(checkpoints_list) == 0: - shared.log.warning("Cannot generate without a checkpoint") - shared.log.info("Set system paths to use existing folders") - shared.log.info(" or use --models-dir to specify base folder with all models") - shared.log.info(" or use --ckpt-dir to specify folder with sd models") - shared.log.info(" or use --ckpt to force using specific model") - return None - # checkpoint_info = next(iter(checkpoints_list.values())) - if model_checkpoint is not None: - if model_checkpoint != 'model.safetensors' and model_checkpoint != 'stabilityai/stable-diffusion-xl-base-1.0': - shared.log.info(f'Load {op}: search="{model_checkpoint}" not found') - else: - shared.log.info("Selecting first available checkpoint") - # shared.log.warning(f"Loading fallback checkpoint: {checkpoint_info.title}") - # shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title - else: - shared.log.info(f'Load {op}: select="{checkpoint_info.title if checkpoint_info is not None else None}"') - return checkpoint_info - - -checkpoint_dict_replacements = { - 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.', - 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.', - 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.', -} - - -def transform_checkpoint_dict_key(k): - for text, replacement in checkpoint_dict_replacements.items(): - if k.startswith(text): - k = replacement + k[len(text):] - return k - - -def get_state_dict_from_checkpoint(pl_sd): - pl_sd = pl_sd.pop("state_dict", pl_sd) - pl_sd.pop("state_dict", None) - sd = {} - for k, v in pl_sd.items(): - new_key = transform_checkpoint_dict_key(k) - if new_key is not None: - sd[new_key] = v - pl_sd.clear() - pl_sd.update(sd) - return pl_sd - - -def write_metadata(): - global sd_metadata_pending # pylint: disable=global-statement - if sd_metadata_pending == 0: - shared.log.debug(f'Model metadata: file="{sd_metadata_file}" no changes') - return - shared.writefile(sd_metadata, sd_metadata_file) - shared.log.info(f'Model metadata saved: file="{sd_metadata_file}" items={sd_metadata_pending} time={sd_metadata_timer:.2f}') - sd_metadata_pending = 0 - - -def scrub_dict(dict_obj, keys): - for key in list(dict_obj.keys()): - if not isinstance(dict_obj, dict): - continue - if key in keys: - dict_obj.pop(key, None) - elif isinstance(dict_obj[key], dict): - scrub_dict(dict_obj[key], keys) - elif isinstance(dict_obj[key], list): - for item in dict_obj[key]: - scrub_dict(item, keys) - - -def read_metadata_from_safetensors(filename): - global sd_metadata # pylint: disable=global-statement - if sd_metadata is None: - sd_metadata = shared.readfile(sd_metadata_file, lock=True) if os.path.isfile(sd_metadata_file) else {} - res = sd_metadata.get(filename, None) - if res is not None: - return res - if not filename.endswith(".safetensors"): - return {} - if shared.cmd_opts.no_metadata: - return {} - res = {} - # try: - t0 = time.time() - with open(filename, mode="rb") as file: - try: - metadata_len = file.read(8) - metadata_len = int.from_bytes(metadata_len, "little") - json_start = file.read(2) - if metadata_len <= 2 or json_start not in (b'{"', b"{'"): - shared.log.error(f'Model metadata invalid: file="{filename}"') - json_data = json_start + file.read(metadata_len-2) - json_obj = json.loads(json_data) - for k, v in json_obj.get("__metadata__", {}).items(): - if v.startswith("data:"): - v = 'data' - if k == 'format' and v == 'pt': - continue - large = True if len(v) > 2048 else False - if large and k == 'ss_datasets': - continue - if large and k == 'workflow': - continue - if large and k == 'prompt': - continue - if large and k == 'ss_bucket_info': - continue - if v[0:1] == '{': - try: - v = json.loads(v) - if large and k == 'ss_tag_frequency': - v = { i: len(j) for i, j in v.items() } - if large and k == 'sd_merge_models': - scrub_dict(v, ['sd_merge_recipe']) - except Exception: - pass - res[k] = v - except Exception as e: - shared.log.error(f'Model metadata: file="{filename}" {e}') - sd_metadata[filename] = res - global sd_metadata_pending # pylint: disable=global-statement - sd_metadata_pending += 1 - t1 = time.time() - global sd_metadata_timer # pylint: disable=global-statement - sd_metadata_timer += (t1 - t0) - # except Exception as e: - # shared.log.error(f"Error reading metadata from: {filename} {e}") - return res - - def read_state_dict(checkpoint_file, map_location=None, what:str='model'): # pylint: disable=unused-argument if not os.path.isfile(checkpoint_file): shared.log.error(f'Load dict: path="{checkpoint_file}" not a file') @@ -449,26 +83,55 @@ def get_safetensor_keys(filename): return keys +def get_state_dict_from_checkpoint(pl_sd): + checkpoint_dict_replacements = { + 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.', + 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.', + 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.', + } + + def transform_checkpoint_dict_key(k): + for text, replacement in checkpoint_dict_replacements.items(): + if k.startswith(text): + k = replacement + k[len(text):] + return k + + pl_sd = pl_sd.pop("state_dict", pl_sd) + pl_sd.pop("state_dict", None) + sd = {} + for k, v in pl_sd.items(): + new_key = transform_checkpoint_dict_key(k) + if new_key is not None: + sd[new_key] = v + pl_sd.clear() + pl_sd.update(sd) + return pl_sd + + def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer): if not os.path.isfile(checkpoint_info.filename): return None + """ if checkpoint_info in checkpoints_loaded: shared.log.info("Load model: cache") checkpoints_loaded.move_to_end(checkpoint_info, last=True) # FIFO -> LRU cache return checkpoints_loaded[checkpoint_info] + """ res = read_state_dict(checkpoint_info.filename, what='model') + """ if shared.opts.sd_checkpoint_cache > 0 and not shared.native: # cache newly loaded model checkpoints_loaded[checkpoint_info] = res # clean up cache if limit is reached while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: checkpoints_loaded.popitem(last=False) + """ timer.record("load") return res def load_model_weights(model: torch.nn.Module, checkpoint_info: CheckpointInfo, state_dict, timer): - _pipeline, _model_type = detect_pipeline(checkpoint_info.path, 'model') + _pipeline, _model_type = sd_detect.detect_pipeline(checkpoint_info.path, 'model') shared.log.debug(f'Load model: memory={memory_stats()}') timer.record("hash") if model_data.sd_dict == 'None': @@ -520,41 +183,6 @@ def load_model_weights(model: torch.nn.Module, checkpoint_info: CheckpointInfo, return True -def enable_midas_autodownload(): - """ - Gives the ldm.modules.midas.api.load_model function automatic downloading. - - When the 512-depth-ema model, and other future models like it, is loaded, - it calls midas.api.load_model to load the associated midas depth model. - This function applies a wrapper to download the model to the correct - location automatically. - """ - import ldm.modules.midas.api - midas_path = os.path.join(paths.models_path, 'midas') - for k, v in ldm.modules.midas.api.ISL_PATHS.items(): - file_name = os.path.basename(v) - ldm.modules.midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name) - midas_urls = { - "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", - "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt", - "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt", - "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt", - } - ldm.modules.midas.api.load_model_inner = ldm.modules.midas.api.load_model - - def load_model_wrapper(model_type): - path = ldm.modules.midas.api.ISL_PATHS[model_type] - if not os.path.exists(path): - if not os.path.exists(midas_path): - mkdir(midas_path) - shared.log.info(f"Downloading midas model weights for {model_type} to {path}") - request.urlretrieve(midas_urls[model_type], path) - shared.log.info(f"{model_type} downloaded") - return ldm.modules.midas.api.load_model_inner(model_type) - - ldm.modules.midas.api.load_model = load_model_wrapper - - def repair_config(sd_config): if "use_ema" not in sd_config.model.params: sd_config.model.params.use_ema = False @@ -580,7 +208,6 @@ def change_backend(): unload_model_weights() shared.backend = shared.Backend.ORIGINAL if shared.opts.sd_backend == 'original' else shared.Backend.DIFFUSERS shared.native = shared.backend == shared.Backend.DIFFUSERS - checkpoints_loaded.clear() from modules.sd_samplers import list_samplers list_samplers() list_models() @@ -588,118 +215,6 @@ def change_backend(): refresh_vae_list() -def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False): - guess = shared.opts.diffusers_pipeline - warn = shared.log.warning if warning else lambda *args, **kwargs: None - size = 0 - pipeline = None - if guess == 'Autodetect': - try: - guess = 'Stable Diffusion XL' if 'XL' in f.upper() else 'Stable Diffusion' - # guess by size - if os.path.isfile(f) and f.endswith('.safetensors'): - size = round(os.path.getsize(f) / 1024 / 1024) - if (size > 0 and size < 128): - warn(f'Model size smaller than expected: {f} size={size} MB') - elif (size >= 316 and size <= 324) or (size >= 156 and size <= 164): # 320 or 160 - warn(f'Model detected as VAE model, but attempting to load as model: {op}={f} size={size} MB') - guess = 'VAE' - elif (size >= 4970 and size <= 4976): # 4973 - guess = 'Stable Diffusion 2' # SD v2 but could be eps or v-prediction - # elif size < 0: # unknown - # guess = 'Stable Diffusion 2B' - elif (size >= 5791 and size <= 5799): # 5795 - if op == 'model': - warn(f'Model detected as SD-XL refiner model, but attempting to load a base model: {op}={f} size={size} MB') - guess = 'Stable Diffusion XL Refiner' - elif (size >= 6611 and size <= 7220): # 6617, HassakuXL is 6776, monkrenRealisticINT_v10 is 7217 - guess = 'Stable Diffusion XL' - elif (size >= 3361 and size <= 3369): # 3368 - guess = 'Stable Diffusion Upscale' - elif (size >= 4891 and size <= 4899): # 4897 - guess = 'Stable Diffusion XL Inpaint' - elif (size >= 9791 and size <= 9799): # 9794 - guess = 'Stable Diffusion XL Instruct' - elif (size > 3138 and size < 3142): #3140 - guess = 'Stable Diffusion XL' - elif (size > 5692 and size < 5698) or (size > 4134 and size < 4138) or (size > 10362 and size < 10366) or (size > 15028 and size < 15228): - guess = 'Stable Diffusion 3' - elif (size > 20000 and size < 40000): - guess = 'FLUX' - # guess by name - """ - if 'LCM_' in f.upper() or 'LCM-' in f.upper() or '_LCM' in f.upper() or '-LCM' in f.upper(): - if shared.backend == shared.Backend.ORIGINAL: - warn(f'Model detected as LCM model, but attempting to load using backend=original: {op}={f} size={size} MB') - guess = 'Latent Consistency Model' - """ - if 'instaflow' in f.lower(): - guess = 'InstaFlow' - if 'segmoe' in f.lower(): - guess = 'SegMoE' - if 'hunyuandit' in f.lower(): - guess = 'HunyuanDiT' - if 'pixart-xl' in f.lower(): - guess = 'PixArt-Alpha' - if 'stable-diffusion-3' in f.lower(): - guess = 'Stable Diffusion 3' - if 'stable-cascade' in f.lower() or 'stablecascade' in f.lower() or 'wuerstchen3' in f.lower() or ('sotediffusion' in f.lower() and "v2" in f.lower()): - if devices.dtype == torch.float16: - warn('Stable Cascade does not support Float16') - guess = 'Stable Cascade' - if 'pixart-sigma' in f.lower(): - guess = 'PixArt-Sigma' - if 'lumina-next' in f.lower(): - guess = 'Lumina-Next' - if 'kolors' in f.lower(): - guess = 'Kolors' - if 'auraflow' in f.lower(): - guess = 'AuraFlow' - if 'cogview' in f.lower(): - guess = 'CogView' - if 'meissonic' in f.lower(): - guess = 'Meissonic' - pipeline = 'custom' - if 'omnigen' in f.lower(): - guess = 'OmniGen' - pipeline = 'custom' - if 'flux' in f.lower(): - guess = 'FLUX' - if size > 11000 and size < 20000: - warn(f'Model detected as FLUX UNET model, but attempting to load a base model: {op}={f} size={size} MB') - # switch for specific variant - if guess == 'Stable Diffusion' and 'inpaint' in f.lower(): - guess = 'Stable Diffusion Inpaint' - elif guess == 'Stable Diffusion' and 'instruct' in f.lower(): - guess = 'Stable Diffusion Instruct' - if guess == 'Stable Diffusion XL' and 'inpaint' in f.lower(): - guess = 'Stable Diffusion XL Inpaint' - elif guess == 'Stable Diffusion XL' and 'instruct' in f.lower(): - guess = 'Stable Diffusion XL Instruct' - # get actual pipeline - pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline - if not quiet: - shared.log.info(f'Autodetect {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}" size={size}MB') - except Exception as e: - shared.log.error(f'Autodetect {op}: file="{f}" {e}') - if debug_load: - errors.display(e, f'Load {op}: {f}') - return None, None - else: - try: - size = round(os.path.getsize(f) / 1024 / 1024) - pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline - if not quiet: - shared.log.info(f'Load {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}" size={size}MB') - except Exception as e: - shared.log.error(f'Load {op}: detect="{guess}" file="{f}" {e}') - - if pipeline is None: - shared.log.warning(f'Load {op}: detect="{guess}" file="{f}" size={size} not recognized') - pipeline = diffusers.StableDiffusionPipeline - return pipeline, guess - - def copy_diffuser_options(new_pipe, orig_pipe): new_pipe.sd_checkpoint_info = getattr(orig_pipe, 'sd_checkpoint_info', None) new_pipe.sd_model_checkpoint = getattr(orig_pipe, 'sd_model_checkpoint', None) @@ -997,35 +512,6 @@ def move_base(model, device): return R -def get_load_config(model_file, model_type, config_type='yaml'): - if config_type == 'yaml': - yaml = os.path.splitext(model_file)[0] + '.yaml' - if os.path.exists(yaml): - return yaml - if model_type == 'Stable Diffusion': - return 'configs/v1-inference.yaml' - if model_type == 'Stable Diffusion XL': - return 'configs/sd_xl_base.yaml' - if model_type == 'Stable Diffusion XL Refiner': - return 'configs/sd_xl_refiner.yaml' - if model_type == 'Stable Diffusion 2': - return None # dont know if its eps or v so let diffusers sort it out - # return 'configs/v2-inference-512-base.yaml' - # return 'configs/v2-inference-768-v.yaml' - elif config_type == 'json': - if not shared.opts.diffuser_cache_config: - return None - if model_type == 'Stable Diffusion': - return 'configs/sd15' - if model_type == 'Stable Diffusion XL': - return 'configs/sdxl' - if model_type == 'Stable Diffusion 3': - return 'configs/sd3' - if model_type == 'FLUX': - return 'configs/flux' - return None - - def patch_diffuser_config(sd_model, model_file): def load_config(fn, k): model_file = os.path.splitext(fn)[0] @@ -1216,7 +702,7 @@ def load_diffuser_file(model_type, pipeline, checkpoint_info, diffusers_load_con if shared.opts.diffusers_force_zeros: diffusers_load_config['force_zeros_for_empty_prompt '] = shared.opts.diffusers_force_zeros else: - model_config = get_load_config(checkpoint_info.path, model_type, config_type='json') + model_config = sd_detect.get_load_config(checkpoint_info.path, model_type, config_type='json') if model_config is not None: if debug_load: shared.log.debug(f'Load {op}: config="{model_config}"') @@ -1307,7 +793,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No return # detect pipeline - pipeline, model_type = detect_pipeline(checkpoint_info.path, op) + pipeline, model_type = sd_detect.detect_pipeline(checkpoint_info.path, op) # preload vae so it can be used as param vae = None @@ -1782,7 +1268,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None, shared.log.info(f"Model loaded in {timer.summary()}") current_checkpoint_info = None devices.torch_gc(force=True) - shared.log.info(f'Model load finished: {memory_stats()} cached={len(checkpoints_loaded.keys())}') + shared.log.info(f'Model load finished: {memory_stats()}') def reload_text_encoder(initial=False): @@ -1842,7 +1328,7 @@ def reload_model_weights(sd_model=None, info=None, reuse_dict=False, op='model', state_dict = get_checkpoint_state_dict(checkpoint_info, timer) if not shared.native else None checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info) timer.record("config") - if sd_model is None or checkpoint_config != getattr(sd_model, 'used_config', None): + if sd_model is None or checkpoint_config != getattr(sd_model, 'used_config', None) or force: sd_model = None if not shared.native: load_model(checkpoint_info, already_loaded_state_dict=state_dict, timer=timer, op=op) @@ -1936,82 +1422,6 @@ def unload_model_weights(op='model'): shared.log.debug(f'Unload weights {op}: {memory_stats()}') -def apply_token_merging(sd_model): - current_tome = getattr(sd_model, 'applied_tome', 0) - current_todo = getattr(sd_model, 'applied_todo', 0) - - if shared.opts.token_merging_method == 'ToMe' and shared.opts.tome_ratio > 0: - if current_tome == shared.opts.tome_ratio: - return - if shared.opts.hypertile_unet_enabled and not shared.cmd_opts.experimental: - shared.log.warning('Token merging not supported with HyperTile for UNet') - return - try: - import installer - installer.install('tomesd', 'tomesd', ignore=False) - import tomesd - tomesd.apply_patch( - sd_model, - ratio=shared.opts.tome_ratio, - use_rand=False, # can cause issues with some samplers - merge_attn=True, - merge_crossattn=False, - merge_mlp=False - ) - shared.log.info(f'Applying ToMe: ratio={shared.opts.tome_ratio}') - sd_model.applied_tome = shared.opts.tome_ratio - except Exception: - shared.log.warning(f'Token merging not supported: pipeline={sd_model.__class__.__name__}') - else: - sd_model.applied_tome = 0 - - if shared.opts.token_merging_method == 'ToDo' and shared.opts.todo_ratio > 0: - if current_todo == shared.opts.todo_ratio: - return - if shared.opts.hypertile_unet_enabled and not shared.cmd_opts.experimental: - shared.log.warning('Token merging not supported with HyperTile for UNet') - return - try: - from modules.todo.todo_utils import patch_attention_proc - token_merge_args = { - "ratio": shared.opts.todo_ratio, - "merge_tokens": "keys/values", - "merge_method": "downsample", - "downsample_method": "nearest", - "downsample_factor": 2, - "timestep_threshold_switch": 0.0, - "timestep_threshold_stop": 0.0, - "downsample_factor_level_2": 1, - "ratio_level_2": 0.0, - } - patch_attention_proc(sd_model.unet, token_merge_args=token_merge_args) - shared.log.info(f'Applying ToDo: ratio={shared.opts.todo_ratio}') - sd_model.applied_todo = shared.opts.todo_ratio - except Exception: - shared.log.warning(f'Token merging not supported: pipeline={sd_model.__class__.