diff --git a/extensions-builtin/multidiffusion-upscaler-for-automatic1111 b/extensions-builtin/multidiffusion-upscaler-for-automatic1111 index 2473d6b00..51cb83ce2 160000 --- a/extensions-builtin/multidiffusion-upscaler-for-automatic1111 +++ b/extensions-builtin/multidiffusion-upscaler-for-automatic1111 @@ -1 +1 @@ -Subproject commit 2473d6b005a516fb4bd51b331abd04b0289fdf07 +Subproject commit 51cb83ce2a53bf147a9091ed5269dcce8f1662d7 diff --git a/extensions-builtin/sd-webui-controlnet b/extensions-builtin/sd-webui-controlnet index dc2d91316..cdba83b6e 160000 --- a/extensions-builtin/sd-webui-controlnet +++ b/extensions-builtin/sd-webui-controlnet @@ -1 +1 @@ -Subproject commit dc2d91316307b6d6af83983f3a71f8ba66749609 +Subproject commit cdba83b6e1a59f3b59bbcf5f0a6e0a585d666a01 diff --git a/installer.py b/installer.py index 92b6c9cfd..25a6e18c2 100644 --- a/installer.py +++ b/installer.py @@ -261,7 +261,7 @@ def check_torch(): os.environ.setdefault('PYTORCH_HIP_ALLOC_CONF', 'garbage_collection_threshold:0.9,max_split_size_mb:512') torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.0 torchvision==0.15.1 --index-url https://download.pytorch.org/whl/rocm5.4.2') xformers_package = os.environ.get('XFORMERS_PACKAGE', 'none') - elif allow_ipex and (shutil.which('sycl-ls') is not None or os.path.exists('/opt/intel/oneapi') or args.use_ipex): + elif allow_ipex and args.use_ipex and shutil.which('sycl-ls') is not None: log.info('Intel OneAPI Toolkit detected') torch_command = os.environ.get('TORCH_COMMAND', 'torch==1.13.0a0 torchvision==0.14.1a0 intel_extension_for_pytorch==1.13.120+xpu -f https://developer.intel.com/ipex-whl-stable-xpu') xformers_package = os.environ.get('XFORMERS_PACKAGE', 'none') diff --git a/javascript/style.css b/javascript/style.css index dc9b07208..fab461254 100644 --- a/javascript/style.css +++ b/javascript/style.css @@ -696,3 +696,5 @@ footer { .controlnet_control_mode_radio .wrap:last-of-type { flex-direction: column; } + +#modelmerger_interp_description { margin-top: 1em; margin-bottom: 1em; } diff --git a/modules/extras.py b/modules/extras.py index 1db100dec..683064661 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -98,13 +98,13 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ } filename_generator, theta_func1, theta_func2 = theta_funcs[interp_method] shared.state.job_count = (1 if theta_func1 else 0) + (1 if theta_func2 else 0) - if not primary_model_name: + if not primary_model_name or primary_model_name == 'None': return fail("Failed: Merging requires a primary model.") primary_model_info = sd_models.checkpoints_list[primary_model_name] - if theta_func2 and not secondary_model_name: + if theta_func2 and (not secondary_model_name or secondary_model_name == 'None'): return fail("Failed: Merging requires a secondary model.") secondary_model_info = sd_models.checkpoints_list[secondary_model_name] if theta_func2 else None - if theta_func1 and not tertiary_model_name: + if theta_func1 and (not tertiary_model_name or tertiary_model_name == 'None'): return fail(f"Failed: Interpolation method ({interp_method}) requires a tertiary model.") tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None result_is_inpainting_model = False diff --git a/modules/hf_hub.py b/modules/hf_hub.py new file mode 100644 index 000000000..b0fc9040c --- /dev/null +++ b/modules/hf_hub.py @@ -0,0 +1,17 @@ +import sys +import huggingface_hub as hf +from rich import print # pylint: disable=redefined-builtin + +if __name__ == "__main__": + sys.argv.pop(0) + keyword = sys.argv[0] if len(sys.argv) > 0 else '' + hf_api = hf.HfApi() + model_filter = hf.ModelFilter( + model_name=keyword, + task='text-to-image', + tags='stable-diffusion', + library=['diffusers', 'stable-diffusion'], + ) + res = hf_api.list_models(filter=model_filter, full=True, limit=50, sort="downloads", direction=-1) + models = [{ 'name': m.modelId, 'downloads': m.downloads, 'mtime': m.lastModified, 'url': f'https://huggingface.co/{m.modelId}' } for m in res] + print('Online', models) diff --git a/modules/img2img.py b/modules/img2img.py index 9a6370760..dda82dfcd 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -71,8 +71,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s