__name__}') - else: - sd_model.applied_todo = 0 - - -def remove_token_merging(sd_model): - current_tome = getattr(sd_model, 'applied_tome', 0) - current_todo = getattr(sd_model, 'applied_todo', 0) - try: - if current_tome > 0: - import tomesd - tomesd.remove_patch(sd_model) - sd_model.applied_tome = 0 - except Exception: - pass - try: - if current_todo > 0: - from modules.todo.todo_utils import remove_patch - remove_patch(sd_model) - sd_model.applied_todo = 0 - except Exception: - pass - - def path_to_repo(fn: str = ''): if isinstance(fn, CheckpointInfo): fn = fn.name diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 52ba77bba..f266f8c38 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -2,7 +2,7 @@ import os import glob from copy import deepcopy import torch -from modules import shared, errors, paths, devices, script_callbacks, sd_models +from modules import shared, errors, paths, devices, script_callbacks, sd_models, sd_detect vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} @@ -206,8 +206,8 @@ def load_vae_diffusers(model_file, vae_file=None, vae_source="unknown-source"): diffusers_load_config['variant'] = shared.opts.diffusers_vae_load_variant if shared.opts.diffusers_vae_upcast != 'default': diffusers_load_config['force_upcast'] = True if shared.opts.diffusers_vae_upcast == 'true' else False - _pipeline, model_type = sd_models.detect_pipeline(model_file, 'vae') - vae_config = sd_models.get_load_config(model_file, model_type, config_type='json') + _pipeline, model_type = sd_detect.detect_pipeline(model_file, 'vae') + vae_config = sd_detect.get_load_config(model_file, model_type, config_type='json') if vae_config is not None: diffusers_load_config['config'] = os.path.join(vae_config, 'vae') shared.log.info(f'Load module: type=VAE model="{vae_file}" source={vae_source} config={diffusers_load_config}') diff --git a/modules/shared.py b/modules/shared.py index a3a9a5482..ae94f26cf 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -280,12 +280,13 @@ def options_section(section_identifier, options_dict): return options_dict -def list_checkpoint_tiles(): +def list_checkpoint_titles(): import modules.sd_models # pylint: disable=W0621 - return modules.sd_models.checkpoint_tiles() + return modules.sd_models.checkpoint_titles() -default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.safetensors" +list_checkpoint_tiles = list_checkpoint_titles # alias for legacy typo +default_checkpoint = list_checkpoint_titles()[0] if len(list_checkpoint_titles()) > 0 else "model.safetensors" def is_url(string): @@ -427,12 +428,12 @@ startup_offload_mode, startup_cross_attention, startup_sdp_options = get_default options_templates.update(options_section(('sd', "Execution & Models"), { "sd_backend": OptionInfo(default_backend, "Execution backend", gr.Radio, {"choices": ["diffusers", "original"] }), - "sd_model_checkpoint": OptionInfo(default_checkpoint, "Base model", DropdownEditable, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints), - "sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints), + "sd_model_checkpoint": OptionInfo(default_checkpoint, "Base model", DropdownEditable, lambda: {"choices": list_checkpoint_titles()}, refresh=refresh_checkpoints), + "sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_titles()}, refresh=refresh_checkpoints), "sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list), "sd_unet": OptionInfo("None", "UNET model", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list), "sd_text_encoder": OptionInfo('None', "Text encoder model", gr.Dropdown, lambda: {"choices": shared_items.sd_te_items()}, refresh=shared_items.refresh_te_list), - "sd_model_dict": OptionInfo('None', "Use separate base dict", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints), + "sd_model_dict": OptionInfo('None', "Use separate base dict", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_titles()}, refresh=refresh_checkpoints), "sd_checkpoint_autoload": OptionInfo(True, "Model autoload on start"), "sd_checkpoint_autodownload": OptionInfo(True, "Model auto-download on demand"), "sd_textencoder_cache": OptionInfo(True, "Cache text encoder results"), diff --git a/modules/token_merge.py b/modules/token_merge.py new file mode 100644 index 000000000..f97c1fc8e --- /dev/null +++ b/modules/token_merge.py @@ -0,0 +1,77 @@ +from modules import shared + + +def apply_token_merging(sd_model): + current_tome = getattr(sd_model, 'applied_tome', 0) + current_todo = getattr(sd_model, 'applied_todo', 0) + + if shared.opts.token_merging_method == 'ToMe' and shared.opts.tome_ratio > 0: + if current_tome == shared.opts.tome_ratio: + return + if shared.opts.hypertile_unet_enabled and not shared.cmd_opts.experimental: + shared.log.warning('Token merging not supported with HyperTile for UNet') + return + try: + import installer + installer.install('tomesd', 'tomesd', ignore=False) + import tomesd + tomesd.apply_patch( + sd_model, + ratio=shared.opts.tome_ratio, + use_rand=False, # can cause issues with some samplers + merge_attn=True, + merge_crossattn=False, + merge_mlp=False + ) + shared.log.info(f'Applying ToMe: ratio={shared.opts.tome_ratio}') + sd_model.applied_tome = shared.opts.tome_ratio + except Exception: + shared.log.warning(f'Token merging not supported: pipeline={sd_model.__class__.__name__}') + else: + sd_model.applied_tome = 0 + + if shared.opts.token_merging_method == 'ToDo' and shared.opts.todo_ratio > 0: + if current_todo == shared.opts.todo_ratio: + return + if shared.opts.hypertile_unet_enabled and not shared.cmd_opts.experimental: + shared.log.warning('Token merging not supported with HyperTile for UNet') + return + try: + from modules.todo.todo_utils import patch_attention_proc + token_merge_args = { + "ratio": shared.opts.todo_ratio, + "merge_tokens": "keys/values", + "merge_method": "downsample", + "downsample_method": "nearest", + "downsample_factor": 2, + "timestep_threshold_switch": 0.0, + "timestep_threshold_stop": 0.0, + "downsample_factor_level_2": 1, + "ratio_level_2": 0.0, + } + patch_attention_proc(sd_model.unet, token_merge_args=token_merge_args) + shared.log.info(f'Applying ToDo: ratio={shared.opts.todo_ratio}') + sd_model.applied_todo = shared.opts.todo_ratio + except Exception: + shared.log.warning(f'Token merging not supported: pipeline={sd_model.__class__.__name__}') + else: + sd_model.applied_todo = 0 + + +def remove_token_merging(sd_model): + current_tome = getattr(sd_model, 'applied_tome', 0) + current_todo = getattr(sd_model, 'applied_todo', 0) + try: + if current_tome > 0: + import tomesd + tomesd.remove_patch(sd_model) + sd_model.applied_tome = 0 + except Exception: + pass + try: + if current_todo > 0: + from modules.todo.todo_utils import remove_patch + remove_patch(sd_model) + sd_model.applied_todo = 0 + except Exception: + pass diff --git a/modules/ui_control.py b/modules/ui_control.py index 388e8ede9..4d7c59bee 100644 --- a/modules/ui_control.py +++ b/modules/ui_control.py @@ -612,6 +612,7 @@ def create_ui(_blocks: gr.Blocks=None): (mask_controls[6], "Mask auto"), # advanced (cfg_scale, "CFG scale"), + (cfg_end, "CFG end"), (clip_skip, "Clip skip"), (image_cfg_scale, "Image CFG scale"), (diffusers_guidance_rescale, "CFG rescale"), diff --git a/modules/ui_img2img.py b/modules/ui_img2img.py index d46ea4dd3..44f48c3c6 100644 --- a/modules/ui_img2img.py +++ b/modules/ui_img2img.py @@ -263,6 +263,7 @@ def create_ui(): (refiner_start, "Refiner start"), # advanced (cfg_scale, "CFG scale"), + (cfg_end, "CFG end"), (image_cfg_scale, "Image CFG scale"), (clip_skip, "Clip skip"), (diffusers_guidance_rescale, "CFG rescale"), diff --git a/modules/ui_models.py b/modules/ui_models.py index 051ca39a7..e9be428b4 100644 --- a/modules/ui_models.py +++ b/modules/ui_models.py @@ -59,8 +59,8 @@ def create_ui(): with gr.Tab(label="Convert"): with gr.Row(): - model_name = gr.Dropdown(sd_models.checkpoint_tiles(), label="Original model") - create_refresh_button(model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_Z") + model_name = gr.Dropdown(sd_models.checkpoint_titles(), label="Original model") + create_refresh_button(model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_titles()}, "refresh_checkpoint_Z") with gr.Row(): custom_name = gr.Textbox(label="Output model name") with gr.Row(): @@ -98,7 +98,7 @@ def create_ui(): with gr.Tab(label="Merge"): def sd_model_choices(): - return ['None'] + sd_models.checkpoint_tiles() + return ['None'] + sd_models.checkpoint_titles() with gr.Row(equal_height=False): with gr.Column(variant='compact'): @@ -213,10 +213,10 @@ def create_ui(): del kwargs['dummy_component'] if kwargs.get("custom_name", None) is None: log.error('Merge: no output model specified') - return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "No output model specified"] + return [*[gr.Dropdown.update(choices=sd_models.checkpoint_titles()) for _ in range(4)], "No output model specified"] elif kwargs.get("primary_model_name", None) is None or kwargs.get("secondary_model_name", None) is None: log.error('Merge: no models selected') - return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "No models selected"] + return [*[gr.Dropdown.update(choices=sd_models.checkpoint_titles()) for _ in range(4)], "No models selected"] else: log.debug(f'Merge start: {kwargs}') try: @@ -224,7 +224,7 @@ def create_ui(): except Exception as e: modules.errors.display(e, 'Merge') sd_models.list_models() # to remove the potentially missing models from the list - return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"] + return [*[gr.Dropdown.update(choices=sd_models.checkpoint_titles()) for _ in range(4)], f"Error merging checkpoints: {e}"] return results def tertiary(mode): diff --git a/modules/ui_txt2img.py b/modules/ui_txt2img.py index 1ae3a8dad..ece8829c5 100644 --- a/modules/ui_txt2img.py +++ b/modules/ui_txt2img.py @@ -116,6 +116,7 @@ def create_ui(): (subseed_strength, "Variation strength"), # advanced (cfg_scale, "CFG scale"), + (cfg_end, "CFG end"), (clip_skip, "Clip skip"), (image_cfg_scale, "Image CFG scale"), (diffusers_guidance_rescale, "CFG rescale"), diff --git a/scripts/x_adapter.py b/scripts/x_adapter.py index 7c1341701..553a20d30 100644 --- a/scripts/x_adapter.py +++ b/scripts/x_adapter.py @@ -22,7 +22,7 @@ class Script(scripts.Script): with gr.Row(): gr.HTML('  X-Adapter
') with gr.Row(): - model = gr.Dropdown(label='Adapter model', choices=['None'] + sd_models.checkpoint_tiles(), value='None') + model = gr.Dropdown(label='Adapter model', choices=['None'] + sd_models.checkpoint_titles(), value='None') sampler = gr.Dropdown(label='Adapter sampler', choices=[s.name for s in sd_samplers.samplers], value='Default') with gr.Row(): width = gr.Slider(label='Adapter width', minimum=64, maximum=2048, step=8, value=1024) @@ -34,7 +34,7 @@ class Script(scripts.Script): lora = gr.Textbox('', label='Adapter LoRA', default='') return model, sampler, width, height, start, scale, lora - def run(self, p: processing.StableDiffusionProcessing, model, sampler, width, height, start, scale, lora): # pylint: disable=arguments-differ + def run(self, p: processing.StableDiffusionProcessing, model, sampler, width, height, start, scale, lora): # pylint: disable=arguments-differ, unused-argument from modules.xadapter.xadapter_hijacks import PositionNet diffusers.models.embeddings.PositionNet = PositionNet # patch diffusers==0.26 from diffusers==0.20 from modules.xadapter.adapter import Adapter_XL diff --git a/scripts/xyz_grid_classes.py b/scripts/xyz_grid_classes.py index 8a78d2c40..335d66186 100644 --- a/scripts/xyz_grid_classes.py +++ b/scripts/xyz_grid_classes.py @@ -99,7 +99,7 @@ axis_options = [ AxisOption("[Param] Height", int, apply_field("height")), AxisOption("[Param] Seed", int, apply_seed), AxisOption("[Param] Steps", int, apply_field("steps")), - AxisOption("[Param] CFG scale", float, apply_field("cfg_scale")), + AxisOption("[Param] Guidance scale", float, apply_field("cfg_scale")), AxisOption("[Param] Guidance end", float, apply_field("cfg_end")), AxisOption("[Param] Variation seed", int, apply_field("subseed")), AxisOption("[Param] Variation strength", float, apply_field("subseed_strength")), @@ -125,7 +125,7 @@ axis_options = [ AxisOption("[Refine] Sampler", str, apply_hr_sampler_name, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), AxisOption("[Refine] Denoising strength", float, apply_field("denoising_strength")), AxisOption("[Refine] Hires steps", int, apply_field("hr_second_pass_steps")), - AxisOption("[Refine] CFG scale", float, apply_field("image_cfg_scale")), + AxisOption("[Refine] Guidance scale", float, apply_field("image_cfg_scale")), AxisOption("[Refine] Guidance rescale", float, apply_field("diffusers_guidance_rescale")), AxisOption("[Refine] Refiner start", float, apply_field("refiner_start")), AxisOption("[Refine] Refiner steps", float, apply_field("refiner_steps")), From fda2cafcbe3a784ca47b1f17db933f43ac695d60 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 26 Oct 2024 13:54:00 -0400 Subject: [PATCH 06/81] update modernui Signed-off-by: Vladimir Mandic --- extensions-builtin/sdnext-modernui | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui index 906bd2a98..9e9b2d8e5 160000 --- a/extensions-builtin/sdnext-modernui +++ b/extensions-builtin/sdnext-modernui @@ -1 +1 @@ -Subproject commit 906bd2a98ba0736c235925f2beaea787a050aeed +Subproject commit 9e9b2d8e57c21d7b6d35ca2c4e0fb38009d8aed0 From 0fb6cf2decba4623513a29ededd914611e2c5638 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 26 Oct 2024 20:35:47 -0400 Subject: [PATCH 07/81] add legacy typo Signed-off-by: Vladimir Mandic --- modules/sd_models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/sd_models.py b/modules/sd_models.py index 9662a005d..529c1b6a5 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -31,6 +31,7 @@ debug_move = shared.log.trace if os.environ.get('SD_MOVE_DEBUG', None) is not No debug_load = os.environ.get('SD_LOAD_DEBUG', None) debug_process = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None diffusers_version = int(diffusers.__version__.split('.')[1]) +checkpoint_tiles = checkpoint_titles # legacy compatibility class NoWatermark: From b569d68b4f5eb197272e89b9198753d1c3c78721 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 26 Oct 2024 21:51:36 -0400 Subject: [PATCH 08/81] add ostris: experimental Signed-off-by: Vladimir Mandic --- extensions-builtin/Lora/networks.py | 2 ++ installer.py | 2 +- javascript/sdnext.css | 2 +- modules/sd_samplers_common.py | 4 +-- modules/sd_vae_ostris.py | 41 +++++++++++++++++++++++++++++ modules/sd_vae_taesd.py | 12 ++++++--- 6 files changed, 55 insertions(+), 8 deletions(-) create mode 100644 modules/sd_vae_ostris.py diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py index c0a8555e1..83aa6b40b 100644 --- a/extensions-builtin/Lora/networks.py +++ b/extensions-builtin/Lora/networks.py @@ -127,6 +127,8 @@ def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_ def load_network(name, network_on_disk) -> network.Network: + if not shared.sd_loaded: + return t0 = time.time() cached = lora_cache.get(name, None) if debug: diff --git a/installer.py b/installer.py index aca36056b..2cc1c74a7 100644 --- a/installer.py +++ b/installer.py @@ -731,7 +731,7 @@ def check_torch(): else: if args.use_zluda: log.warning("ZLUDA failed to initialize: no HIP SDK found") - log.info('Using CPU-only Torch') + log.warning('Torch: CPU-only version installed') torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision') if 'torch' in torch_command and not args.version: install(torch_command, 'torch torchvision', quiet=True) diff --git a/javascript/sdnext.css b/javascript/sdnext.css index 3c81e7d8e..cbd3cac83 100644 --- a/javascript/sdnext.css +++ b/javascript/sdnext.css @@ -38,7 +38,7 @@ td > div > span { overflow-y: auto; max-height: 3em; overflow-x: hidden; } .gradio-button.secondary-down, .gradio-button.secondary-down:hover { box-shadow: 1px 1px 1px rgba(0,0,0,0.25) inset, 0px 0px 3px rgba(0,0,0,0.15) inset; } .gradio-button.secondary-down:hover { background: var(--button-secondary-background-fill-hover); color: var(--button-secondary-text-color-hover); } .gradio-button.tool { max-width: min-content; min-width: min-content !important; font-size: 20px !important; color: var(--body-text-color) !important; align-self: end; margin-bottom: 4px; } -.gradio-checkbox { margin: 0.75em 1.5em 0 0; align-self: center; } +.gradio-checkbox { margin-right: 1em !important; align-self: center; } .gradio-column { min-width: min(160px, 100%) !important; } .gradio-container { max-width: unset !important; padding: var(--block-label-padding) !important; } .gradio-container .prose a, .gradio-container .prose a:visited{ color: unset; text-decoration: none; } diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index 1b1cd189a..a487fe9b7 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -44,7 +44,7 @@ def single_sample_to_image(sample, approximation=None): if sample.dtype == torch.bfloat16 and (approximation == 0 or approximation == 1): sample = sample.to(torch.float16) except Exception as e: - warn_once(f'live preview: {e}') + warn_once(f'Preview: {e}') if len(sample.shape) > 4: # likely unknown video latent (e.g. svd) return Image.new(mode="RGB", size=(512, 512)) @@ -82,7 +82,7 @@ def single_sample_to_image(sample, approximation=None): transform = T.ToPILImage() image = transform(x_sample) except Exception as e: - warn_once(f'live preview: {e}') + warn_once(f'Preview: {e}') image = Image.new(mode="RGB", size=(512, 512)) return image diff --git a/modules/sd_vae_ostris.py b/modules/sd_vae_ostris.py new file mode 100644 index 000000000..70542e9f5 --- /dev/null +++ b/modules/sd_vae_ostris.py @@ -0,0 +1,41 @@ +import time +import torch +import diffusers +from huggingface_hub import hf_hub_download +from safetensors.torch import load_file +from modules import shared, devices + + +decoder_id = "ostris/vae-kl-f8-d16" +adapter_id = "ostris/16ch-VAE-Adapters" + + +def load_vae(pipe): + if shared.sd_model_type == 'sd': + adapter_file = "16ch-VAE-Adapter-SD15-alpha.safetensors" + elif shared.sd_model_type == 'sdxl': + adapter_file = "16ch-VAE-Adapter-SDXL-alpha_v02.safetensors" + else: + shared.log.error('VAE: type=osiris unsupported model type') + return + t0 = time.time() + ckpt_file = hf_hub_download(adapter_id, adapter_file, cache_dir=shared.opts.hfcache_dir) + ckpt = load_file(ckpt_file) + lora_state_dict = {k: v for k, v in ckpt.items() if "lora" in k} + unet_state_dict = {k.replace("unet_", ""): v for k, v in ckpt.items() if "unet_" in k} + + pipe.unet.conv_in = torch.nn.Conv2d(16, 320, 3, 1, 1) + pipe.unet.conv_out = torch.nn.Conv2d(320, 16, 3, 1, 1) + pipe.unet.load_state_dict(unet_state_dict, strict=False) + pipe.unet.conv_in.to(devices.dtype) + pipe.unet.conv_out.to(devices.dtype) + pipe.unet.config.in_channels = 16 + pipe.unet.config.out_channels = 16 + + pipe.load_lora_weights(lora_state_dict, adapter_name=adapter_id) + # pipe.set_adapters(adapter_names=[adapter_id], adapter_weights=[0.8]) + pipe.fuse_lora(adapter_names=[adapter_id], lora_scale=0.8, fuse_unet=True) + + pipe.vae = diffusers.AutoencoderKL.from_pretrained(decoder_id, torch_dtype=devices.dtype, cache_dir=shared.opts.hfcache_dir) + t1 = time.time() + shared.log.info(f'VAE load: type=osiris decoder="{decoder_id}" adapter="{adapter_id}" time={t1-t0:.2f}s') diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py index 5cd7fab7c..67886229f 100644 --- a/modules/sd_vae_taesd.py +++ b/modules/sd_vae_taesd.py @@ -171,20 +171,24 @@ def decode(latents): try: with devices.inference_context(): latents = latents.detach().clone().to(devices.device, dtype) - if len(latents.shape) == 3: + if len(latents.shape) == 3 and latents.shape[0] == 4: latents = latents.unsqueeze(0) image = vae.decoder(latents).clamp(0, 1).detach() image = 2.0 * image - 1.0 # typical normalized range except for preview which runs denormalization return image[0] - elif len(latents.shape) == 4: + elif len(latents.shape) == 4 and latents.shape[1] == 4: image = vae.decoder(latents).clamp(0, 1).detach() image = 2.0 * image - 1.0 # typical normalized range except for preview which runs denormalization return image else: - shared.log.error(f'TAESD decode unsupported latent type: {latents.shape}') + if not previous_warnings: + shared.log.error(f'TAESD decode unsupported latent type: {latents.shape}') + previous_warnings = True return latents except Exception as e: - shared.log.error(f'VAE decode taesd: {e}') + if not previous_warnings: + shared.log.error(f'VAE decode taesd: {e}') + previous_warnings = True return latents From c9021ea7d3e204849730834558c9359972681516 Mon Sep 17 00:00:00 2001 From: Seunghoon Lee Date: Sun, 27 Oct 2024 19:38:25 +0900 Subject: [PATCH 09/81] optimum 1.23 --- modules/onnx_impl/__init__.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/modules/onnx_impl/__init__.py b/modules/onnx_impl/__init__.py index 013387d8f..42f22ea3f 100644 --- a/modules/onnx_impl/__init__.py +++ b/modules/onnx_impl/__init__.py @@ -184,8 +184,8 @@ def preprocess_pipeline(p): return shared.sd_model -def ORTDiffusionModelPart_to(self, *args, **kwargs): - self.parent_model = self.parent_model.to(*args, **kwargs) +def ORTPipelinePart_to(self, *args, **kwargs): + self.parent_pipeline = self.parent_pipeline.to(*args, **kwargs) return self @@ -241,9 +241,9 @@ def initialize_onnx(): diffusers.ORTStableDiffusionXLPipeline = diffusers.OnnxStableDiffusionXLPipeline # Huggingface model compatibility diffusers.ORTStableDiffusionXLImg2ImgPipeline = diffusers.OnnxStableDiffusionXLImg2ImgPipeline - optimum.onnxruntime.modeling_diffusion._ORTDiffusionModelPart.to = ORTDiffusionModelPart_to # pylint: disable=protected-access - except Exception: - pass + optimum.onnxruntime.modeling_diffusion.ORTPipelinePart.to = ORTPipelinePart_to # pylint: disable=protected-access + except Exception as e: + log.debug(f'ONNX failed to initialize XL pipelines: {e}') initialized = True From 706852e7c50855886bddd47b58d2161834d4202d Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sun, 27 Oct 2024 09:23:51 -0400 Subject: [PATCH 10/81] fix taesd live preview Signed-off-by: Vladimir Mandic --- modules/sd_models.py | 8 ++++++-- modules/sd_vae_taesd.py | 4 ++-- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 529c1b6a5..8e9535ec3 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -1,5 +1,6 @@ import io import sys +import time import json import copy import inspect @@ -453,7 +454,6 @@ def move_model(model, device=None, force=False): if hasattr(model.vae, '_hf_hook'): debug_move(f'Model move: to={device} class={model.vae.__class__} fn={fn}') # pylint: disable=protected-access model.vae._hf_hook.execution_device = device # pylint: disable=protected-access - debug_move(f'Model move: device={device} class={model.__class__} accelerate={getattr(model, "has_accelerate", False)} fn={fn}') # pylint: disable=protected-access if hasattr(model, "components"): # accelerate patch for name, m in model.components.items(): if not hasattr(m, "_hf_hook"): # not accelerate hook @@ -472,8 +472,9 @@ def move_model(model, device=None, force=False): if hasattr(model, "device") and devices.normalize_device(model.device) == devices.normalize_device(device): return try: + t0 = time.time() try: - model.to(device) + model.to(device, non_blocking=True) if hasattr(model, "prior_pipe"): model.prior_pipe.to(device) except Exception as e0: @@ -493,8 +494,11 @@ def move_model(model, device=None, force=False): pass # ignore model move if sequential offload is enabled else: raise e0 + t1 = time.time() except Exception as e1: shared.log.error(f'Model move: device={device} {e1}') + if os.environ.get('SD_MOVE_DEBUG', None) or (t1-t0) > 0.1: + shared.log.debug(f'Model move: device={device} class={model.__class__.__name__} accelerate={getattr(model, "has_accelerate", False)} fn={fn} time={t1-t0:.2f}') # pylint: disable=protected-access devices.torch_gc() diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py index 67886229f..f99f296a8 100644 --- a/modules/sd_vae_taesd.py +++ b/modules/sd_vae_taesd.py @@ -171,12 +171,12 @@ def decode(latents): try: with devices.inference_context(): latents = latents.detach().clone().to(devices.device, dtype) - if len(latents.shape) == 3 and latents.shape[0] == 4: + if len(latents.shape) == 3: latents = latents.unsqueeze(0) image = vae.decoder(latents).clamp(0, 1).detach() image = 2.0 * image - 1.0 # typical normalized range except for preview which runs denormalization return image[0] - elif len(latents.shape) == 4 and latents.shape[1] == 4: + elif len(latents.shape) == 4: image = vae.decoder(latents).clamp(0, 1).detach() image = 2.0 * image - 1.0 # typical normalized range except for preview which runs denormalization return image From 2410012812aa929f40f22dab3405e03c55bc3b2b Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sun, 27 Oct 2024 11:33:40 -0400 Subject: [PATCH 11/81] update ipadapters Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 29 ++++++----- installer.py | 7 +-- modules/ipadapter.py | 103 ++++++++++++++++++++++++--------------- modules/sd_vae_approx.py | 2 +- modules/sd_vae_taesd.py | 6 +-- modules/ui_common.py | 11 +++-- scripts/ipadapter.py | 11 +++-- 7 files changed, 104 insertions(+), 65 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5381b08b5..8040b1772 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,13 +1,9 @@ # Change Log for SD.Next -## Update for 2024-10-26 +## Update for 2024-10-27 -Improvements: -- Torch CUDA set device memory limit - in *settings -> compute settings -> torch memory limit* - default=0 meaning no limit, if set torch will limit memory usage to specified fraction - *note*: this is not a hard limit, torch will try to stay under this value -- Model selector: +improvements: +- model selector: - change-in-behavior - when typing, it will auto-load model as soon as exactly one match is found - allows entering model that are not on the list which triggers huggingface search @@ -18,17 +14,26 @@ Improvements: e.g. `https://civitai.com/api/download/models/72396?type=Model&format=SafeTensor&size=full&fp=fp16` - auto-search-and-download can be disabled in settings -> models -> auto-download this also disables reference models as they are auto-downloaded on first use as well -- SD3 loader enhancements +- sd3 loader enhancements - report when loading incomplete model - handle missing model components - handle component preloading - native lora handler - gguf transformer loader (prototype) -- OpenVINO: add accuracy option -- ZLUDA: guess GPU arch -- Major model load refactor +- ipadapter: + - list available adapters based on loaded model type + - add adapter `ostris consistency` for sd15/sdxl +- torch + - CUDA set device memory limit + in *settings -> compute settings -> torch memory limit* + default=0 meaning no limit, if set torch will limit memory usage to specified fraction + *note*: this is not a hard limit, torch will try to stay under this value +- compute backends: + - OpenVINO: add accuracy option + - ZLUDA: guess GPU arch +- major model load refactor -Fixes: +fixes: - fix send-to-control - fix k-diffusion - fix sd3 img2img and hires diff --git a/installer.py b/installer.py index 2cc1c74a7..207dde388 100644 --- a/installer.py +++ b/installer.py @@ -254,11 +254,12 @@ def uninstall(package, quiet = False): @lru_cache() def pip(arg: str, ignore: bool = False, quiet: bool = False, uv = True): originalArg = arg - uv = uv and args.uv - pipCmd = "uv pip" if uv else "pip" arg = arg.replace('>=', '==') + package = arg.replace("install", "").replace("--upgrade", "").replace("--no-deps", "").replace("--force", "").replace(" ", " ").strip() + uv = uv and args.uv and not package.startswith('git+') + pipCmd = "uv pip" if uv else "pip" if not quiet and '-r ' not in arg: - log.info(f'Install: package="{arg.replace("install", "").replace("--upgrade", "").replace("--no-deps", "").replace("--force", "").replace(" ", " ").strip()}" mode={"uv" if uv else "pip"}') + log.info(f'Install: package="{package}" mode={"uv" if uv else "pip"}') env_args = os.environ.get("PIP_EXTRA_ARGS", "") all_args = f'{pip_log}{arg} {env_args}'.strip() if not quiet: diff --git a/modules/ipadapter.py b/modules/ipadapter.py index 0ab27f03c..0a010c41c 100644 --- a/modules/ipadapter.py +++ b/modules/ipadapter.py @@ -3,8 +3,6 @@ Lightweight IP-Adapter applied to existing pipeline in Diffusers - Downloads image_encoder or first usage (2.5GB) - Introduced via: https://github.com/huggingface/diffusers/pull/5713 - IP adapters: https://huggingface.co/h94/IP-Adapter -TODO ipadapter items: -- SD/SDXL autodetect """ import os @@ -14,21 +12,41 @@ from PIL import Image from modules import processing, shared, devices, sd_models -base_repo = "h94/IP-Adapter" +clip_repo = "h94/IP-Adapter" clip_loaded = None -ADAPTERS = { - 'None': 'none', - 'Base': 'ip-adapter_sd15.safetensors', - 'Base ViT-G': 'ip-adapter_sd15_vit-G.safetensors', - 'Light': 'ip-adapter_sd15_light.safetensors', - 'Plus': 'ip-adapter-plus_sd15.safetensors', - 'Plus Face': 'ip-adapter-plus-face_sd15.safetensors', - 'Full Face': 'ip-adapter-full-face_sd15.safetensors', - 'Base SDXL': 'ip-adapter_sdxl.safetensors', - 'Base ViT-H SDXL': 'ip-adapter_sdxl_vit-h.safetensors', - 'Plus ViT-H SDXL': 'ip-adapter-plus_sdxl_vit-h.safetensors', - 'Plus Face ViT-H SDXL': 'ip-adapter-plus-face_sdxl_vit-h.safetensors', +ADAPTERS_NONE = { + 'None': { 'name': 'none', 'repo': 'none', 'subfolder': 'none' }, } +ADAPTERS_SD15 = { + 'None': { 'name': 'none', 'repo': 'none', 'subfolder': 'none' }, + 'Base': { 'name': 'ip-adapter_sd15.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'models' }, + 'Base ViT-G': { 'name': 'ip-adapter_sd15_vit-G.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'models' }, + 'Light': { 'name': 'ip-adapter_sd15_light.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'models' }, + 'Plus': { 'name': 'ip-adapter-plus_sd15.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'models' }, + 'Plus Face': { 'name': 'ip-adapter-plus-face_sd15.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'models' }, + 'Full Face': { 'name': 'ip-adapter-full-face_sd15.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'models' }, + 'Ostris Composition ViT-H': { 'name': 'ip_plus_composition_sd15.safetensors', 'repo': 'ostris/ip-composition-adapter', 'subfolder': '' }, +} +ADAPTERS_SDXL = { + 'None': { 'name': 'none', 'repo': 'none', 'subfolder': 'none' }, + 'Base SDXL': { 'name': 'ip-adapter_sdxl.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'sdxl_models' }, + 'Base ViT-H SDXL': { 'name': 'ip-adapter_sdxl_vit-h.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'sdxl_models' }, + 'Plus ViT-H SDXL': { 'name': 'ip-adapter-plus_sdxl_vit-h.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'sdxl_models' }, + 'Plus Face ViT-H SDXL': { 'name': 'ip-adapter-plus-face_sdxl_vit-h.safetensors', 'repo': 'h94/IP-Adapter', 'subfolder': 'sdxl_models' }, + 'Ostris Composition ViT-H SDXL': { 'name': 'ip_plus_composition_sdxl.safetensors', 'repo': 'ostris/ip-composition-adapter', 'subfolder': '' }, +} +ADAPTERS = { **ADAPTERS_SD15, **ADAPTERS_SDXL } + + +def get_adapters(): + global ADAPTERS # pylint: disable=global-statement + if shared.sd_model_type == 'sd': + ADAPTERS = ADAPTERS_SD15 + elif shared.sd_model_type == 'sdxl': + ADAPTERS = ADAPTERS_SDXL + else: + ADAPTERS = ADAPTERS_NONE + return list(ADAPTERS) def get_images(input_images): @@ -117,13 +135,13 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt if hasattr(p, 'ip_adapter_names'): if isinstance(p.ip_adapter_names, str): p.ip_adapter_names = [p.ip_adapter_names] - adapters = [ADAPTERS.get(adapter, None) for adapter in p.ip_adapter_names if adapter is not None and adapter.lower() != 'none'] + adapters = [ADAPTERS.get(adapter_name, None) for adapter_name in p.ip_adapter_names if adapter_name is not None and adapter_name.lower() != 'none'] adapter_names = p.ip_adapter_names else: if isinstance(adapter_names, str): adapter_names = [adapter_names] adapters = [ADAPTERS.get(adapter, None) for adapter in adapter_names] - adapters = [adapter for adapter in adapters if adapter is not None and adapter.lower() != 'none'] + adapters = [adapter for adapter in adapters if adapter is not None and adapter['name'].lower() != 'none'] if len(adapters) == 0: unapply(pipe) if hasattr(p, 'ip_adapter_images'): @@ -189,41 +207,48 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt for adapter_name in adapter_names: # which clip to use - if 'ViT' not in adapter_name: - clip_repo = base_repo - clip_subfolder = 'models/image_encoder' if shared.sd_model_type == 'sd' else 'sdxl_models/image_encoder' # defaults per model + if 'ViT' not in adapter_name: # defaults per model + if shared.sd_model_type == 'sd': + clip_subfolder = 'models/image_encoder' + else: + clip_subfolder = 'sdxl_models/image_encoder' elif 'ViT-H' in adapter_name: - clip_repo = base_repo clip_subfolder = 'models/image_encoder' # this is vit-h elif 'ViT-G' in adapter_name: - clip_repo = base_repo clip_subfolder = 'sdxl_models/image_encoder' # this is vit-g else: shared.log.error(f'IP adapter: unknown model type: {adapter_name}') return False - # load feature extractor used by ip adapter - if pipe.feature_extractor is None: + # load feature extractor used by ip adapter + if pipe.feature_extractor is None: + try: from transformers import CLIPImageProcessor shared.log.debug('IP adapter load: feature extractor') pipe.feature_extractor = CLIPImageProcessor() - # load image encoder used by ip adapter - if pipe.image_encoder is None or clip_loaded != f'{clip_repo}/{clip_subfolder}': - try: - from transformers import CLIPVisionModelWithProjection - shared.log.debug(f'IP adapter load: image encoder="{clip_repo}/{clip_subfolder}"') - pipe.image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_repo, subfolder=clip_subfolder, torch_dtype=devices.dtype, cache_dir=shared.opts.diffusers_dir, use_safetensors=True) - clip_loaded = f'{clip_repo}/{clip_subfolder}' - except Exception as e: - shared.log.error(f'IP adapter: failed to load image encoder: {e}') - return False - sd_models.move_model(pipe.image_encoder, devices.device) + except Exception as e: + shared.log.error(f'IP adapter load: feature extractor {e}') + return False + + # load image encoder used by ip adapter + if pipe.image_encoder is None or clip_loaded != f'{clip_repo}/{clip_subfolder}': + try: + from transformers import CLIPVisionModelWithProjection + shared.log.debug(f'IP adapter load: image encoder="{clip_repo}/{clip_subfolder}"') + pipe.image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_repo, subfolder=clip_subfolder, torch_dtype=devices.dtype, cache_dir=shared.opts.diffusers_dir, use_safetensors=True) + clip_loaded = f'{clip_repo}/{clip_subfolder}' + except Exception as e: + shared.log.error(f'IP adapter load: image encoder="{clip_repo}/{clip_subfolder}" {e}') + return False + sd_models.move_model(pipe.image_encoder, devices.device) # main code - t0 = time.time() - ip_subfolder = 'models' if shared.sd_model_type == 'sd' else 'sdxl_models' try: - pipe.load_ip_adapter([base_repo], subfolder=[ip_subfolder], weight_name=adapters) + t0 = time.time() + repos = [adapter['repo'] for adapter in adapters] + subfolders = [adapter['subfolder'] for adapter in adapters] + names = [adapter['name'] for adapter in adapters] + pipe.load_ip_adapter(repos, subfolder=subfolders, weight_name=names) if hasattr(p, 'ip_adapter_layers'): pipe.set_ip_adapter_scale(p.ip_adapter_layers) ip_str = ';'.join(adapter_names) + ':' + json.dumps(p.ip_adapter_layers) @@ -240,5 +265,5 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt t1 = time.time() shared.log.info(f'IP adapter: {ip_str} image={adapter_images} mask={adapter_masks is not None} time={t1-t0:.2f}') except Exception as e: - shared.log.error(f'IP adapter failed to load: repo="{base_repo}" folder="{ip_subfolder}" weights={adapters} names={adapter_names} {e}') + shared.log.error(f'IP adapter load: adapters={adapter_names} repo={repos} folders={subfolders} names={names} {e}') return True diff --git a/modules/sd_vae_approx.py b/modules/sd_vae_approx.py index 2b4399edb..78fe8f08b 100644 --- a/modules/sd_vae_approx.py +++ b/modules/sd_vae_approx.py @@ -46,7 +46,7 @@ def nn_approximation(sample): # Approximate NN sd_vae_approx_model.load_state_dict(approx_weights) sd_vae_approx_model.eval() sd_vae_approx_model.to(device, dtype) - shared.log.debug(f'VAE load: type=approximate model={model_path}') + shared.log.debug(f'VAE load: type=approximate model="{model_path}"') try: in_sample = sample.to(device, dtype).unsqueeze(0) sd_vae_approx_model.to(device, dtype) diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py index f99f296a8..4d213ad48 100644 --- a/modules/sd_vae_taesd.py +++ b/modules/sd_vae_taesd.py @@ -160,11 +160,11 @@ def decode(latents): download_model(model_path) if os.path.exists(model_path): taesd_models[f'{model_class}-decoder'] = TAESD(decoder_path=model_path, encoder_path=None) - shared.log.debug(f'VAE load: type=taesd model={model_path}') + shared.log.debug(f'VAE load: type=taesd model="{model_path}"') vae = taesd_models[f'{model_class}-decoder'] vae.decoder.to(devices.device, dtype) else: - shared.log.error(f'VAE load: type=taesd model={model_path} not found') + shared.log.error(f'VAE load: type=taesd model="{model_path}" not found') return latents if vae is None: return latents @@ -208,7 +208,7 @@ def encode(image): model_path = os.path.join(paths.models_path, "TAESD", f"tae{model_class}_encoder.pth") download_model(model_path) if os.path.exists(model_path): - shared.log.debug(f'VAE load: type=taesd model={model_path}') + shared.log.debug(f'VAE load: type=taesd model="{model_path}"') taesd_models[f'{model_class}-encoder'] = TAESD(encoder_path=model_path, decoder_path=None) vae = taesd_models[f'{model_class}-encoder'] vae.encoder.to(devices.device, devices.dtype_vae) diff --git a/modules/ui_common.py b/modules/ui_common.py index 5e873355b..9ad87c17f 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -319,13 +319,18 @@ def create_output_panel(tabname, preview=True, prompt=None, height=None): return result_gallery, generation_info, html_info, html_info_formatted, html_log -def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id, visible: bool = True): +def create_refresh_button(refresh_component, refresh_method, refreshed_args = None, elem_id = None, visible: bool = True): def refresh(): refresh_method() - args = refreshed_args() if callable(refreshed_args) else refreshed_args + if refreshed_args is None: + args = {"choices": refresh_method()} # pylint: disable=unnecessary-lambda-assignment + elif callable(refreshed_args): + args = refreshed_args() + else: + args = refreshed_args for k, v in args.items(): setattr(refresh_component, k, v) - return gr.update(**(args or {})) + return gr.update(**args) refresh_button = ui_components.ToolButton(value=ui_symbols.refresh, elem_id=elem_id, visible=visible) refresh_button.click(fn=refresh, inputs=[], outputs=[refresh_component]) diff --git a/scripts/ipadapter.py b/scripts/ipadapter.py index c7fcb3053..60c70b9dc 100644 --- a/scripts/ipadapter.py +++ b/scripts/ipadapter.py @@ -1,7 +1,7 @@ import json from PIL import Image import gradio as gr -from modules import scripts, processing, shared, ipadapter +from modules import scripts, processing, shared, ipadapter, ui_common MAX_ADAPTERS = 4 @@ -60,9 +60,12 @@ class Script(scripts.Script): for i in range(MAX_ADAPTERS): with gr.Accordion(f'Adapter {i+1}', visible=i==0) as unit: with gr.Row(): - adapters.append(gr.Dropdown(label='Adapter', choices=list(ipadapter.ADAPTERS), value='None')) - scales.append(gr.Slider(label='Scale', minimum=0.0, maximum=1.0, step=0.01, value=0.5)) - crops.append(gr.Checkbox(label='Crop', default=False, interactive=True)) + adapter = gr.Dropdown(label='Adapter', choices=list(ipadapter.get_adapters()), value='None') + adapters.append(adapter) + ui_common.create_refresh_button(adapter, ipadapter.get_adapters) + with gr.Row(): + scales.append(gr.Slider(label='Strength', minimum=0.0, maximum=1.0, step=0.01, value=0.5)) + crops.append(gr.Checkbox(label='Crop to portrait', default=False, interactive=True)) with gr.Row(): starts.append(gr.Slider(label='Start', minimum=0.0, maximum=1.0, step=0.1, value=0)) ends.append(gr.Slider(label='End', minimum=0.0, maximum=1.0, step=0.1, value=1)) From 0756e410c9f816568a519de0c268b933e8b136fb Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sun, 27 Oct 2024 13:10:06 -0400 Subject: [PATCH 12/81] add k-diffusion samplers to diffusers Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 7 ++-- modules/loader.py | 3 ++ modules/sd_models.py | 1 + modules/sd_samplers.py | 2 ++ scripts/apg.py | 5 ++- scripts/k_diff.py | 72 ++++++++++++++++++++++++++++++++++++++++++ 6 files changed, 87 insertions(+), 3 deletions(-) create mode 100644 scripts/k_diff.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 8040b1772..764ab83a7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -14,12 +14,15 @@ improvements: e.g. `https://civitai.com/api/download/models/72396?type=Model&format=SafeTensor&size=full&fp=fp16` - auto-search-and-download can be disabled in settings -> models -> auto-download this also disables reference models as they are auto-downloaded on first use as well -- sd3 loader enhancements +- sd3 enhancements: - report when loading incomplete model - - handle missing model components + - handle missing model components during load - handle component preloading - native lora handler - gguf transformer loader (prototype) +- samplers: + - support for original k-diffusion samplers + select scripts -> k-diffusion -> sampler - ipadapter: - list available adapters based on loaded model type - add adapter `ostris consistency` for sd15/sdxl diff --git a/modules/loader.py b/modules/loader.py index 05e5ec394..0711c2906 100644 --- a/modules/loader.py +++ b/modules/loader.py @@ -63,6 +63,9 @@ errors.install([gradio]) import pydantic # pylint: disable=W0611,C0411 timer.startup.record("pydantic") +import diffusers.utils.import_utils # pylint: disable=W0611,C0411 +diffusers.utils.import_utils._k_diffusion_available = True # pylint: disable=protected-access # monkey-patch since we use k-diffusion from git +diffusers.utils.import_utils._k_diffusion_version = '0.0.12' # pylint: disable=protected-access import diffusers # pylint: disable=W0611,C0411 import diffusers.loaders.single_file # pylint: disable=W0611,C0411 import huggingface_hub # pylint: disable=W0611,C0411 diff --git a/modules/sd_models.py b/modules/sd_models.py index 8e9535ec3..bc07e10f6 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -496,6 +496,7 @@ def move_model(model, device=None, force=False): raise e0 t1 = time.time() except Exception as e1: + t1 = time.time() shared.log.error(f'Model move: device={device} {e1}') if os.environ.get('SD_MOVE_DEBUG', None) or (t1-t0) > 0.1: shared.log.debug(f'Model move: device={device} class={model.__class__.__name__} accelerate={getattr(model, "has_accelerate", False)} fn={fn} time={t1-t0:.2f}') # pylint: disable=protected-access diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 82171e0b7..bbc2f360b 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -84,6 +84,8 @@ def create_sampler(name, model): if 'AuraFlow' in model.__class__.__name__: shared.log.warning(f'AuraFlow: sampler="{name}" unsupported') return None + if 'KDiffusion' in model.__class__.__name__: + return None if not hasattr(model, 'scheduler_config'): model.scheduler_config = sampler.sampler.config.copy() if hasattr(sampler.sampler, 'config') else {} model.scheduler = sampler.sampler diff --git a/scripts/apg.py b/scripts/apg.py index 3325d9333..c7e60c982 100644 --- a/scripts/apg.py +++ b/scripts/apg.py @@ -62,10 +62,13 @@ class Script(scripts.Script): def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, eta, momentum, threshold): # pylint: disable=arguments-differ, unused-argument from modules import apg + if self.orig_pipe is None: + return processed # restore pipeline - if shared.sd_model_type == "sdxl": + if shared.sd_model_type == "sdxl" or shared.sd_model_type == "sd": shared.sd_model = self.orig_pipe elif shared.sd_model_type == "sc": shared.sd_model.prior_pipe = self.orig_pipe apg.buffer = None + self.orig_pipe = None return processed diff --git a/scripts/k_diff.py b/scripts/k_diff.py new file mode 100644 index 000000000..3ce8207da --- /dev/null +++ b/scripts/k_diff.py @@ -0,0 +1,72 @@ +import inspect +import importlib +import gradio as gr +import diffusers +from modules import scripts, processing, shared, sd_models + + +class Script(scripts.Script): + supported_models = ['sd', 'sdxl'] + orig_pipe = None + library = importlib.import_module('k_diffusion') + + def title(self): + return 'K-Diffusion' + + def show(self, is_img2img): + return not is_img2img if shared.native else False + + def ui(self, _is_img2img): # ui elements + with gr.Row(): + gr.HTML('  K-Diffusion samplers