shared.log.warning('Model not loaded') return if init_img is None: - shared.log.warning('Init image not set') - return + shared.log.debug('Init image not set') + shared.log.debug(f'img2img: id_task={id_task}|mode={mode}|prompt={prompt}|negative_prompt={negative_prompt}|prompt_styles={prompt_styles}|init_img={init_img}|sketch={sketch}|init_img_with_mask={init_img_with_mask}|inpaint_color_sketch={inpaint_color_sketch}|inpaint_color_sketch_orig={inpaint_color_sketch_orig}|init_img_inpaint={init_img_inpaint}|init_mask_inpaint={init_mask_inpaint}|steps={steps}|sampler_index={sampler_index}|mask_blur={mask_blur}|mask_alpha={mask_alpha}|inpainting_fill={inpainting_fill}|restore_faces={restore_faces}|tiling={tiling}|n_iter={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|image_cfg_scale={image_cfg_scale}|clip_skip={clip_skip}|denoising_strength={denoising_strength}|seed={seed}|subseed{subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|seed_enable_extras={seed_enable_extras}|selected_scale_tab={selected_scale_tab}|height={height}|width={width}|scale_by={scale_by}|resize_mode={resize_mode}|inpaint_full_res={inpaint_full_res}|inpaint_full_res_padding={inpaint_full_res_padding}|inpainting_mask_invert={inpainting_mask_invert}|img2img_batch_input_dir={img2img_batch_input_dir}|img2img_batch_output_dir={img2img_batch_output_dir}|img2img_batch_inpaint_mask_dir={img2img_batch_inpaint_mask_dir}|override_settings_texts={override_settings_texts}|args={args}') if sampler_index is None: diff --git a/modules/modelloader.py b/modules/modelloader.py index fc8dc7ab4..188ee6db2 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -7,8 +7,50 @@ from modules import shared from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone from modules.paths import script_path, models_path +diffuser_repos = [] -def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None, diffusors=False) -> list: +def load_diffusers(model_path: str, command_path: str = None): + import huggingface_hub as hf + places = [] + places.append(model_path) + if command_path is not None and command_path != model_path and os.path.isdir(command_path): + places.append(command_path) + diffuser_repos.clear() + output = [] + try: + for place in places: + res = hf.scan_cache_dir(cache_dir=place) + for r in list(res.repos): + diffuser_repos.append({ 'name': r.repo_id, 'filename': r.repo_id, 'path': str(r.repo_path), 'size': r.size_on_disk, 'mtime': r.last_modified, 'hash': list(r.revisions)[-1].commit_hash }) + output.append(str(r.repo_id)) + except Exception as e: + shared.log.error(f"Error listing diffusers: {place} {e}") + shared.log.debug(f'Scanning diffusers cache: {len(output)} {model_path} {command_path}') + return output + + +def find_diffuser(name: str): + import huggingface_hub as hf + + if name in diffuser_repos: + return name + if shared.cmd_opts.no_download: + return None + api = hf.HfApi() + filt = hf.ModelFilter( + model_name=name, + task='text-to-image', + tags='stable-diffusion', + library=['diffusers', 'stable-diffusion'], + ) + models = list(api.list_models(filter=filt, full=True, limit=50, sort="downloads", direction=-1)) + shared.log.debug(f'Searching diffusers models: {name} {len(models) > 0}') + if len(models) > 0: + return models[0].modelId + return None + + +def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list: """ A one-and done loader to try finding the desired models in specified directories. @@ -23,41 +65,27 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None places.append(model_path) if command_path is not None and command_path != model_path and os.path.isdir(command_path): places.append(command_path) - - def get_checkpoints(): - output = [] - try: - for place in places: - for full_path in shared.walk_files(place, allowed_extensions=ext_filter): - if os.path.islink(full_path) and not os.path.exists(full_path): - print(f"Skipping broken symlink: {full_path}") - continue - if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]): - continue - if full_path not in output: - output.append(full_path) - if model_url is not None and len(output) == 0: - if download_name is not None: - from basicsr.utils.download_util import load_file_from_url - dl = load_file_from_url(model_url, model_path, True, download_name) - output.append(dl) - else: - output.append(model_url) - except Exception: - pass - return output - - def get_diffusors(): - output = [] + output = [] + try: for place in places: - output = os.listdir(place) - output = [os.path.join(place, x) for x in output] - return output - - if not diffusors: - return get_checkpoints() - else: - return get_diffusors() + for full_path in shared.walk_files(place, allowed_extensions=ext_filter): + if os.path.islink(full_path) and not os.path.exists(full_path): + print(f"Skipping broken symlink: {full_path}") + continue + if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]): + continue + if full_path not in output: + output.append(full_path) + if model_url is not None and len(output) == 0: + if download_name is not None: + from basicsr.utils.download_util import load_file_from_url + dl = load_file_from_url(model_url, model_path, True, download_name) + output.append(dl) + else: + output.append(model_url) + except Exception as e: + shared.log.error(f"Error listing models: {places} {e}") + return output def friendly_name(file: str): diff --git a/modules/models/diffusion/uni_pc/sampler.py b/modules/models/diffusion/uni_pc/sampler.py index e46befd06..b46848183 100644 --- a/modules/models/diffusion/uni_pc/sampler.py +++ b/modules/models/diffusion/uni_pc/sampler.py @@ -1,6 +1,5 @@ """SAMPLING ONLY.""" -import numpy as np import torch from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC, get_time_steps diff --git a/modules/sd_models.py b/modules/sd_models.py index bfbb7eb82..80626abde 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -10,6 +10,7 @@ import torch import safetensors.torch from omegaconf import OmegaConf import tomesd +from transformers import logging as transformers_logging import ldm.modules.midas as midas from ldm.util import instantiate_from_config from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config @@ -18,7 +19,7 @@ from modules.timer import Timer from modules.memstats import memory_stats from modules.paths_internal import models_path - +transformers_logging.set_verbosity_error() model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(paths.models_path, model_dir)) checkpoints_list = {} @@ -29,29 +30,37 @@ skip_next_load = False class CheckpointInfo: # TODO Diffusers def __init__(self, filename): + name = '' + self.name = None + self.hash = None self.filename = filename abspath = os.path.abspath(filename) - if shared.opts.ckpt_dir is not None and abspath.startswith(shared.opts.ckpt_dir): - name = abspath.replace(shared.opts.ckpt_dir, '') - elif abspath.startswith(model_path): - name = abspath.replace(model_path, '') - else: - name = os.path.basename(filename) - if name.startswith("\\") or name.startswith("/"): - name = name[1:] - self.name = name - self.name_for_extra = os.path.splitext(os.path.basename(filename))[0] - self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] if shared.opts.sd_backend == 'Original': + if shared.opts.ckpt_dir is not None and abspath.startswith(shared.opts.ckpt_dir): + name = abspath.replace(shared.opts.ckpt_dir, '') + elif abspath.startswith(model_path): + name = abspath.replace(model_path, '') + else: + name = os.path.basename(filename) + if name.startswith("\\") or name.startswith("/"): + name = name[1:] + self.name = name self.hash = model_hash(self.filename) self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}") - else: # TODO Diffusers calculate hash + else: # TODO Diffusers # sd_model.unet.config._name_or_path.split("/")[-2] - self.hash = 'ABCDEFGH' - self.sha256 = 'ABCDEFGH' + repo = [r for r in modelloader.diffuser_repos if filename == r['filename']] + if len(repo) == 0: + shared.log.error(f'Cannot find diffuser model: {filename}') + return + self.name = repo[0]['name'] + self.hash = repo[0]['hash'][:8] + self.sha256 = repo[0]['hash'] + self.name_for_extra = os.path.splitext(os.path.basename(filename))[0] + self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] self.shorthash = self.sha256[0:10] if self.sha256 else None - self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]' - self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else []) + self.title = self.name if self.shorthash is None else