') + with gr.Row(): + sampler = gr.Dropdown(label="Sampler", choices=self.samplers()) + return [sampler] + + def samplers(self): + samplers = [] + sampling = getattr(self.library, 'sampling', None) + if sampling is None: + return samplers + for s in dir(sampling): + if s.startswith('sample_'): + samplers.append(s.replace('sample_', '')) + return samplers + + def callback(self, d): + _step = d['i'] + + def run(self, p: processing.StableDiffusionProcessing, sampler: str): # pylint: disable=arguments-differ + if shared.sd_model_type not in self.supported_models: + shared.log.warning(f'K-Diffusion: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={self.supported_models}') + return None + cls = None + if shared.sd_model_type == "sd": + cls = diffusers.pipelines.StableDiffusionKDiffusionPipeline + if shared.sd_model_type == "sdxl": + cls = diffusers.pipelines.StableDiffusionXLKDiffusionPipeline + if cls is None: + return None + self.orig_pipe = shared.sd_model + shared.sd_model = sd_models.switch_pipe(cls, shared.sd_model) + sampler = 'sample_' + sampler + + sampling = getattr(self.library, "sampling", None) + shared.sd_model.sampler = getattr(sampling, sampler) + + params = inspect.signature(shared.sd_model.sampler).parameters.values() + params = {param.name: param.default for param in params if param.default != inspect.Parameter.empty} + # if 'callback' in list(params): + # params['callback'] = self.callback + # if 'disable' in list(params): + # params['disable'] = False + shared.log.info(f'K-diffusion apply: class={shared.sd_model.__class__.__name__} sampler={sampler} params={params}') + p.extra_generation_params["Sampler"] = sampler + + def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, sampler): # pylint: disable=arguments-differ, unused-argument + if self.orig_pipe is None: + return processed + if shared.sd_model_type == "sdxl" or shared.sd_model_type == "sd": + shared.sd_model = self.orig_pipe + self.orig_pipe = None + return processed From d6237ba8aa60a8e158eb6dce2b20f0ec1a1ef107 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sun, 27 Oct 2024 17:51:23 -0400 Subject: [PATCH 13/81] update modernui Signed-off-by: Vladimir Mandic --- extensions-builtin/sdnext-modernui | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/extensions-builtin/sdnext-modernui b/extensions-builtin/sdnext-modernui index 9e9b2d8e5..9b721248d 160000 --- a/extensions-builtin/sdnext-modernui +++ b/extensions-builtin/sdnext-modernui @@ -1 +1 @@ -Subproject commit 9e9b2d8e57c21d7b6d35ca2c4e0fb38009d8aed0 +Subproject commit 9b721248d55021cbb3e2976ccfa984a8e5b96f39 From 3a2a639a66f79461ca74e736405743d894c7be01 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Mon, 28 Oct 2024 10:01:24 -0400 Subject: [PATCH 14/81] add prompt placeholder to detailer Signed-off-by: Vladimir Mandic --- modules/postprocess/yolo.py | 12 ++++++++++-- wiki | 2 +- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/modules/postprocess/yolo.py b/modules/postprocess/yolo.py index b162240e0..44f72237e 100644 --- a/modules/postprocess/yolo.py +++ b/modules/postprocess/yolo.py @@ -191,12 +191,20 @@ class YoloRestorer(Detailer): pp = None shared.opts.data['mask_apply_overlay'] = True resolution = 512 if shared.sd_model_type in ['none', 'sd', 'lcm', 'unknown'] else 1024 + orig_prompt: str = orig_p.get('all_prompts', [''])[0] + orig_negative: str = orig_p.get('all_negative_prompts', [''])[0] prompt: str = orig_p.get('refiner_prompt', '') negative: str = orig_p.get('refiner_negative', '') if len(prompt) == 0: - prompt = orig_p.get('all_prompts', [''])[0] + prompt = orig_prompt + else: + prompt = prompt.replace('[PROMPT]', orig_prompt) + prompt = prompt.replace('[prompt]', orig_prompt) if len(negative) == 0: - negative = orig_p.get('all_negative_prompts', [''])[0] + negative = orig_negative + else: + negative = negative.replace('[PROMPT]', orig_negative) + negative = negative.replace('[prompt]', orig_negative) prompt_lines = prompt.split('\n') negative_lines = negative.split('\n') prompt = prompt_lines[i % len(prompt_lines)] diff --git a/wiki b/wiki index 53def8203..84d0a46e9 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 53def8203b6799cbd659327c1af6aa5af9cb9a70 +Subproject commit 84d0a46e9d686dd971d51ae2c1d29e60e57bf233 From 42fb7168df5d6322882e5faf8f727ba12425fb1c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Mon, 28 Oct 2024 11:43:22 -0400 Subject: [PATCH 15/81] update changelog Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 4 +++- modules/scripts.py | 6 ++++++ wiki | 2 +- 3 files changed, 10 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 764ab83a7..c955f0ce3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # Change Log for SD.Next -## Update for 2024-10-27 +## Update for 2024-10-28 improvements: - model selector: @@ -26,6 +26,8 @@ improvements: - ipadapter: - list available adapters based on loaded model type - add adapter `ostris consistency` for sd15/sdxl +- detailer: + - add `[prompt]` to refine/defailer prompts as placeholder referencing original prompt - torch - CUDA set device memory limit in *settings -> compute settings -> torch memory limit* diff --git a/modules/scripts.py b/modules/scripts.py index 15da9c070..8a67d0a50 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -3,6 +3,7 @@ import re import sys import time from collections import namedtuple +from dataclasses import dataclass import gradio as gr from modules import paths, script_callbacks, extensions, script_loading, scripts_postprocessing, errors, timer @@ -23,6 +24,11 @@ class PostprocessBatchListArgs: self.images = images +@dataclass +class OnComponent: + component: gr.blocks.Block + + class Script: parent = None name = None diff --git a/wiki b/wiki index 84d0a46e9..23cc41bc4 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 84d0a46e9d686dd971d51ae2c1d29e60e57bf233 +Subproject commit 23cc41bc44ce06a9f6620452817f3cbfbed86f84 From 90c4a6126be0c8576422805b62603162cb3c5172 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Mon, 28 Oct 2024 13:43:09 -0400 Subject: [PATCH 16/81] ip-instruct: experimental Signed-off-by: Vladimir Mandic --- .gitignore | 4 +- repositories/ip_instruct | 1 + scripts/ipinstruct.py | 111 +++++++++++++++++++++++++++++++++++++++ wiki | 2 +- 4 files changed, 116 insertions(+), 2 deletions(-) create mode 160000 repositories/ip_instruct create mode 100644 scripts/ipinstruct.py diff --git a/.gitignore b/.gitignore index 2bc819860..4d0ab36c6 100644 --- a/.gitignore +++ b/.gitignore @@ -44,6 +44,9 @@ tunableop_results*.csv !webui.sh !package.json +# dynamically generated +/repositories/ip-instruct/ + # all dynamic stuff /extensions/**/* /outputs/**/* @@ -59,7 +62,6 @@ tunableop_results*.csv .vscode/ .idea/ /localizations - .*/ # force included diff --git a/repositories/ip_instruct b/repositories/ip_instruct new file mode 160000 index 000000000..57efbe40a --- /dev/null +++ b/repositories/ip_instruct @@ -0,0 +1 @@ +Subproject commit 57efbe40a4a05d5688a869804244f9312a660efb diff --git a/scripts/ipinstruct.py b/scripts/ipinstruct.py new file mode 100644 index 000000000..2ba4c4ff5 --- /dev/null +++ b/scripts/ipinstruct.py @@ -0,0 +1,111 @@ +""" +Repo: +Models: +adapter: `sd15`=0.35GB `sdxl`=2.12GB `sd3`=1.56GB +encoder: `laion/CLIP-ViT-H-14-laion2B-s32B-b79K`=3.94GB +""" +import os +import importlib +import gradio as gr +from modules import scripts, processing, shared, sd_models, devices + + +repo = 'https://github.com/vladmandic/IP-Instruct' +repo_id = 'CiaraRowles/IP-Adapter-Instruct' +encoder = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" +folder = os.path.join('repositories', 'ip_instruct') + + +class Script(scripts.Script): + def __init__(self): + super().__init__() + self.orig_pipe = None + self.lib = None + + def title(self): + return 'IP Instruct' + + def show(self, is_img2img): + if shared.cmd_opts.experimental: + return not is_img2img if shared.native else False + else: + return False + + def install(self): + if not os.path.exists(folder): + from installer import clone + clone(repo, folder) + if self.lib is None: + self.lib = importlib.import_module('ip_instruct.ip_adapter') + + + def ui(self, _is_img2img): # ui elements + with gr.Row(): + gr.HTML('  IP Adapter Instruct
') + with gr.Row(): + query = gr.Textbox(lines=1, label='Query', placeholder='use the composition from the image') + with gr.Row(): + image = gr.Image(value=None, label='Image', type='pil', source='upload', width=256, height=256) + with gr.Row(): + strength = gr.Slider(label="Strength", value=1.0, minimum=0, maximum=2.0, step=0.05) + tokens = gr.Slider(label="Tokens", value=4, minimum=1, maximum=32, step=1) + with gr.Row(): + instruct_guidance = gr.Slider(label="Guidance", value=6.0, minimum=1.0, maximum=15.0, step=0.05) + image_guidance = gr.Slider(label="Guidance", value=0.5, minimum=0, maximum=1.0, step=0.05) + return [query, image, strength, tokens, instruct_guidance, image_guidance] + + def run(self, p: processing.StableDiffusionProcessing, query, image, strength, tokens, instruct_guidance, image_guidance): # pylint: disable=arguments-differ + supported_model_list = ['sd', 'sdxl', 'sd3'] + if shared.sd_model_type not in supported_model_list: + shared.log.warning(f'IP-Instruct: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}') + return None + self.install() + if self.lib is None: + shared.log.error('IP-Instruct: failed to import library') + return None + self.orig_pipe = shared.sd_model + if shared.sd_model_type == 'sdxl': + pipe = self.lib.StableDiffusionXLPipelineExtraCFG + cls = self.lib.IPAdapterInstructSDXL + ckpt = "ip-adapter-instruct-sdxl.bin" + elif shared.sd_model_type == 'sd3': + pipe = self.lib.StableDiffusion3PipelineExtraCFG + cls = self.lib.IPAdapter_sd3_Instruct + ckpt = "ip-adapter-instruct-sd3.bin" + else: + pipe = self.lib.StableDiffusionPipelineCFG + cls = self.lib.IPAdapterInstruct + ckpt = "ip-adapter-instruct-sd15.bin" + + shared.sd_model = sd_models.switch_pipe(pipe, shared.sd_model) + + import huggingface_hub as hf + ip_ckpt = hf.hf_hub_download(repo_id=repo_id, filename=ckpt, cache_dir=shared.opts.hfcache_dir) + ip_model = cls(shared.sd_model, encoder, ip_ckpt, device=devices.device, dtypein=devices.dtype, num_tokens=tokens) + processing.fix_seed(p) + shared.log.debug(f'IP-Instruct: class={shared.sd_model.__class__.__name__} wrapper={ip_model.__class__.__name__} encoder={encoder} adapter={ckpt}') + shared.log.info(f'IP-Instruct: image={image} query="{query}" strength={strength} tokens={tokens} instruct_guidance={instruct_guidance} image_guidance={image_guidance}') + + image_list = ip_model.generate( + query = query, + scale = strength, + instruct_guidance_scale = instruct_guidance, + image_guidance_scale = image_guidance, + + prompt = p.prompt, + pil_image = image, + num_samples = 1, + num_inference_steps = p.steps, + seed = p.seed, + guidance_scale = p.cfg_scale, + auto_scale = False, + simple_cfg_mode = False, + ) + processed = processing.Processed(p, images_list=image_list, seed=p.seed, subseed=p.subseed, index_of_first_image=0) # manually created processed object + p.extra_generation_params["IPInstruct"] = f'' + return processed + + def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, **kwargs): # pylint: disable=unused-argument + if self.orig_pipe is not None: + shared.sd_model = self.orig_pipe + return processed diff --git a/wiki b/wiki index 23cc41bc4..b2d3110d4 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 23cc41bc44ce06a9f6620452817f3cbfbed86f84 +Subproject commit b2d3110d42ef1417008295425437721b25369cb1 From 3650af8b863a321efffa03716dd1fb94eb28d450 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Mon, 28 Oct 2024 14:31:44 -0400 Subject: [PATCH 17/81] cleanup Signed-off-by: Vladimir Mandic --- modules/processing_correction.py | 17 ++++++++++------- repositories/ip_instruct | 2 +- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/modules/processing_correction.py b/modules/processing_correction.py index c52f30ab3..e715d8c49 100644 --- a/modules/processing_correction.py +++ b/modules/processing_correction.py @@ -7,7 +7,8 @@ import os import torch from modules import shared, sd_vae_taesd, devices -debug = shared.log.trace if os.environ.get('SD_HDR_DEBUG', None) is not None else lambda *args, **kwargs: None +debug_enabled = os.environ.get('SD_HDR_DEBUG', None) is not None +debug = shared.log.trace if debug_enabled else lambda *args, **kwargs: None debug('Trace: HDR') @@ -119,16 +120,18 @@ def correction_callback(p, timestep, kwargs): if not any([p.hdr_clamp, p.hdr_mode, p.hdr_maximize, p.hdr_sharpen, p.hdr_color, p.hdr_brightness, p.hdr_tint_ratio]): return kwargs latents = kwargs["latents"] - debug('') - debug(f' Timestep: {timestep}') + if debug_enabled: + debug('') + debug(f' Timestep: {timestep}') # debug(f'HDR correction: latents={latents.shape}') if len(latents.shape) == 4: # standard batched latent for i in range(latents.shape[0]): latents[i] = correction(p, timestep, latents[i]) - debug(f"Full Mean: {latents[i].mean().item()}") - debug(f"Channel Means: {latents[i].mean(dim=(-1, -2), keepdim=True).flatten().float().cpu().numpy()}") - debug(f"Channel Mins: {latents[i].min(-1, keepdim=True)[0].min(-2, keepdim=True)[0].flatten().float().cpu().numpy()}") - debug(f"Channel Maxes: {latents[i].max(-1, keepdim=True)[0].min(-2, keepdim=True)[0].flatten().float().cpu().numpy()}") + if debug_enabled: + debug(f"Full Mean: {latents[i].mean().item()}") + debug(f"Channel Means: {latents[i].mean(dim=(-1, -2), keepdim=True).flatten().float().cpu().numpy()}") + debug(f"Channel Mins: {latents[i].min(-1, keepdim=True)[0].min(-2, keepdim=True)[0].flatten().float().cpu().numpy()}") + debug(f"Channel Maxes: {latents[i].max(-1, keepdim=True)[0].min(-2, keepdim=True)[0].flatten().float().cpu().numpy()}") elif len(latents.shape) == 5 and latents.shape[0] == 1: # probably animatediff latents = latents.squeeze(0).permute(1, 0, 2, 3) for i in range(latents.shape[0]): diff --git a/repositories/ip_instruct b/repositories/ip_instruct index 57efbe40a..004d91f09 160000 --- a/repositories/ip_instruct +++ b/repositories/ip_instruct @@ -1 +1 @@ -Subproject commit 57efbe40a4a05d5688a869804244f9312a660efb +Subproject commit 004d91f09c6492133573404664c0341cbd4d19b8 From 81dd86bf64119c31b87d0b4f583a6f9d44ccb67c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Mon, 28 Oct 2024 15:32:01 -0400 Subject: [PATCH 18/81] update Signed-off-by: Vladimir Mandic --- repositories/ip_instruct | 1 - scripts/k_diff.py | 7 ++++++- 2 files changed, 6 insertions(+), 2 deletions(-) delete mode 160000 repositories/ip_instruct diff --git a/repositories/ip_instruct b/repositories/ip_instruct deleted file mode 160000 index 004d91f09..000000000 --- a/repositories/ip_instruct +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 004d91f09c6492133573404664c0341cbd4d19b8 diff --git a/scripts/k_diff.py b/scripts/k_diff.py index 3ce8207da..711115aa1 100644 --- a/scripts/k_diff.py +++ b/scripts/k_diff.py @@ -8,7 +8,10 @@ from modules import scripts, processing, shared, sd_models class Script(scripts.Script): supported_models = ['sd', 'sdxl'] orig_pipe = None - library = importlib.import_module('k_diffusion') + try: + library = importlib.import_module('k_diffusion') + except Exception: + library = None def title(self): return 'K-Diffusion' @@ -40,6 +43,8 @@ class Script(scripts.Script): if shared.sd_model_type not in self.supported_models: shared.log.warning(f'K-Diffusion: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={self.supported_models}') return None + if self.library is None: + return cls = None if shared.sd_model_type == "sd": cls = diffusers.pipelines.StableDiffusionKDiffusionPipeline From ce2234178d760104ef6441192c24940eed67aa4a Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 11:02:39 -0400 Subject: [PATCH 19/81] add sd35 medium Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 8 +++++++- html/reference.json | 12 ++++++++++-- installer.py | 2 +- modules/model_sd3.py | 26 +++++++++++++++++++++++--- modules/modelloader.py | 2 +- wiki | 2 +- 6 files changed, 43 insertions(+), 9 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index c955f0ce3..cfa77c3bf 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # Change Log for SD.Next -## Update for 2024-10-28 +## Update for 2024-10-29 improvements: - model selector: @@ -19,6 +19,7 @@ improvements: - handle missing model components during load - handle component preloading - native lora handler + - support for all sd35 variants: *medium/large/large-turbo* - gguf transformer loader (prototype) - samplers: - support for original k-diffusion samplers @@ -37,6 +38,11 @@ improvements: - OpenVINO: add accuracy option - ZLUDA: guess GPU arch - major model load refactor +- wiki: new articles + - [Gated Access Wiki](https://github.com/vladmandic/automatic/wiki/Gated) + - [Quantization Wiki](https://github.com/vladmandic/automatic/wiki/Quantization) + - [Offloading Wiki](https://github.com/vladmandic/automatic/wiki/Offload) + fixes: - fix send-to-control diff --git a/html/reference.json b/html/reference.json index 8d26433e7..4a549586f 100644 --- a/html/reference.json +++ b/html/reference.json @@ -119,11 +119,19 @@ "preview": "stabilityai--stable-diffusion-3.jpg", "extras": "sampler: Default, cfg_scale: 7.0" }, + "StabilityAI Stable Diffusion 3.5 Medium": { + "path": "stabilityai/stable-diffusion-3.5-medium", + "skip": true, + "variant": "fp16", + "desc": "Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer with improvements (MMDiT-X) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.", + "preview": "stabilityai--stable-diffusion-3_5.jpg", + "extras": "sampler: Default, cfg_scale: 7.0" + }, "StabilityAI Stable Diffusion 3.5 Large": { "path": "stabilityai/stable-diffusion-3.5-large", "skip": true, "variant": "fp16", - "desc": "Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency", + "desc": "Stable Diffusion 3.5 Large is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.", "preview": "stabilityai--stable-diffusion-3_5.jpg", "extras": "sampler: Default, cfg_scale: 7.0" }, @@ -131,7 +139,7 @@ "path": "stabilityai/stable-diffusion-3.5-large-turbo", "skip": true, "variant": "fp16", - "desc": "Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency", + "desc": "Stable Diffusion 3.5 Large Turbo is a Multimodal Diffusion Transformer (MMDiT) text-to-image model with Adversarial Diffusion Distillation (ADD) that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency, with a focus on fewer inference steps.", "preview": "stabilityai--stable-diffusion-3_5.jpg", "extras": "sampler: Default, cfg_scale: 7.0" }, diff --git a/installer.py b/installer.py index 207dde388..f65b0e58f 100644 --- a/installer.py +++ b/installer.py @@ -455,7 +455,7 @@ def check_python(supported_minors=[9, 10, 11, 12], reason=None): # check diffusers version def check_diffusers(): - sha = '435f6b7e47c031f98b8374b1689e1abeb17bfdb6' + sha = '0d1d267b12e47b40b0e8f265339c76e0f45f8c49' pkg = pkg_resources.working_set.by_key.get('diffusers', None) minor = int(pkg.version.split('.')[1] if pkg is not None else 0) cur = opts.get('diffusers_version', '') if minor > 0 else '' diff --git a/modules/model_sd3.py b/modules/model_sd3.py index da99e6c4b..8e358b8b1 100644 --- a/modules/model_sd3.py +++ b/modules/model_sd3.py @@ -49,6 +49,24 @@ def load_overrides(kwargs, cache_dir): return kwargs +def create_bnb_config(kwargs): + if len(shared.opts.bnb_quantization) > 0: + if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs: + from modules.model_quant import load_bnb + load_bnb('Load model: type=SD3') + bnb_config = diffusers.BitsAndBytesConfig( + load_in_8bit=shared.opts.bnb_quantization_type in ['fp8'], + load_in_4bit=shared.opts.bnb_quantization_type in ['nf4', 'fp4'], + bnb_4bit_quant_storage=shared.opts.bnb_quantization_storage, + bnb_4bit_quant_type=shared.opts.bnb_quantization_type, + bnb_4bit_compute_dtype=devices.dtype + ) + kwargs['quantization_config'] = bnb_config + shared.log.debug(f'Quantization: module=all type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}') + + return kwargs + + def load_quants(kwargs, repo_id, cache_dir): if len(shared.opts.bnb_quantization) > 0: from modules.model_quant import load_bnb @@ -127,7 +145,7 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None): kwargs = load_quants(kwargs, repo_id, cache_dir) loader = diffusers.StableDiffusion3Pipeline.from_pretrained - if fn is not None and os.path.exists(fn): + if fn is not None and os.path.exists(fn) and os.path.isfile(fn): if fn.endswith('.safetensors'): loader = diffusers.StableDiffusion3Pipeline.from_single_file kwargs = load_missing(kwargs, fn, cache_dir) @@ -139,8 +157,10 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None): else: kwargs['variant'] = 'fp16' - shared.log.debug(f'Load model: type=SD3 preloaded={list(kwargs)}') + shared.log.debug(f'Load model: type=SD3 kwargs={list(kwargs)}') + kwargs = create_bnb_config(kwargs) + print('HERE', repo_id, kwargs) pipe = loader( repo_id, torch_dtype=devices.dtype, @@ -148,5 +168,5 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None): config=config, **kwargs, ) - devices.torch_gc() + devices.torch_gc(force=True) return pipe diff --git a/modules/modelloader.py b/modules/modelloader.py index 70ea7cabb..8eab91597 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -318,7 +318,7 @@ def load_diffusers_models(clear=True): def find_diffuser(name: str, full=False): repo = [r for r in diffuser_repos if name == r['name'] or name == r['friendly'] or name == r['path']] if len(repo) > 0: - return repo['name'] + return [repo[0]['name']] hf_api = hf.HfApi() models = list(hf_api.list_models(model_name=name, library=['diffusers'], full=True, limit=20, sort="downloads", direction=-1)) shared.log.debug(f'Searching diffusers models: {name} {len(models) > 0}') diff --git a/wiki b/wiki index b2d3110d4..6e278d8aa 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit b2d3110d42ef1417008295425437721b25369cb1 +Subproject commit 6e278d8aa2d559b9b46357086c341b9bd8cf33fd From f98d139c36547277a9c7e43863c41716d9233b99 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 11:11:09 -0400 Subject: [PATCH 20/81] update torch==2.5.1 Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 3 ++- installer.py | 7 ++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index cfa77c3bf..a69641163 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -23,13 +23,14 @@ improvements: - gguf transformer loader (prototype) - samplers: - support for original k-diffusion samplers - select scripts -> k-diffusion -> sampler + select via *scripts -> k-diffusion -> sampler* - ipadapter: - list available adapters based on loaded model type - add adapter `ostris consistency` for sd15/sdxl - detailer: - add `[prompt]` to refine/defailer prompts as placeholder referencing original prompt - torch + - use `torch==2.5.1` by default on supported platforms - CUDA set device memory limit in *settings -> compute settings -> torch memory limit* default=0 meaning no limit, if set torch will limit memory usage to specified fraction diff --git a/installer.py b/installer.py index f65b0e58f..d95d14f2b 100644 --- a/installer.py +++ b/installer.py @@ -490,7 +490,7 @@ def install_cuda(): log.info('CUDA: nVidia toolkit detected') install('onnxruntime-gpu', 'onnxruntime-gpu', ignore=True, quiet=True) # return os.environ.get('TORCH_COMMAND', 'torch torchvision --index-url https://download.pytorch.org/whl/cu124') - return os.environ.get('TORCH_COMMAND', 'torch==2.4.1+cu124 torchvision==0.19.1+cu124 --index-url https://download.pytorch.org/whl/cu124') + return os.environ.get('TORCH_COMMAND', 'torch==2.5.1+cu124 torchvision==0.20.1+cu124 --index-url https://download.pytorch.org/whl/cu124') def install_rocm_zluda(): @@ -572,8 +572,9 @@ def install_rocm_zluda(): log.info('Using CPU-only torch') torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision') else: - if rocm.version is None or float(rocm.version) >= 6.1: # assume the latest if version check fails - #torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision --index-url https://download.pytorch.org/whl/rocm6.1') + if rocm.version is None or float(rocm.version) > 6.1: # assume the latest if version check fails + torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.5.1+rocm6.2 torchvision==0.20.1+rocm6.2 --index-url https://download.pytorch.org/whl/rocm6.2') + elif rocm.version == "6.1": # lock to 2.4.1, older rocm (5.7) uses torch 2.3 torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.4.1+rocm6.1 torchvision==0.19.1+rocm6.1 --index-url https://download.pytorch.org/whl/rocm6.1') elif rocm.version == "6.0": # lock to 2.4.1, older rocm (5.7) uses torch 2.3 torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.4.1+rocm6.0 torchvision==0.19.1+rocm6.0 --index-url https://download.pytorch.org/whl/rocm6.0') From 31e0bf8ea79413234a718e69eec390f64d7b1deb Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 11:45:55 -0400 Subject: [PATCH 21/81] update requirements Signed-off-by: Vladimir Mandic --- installer.py | 1 + requirements.txt | 11 +++++------ 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/installer.py b/installer.py index d95d14f2b..1595eef74 100644 --- a/installer.py +++ b/installer.py @@ -820,6 +820,7 @@ def install_packages(): log.info('Verifying packages') clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git") install(clip_package, 'clip', quiet=True) + install('open-clip-torch', no_deps=True, quiet=True) # tensorflow_package = os.environ.get('TENSORFLOW_PACKAGE', 'tensorflow==2.13.0') # tensorflow_package = os.environ.get('TENSORFLOW_PACKAGE', None) # if tensorflow_package is not None: diff --git a/requirements.txt b/requirements.txt index fd8e3ab4a..208cfba3a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -36,11 +36,11 @@ torchsde==0.2.6 antlr4-python3-runtime==4.9.3 requests==2.32.3 tqdm==4.66.5 -accelerate==1.0.0 +accelerate==1.0.1 opencv-contrib-python-headless==4.9.0.80 einops==0.4.1 gradio==3.43.2 -huggingface_hub==0.25.2 +huggingface_hub==0.26.2 numexpr==2.8.8 numpy==1.26.4 numba==0.59.1 @@ -49,7 +49,7 @@ scipy pandas protobuf==4.25.3 pytorch_lightning==1.9.4 -tokenizers==0.20.0 +tokenizers==0.20.1 transformers==4.46.0 urllib3==1.26.19 Pillow==10.4.0 @@ -61,8 +61,7 @@ torchdiffeq dctorch scikit-image seam-carving -open-clip-torch -# TODO temporary block for torch==2.5.0 -torchvision!=0.20.0 +# block torch!=2.5.0 +torchvision!=0.20.0 From 13f6fb388c5687e7095bdc698c99849133662c0a Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 11:52:00 -0400 Subject: [PATCH 22/81] revert torch on rocm Signed-off-by: Vladimir Mandic --- installer.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/installer.py b/installer.py index 1595eef74..ab883ea97 100644 --- a/installer.py +++ b/installer.py @@ -573,7 +573,8 @@ def install_rocm_zluda(): torch_command = os.environ.get('TORCH_COMMAND', 'torch torchvision') else: if rocm.version is None or float(rocm.version) > 6.1: # assume the latest if version check fails - torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.5.1+rocm6.2 torchvision==0.20.1+rocm6.2 --index-url https://download.pytorch.org/whl/rocm6.2') + # torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.5.1+rocm6.2 torchvision==0.20.1+rocm6.2 --index-url https://download.pytorch.org/whl/rocm6.2') + torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.4.1+rocm6.1 torchvision==0.19.1+rocm6.1 --index-url https://download.pytorch.org/whl/rocm6.1') elif rocm.version == "6.1": # lock to 2.4.1, older rocm (5.7) uses torch 2.3 torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.4.1+rocm6.1 torchvision==0.19.1+rocm6.1 --index-url https://download.pytorch.org/whl/rocm6.1') elif rocm.version == "6.0": # lock to 2.4.1, older rocm (5.7) uses torch 2.3 From 1f57462ad3c9baf2b4af887f2304f948af76c2dd Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 11:57:49 -0400 Subject: [PATCH 23/81] cleanup Signed-off-by: Vladimir Mandic --- modules/model_sd3.py | 1 - 1 file changed, 1 deletion(-) diff --git a/modules/model_sd3.py b/modules/model_sd3.py index 8e358b8b1..4dd4773da 100644 --- a/modules/model_sd3.py +++ b/modules/model_sd3.py @@ -160,7 +160,6 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None): shared.log.debug(f'Load model: type=SD3 kwargs={list(kwargs)}') kwargs = create_bnb_config(kwargs) - print('HERE', repo_id, kwargs) pipe = loader( repo_id, torch_dtype=devices.dtype, From 58220b6497a5c3621adf982d6b063f1b8b507923 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 12:30:20 -0400 Subject: [PATCH 24/81] flux enabled bnb quant on-the-fly Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 3 +++ installer.py | 4 ++-- modules/loader.py | 2 ++ modules/model_flux.py | 25 +++++++++++++------------ modules/model_quant.py | 18 ++++++++++++++++++ modules/model_sd3.py | 25 +++---------------------- 6 files changed, 41 insertions(+), 36 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index a69641163..1141bf6a6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -15,12 +15,15 @@ improvements: - auto-search-and-download can be disabled in settings -> models -> auto-download this also disables reference models as they are auto-downloaded on first use as well - sd3 enhancements: + - allow on-the-fly bnb quantization during load - report when loading incomplete model - handle missing model components during load - handle component preloading - native lora handler - support for all sd35 variants: *medium/large/large-turbo* - gguf transformer loader (prototype) +- flux.1 enhancements: + - allow on-the-fly bnb quantization during load - samplers: - support for original k-diffusion samplers select via *scripts -> k-diffusion -> sampler* diff --git a/installer.py b/installer.py index ab883ea97..a17efa83a 100644 --- a/installer.py +++ b/installer.py @@ -227,9 +227,9 @@ def installed(package, friendly: str = None, reload = False, quiet = False): exact = pkg_version == p[1] if not exact and not quiet: if args.experimental: - log.warning(f"Package: {p[0]} {pkg_version} required {p[1]} allowing experimental") + log.warning(f"Package: {p[0]} installed={pkg_version} required={p[1]} allowing experimental") else: - log.warning(f"Package: {p[0]} {pkg_version} required {p[1]} version mismatch") + log.warning(f"Package: {p[0]} installed={pkg_version} required={p[1]} version mismatch") ok = ok and (exact or args.experimental) else: if not quiet: diff --git a/modules/loader.py b/modules/loader.py index 0711c2906..acbd47f37 100644 --- a/modules/loader.py +++ b/modules/loader.py @@ -66,6 +66,8 @@ timer.startup.record("pydantic") import diffusers.utils.import_utils # pylint: disable=W0611,C0411 diffusers.utils.import_utils._k_diffusion_available = True # pylint: disable=protected-access # monkey-patch since we use k-diffusion from git diffusers.utils.import_utils._k_diffusion_version = '0.0.12' # pylint: disable=protected-access +diffusers.utils.import_utils._bitsandbytes_available = True # pylint: disable=protected-access +diffusers.utils.import_utils._bitsandbytes_version = '0.43.3' # pylint: disable=protected-access import diffusers # pylint: disable=W0611,C0411 import diffusers.loaders.single_file # pylint: disable=W0611,C0411 import huggingface_hub # pylint: disable=W0611,C0411 diff --git a/modules/model_flux.py b/modules/model_flux.py index 9bbc24f83..c605702c8 100644 --- a/modules/model_flux.py +++ b/modules/model_flux.py @@ -287,25 +287,26 @@ def load_flux(checkpoint_info, diffusers_load_config): # triggered by opts.sd_ch errors.display(e, 'FLUX Quanto:') # initialize pipeline with pre-loaded components - components = {} - transformer, text_encoder_2 = quant_flux_bnb(checkpoint_info, transformer, text_encoder_2) + kwargs = {} + # transformer, text_encoder_2 = quant_flux_bnb(checkpoint_info, transformer, text_encoder_2) if transformer is not None: - components['transformer'] = transformer + kwargs['transformer'] = transformer sd_unet.loaded_unet = shared.opts.sd_unet if text_encoder_1 is not None: - components['text_encoder'] = text_encoder_1 + kwargs['text_encoder'] = text_encoder_1 model_te.loaded_te = shared.opts.sd_text_encoder if text_encoder_2 is not None: - components['text_encoder_2'] = text_encoder_2 + kwargs['text_encoder_2'] = text_encoder_2 model_te.loaded_te = shared.opts.sd_text_encoder if vae is not None: - components['vae'] = vae - shared.log.debug(f'Load model: type=FLUX preloaded={list(components)}') + kwargs['vae'] = vae + shared.log.debug(f'Load model: type=FLUX preloaded={list(kwargs)}') if repo_id == 'sayakpaul/flux.1-dev-nf4': repo_id = 'black-forest-labs/FLUX.1-dev' # workaround since sayakpaul model is missing model_index.json - for c in components: - if components[c].dtype == torch.float32 and devices.dtype != torch.float32: - shared.log.warning(f'Load model: type=FLUX component={c} dtype={components[c].dtype} cast dtype={devices.dtype}') - components[c] = components[c].to(dtype=devices.dtype) - pipe = diffusers.FluxPipeline.from_pretrained(repo_id, cache_dir=shared.opts.diffusers_dir, **components, **diffusers_load_config) + for c in kwargs: + if kwargs[c].dtype == torch.float32 and devices.dtype != torch.float32: + shared.log.warning(f'Load model: type=FLUX component={c} dtype={kwargs[c].dtype} cast dtype={devices.dtype}') + kwargs[c] = kwargs[c].to(dtype=devices.dtype) + kwargs = model_quant.create_bnb_config(kwargs) + pipe = diffusers.FluxPipeline.from_pretrained(repo_id, cache_dir=shared.opts.diffusers_dir, **kwargs, **diffusers_load_config) return pipe diff --git a/modules/model_quant.py b/modules/model_quant.py index 1348662de..0e54e496b 100644 --- a/modules/model_quant.py +++ b/modules/model_quant.py @@ -1,4 +1,5 @@ import sys +import diffusers from installer import install, log @@ -6,6 +7,23 @@ bnb = None quanto = None +def create_bnb_config(kwargs): + from modules import shared, devices + if len(shared.opts.bnb_quantization) > 0: + if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs: + load_bnb('Load model') + bnb_config = diffusers.BitsAndBytesConfig( + load_in_8bit=shared.opts.bnb_quantization_type in ['fp8'], + load_in_4bit=shared.opts.bnb_quantization_type in ['nf4', 'fp4'], + bnb_4bit_quant_storage=shared.opts.bnb_quantization_storage, + bnb_4bit_quant_type=shared.opts.bnb_quantization_type, + bnb_4bit_compute_dtype=devices.dtype + ) + kwargs['quantization_config'] = bnb_config + shared.log.debug(f'Quantization: module=all type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}') + return kwargs + + def load_bnb(msg='', silent=False): global bnb # pylint: disable=global-statement if bnb is not None: diff --git a/modules/model_sd3.py b/modules/model_sd3.py index 4dd4773da..e369b197a 100644 --- a/modules/model_sd3.py +++ b/modules/model_sd3.py @@ -1,7 +1,7 @@ import os import diffusers import transformers -from modules import shared, devices, sd_models, sd_unet, model_te +from modules import shared, devices, sd_models, sd_unet, model_te, model_quant def load_overrides(kwargs, cache_dir): @@ -49,28 +49,9 @@ def load_overrides(kwargs, cache_dir): return kwargs -def create_bnb_config(kwargs): - if len(shared.opts.bnb_quantization) > 0: - if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs: - from modules.model_quant import load_bnb - load_bnb('Load model: type=SD3') - bnb_config = diffusers.BitsAndBytesConfig( - load_in_8bit=shared.opts.bnb_quantization_type in ['fp8'], - load_in_4bit=shared.opts.bnb_quantization_type in ['nf4', 'fp4'], - bnb_4bit_quant_storage=shared.opts.bnb_quantization_storage, - bnb_4bit_quant_type=shared.opts.bnb_quantization_type, - bnb_4bit_compute_dtype=devices.dtype - ) - kwargs['quantization_config'] = bnb_config - shared.log.debug(f'Quantization: module=all type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}') - - return kwargs - - def load_quants(kwargs, repo_id, cache_dir): if len(shared.opts.bnb_quantization) > 0: - from modules.model_quant import load_bnb - load_bnb('Load model: type=SD3') + model_quant.load_bnb('Load model: type=SD3') bnb_config = diffusers.BitsAndBytesConfig( load_in_8bit=shared.opts.bnb_quantization_type in ['fp8'], load_in_4bit=shared.opts.bnb_quantization_type in ['nf4', 'fp4'], @@ -159,7 +140,7 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None): shared.log.debug(f'Load model: type=SD3 kwargs={list(kwargs)}') - kwargs = create_bnb_config(kwargs) + kwargs = model_quant.create_bnb_config(kwargs) pipe = loader( repo_id, torch_dtype=devices.dtype, From 7b150ba361014260bdedd19dbbb60ee839b09029 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 12:36:03 -0400 Subject: [PATCH 25/81] fix bnb loader Signed-off-by: Vladimir Mandic --- modules/loader.py | 2 -- modules/model_quant.py | 4 +++- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/loader.py b/modules/loader.py index acbd47f37..0711c2906 100644 --- a/modules/loader.py +++ b/modules/loader.py @@ -66,8 +66,6 @@ timer.startup.record("pydantic") import diffusers.utils.import_utils # pylint: disable=W0611,C0411 diffusers.utils.import_utils._k_diffusion_available = True # pylint: disable=protected-access # monkey-patch since we use k-diffusion from git diffusers.utils.import_utils._k_diffusion_version = '0.0.12' # pylint: disable=protected-access -diffusers.utils.import_utils._bitsandbytes_available = True # pylint: disable=protected-access -diffusers.utils.import_utils._bitsandbytes_version = '0.43.3' # pylint: disable=protected-access import diffusers # pylint: disable=W0611,C0411 import diffusers.loaders.single_file # pylint: disable=W0611,C0411 import huggingface_hub # pylint: disable=W0611,C0411 diff --git a/modules/model_quant.py b/modules/model_quant.py index 0e54e496b..fab3a93a0 100644 --- a/modules/model_quant.py +++ b/modules/model_quant.py @@ -11,7 +11,7 @@ def create_bnb_config(kwargs): from modules import shared, devices if len(shared.opts.bnb_quantization) > 0: if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs: - load_bnb('Load model') + load_bnb() bnb_config = diffusers.BitsAndBytesConfig( load_in_8bit=shared.opts.bnb_quantization_type in ['fp8'], load_in_4bit=shared.opts.bnb_quantization_type in ['nf4', 'fp4'], @@ -34,6 +34,8 @@ def load_bnb(msg='', silent=False): try: import bitsandbytes bnb = bitsandbytes + diffusers.utils.import_utils._bitsandbytes_available = True # pylint: disable=protected-access + diffusers.utils.import_utils._bitsandbytes_version = '0.43.3' # pylint: disable=protected-access return bnb except Exception as e: if len(msg) > 0: From 07f3ff7659a9e1af18bb0822fb783b9a87224fe0 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 12:58:07 -0400 Subject: [PATCH 26/81] cleanup Signed-off-by: Vladimir Mandic --- modules/sd_models.py | 7 ++++--- scripts/ipinstruct.py | 2 +- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index bc07e10f6..5be48e772 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -1391,9 +1391,10 @@ def disable_offload(sd_model): from accelerate.hooks import remove_hook_from_module if not getattr(sd_model, 'has_accelerate', False): return - for _name, model in sd_model.components.items(): - if isinstance(model, torch.nn.Module): - remove_hook_from_module(model, recurse=True) + if hasattr(sd_model, 'components'): + for _name, model in sd_model.components.items(): + if isinstance(model, torch.nn.Module): + remove_hook_from_module(model, recurse=True) sd_model.has_accelerate = False diff --git a/scripts/ipinstruct.py b/scripts/ipinstruct.py index 2ba4c4ff5..4a94197b1 100644 --- a/scripts/ipinstruct.py +++ b/scripts/ipinstruct.py @@ -102,7 +102,7 @@ class Script(scripts.Script): simple_cfg_mode = False, ) processed = processing.Processed(p, images_list=image_list, seed=p.seed, subseed=p.subseed, index_of_first_image=0) # manually created processed object - p.extra_generation_params["IPInstruct"] = f'' + # p.extra_generation_params["IPInstruct"] = f'' return processed def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, **kwargs): # pylint: disable=unused-argument From c5f5c5e0d0a56198f1a51fb2cccf4d023ed4697c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 13:22:01 -0400 Subject: [PATCH 27/81] fix diffusers load from folder Signed-off-by: Vladimir Mandic --- modules/sd_checkpoint.py | 3 ++ modules/sd_models.py | 110 +++++++++++++++++++-------------------- 2 files changed, 58 insertions(+), 55 deletions(-) diff --git a/modules/sd_checkpoint.py b/modules/sd_checkpoint.py index 45834a1f0..e1787246f 100644 --- a/modules/sd_checkpoint.py +++ b/modules/sd_checkpoint.py @@ -96,6 +96,9 @@ class CheckpointInfo: self.register() return self.shorthash + def __str__(self): + return f'checkpoint: type={self.type} title="{self.title}" path="{self.path}"' + def setup_model(): list_models() diff --git a/modules/sd_models.py b/modules/sd_models.py index 5be48e772..7aa70b90f 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -634,65 +634,65 @@ def load_diffuser_folder(model_type, pipeline, checkpoint_info, diffusers_load_c files = shared.walk_files(checkpoint_info.path, ['.safetensors', '.bin', '.ckpt']) if 'variant' not in diffusers_load_config and any('diffusion_pytorch_model.fp16' in f for f in files): # deal with diffusers lack of variant fallback when loading diffusers_load_config['variant'] = 'fp16' - if model_type is not None and pipeline is not None and 'ONNX' in model_type: # forced pipeline - try: - sd_model = pipeline.from_pretrained(checkpoint_info.path) - except Exception as e: - shared.log.error(f'Load {op}: type=ONNX path="{checkpoint_info.path}" {e}') - if debug_load: - errors.display(e, 'Load') - return None - else: - err1, err2, err3 = None, None, None - if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path): - if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')): - shared.log.debug(f'Load {op}: type=pickle') - diffusers_load_config['use_safetensors'] = False + if model_type is not None and pipeline is not None and 'ONNX' in model_type: # forced pipeline + try: + sd_model = pipeline.from_pretrained(checkpoint_info.path) + except Exception as e: + shared.log.error(f'Load {op}: type=ONNX path="{checkpoint_info.path}" {e}') if debug_load: - shared.log.debug(f'Load {op}: args={diffusers_load_config}') - try: # 1 - autopipeline, best choice but not all pipelines are available - try: + errors.display(e, 'Load') + return None + else: + err1, err2, err3 = None, None, None + if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path): + if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')): + shared.log.debug(f'Load {op}: type=pickle') + diffusers_load_config['use_safetensors'] = False + if debug_load: + shared.log.debug(f'Load {op}: args={diffusers_load_config}') + try: # 1 - autopipeline, best choice but not all pipelines are available + try: + sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) + sd_model.model_type = sd_model.