f'{self.name} [{self.shorthash}]' + self.ids = [self.hash, self.model_name, self.title, self.name, f'{self.name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else []) self.metadata = {} _, ext = os.path.splitext(self.filename) if ext.lower() == ".safetensors": @@ -78,14 +87,6 @@ class CheckpointInfo: # TODO Diffusers return self.shorthash -try: - # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. - from transformers import logging - logging.set_verbosity_error() -except Exception: - pass - - def setup_model(): if not os.path.exists(model_path): os.makedirs(model_path) @@ -107,20 +108,22 @@ def list_models(): if shared.opts.sd_backend == 'Original': model_list = modelloader.load_models(model_path=os.path.join(models_path, 'Stable-diffusion'), model_url=None, command_path=shared.opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name=None, ext_blacklist=[".vae.ckpt", ".vae.safetensors"]) else: - model_list = modelloader.load_models(model_path=os.path.join(models_path, 'Diffusers'), model_url=None, command_path=shared.opts.diffusers_dir, ext_filter=[".ckpt", ".safetensors"], download_name=None, ext_blacklist=[".vae.ckpt", ".vae.safetensors"]) + model_list = modelloader.load_diffusers(model_path=os.path.join(models_path, 'Diffusers'), command_path=shared.opts.diffusers_dir) + 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.cmd_opts.ckpt is not None: if not os.path.exists(shared.cmd_opts.ckpt) and shared.opts.sd_backend == 'Original': if shared.cmd_opts.ckpt.lower() != "none": shared.log.warning(f"Requested checkpoint not found: {shared.cmd_opts.ckpt}") else: checkpoint_info = CheckpointInfo(shared.cmd_opts.ckpt) - checkpoint_info.register() - shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title + 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"Checkpoint not found: {shared.cmd_opts.ckpt}") - for filename in sorted(model_list, key=str.lower): - checkpoint_info = CheckpointInfo(filename) - checkpoint_info.register() shared.log.info(f'Available models: {shared.opts.ckpt_dir} {len(checkpoints_list)}') if len(checkpoints_list) == 0: if not shared.cmd_opts.no_download: @@ -162,7 +165,7 @@ def model_hash(filename): def select_checkpoint(): model_checkpoint = shared.opts.sd_model_checkpoint checkpoint_info = checkpoint_aliases.get(model_checkpoint, None) - if checkpoint_info is not None or shared.cmd_opts.ckpt is not None: + if checkpoint_info is not None: shared.log.debug(f'Select checkpoint: {checkpoint_info.title if checkpoint_info is not None else None}') return checkpoint_info if len(checkpoints_list) == 0: @@ -171,7 +174,7 @@ def select_checkpoint(): exit(1) checkpoint_info = next(iter(checkpoints_list.values())) if model_checkpoint is not None: - shared.log.warning(f"Default checkpoint not found: {model_checkpoint}") + shared.log.warning(f"Selected checkpoint not found: {model_checkpoint}") shared.log.warning(f"Loading fallback checkpoint: {checkpoint_info.title}") shared.log.debug(f'Select checkpoint: {checkpoint_info.title if checkpoint_info is not None else None}') return checkpoint_info @@ -225,6 +228,8 @@ def read_metadata_from_safetensors(filename): def read_state_dict(checkpoint_file, map_location=None): # pylint: disable=unused-argument + if shared.opts.sd_backend == 'Diffusers': + return None try: pl_sd = None with progress.open(checkpoint_file, 'rb', description=f'Loading weights: [cyan]{checkpoint_file}', auto_refresh=True) as f: @@ -365,6 +370,7 @@ sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_w class SdModelData: def __init__(self): self.sd_model = None + self.initial = True self.lock = threading.Lock() def get_sd_model(self): @@ -374,9 +380,10 @@ class SdModelData: if shared.opts.sd_backend == 'Original': load_model() elif shared.opts.sd_backend == 'Diffusers': - load_diffusers() + load_diffuser() else: shared.log.error(f"Unknown Stable Diffusion backend: {shared.opts.sd_backend}") + self.initial = False except Exception as e: shared.log.error("Failed to load stable diffusion model") errors.display(e, "loading stable diffusion model") @@ -390,10 +397,12 @@ class SdModelData: model_data = SdModelData() -def load_diffusers(checkpoint_info=None, already_loaded_state_dict=None, timer=None): +def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=None): # pylint: disable=unused-argument if timer is None: timer = Timer() import diffusers + import logging + logging.getLogger("diffusers").setLevel(logging.ERROR) timer.record("diffusers") diffusor_config = { "force_download": False, @@ -404,23 +413,37 @@ def load_diffusers(checkpoint_info=None, already_loaded_state_dict=None, timer=N "cache_dir": shared.opts.diffusers_dir, "torch_dtype": devices.dtype, } - shared.log.warning("Using experimental Diffusers backend for Stable Diffusion") if shared.opts.data['sd_model_checkpoint'] == 'model.ckpt': shared.opts.data['sd_model_checkpoint'] = "runwayml/stable-diffusion-v1-5" sd_model = None try: - checkpoint_info = checkpoint_info or select_checkpoint() - scheduler = diffusers.UniPCMultistepScheduler.from_pretrained(checkpoint_info.filename, subfolder="scheduler") - scheduler.name = 'UniPC' - sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.filename, scheduler=scheduler, **diffusor_config) - sd_model.to(devices.device) + if shared.cmd_opts.ckpt is not None and model_data.initial: # initial load + model_name = modelloader.find_diffuser(shared.cmd_opts.ckpt) + if model_name is not None: + shared.log.info(f'Loading diffuser model: {model_name}') + scheduler = diffusers.UniPCMultistepScheduler.from_pretrained(model_name, subfolder="scheduler") + sd_model = diffusers.DiffusionPipeline.from_pretrained(model_name, scheduler=scheduler, **diffusor_config) + list_models() # rescan for downloaded model + checkpoint_info = CheckpointInfo(model_name) + if sd_model is None: + checkpoint_info = checkpoint_info or select_checkpoint() + shared.log.info(f'Loading diffuser model: {checkpoint_info.filename}') + scheduler = diffusers.UniPCMultistepScheduler.from_pretrained(checkpoint_info.filename, subfolder="scheduler") + sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.filename, scheduler=scheduler, **diffusor_config) sd_model.sd_checkpoint_info = checkpoint_info + sd_model.sd_model_checkpoint = checkpoint_info.filename sd_model.sd_model_hash = checkpoint_info.hash + scheduler.name = 'UniPC' + sd_model.to(devices.device) except Exception as e: shared.log.error("Failed to load diffusers model") errors.display(e, "loading Diffusers model") shared.sd_model = sd_model timer.record("load") + shared.log.info(f"Model loaded in {timer.summary()}") + devices.torch_gc(force=True) + shared.log.info(f'Model load finished: {memory_stats()}') + def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None): @@ -515,9 +538,9 @@ def reload_model_weights(sd_model=None, info=None): lowvram.send_everything_to_cpu() else: sd_model.to(devices.cpu) - sd_hijack.model_hijack.undo_hijack(sd_model) if shared.opts.model_reuse_dict and sd_model is not None: shared.log.info('Reusing previous model dictionary') + sd_hijack.model_hijack.undo_hijack(sd_model) # TODO double undo hijack else: unload_model_weights() sd_model = None @@ -528,7 +551,10 @@ def reload_model_weights(sd_model=None, info=None): if sd_model is None or checkpoint_config != sd_model.used_config: del sd_model checkpoints_loaded.clear() - load_model(checkpoint_info, already_loaded_state_dict=state_dict, timer=timer) + if shared.opts.sd_backend == 'Original': + load_model(checkpoint_info, already_loaded_state_dict=state_dict, timer=timer) + else: + load_diffuser(checkpoint_info, already_loaded_state_dict=state_dict, timer=timer) return model_data.sd_model try: load_model_weights(sd_model, checkpoint_info, state_dict, timer) @@ -550,7 +576,8 @@ def unload_model_weights(sd_model=None, _info=None): from modules import sd_hijack if model_data.sd_model: model_data.sd_model.to(devices.cpu) - sd_hijack.model_hijack.undo_hijack(model_data.sd_model) + if shared.opts.sd_backend == 'Original': + sd_hijack.model_hijack.undo_hijack(model_data.sd_model) model_data.sd_model = None sd_model = None devices.torch_gc(force=True) diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index bb8ad4d06..0928b8ee1 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -10,6 +10,9 @@ from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback +# from tqdm.rich import trange +# k_diffusion.sampling.trange = trange + samplers_k_diffusion = [ ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}), ('Euler', 