__class__.__name__ + except ValueError as e: + if 'no variant default' in str(e): + shared.log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant') + diffusers_load_config.pop('variant', None) sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) sd_model.model_type = sd_model.__class__.__name__ - except ValueError as e: - if 'no variant default' in str(e): - shared.log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant') - diffusers_load_config.pop('variant', None) - sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) - sd_model.model_type = sd_model.__class__.__name__ - elif 'safetensors found in directory' in str(err1): - shared.log.warning(f'Load {op}: type=pickle') - diffusers_load_config['use_safetensors'] = False - sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) - sd_model.model_type = sd_model.__class__.__name__ - else: - raise ValueError from e # reraise - except Exception as e: - err1 = e - if debug_load: - errors.display(e, 'Load AutoPipeline') - # shared.log.error(f'AutoPipeline: {e}') - try: # 2 - diffusion pipeline, works for most non-linked pipelines - if err1 is not None: - sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) + elif 'safetensors found in directory' in str(err1): + shared.log.warning(f'Load {op}: type=pickle') + diffusers_load_config['use_safetensors'] = False + sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) sd_model.model_type = sd_model.__class__.__name__ - except Exception as e: - err2 = e - if debug_load: - errors.display(e, "Load DiffusionPipeline") - # shared.log.error(f'DiffusionPipeline: {e}') - try: # 3 - try basic pipeline just in case - if err2 is not None: - sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) - sd_model.model_type = sd_model.__class__.__name__ - except Exception as e: - err3 = e # ignore last error - shared.log.error(f"StableDiffusionPipeline: {e}") - if debug_load: - errors.display(e, "Load StableDiffusionPipeline") - if err3 is not None: - shared.log.error(f'Load {op}: {checkpoint_info.path} auto={err1} diffusion={err2}') - return None + else: + raise ValueError from e # reraise + except Exception as e: + err1 = e + if debug_load: + errors.display(e, 'Load AutoPipeline') + # shared.log.error(f'AutoPipeline: {e}') + try: # 2 - diffusion pipeline, works for most non-linked pipelines + if err1 is not None: + sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) + sd_model.model_type = sd_model.__class__.__name__ + except Exception as e: + err2 = e + if debug_load: + errors.display(e, "Load DiffusionPipeline") + # shared.log.error(f'DiffusionPipeline: {e}') + try: # 3 - try basic pipeline just in case + if err2 is not None: + sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config) + sd_model.model_type = sd_model.__class__.__name__ + except Exception as e: + err3 = e # ignore last error + shared.log.error(f"StableDiffusionPipeline: {e}") + if debug_load: + errors.display(e, "Load StableDiffusionPipeline") + if err3 is not None: + shared.log.error(f'Load {op}: {checkpoint_info.path} auto={err1} diffusion={err2}') + return None return sd_model From 76a0a632434604f012c90372802db6813de26b0b Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 13:53:21 -0400 Subject: [PATCH 28/81] offload exclude lsit Signed-off-by: Vladimir Mandic --- modules/sd_models.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/modules/sd_models.py b/modules/sd_models.py index 7aa70b90f..734f4ab57 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -375,6 +375,9 @@ def set_diffuser_offload(sd_model, op: str = 'model'): def apply_balanced_offload(sd_model): from accelerate import infer_auto_device_map, dispatch_model from accelerate.hooks import add_hook_to_module, remove_hook_from_module, ModelHook + excluded = ['OmniGenPipeline'] + if sd_model.__class__.__name__ in excluded: + return sd_model class dispatch_from_cpu_hook(ModelHook): def init_hook(self, module): From 5d369d2816dcb41714f04a282d7795ca447d02cd Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 14:20:22 -0400 Subject: [PATCH 29/81] update changelog Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 19 +++++++++++++++++-- wiki | 2 +- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1141bf6a6..39892eb0f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,23 @@ ## Update for 2024-10-29 -improvements: +### Highlights for 2024-10-29 + +- Support for **all SD3.x variants** + *SD3.0-Medium, SD3.5-Medium, SD3.5-Large, SD3.0-Large-Turbo* +- Allow quantization using `bitsandbytes` on-the-fly during models load + Load any variant of SD3.x or FLUX.1 and apply quantization during load without the need for pre-quantized models +- Allow for custom model URL in standard model selector + Can be used to specify any model from *HuggingFace* or *CivitAI* +- Full support for `torch==2.5.1` +- New wiki articles: [Gated Access](https://github.com/vladmandic/automatic/wiki/Gated), [Quantization](https://github.com/vladmandic/automatic/wiki/Quantization), [Offloading](https://github.com/vladmandic/automatic/wiki/Offload) + +Plus tons of smaller improvements and cumulative fixes reported since last release + +[README](https://github.com/vladmandic/automatic/blob/master/README.md) | [CHANGELOG](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867) + +### Details for 2024-10-29 + - model selector: - change-in-behavior - when typing, it will auto-load model as soon as exactly one match is found @@ -47,7 +63,6 @@ improvements: - [Quantization Wiki](https://github.com/vladmandic/automatic/wiki/Quantization) - [Offloading Wiki](https://github.com/vladmandic/automatic/wiki/Offload) - fixes: - fix send-to-control - fix k-diffusion diff --git a/wiki b/wiki index 6e278d8aa..4360bc7fc 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 6e278d8aa2d559b9b46357086c341b9bd8cf33fd +Subproject commit 4360bc7fcfd5dc301b825a6c5dcf4274c8eba983 From d205b5cf3e364bd81526e097cfe4d8834ee27944 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 29 Oct 2024 15:31:50 -0400 Subject: [PATCH 30/81] update Signed-off-by: Vladimir Mandic --- modules/sd_models.py | 2 +- requirements.txt | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 734f4ab57..dc0679c58 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -477,7 +477,7 @@ def move_model(model, device=None, force=False): try: t0 = time.time() try: - model.to(device, non_blocking=True) + model.to(device) if hasattr(model, "prior_pipe"): model.prior_pipe.to(device) except Exception as e0: diff --git a/requirements.txt b/requirements.txt index 208cfba3a..d376fe320 100644 --- a/requirements.txt +++ b/requirements.txt @@ -61,6 +61,7 @@ torchdiffeq dctorch scikit-image seam-carving +sentencepiece # block torch!=2.5.0 From f9ca39c6305942217c4ae8aa33bcc00edb059dfe Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 30 Oct 2024 08:42:55 -0400 Subject: [PATCH 31/81] readd model_hash to sd_models Signed-off-by: Vladimir Mandic --- modules/sd_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index dc0679c58..a925b207e 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -19,7 +19,7 @@ from modules import paths, shared, shared_state, modelloader, devices, script_ca from modules.timer import Timer from modules.memstats import memory_stats from modules.modeldata import model_data -from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoints_list, checkpoint_titles, get_closet_checkpoint_match, update_model_hashes, setup_model, write_metadata, read_metadata_from_safetensors # pylint: disable=unused-import +from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoints_list, checkpoint_titles, get_closet_checkpoint_match, model_hash, update_model_hashes, setup_model, write_metadata, read_metadata_from_safetensors # pylint: disable=unused-import model_dir = "Stable-diffusion" From b3d75d2c7d2456196bfd7ea80b4b9d35335a61f5 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 30 Oct 2024 09:44:07 -0400 Subject: [PATCH 32/81] hotkeys Signed-off-by: Vladimir Mandic --- javascript/script.js | 24 ++++++++++++++---------- wiki | 2 +- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/javascript/script.js b/javascript/script.js index 6f52f4a3d..104567dd7 100644 --- a/javascript/script.js +++ b/javascript/script.js @@ -133,19 +133,23 @@ document.addEventListener('DOMContentLoaded', () => { }); /** - * Add a ctrl+enter as a shortcut to start a generation + * Add a listener to the document for keydown events */ document.addEventListener('keydown', (e) => { - let handled = false; - if (e.key !== undefined) { - if ((e.key === 'Enter' && (e.metaKey || e.ctrlKey || e.altKey))) handled = true; - } else if (e.keyCode !== undefined) { - if ((e.keyCode === 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true; - } - if (handled) { - const button = getUICurrentTabContent().querySelector('button[id$=_generate]'); - if (button) button.click(); + let elem; + if (e.key === 'Escape') elem = getUICurrentTabContent().querySelector('button[id$=_interrupt]'); + if (e.key === 'Enter' && e.ctrlKey) elem = getUICurrentTabContent().querySelector('button[id$=_generate]'); + if (e.key === 'Backspace' && e.ctrlKey) elem = getUICurrentTabContent().querySelector('button[id$=_reprocess]'); + if (e.key === ' ' && e.ctrlKey) elem = getUICurrentTabContent().querySelector('button[id$=_extra_networks_btn]'); + if (e.key === 's' && e.ctrlKey) elem = getUICurrentTabContent().querySelector('button[id^=save_]'); + if (e.key === 'Insert' && e.ctrlKey) elem = getUICurrentTabContent().querySelector('button[id^=save_]'); + if (e.key === 'Delete' && e.ctrlKey) elem = getUICurrentTabContent().querySelector('button[id^=delete_]'); + // if (e.key === 'm' && e.ctrlKey) elem = gradioApp().getElementById('setting_sd_model_checkpoint'); + if (elem) { e.preventDefault(); + log('hotkey', { key: e.key, meta: e.metaKey, ctrl: e.ctrlKey, alt: e.altKey }, elem?.id, elem.nodeName); + if (elem.nodeName === 'BUTTON') elem.click(); + else elem.focus(); } }); diff --git a/wiki b/wiki index 4360bc7fc..f5a169ff9 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 4360bc7fcfd5dc301b825a6c5dcf4274c8eba983 +Subproject commit f5a169ff9e30f9157feec9d3e142d80d7e2ca31a From faa0bf53580ac910cb26d415f21933c8c55addfb Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 30 Oct 2024 12:26:07 -0400 Subject: [PATCH 33/81] add xyz optional time info Signed-off-by: Vladimir Mandic --- modules/images.py | 10 +++++++++- modules/images_grid.py | 18 +++++++++--------- scripts/xyz_grid.py | 29 +++++++++++++++-------------- scripts/xyz_grid_classes.py | 16 ++++++++-------- scripts/xyz_grid_draw.py | 22 ++++++++++++++++------ scripts/xyz_grid_on.py | 12 +++++++----- 6 files changed, 64 insertions(+), 43 deletions(-) diff --git a/modules/images.py b/modules/images.py index add7c5c7d..8e7ffd662 100644 --- a/modules/images.py +++ b/modules/images.py @@ -10,7 +10,7 @@ import threading import numpy as np import piexif import piexif.helper -from PIL import Image, PngImagePlugin, ExifTags +from PIL import Image, PngImagePlugin, ExifTags, ImageDraw from modules import sd_samplers, shared, script_callbacks, errors, paths from modules.images_grid import image_grid, get_grid_size, split_grid, combine_grid, check_grid_size, get_font, draw_grid_annotations, draw_prompt_matrix, GridAnnotation, Grid # pylint: disable=unused-import from modules.images_resize import resize_image # pylint: disable=unused-import @@ -361,6 +361,14 @@ def flatten(img, bgcolor): return img.convert('RGB') +def draw_overlay(im, text): + d = ImageDraw.Draw(im) + fontsize = (im.width + im.height) // 50 + font = get_font(fontsize) + d.text((fontsize//2, fontsize//2), text, font=font, fill=shared.opts.font_color) + return im + + def set_watermark(image, watermark): if shared.opts.image_watermark_position != 'none': # visible watermark wm_image = None diff --git a/modules/images_grid.py b/modules/images_grid.py index 194b0d1cd..51371a0fe 100644 --- a/modules/images_grid.py +++ b/modules/images_grid.py @@ -113,7 +113,7 @@ def get_font(fontsize): return ImageFont.truetype("javascript/notosans-nerdfont-regular.ttf", fontsize) -def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0, title=None): +def draw_grid_annotations(im, width, height, x_texts, y_texts, margin=0, title=None): def wrap(drawing, text, font, line_length): lines = [''] for word in text.split(): @@ -140,15 +140,15 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0, tit line_spacing = fontsize // 2 font = get_font(fontsize) color_inactive = (127, 127, 127) - pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4 - cols = len(hor_texts) - rows = len(ver_texts) + pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in y_texts]) == 0 else width * 3 // 4 + cols = len(x_texts) + rows = len(y_texts) # assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}' # assert rows == len(hor_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}' calc_img = Image.new("RGB", (1, 1), shared.opts.grid_background) calc_d = ImageDraw.Draw(calc_img) title_texts = [title] if title else [[GridAnnotation()]] - for texts, allowed_width in zip(hor_texts + ver_texts + title_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts) + [(width+margin)*cols]): + for texts, allowed_width in zip(x_texts + y_texts + title_texts, [width] * len(x_texts) + [pad_left] * len(y_texts) + [(width+margin)*cols]): items = [] + texts texts.clear() for line in items: @@ -158,8 +158,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0, tit bbox = calc_d.multiline_textbbox((0, 0), line.text, font=font) line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1]) line.allowed_width = allowed_width - hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts] - ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts] + hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in x_texts] + ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in y_texts] pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2 title_pad = 0 if title: @@ -178,11 +178,11 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0, tit for col in range(cols): x = pad_left + (width + margin) * col + width / 2 y = (pad_top / 2 - hor_text_heights[col] / 2) + title_pad - draw_texts(d, x, y, hor_texts[col], font, fontsize) + draw_texts(d, x, y, x_texts[col], font, fontsize) for row in range(rows): x = pad_left / 2 y = (pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2) + title_pad - draw_texts(d, x, y, ver_texts[row], font, fontsize) + draw_texts(d, x, y, y_texts[row], font, fontsize) return result diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index e8f649182..1ed305dd7 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -48,10 +48,11 @@ class Script(scripts.Script): csv_mode = gr.Checkbox(label='Text inputs', value=False, elem_id=self.elem_id("csv_mode"), container=False) draw_legend = gr.Checkbox(label='Legend', value=True, elem_id=self.elem_id("draw_legend"), container=False) no_fixed_seeds = gr.Checkbox(label='Random seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"), container=False) + include_time = gr.Checkbox(label='Add time info', value=False, elem_id=self.elem_id("include_time"), container=False) with gr.Column(): - no_grid = gr.Checkbox(label='Skip grid', value=False, elem_id=self.elem_id("no_xyz_grid"), container=False) - include_lone_images = gr.Checkbox(label='Sub-images', value=False, elem_id=self.elem_id("include_lone_images"), container=False) - include_sub_grids = gr.Checkbox(label='Sub-grids', value=False, elem_id=self.elem_id("include_sub_grids"), container=False) + include_grid = gr.Checkbox(label='Include main grid', value=True, elem_id=self.elem_id("no_xyz_grid"), container=False) + include_subgrids = gr.Checkbox(label='Include sub grids', value=False, elem_id=self.elem_id("include_sub_grids"), container=False) + include_images = gr.Checkbox(label='Include images', value=False, elem_id=self.elem_id("include_lone_images"), container=False) with gr.Row(): margin_size = gr.Slider(label="Grid margins", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size")) with gr.Row(): @@ -130,10 +131,9 @@ class Script(scripts.Script): (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)), ) - return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, no_grid, include_lone_images, include_sub_grids, margin_size] + return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, include_time, margin_size] - def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, no_grid, include_lone_images, include_sub_grids, margin_size): # pylint: disable=W0221 - shared.log.debug(f'xyzgrid: x_type={x_type}|x_values={x_values}|x_values_dropdown={x_values_dropdown}|y_type={y_type}|{y_values}={y_values}|{y_values_dropdown}={y_values_dropdown}|z_type={z_type}|z_values={z_values}|z_values_dropdown={z_values_dropdown}|draw_legend={draw_legend}|include_lone_images={include_lone_images}|include_sub_grids={include_sub_grids}|no_grid={no_grid}|margin_size={margin_size}') + def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, include_time, margin_size): # pylint: disable=W0221 if not no_fixed_seeds: processing.fix_seed(p) if not shared.opts.return_grid: @@ -307,38 +307,39 @@ class Script(scripts.Script): z_labels=[z_opt.format_value(p, z_opt, z) for z in zs], cell=cell, draw_legend=draw_legend, - include_lone_images=include_lone_images, - include_sub_grids=include_sub_grids, + include_lone_images=include_images, + include_sub_grids=include_subgrids, first_axes_processed=first_axes_processed, second_axes_processed=second_axes_processed, margin_size=margin_size, - no_grid=no_grid, + no_grid=not include_grid, + include_time=include_time, ) if not processed.images: return processed # It broke, no further handling needed. z_count = len(zs) processed.infotexts[:1+z_count] = grid_infotext[:1+z_count] # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids) - if not include_lone_images: + if not include_images: # Don't need sub-images anymore, drop from list: - if no_grid and include_sub_grids: + if not include_grid and include_subgrids: processed.images = processed.images[:z_count] # we don't have the main grid image, and need zero additional sub-images else: processed.images = processed.images[:z_count+1] # we either have the main grid image, or need one sub-images if shared.opts.grid_save: # Auto-save main and sub-grids: - grid_count = z_count + ( 1 if not no_grid and z_count > 1 else 0 ) + grid_count = z_count + ( 1 if include_grid and z_count > 1 else 0 ) for g in range(grid_count): adj_g = g-1 if g > 0 else g info = processed.infotexts[g] prompt = processed.all_prompts[adj_g] seed = processed.all_seeds[adj_g] images.save_image(processed.images[g], p.outpath_grids, "grid", info=info, extension=shared.opts.grid_format, prompt=prompt, seed=seed, grid=True, p=processed) - if not include_sub_grids: # Done with sub-grids, drop all related information: + if not include_subgrids: # Done with sub-grids, drop all related information: for _sg in range(z_count): del processed.images[1] del processed.all_prompts[1] del processed.all_seeds[1] del processed.infotexts[1] - elif no_grid: + elif include_grid: del processed.infotexts[0] return processed diff --git a/scripts/xyz_grid_classes.py b/scripts/xyz_grid_classes.py index 335d66186..202482157 100644 --- a/scripts/xyz_grid_classes.py +++ b/scripts/xyz_grid_classes.py @@ -84,13 +84,13 @@ class SharedSettingsStackHelper(object): axis_options = [ AxisOption("Nothing", str, do_nothing, fmt=format_nothing), - AxisOption("[Model] Model", str, apply_checkpoint, cost=1.0, fmt=format_value, choices=lambda: sorted(sd_models.checkpoints_list)), + AxisOption("[Model] Model", str, apply_checkpoint, cost=1.0, fmt=format_value_add_label, choices=lambda: sorted(sd_models.checkpoints_list)), AxisOption("[Model] UNET", str, apply_unet, cost=0.8, choices=lambda: ['None'] + list(sd_unet.unet_dict)), AxisOption("[Model] VAE", str, apply_vae, cost=0.6, choices=lambda: ['None'] + list(sd_vae.vae_dict)), - AxisOption("[Model] Refiner", str, apply_refiner, cost=0.8, fmt=format_value, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list)), + AxisOption("[Model] Refiner", str, apply_refiner, cost=0.8, fmt=format_value_add_label, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list)), AxisOption("[Model] Text encoder", str, apply_te, cost=0.7, choices=shared_items.sd_te_items), - AxisOption("[Model] Dictionary", str, apply_dict, fmt=format_value, cost=0.9, choices=lambda: ['None'] + list(sd_models.checkpoints_list)), - AxisOption("[Prompt] Search & replace", str, apply_prompt, fmt=format_value), + AxisOption("[Model] Dictionary", str, apply_dict, fmt=format_value_add_label, cost=0.9, choices=lambda: ['None'] + list(sd_models.checkpoints_list)), + AxisOption("[Prompt] Search & replace", str, apply_prompt, fmt=format_value_add_label), AxisOption("[Prompt] Prompt order", str_permutations, apply_order, fmt=format_value_join_list), AxisOption("[Network] LoRA", str, apply_lora, cost=0.5, choices=list_lora), AxisOption("[Network] LoRA strength", float, apply_setting('extra_networks_default_multiplier')), @@ -109,8 +109,8 @@ axis_options = [ AxisOption("[Process] Model args", str, apply_task_args), AxisOption("[Process] Processing args", str, apply_processing), AxisOption("[Process] Server options", str, apply_options), - AxisOptionTxt2Img("[Sampler] Name", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), - AxisOptionImg2Img("[Sampler] Name", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]), + AxisOptionTxt2Img("[Sampler] Name", str, apply_sampler, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), + AxisOptionImg2Img("[Sampler] Name", str, apply_sampler, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]), AxisOption("[Sampler] Sigma method", str, apply_setting("schedulers_sigma"), choices=lambda: ['default', 'karras', 'beta', 'exponential']), AxisOption("[Sampler] Timestep spacing", str, apply_setting("schedulers_timestep_spacing"), choices=lambda: ['default', 'linspace', 'leading', 'trailing']), AxisOption("[Sampler] Timestep range", int, apply_setting("schedulers_timesteps_range")), @@ -122,7 +122,7 @@ axis_options = [ AxisOption("[Sampler] eta delta", float, apply_setting("eta_noise_seed_delta")), AxisOption("[Sampler] eta multiplier", float, apply_setting("scheduler_eta")), AxisOption("[Refine] Upscaler", str, apply_field("hr_upscaler"), cost=0.3, choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), - AxisOption("[Refine] Sampler", str, apply_hr_sampler_name, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), + AxisOption("[Refine] Sampler", str, apply_hr_sampler_name, fmt=format_value_add_label, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), AxisOption("[Refine] Denoising strength", float, apply_field("denoising_strength")), AxisOption("[Refine] Hires steps", int, apply_field("hr_second_pass_steps")), AxisOption("[Refine] Guidance scale", float, apply_field("image_cfg_scale")), @@ -131,7 +131,7 @@ axis_options = [ AxisOption("[Refine] Refiner steps", float, apply_field("refiner_steps")), AxisOption("[Postprocess] Upscaler", str, apply_upscaler, cost=0.4, choices=lambda: [x.name for x in shared.sd_upscalers][1:]), AxisOption("[Postprocess] Context", str, apply_context, choices=lambda: ["Add with forward", "Remove with forward", "Add with backward", "Remove with backward"]), - AxisOption("[Postprocess] Detailer", str, apply_detailer, fmt=format_value), + AxisOption("[Postprocess] Detailer", str, apply_detailer, fmt=format_value_add_label), AxisOption("[HDR] Mode", int, apply_field("hdr_mode")), AxisOption("[HDR] Brightness", float, apply_field("hdr_brightness")), AxisOption("[HDR] Color", float, apply_field("hdr_color")), diff --git a/scripts/xyz_grid_draw.py b/scripts/xyz_grid_draw.py index 8f95e696b..9a9f2246c 100644 --- a/scripts/xyz_grid_draw.py +++ b/scripts/xyz_grid_draw.py @@ -4,23 +4,28 @@ from PIL import Image from modules import shared, images, processing -def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size, no_grid): # pylint: disable=unused-argument - hor_texts = [[images.GridAnnotation(x)] for x in x_labels] - ver_texts = [[images.GridAnnotation(y)] for y in y_labels] - title_texts = [[images.GridAnnotation(z)] for z in z_labels] +def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size, no_grid: False, include_time: False): # pylint: disable=unused-argument + x_texts = [[images.GridAnnotation(x)] for x in x_labels] + y_texts = [[images.GridAnnotation(y)] for y in y_labels] + z_texts = [[images.GridAnnotation(z)] for z in z_labels] list_size = (len(xs) * len(ys) * len(zs)) processed_result = None shared.state.job_count = list_size * p.n_iter t0 = time.time() + i = 0 def process_cell(x, y, z, ix, iy, iz): - nonlocal processed_result + nonlocal processed_result, i + i += 1 + shared.log.debug(f'XYZ grid process: x={ix+1}/{len(xs)} y={iy+1}/{len(ys)} z={iz+1}/{len(zs)} total={i/list_size:.2f}') def index(ix, iy, iz): return ix + iy * len(xs) + iz * len(xs) * len(ys) shared.state.job = 'grid' + p0 = time.time() processed: processing.Processed = cell(x, y, z, ix, iy, iz) + p1 = time.time() if processed_result is None: processed_result = copy(processed) if processed_result is None: @@ -30,13 +35,17 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend processed_result.all_prompts = [None] * list_size processed_result.all_seeds = [None] * list_size processed_result.infotexts = [None] * list_size + processed_result.time = [0] * list_size processed_result.index_of_first_image = 1 idx = index(ix, iy, iz) if processed is not None and processed.images: processed_result.images[idx] = processed.images[0] + if include_time: + processed_result.images[idx] = images.draw_overlay(processed_result.images[idx], f'time: {p1 - p0:.2f}') processed_result.all_prompts[idx] = processed.prompt processed_result.all_seeds[idx] = processed.seed processed_result.infotexts[idx] = processed.infotexts[0] + processed_result.time[idx] = round(p1 - p0, 2) else: cell_mode = "P" cell_size = (processed_result.width, processed_result.height) @@ -44,6 +53,7 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend cell_mode = processed_result.images[0].mode cell_size = processed_result.images[0].size processed_result.images[idx] = Image.new(cell_mode, cell_size) + return if first_axes_processed == 'x': for ix, x in enumerate(xs): @@ -93,7 +103,7 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend if (not no_grid or include_sub_grids) and images.check_grid_size(to_process): grid = images.image_grid(to_process, rows=len(ys)) if draw_legend: - grid = images.draw_grid_annotations(grid, w, h, hor_texts, ver_texts, margin_size, title=title_texts[i]) + grid = images.draw_grid_annotations(grid, w, h, x_texts, y_texts, margin_size, title=z_texts[i]) processed_result.images.insert(i, grid) processed_result.all_prompts.insert(i, processed_result.all_prompts[idx0]) processed_result.all_seeds.insert(i, processed_result.all_seeds[idx0]) diff --git a/scripts/xyz_grid_on.py b/scripts/xyz_grid_on.py index ac1bc4c2f..3298ee572 100644 --- a/scripts/xyz_grid_on.py +++ b/scripts/xyz_grid_on.py @@ -57,9 +57,10 @@ class Script(scripts.Script): draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"), container=False) csv_mode = gr.Checkbox(label='Use text inputs', value=False, elem_id=self.elem_id("csv_mode"), container=False) no_fixed_seeds = gr.Checkbox(label='Use random seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"), container=False) + include_time = gr.Checkbox(label='Add time info', value=False, elem_id=self.elem_id("include_time"), container=False) with gr.Column(): - include_grid = gr.Checkbox(label='Create main grid', value=True, elem_id=self.elem_id("no_xyz_grid"), container=False) - include_subgrids = gr.Checkbox(label='Create partial grids', value=False, elem_id=self.elem_id("include_sub_grids"), container=False) + include_grid = gr.Checkbox(label='Include main grid', value=True, elem_id=self.elem_id("no_xyz_grid"), container=False) + include_subgrids = gr.Checkbox(label='Include sub grids', value=False, elem_id=self.elem_id("include_sub_grids"), container=False) include_images = gr.Checkbox(label='Include images', value=False, elem_id=self.elem_id("include_lone_images"), container=False) with gr.Row(): margin_size = gr.Slider(label="Grid margins", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size")) @@ -139,9 +140,9 @@ class Script(scripts.Script): (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)), ) - return [enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, margin_size] + return [enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, include_time, margin_size] - def process(self, p, enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, margin_size): # pylint: disable=W0221 + def process(self, p, enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, include_time, margin_size): # pylint: disable=W0221 global active, cache # pylint: disable=W0603 cache = None if not enabled or active: @@ -327,6 +328,7 @@ class Script(scripts.Script): second_axes_processed=second_axes_processed, margin_size=margin_size, no_grid=not include_grid, + include_time=include_time, ) if not processed.images: @@ -355,5 +357,5 @@ class Script(scripts.Script): cache = processed return processed - def process_images(self, p, enabled, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, csv_mode, draw_legend, no_fixed_seeds, include_grid, include_subgrids, include_images, margin_size): # pylint: disable=W0221, W0613 + def process_images(self, p, *args): # pylint: disable=W0221, W0613 return cache From 8b5f510528ab2112e7ecc0f9c0fec9102ac884f3 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 30 Oct 2024 14:24:12 -0400 Subject: [PATCH 34/81] add sd3 controlnets Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 10 + CITATION.cff | 2 +- LICENSE.txt | 864 +++++++--------------------- modules/control/run.py | 4 +- modules/control/units/controlnet.py | 38 +- modules/control/units/detect.py | 11 +- modules/sd_models.py | 11 +- package.json | 2 +- 8 files changed, 268 insertions(+), 674 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 39892eb0f..5deb1a928 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,15 @@ # Change Log for SD.Next +## Update for 2024-10-30 + +- XYZ grid: optional per-image time benchmark info +- UI: add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys) +- SD3: support for ControlNets: + - *InstantX Canny, Pose, Depth, Tile* + - *Alimama Inpainting, SoftEdge* + - *note*: that just like with FLUX.1 or any large model, ControlNet are also large and can push your system over the limit + e.g. SD3 controlnets vary from 1GB to over 4GB in size + ## Update for 2024-10-29 ### Highlights for 2024-10-29 diff --git a/CITATION.cff b/CITATION.cff index f7fd4bba3..c39efe474 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -24,5 +24,5 @@ abstract: >- generation keywords: - stablediffusion diffusers sdnext -license: AGPL-3.0 +license: Apache-2.0 date-released: 2022-12-24 diff --git a/LICENSE.txt b/LICENSE.txt index 211d32e75..f49a4e16e 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,663 +1,201 @@ - GNU AFFERO GENERAL PUBLIC LICENSE - Version 3, 19 November 2007 - - Copyright (c) 2023 AUTOMATIC1111 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - 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if shared.sd_model_type == 'f1' and 'control_image' not in p.task_args: - p.task_args['control_image'] = p.init_images # flux controlnet mandates this + if shared.sd_model_type in ['f1', 'sd3'] and 'control_image' not in p.task_args: + p.task_args['control_image'] = p.init_images # some controlnets mandate this p.task_args['strength'] = p.denoising_strength elif input_type == 1: # Init image same as control p.task_args['control_image'] = p.init_images # switch image and control_image diff --git a/modules/control/units/controlnet.py b/modules/control/units/controlnet.py index 236892eb5..48a3d440d 100644 --- a/modules/control/units/controlnet.py +++ b/modules/control/units/controlnet.py @@ -1,7 +1,7 @@ import os import time from typing import Union -from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, FluxPipeline, ControlNetModel +from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, FluxPipeline, StableDiffusion3Pipeline, ControlNetModel from modules.control.units import detect from modules.shared import log, opts, listdir from modules import errors, sd_models, devices, model_quant @@ -50,7 +50,6 @@ predefined_sdxl = { 'Depth Zoe XL': 'diffusers/controlnet-zoe-depth-sdxl-1.0', 'Depth Mid XL': 'diffusers/controlnet-depth-sdxl-1.0-mid', 'OpenPose XL': 'thibaud/controlnet-openpose-sdxl-1.0/bin', - # 'OpenPose XL': 'thibaud/controlnet-openpose-sdxl-1.0/OpenPoseXL2.safetensors', 'Xinsir Union XL': 'xinsir/controlnet-union-sdxl-1.0', 'Xinsir OpenPose XL': 'xinsir/controlnet-openpose-sdxl-1.0', 'Xinsir Canny XL': 'xinsir/controlnet-canny-sdxl-1.0', @@ -79,11 +78,20 @@ predefined_f1 = { "XLabs-AI Depth": 'XLabs-AI/flux-controlnet-depth-diffusers', "XLabs-AI HED": 'XLabs-AI/flux-controlnet-hed-diffusers' } +predefined_sd3 = { + "InstantX Canny": 'InstantX/SD3-Controlnet-Canny', + "InstantX Pose": 'InstantX/SD3-Controlnet-Pose', + "InstantX Depth": 'InstantX/SD3-Controlnet-Depth', + "InstantX Tile": 'InstantX/SD3-Controlnet-Tile', + "Alimama Inpainting": 'alimama-creative/SD3-Controlnet-Inpainting', + "Alimama SoftEdge": 'alimama-creative/SD3-Controlnet-Softedge', +} models = {} all_models = {} all_models.update(predefined_sd15) all_models.update(predefined_sdxl) all_models.update(predefined_f1) +all_models.update(predefined_sd3) cache_dir = 'models/control/controlnet' @@ -118,9 +126,11 @@ def list_models(refresh=False): models = ['None'] + list(predefined_sd15) + sorted(find_models()) elif modules.shared.sd_model_type == 'f1': models = ['None'] + list(predefined_f1) + sorted(find_models()) + elif modules.shared.sd_model_type == 'sd3': + models = ['None'] + list(predefined_sd3) + sorted(find_models()) else: log.warning(f'Control {what} model list failed: unknown model type') - models = ['None'] + sorted(predefined_sd15) + sorted(predefined_sdxl) + sorted(find_models()) + models = ['None'] + sorted(predefined_sd15) + sorted(predefined_sdxl) + sorted(predefined_f1) + sorted(predefined_sd3) + sorted(find_models()) debug(f'Control list {what}: path={cache_dir} models={models}') return models @@ -151,6 +161,8 @@ class ControlNet(): from diffusers import ControlNetModel as model_class # pylint: disable=reimported # sdxl shares same model class elif modules.shared.sd_model_type == 'f1': from diffusers import FluxControlNetModel as model_class + elif modules.shared.sd_model_type == 'sd3': + from diffusers import SD3ControlNetModel as model_class else: log.error(f'Control {what}: type={modules.shared.sd_model_type} unsupported model') return None @@ -247,7 +259,11 @@ class ControlNet(): class ControlNetPipeline(): - def __init__(self, controlnet: Union[ControlNetModel, list[ControlNetModel]], pipeline: Union[StableDiffusionXLPipeline, StableDiffusionPipeline, FluxPipeline], dtype = None): + def __init__(self, + controlnet: Union[ControlNetModel, list[ControlNetModel]], + pipeline: Union[StableDiffusionXLPipeline, StableDiffusionPipeline, FluxPipeline, StableDiffusion3Pipeline], + dtype = None, + ): t0 = time.time() self.orig_pipeline = pipeline self.pipeline = None @@ -293,6 +309,20 @@ class ControlNetPipeline(): scheduler=pipeline.scheduler, controlnet=controlnet, # can be a list ) + elif detect.is_sd3(pipeline): + from diffusers import StableDiffusion3ControlNetPipeline + self.pipeline = StableDiffusion3ControlNetPipeline( + vae=pipeline.vae, + text_encoder=pipeline.text_encoder, + text_encoder_2=pipeline.text_encoder_2, + text_encoder_3=pipeline.text_encoder_3, + tokenizer=pipeline.tokenizer, + tokenizer_2=pipeline.tokenizer_2, + tokenizer_3=pipeline.tokenizer_3, + transformer=pipeline.transformer, + scheduler=pipeline.scheduler, + controlnet=controlnet, # can be a list + ) else: log.error(f'Control {what} pipeline: class={pipeline.__class__.__name__} unsupported model type') return diff --git a/modules/control/units/detect.py b/modules/control/units/detect.py index 70a1c8000..7cc8d144d 100644 --- a/modules/control/units/detect.py +++ b/modules/control/units/detect.py @@ -20,5 +20,12 @@ def is_f1(model): if model is None: return False if hasattr(model, '__name__'): - return model.__name__ == p.FluxPipeline.__name__ - return isinstance(model, p.FluxPipeline) + return model.__name__ == p.FluxPipeline.__name__ or model.__name__ == p.FluxImg2ImgPipeline.__name__ or model.__name__ == p.FluxInpaintPipeline.__name__ + return isinstance(model, p.FluxPipeline) or isinstance(model, p.FluxImg2ImgPipeline) or isinstance(model, p.FluxInpaintPipeline) + +def is_sd3(model): + if model is None: + return False + if hasattr(model, '__name__'): + return model.__name__ == p.StableDiffusion3Pipeline.__name__ or model.__name__ == p.StableDiffusion3Img2ImgPipeline.__name__ or model.__name__ == p.StableDiffusion3InpaintPipeline.__name__ + return isinstance(model, p.StableDiffusion3Pipeline) or isinstance(model, p.StableDiffusion3Img2ImgPipeline) or isinstance(model, p.StableDiffusion3InpaintPipeline) diff --git a/modules/sd_models.py b/modules/sd_models.py index a925b207e..3afc81e67 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -1052,6 +1052,15 @@ def clean_diffuser_pipe(pipe): def set_diffuser_pipe(pipe, new_pipe_type): + exclude = [ + 'StableDiffusionReferencePipeline', + 'StableDiffusionAdapterPipeline', + 'AnimateDiffPipeline', + 'AnimateDiffSDXLPipeline', + 'OmniGenPipeline', + 'StableDiffusion3ControlNetPipeline', + ] + n = getattr(pipe.__class__, '__name__', '') if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE: clean_diffuser_pipe(pipe) @@ -1060,7 +1069,7 @@ def set_diffuser_pipe(pipe, new_pipe_type): return pipe # skip specific pipelines - if n in ['StableDiffusionReferencePipeline', 'StableDiffusionAdapterPipeline', 'AnimateDiffPipeline', 'AnimateDiffSDXLPipeline', 'OmniGenPipeline']: + if n in exclude: return pipe if 'Onnx' in pipe.__class__.__name__: return pipe diff --git a/package.json b/package.json index 2c0fa6b50..bf5a366ea 100644 --- a/package.json +++ b/package.json @@ -7,7 +7,7 @@ "url": "https://github.com/vladmandic/automatic/issues" }, "homepage": "https://github.com/vladmandic/automatic", - "license": "AGPLv3", + "license": "Apache-2.0", "engines": { "node": ">=14.0.0" }, From a6baf51b136e99b3335871c309cc5178d5311231 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 30 Oct 2024 18:03:38 -0400 Subject: [PATCH 35/81] revert license Signed-off-by: Vladimir Mandic --- LICENSE.txt | 798 +++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 629 insertions(+), 169 deletions(-) diff --git a/LICENSE.txt b/LICENSE.txt index f49a4e16e..0ad25db4b 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,201 +1,661 @@ - 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From abfb197504c57d8e854dfd3e7d13537bc30d4a8e Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 30 Oct 2024 19:30:52 -0400 Subject: [PATCH 36/81] sd35 all-in-one safetensors support Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 3 +++ modules/model_quant.py | 10 ++++++--- modules/model_sd3.py | 10 ++++++--- modules/model_tools.py | 49 ++++++++++++++++++++++++++++++++++++++++++ modules/sd_detect.py | 2 ++ modules/sd_models.py | 10 --------- 6 files changed, 68 insertions(+), 16 deletions(-) create mode 100644 modules/model_tools.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 5deb1a928..71e8a667e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,9 @@ - *Alimama Inpainting, SoftEdge* - *note*: that just like with FLUX.1 or any large model, ControlNet are also large and can push your system over the limit e.g. SD3 controlnets vary from 1GB to over 4GB in size +- SD3: support for all-in-one safetensors + - examples: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031) [medium](https://civitai.com/models/900327) + - *note*: enable bnb on-the-fly quantization for even bigger gains ## Update for 2024-10-29 diff --git a/modules/model_quant.py b/modules/model_quant.py index fab3a93a0..547a3d7ae 100644 --- a/modules/model_quant.py +++ b/modules/model_quant.py @@ -7,10 +7,10 @@ bnb = None quanto = None -def create_bnb_config(kwargs): +def create_bnb_config(kwargs = None): from modules import shared, devices if len(shared.opts.bnb_quantization) > 0: - if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs: + if 'Model' in shared.opts.bnb_quantization and 'transformer' not in (kwargs or {}): load_bnb() bnb_config = diffusers.BitsAndBytesConfig( load_in_8bit=shared.opts.bnb_quantization_type in ['fp8'], @@ -19,8 +19,12 @@ def create_bnb_config(kwargs): bnb_4bit_quant_type=shared.opts.bnb_quantization_type, bnb_4bit_compute_dtype=devices.dtype ) - kwargs['quantization_config'] = bnb_config shared.log.debug(f'Quantization: module=all type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}') + if kwargs is None: + return bnb_config + else: + kwargs['quantization_config'] = bnb_config + return kwargs return kwargs diff --git a/modules/model_sd3.py b/modules/model_sd3.py index e369b197a..fc817dc4b 100644 --- a/modules/model_sd3.py +++ b/modules/model_sd3.py @@ -1,7 +1,7 @@ import os import diffusers import transformers -from modules import shared, devices, sd_models, sd_unet, model_te, model_quant +from modules import shared, devices, sd_models, sd_unet, model_te, model_quant, model_tools def load_overrides(kwargs, cache_dir): @@ -69,7 +69,7 @@ def load_quants(kwargs, repo_id, cache_dir): def load_missing(kwargs, fn, cache_dir): - keys = sd_models.get_safetensor_keys(fn) + keys = model_tools.get_safetensor_keys(fn) size = os.stat(fn).st_size // 1024 // 1024 if size > 15000: repo_id = 'stabilityai/stable-diffusion-3.5-large' @@ -129,7 +129,11 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None): if fn is not None and os.path.exists(fn) and os.path.isfile(fn): if fn.endswith('.safetensors'): loader = diffusers.StableDiffusion3Pipeline.from_single_file - kwargs = load_missing(kwargs, fn, cache_dir) + # required_modules = model_tools.get_modules(diffusers.StableDiffusion3Pipeline) + # have_modules = model_tools.get_safetensor_keys(fn) + # loaded_modules = model_tools.load_modules('stabilityai/stable-diffusion-3.5-medium', required_modules) + # kwargs = {**kwargs, **loaded_modules} + # kwargs = load_missing(kwargs, fn, cache_dir) repo_id = fn elif fn.endswith('.gguf'): kwargs = load_gguf(kwargs, fn) diff --git a/modules/model_tools.py b/modules/model_tools.py new file mode 100644 index 000000000..1212da244 --- /dev/null +++ b/modules/model_tools.py @@ -0,0 +1,49 @@ +import inspect +import diffusers +import transformers +import safetensors.torch +from modules import shared, devices, model_quant + + +def get_safetensor_keys(filename): + keys = [] + try: + with safetensors.torch.safe_open(filename, framework="pt", device="cpu") as f: + keys = f.keys() + except Exception as e: + shared.log.error(f'Load dict: path="{filename}" {e}') + return keys + + +def get_modules(model: callable): + signature = inspect.signature(model.__init__, follow_wrapped=True) + params = {param.name: param.annotation for param in signature.parameters.values() if param.annotation != inspect._empty and hasattr(param.annotation, 'from_pretrained')} # pylint: disable=protected-access + for name, cls in params.items(): + shared.log.debug(f'Analyze: model={model} module={name} class={cls.__name__} loadable={getattr(cls, "from_pretrained", None)}') + return params + + +def load_modules(repo_id: str, params: dict): + cache_dir = shared.opts.hfcache_dir + modules = {} + for name, cls in params.items(): + subfolder = None + kwargs = {} + if cls == diffusers.AutoencoderKL: + subfolder = 'vae' + if cls == transformers.CLIPTextModel: # clip-vit-l + subfolder = 'text_encoder' + if cls == transformers.CLIPTextModelWithProjection: # clip-vit-g + subfolder = 'text_encoder_2' + if cls == transformers.T5EncoderModel: # t5-xxl + subfolder = 'text_encoder_3' + kwargs['quantization_config'] = model_quant.create_bnb_config() + kwargs['variant'] = 'fp16' + if cls == diffusers.SD3Transformer2DModel: + subfolder = 'transformer' + kwargs['quantization_config'] = model_quant.create_bnb_config() + if subfolder is None: + continue + shared.log.debug(f'Load: module={name} class={cls.__name__} repo={repo_id} location={subfolder}') + modules[name] = cls.from_pretrained(repo_id, subfolder=subfolder, cache_dir=cache_dir, torch_dtype=devices.dtype, **kwargs) + return modules diff --git a/modules/sd_detect.py b/modules/sd_detect.py index 7144a7be7..148e788d5 100644 --- a/modules/sd_detect.py +++ b/modules/sd_detect.py @@ -84,6 +84,8 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False): if 'omnigen' in f.lower(): guess = 'OmniGen' pipeline = 'custom' + if 'sd3' in f.lower(): + guess = 'Stable Diffusion 3' if 'flux' in f.lower(): guess = 'FLUX' if size > 11000 and size < 20000: diff --git a/modules/sd_models.py b/modules/sd_models.py index 3afc81e67..7743ce27d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -75,16 +75,6 @@ def read_state_dict(checkpoint_file, map_location=None, what:str='model'): # pyl return sd -def get_safetensor_keys(filename): - keys = [] - try: - with safetensors.torch.safe_open(filename, framework="pt", device="cpu") as f: - keys = f.keys() - except Exception as e: - shared.log.error(f'Load dict: path="{filename}" {e}') - return keys - - def get_state_dict_from_checkpoint(pl_sd): checkpoint_dict_replacements = { 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.', From 4473f538bdb1d66fd4af2ebff5dffe95c17b966e Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Thu, 31 Oct 2024 10:37:42 -0400 Subject: [PATCH 37/81] changelog management, move assets Signed-off-by: Vladimir Mandic --- .eslintrc.json | 2 + CHANGELOG.md | 10 +- README.md | 104 +++------- html/card-no-preview.png | Bin 448624 -> 103803 bytes html/screenshot-color.jpg | Bin 78314 -> 0 bytes html/screenshot-control.jpg | Bin 93916 -> 0 bytes html/screenshot-corrections.jpg | Bin 106478 -> 0 bytes html/screenshot-differential.jpg | Bin 64212 -> 0 bytes html/screenshot-instantid.jpg | Bin 64928 -> 0 bytes html/screenshot-ipadapter-mask.jpg | Bin 200702 -> 0 bytes html/screenshot-ipadapter.jpg | Bin 101550 -> 0 bytes html/screenshot-ipadapter02.jpg | Bin 103711 -> 0 bytes html/screenshot-ledit.jpg | Bin 67337 -> 0 bytes html/screenshot-mask.jpg | Bin 94525 -> 0 bytes html/screenshot-modernui-control.jpg | Bin 157697 -> 0 bytes html/screenshot-modernui-f1.jpg | Bin 165635 -> 0 bytes html/screenshot-modernui-img2img.jpg | Bin 158960 -> 0 bytes html/screenshot-modernui-sd3.jpg | Bin 197627 -> 0 bytes html/screenshot-modernui.jpg | Bin 157944 -> 0 bytes html/screenshot-outpaint.jpg | Bin 102817 -> 0 bytes html/screenshot-processors.jpg | Bin 82001 -> 0 bytes html/screenshot-regional.jpg | Bin 66124 -> 0 bytes html/screenshot-text2image.jpg | Bin 104309 -> 0 bytes installer.py | 71 +++---- javascript/changelog.js | 73 +++++++ javascript/sdnext.css | 6 + javascript/startup.js | 1 + modules/cmd_args.py | 216 ++++++++++++--------- modules/generation_parameters_copypaste.py | 2 +- modules/postprocessing.py | 1 - modules/shared.py | 2 +- modules/ui.py | 19 +- requirements.txt | 2 +- 33 files changed, 293 insertions(+), 216 deletions(-) delete mode 100644 html/screenshot-color.jpg delete mode 100644 html/screenshot-control.jpg delete mode 100644 html/screenshot-corrections.jpg delete mode 100644 html/screenshot-differential.jpg delete mode 100644 html/screenshot-instantid.jpg delete mode 100644 html/screenshot-ipadapter-mask.jpg delete mode 100644 html/screenshot-ipadapter.jpg delete mode 100644 html/screenshot-ipadapter02.jpg delete mode 100644 html/screenshot-ledit.jpg delete mode 100644 html/screenshot-mask.jpg delete mode 100644 html/screenshot-modernui-control.jpg delete mode 100644 html/screenshot-modernui-f1.jpg delete mode 100644 html/screenshot-modernui-img2img.jpg delete mode 100644 html/screenshot-modernui-sd3.jpg delete mode 100644 html/screenshot-modernui.jpg delete mode 100644 html/screenshot-outpaint.jpg delete mode 100644 html/screenshot-processors.jpg delete mode 100644 html/screenshot-regional.jpg delete mode 100644 html/screenshot-text2image.jpg create mode 100644 javascript/changelog.js diff --git a/.eslintrc.json b/.eslintrc.json index 22a9edee5..7ea7b8dae 100644 --- a/.eslintrc.json +++ b/.eslintrc.json @@ -112,6 +112,8 @@ "idbPut": "readonly", "idbDel": "readonly", "idbAdd": "readonly", + // changelog.js + "initChangelog": "readonly", // notification.js "sendNotification": "readonly" }, diff --git a/CHANGELOG.md b/CHANGELOG.md index 71e8a667e..b3f2a9fb7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,17 +1,23 @@ # Change Log for SD.Next -## Update for 2024-10-30 +## Update for 2024-10-31 - XYZ grid: optional per-image time benchmark info - UI: add additional [hotkeys](https://github.com/vladmandic/automatic/wiki/Hotkeys) +- Docs: since [changelog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) is the up-to-date source of info + - go to system -> changelog and search/highligh/navigate directly in UI! - SD3: support for ControlNets: - *InstantX Canny, Pose, Depth, Tile* - *Alimama Inpainting, SoftEdge* - *note*: that just like with FLUX.1 or any large model, ControlNet are also large and can push your system over the limit e.g. SD3 controlnets vary from 1GB to over 4GB in size - SD3: support for all-in-one safetensors - - examples: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031) [medium](https://civitai.com/models/900327) + - *examples*: [large](https://civitai.com/models/882666/sd35-large-google-flan?modelVersionId=1003031), [medium](https://civitai.com/models/900327) - *note*: enable bnb on-the-fly quantization for even bigger gains +- CLI: refactor command line params + - run `webui.sh`/`webui.bat` with `--help` to see all options +- Repo: move screenshots to GH pages +- Update requirements ## Update for 2024-10-29 diff --git a/README.md b/README.md index ac96b2e32..a2caa5cb7 100644 --- a/README.md +++ b/README.md @@ -50,12 +50,13 @@ All individual features are not listed here, instead check [ChangeLog](CHANGELOG
*Main interface using **StandardUI***: -![Screenshot-Dark](html/screenshot-text2image.jpg) +![screenshot-text2image](https://github.com/user-attachments/assets/87ac2813-65c2-45f4-80b8-67b26ccf5cd6) *Main interface using **ModernUI***: -![Screenshot-Dark](html/screenshot-modernui-f1.jpg) -![Screenshot-Dark](html/screenshot-modernui.jpg) -![Screenshot-Dark](html/screenshot-modernui-sd3.jpg) + +![screenshot-modernui-f1](https://github.com/user-attachments/assets/b509a280-8d3b-48b5-8525-363bad8c1ed2) +![screenshot-modernui](https://github.com/user-attachments/assets/fef33127-f733-4e78-b66e-17729539512f) +![screenshot-modernui-sd3](https://github.com/user-attachments/assets/1ed02ecc-23e4-4fda-8ae5-2d7393dc530c) For screenshots and informations on other available themes, see [Themes Wiki](https://github.com/vladmandic/automatic/wiki/Themes) @@ -63,12 +64,13 @@ For screenshots and informations on other available themes, see [Themes Wiki](ht ## Model support -Additional models will be added as they become available and there is public interest in them +Additional models will be added as they become available and there is public interest in them +See [models overview](https://github.com/vladmandic/automatic/wiki/Models) for details on each model, including their architecture, complexity and other info - [RunwayML Stable Diffusion](https://github.com/Stability-AI/stablediffusion/) 1.x and 2.x *(all variants)* - [StabilityAI Stable Diffusion XL](https://github.com/Stability-AI/generative-models) -- [StabilityAI Stable Diffusion 3 Medium](https://stability.ai/news/stable-diffusion-3-medium) -- [Stable Diffusion 3.5 Large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) +- [StabilityAI Stable Diffusion](https://stability.ai/news/stable-diffusion-3-medium) +- [Stable Diffusion 3.x](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) 3.0 Medium, 3.5 Medium, 3.5 Large, 3.5 Large Turbo - [StabilityAI Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) Base, XT 1.0, XT 1.1 - [StabilityAI Stable Cascade](https://github.com/Stability-AI/StableCascade) *Full* and *Lite* - [Black Forest Labs FLUX.1](https://blackforestlabs.ai/announcing-black-forest-labs/) Dev, Schnell @@ -136,13 +138,13 @@ Also supported are modifiers such as: ## Examples *IP Adapters*: -![Screenshot-IPAdapter](html/screenshot-ipadapter.jpg) +![screenshot-ipadapter](https://github.com/user-attachments/assets/92830894-845c-49ec-92d9-18c8a577d04f) *Color grading*: -![Screenshot-Color](html/screenshot-color.jpg) +![screenshot-control](https://github.com/user-attachments/assets/cdad2722-ae7c-4c9c-94d6-5ea35a4b1356) *InstantID*: -![Screenshot-InstantID](html/screenshot-instantid.jpg) +![screenshot-instantid](https://github.com/user-attachments/assets/f38a5660-32b3-4235-9da1-c79eccf5372f) > [!IMPORTANT] > - Loading any model other than standard SD 1.x / SD 2.x requires use of backend **Diffusers** @@ -173,92 +175,34 @@ Also supported are modifiers such as: Once SD.Next is installed, simply run `webui.ps1` or `webui.bat` (*Windows*) or `webui.sh` (*Linux or MacOS*) -List of available parameters, run `webui --help` for the full & up-to-date list: +For list of available command line options, run `webui --help` for the full & up-to-date list - Server options: - --config CONFIG Use specific server configuration file, default: config.json - --ui-config UI_CONFIG Use specific UI configuration file, default: ui-config.json - --medvram Split model stages and keep only active part in VRAM, default: False - --lowvram Split model components and keep only active part in VRAM, default: False - --ckpt CKPT Path to model checkpoint to load immediately, default: None - --vae VAE Path to VAE checkpoint to load immediately, default: None - --data-dir DATA_DIR Base path where all user data is stored, default: - --models-dir MODELS_DIR Base path where all models are stored, default: models - --allow-code Allow custom script execution, default: False - --share Enable UI accessible through Gradio site, default: False - --insecure Enable extensions tab regardless of other options, default: False - --use-cpu USE_CPU [USE_CPU ...] Force use CPU for specified modules, default: [] - --listen Launch web server using public IP address, default: False - --port PORT Launch web server with given server port, default: 7860 - --freeze Disable editing settings - --auth AUTH Set access authentication like "user:pwd,user:pwd"" - --auth-file AUTH_FILE Set access authentication using file, default: None - --autolaunch Open the UI URL in the system's default browser upon launch - --docs Mount API docs, default: False - --api-only Run in API only mode without starting UI - --api-log Enable logging of all API requests, default: False - --device-id DEVICE_ID Select the default CUDA device to use, default: None - --cors-origins CORS_ORIGINS Allowed CORS origins as comma-separated list, default: None - --cors-regex CORS_REGEX Allowed CORS origins as regular expression, default: None - --tls-keyfile TLS_KEYFILE Enable TLS and specify key file, default: None - --tls-certfile TLS_CERTFILE Enable TLS and specify cert file, default: None - --tls-selfsign Enable TLS with self-signed certificates, default: False - --server-name SERVER_NAME Sets hostname of server, default: None - --no-hashing Disable hashing of checkpoints, default: False - --no-metadata Disable reading of metadata from models, default: False - --disable-queue Disable queues, default: False - --subpath SUBPATH Customize the URL subpath for usage with reverse proxy - --backend {original,diffusers} force model pipeline type - --allowed-paths ALLOWED_PATHS [ALLOWED_PATHS ...] add additional paths to paths allowed for web access - - Setup options: - --reset Reset main repository to latest version, default: False - --upgrade Upgrade main repository to latest version, default: False - --requirements Force re-check of requirements, default: False - --quick Bypass version checks, default: False - --use-directml Use DirectML if no compatible GPU is detected, default: False - --use-openvino Use Intel OpenVINO backend, default: False - --use-ipex Force use Intel OneAPI XPU backend, default: False - --use-cuda Force use nVidia CUDA backend, default: False - --use-rocm Force use AMD ROCm backend, default: False - --use-zluda Force use ZLUDA, AMD GPUs only, default: False - --use-xformers Force use xFormers cross-optimization, default: False - --skip-requirements Skips checking and installing requirements, default: False - --skip-extensions Skips running individual extension installers, default: False - --skip-git Skips running all GIT operations, default: False - --skip-torch Skips running Torch checks, default: False - --skip-all Skips running all checks, default: False - --skip-env Skips setting of env variables during startup, default: False - --experimental Allow unsupported versions of libraries, default: False - --reinstall Force reinstallation of all requirements, default: False - --test Run test only and exit - --version Print version information - --ignore Ignore any errors and attempt to continue - --safe Run in safe mode with no user extensions - --uv Use uv as installer, default: False - - Logging options: - --log LOG Set log file, default: None - --debug Run installer with debug logging, default: False - --profile Run profiler, default: False +> [!TIP] +> All command line options can also be set via env variable +> For example `--debug` is same as `set SD_DEBUG=true` ## Notes > [!TIP] > If you don't want to use built-in `venv` support and prefer to run SD.Next in your own environment such as *Docker* container, *Conda* environment or any other virtual environment, you can skip `venv` create/activate and launch SD.Next directly using `python launch.py` (command line flags noted above still apply). +### Quantization + +**SD.Next** comes with broad quantization support, including support for BitsAndBytes, Optimum.Quanto, TorchAO, NNCF and GGUF +See [Quantization Wiki](https://github.com/vladmandic/automatic/wiki/Quantization) + ### Control **SD.Next** comes with built-in control for all types of text2image, image2image, video2video and batch processing *Control interface*: -![Screenshot-Control](html/screenshot-control.jpg) +![screenshot-control](https://github.com/user-attachments/assets/cdad2722-ae7c-4c9c-94d6-5ea35a4b1356) *Control processors*: -![Screenshot-Process](html/screenshot-processors.jpg) +![screenshot-processors](https://github.com/user-attachments/assets/7bccb82b-366e-4bdb-ae57-cc53fac95d3c) *Masking*: -![Screenshot-Mask](html/screenshot-mask.jpg) +![screenshot-mask](https://github.com/user-attachments/assets/4b057e65-64f0-44ea-93b4-c3b69bc55532) ### Extensions diff --git a/html/card-no-preview.png b/html/card-no-preview.png index 352b0a9df358eb6340a48752ed6ffc2710350b95..6b89aad1abf262b46fbc45bc2ed7ed5f82eb8566 100644 GIT binary patch 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