'sample_euler', ['k_euler'], {}), diff --git a/modules/ui.py b/modules/ui.py index 5b61ae83b..33e4dcffd 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -383,10 +383,10 @@ def create_ui(): elif category == "hires_fix": with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options: with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"): - hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*modules.shared.latent_upscale_modes, *[x.name for x in modules.shared.sd_upscalers]], value=modules.shared.latent_upscale_default_mode) + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength") hr_second_pass_steps = gr.Slider(minimum=0, maximum=99, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps") with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"): - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength") + hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*modules.shared.latent_upscale_modes, *[x.name for x in modules.shared.sd_upscalers]], value=modules.shared.latent_upscale_default_mode) hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale") with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact"): hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x") @@ -898,40 +898,36 @@ def create_ui(): with gr.Tab(label="Merge models") as modelmerger_interface: with gr.Row().style(equal_height=False): with gr.Column(variant='compact'): - interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description") - with FormRow(elem_id="modelmerger_models"): - primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)") - create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A") - - secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)") - create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B") - - tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)") - create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C") - - custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name") - interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount") - interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method") - interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description]) - + def sd_model_choices(): + return ['None'] + modules.sd_models.checkpoint_tiles() + primary_model_name = gr.Dropdown(sd_model_choices(), elem_id="modelmerger_primary_model_name", label="Primary model", value="None") + create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_A") + secondary_model_name = gr.Dropdown(sd_model_choices(), elem_id="modelmerger_secondary_model_name", label="Secondary model", value="None") + create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_B") + tertiary_model_name = gr.Dropdown(sd_model_choices(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model", value="None") + create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": sd_model_choices()}, "refresh_checkpoint_C") + custom_name = gr.Textbox(label="New model name", elem_id="modelmerger_custom_name") + with FormRow(): + interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description") + with FormRow(): + interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method") + interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description]) + interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation ratio from Primary to Secondary', value=0.5, elem_id="modelmerger_interp_amount") with FormRow(): checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format") - save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half") - save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata") - + with gr.Box(): + save_as_half = gr.Checkbox(value=True, label="Use FP16", elem_id="modelmerger_save_as_half") + save_metadata = gr.Checkbox(value=True, label="Save metadata", elem_id="modelmerger_save_metadata") with FormRow(): with gr.Column(): - config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method") - + config_source = gr.Radio(choices=["Primary", "Secondary", "Tertiary", "None"], value="Primary", label="Model configuration", type="index", elem_id="modelmerger_config_method") with gr.Column(): with FormRow(): bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae") create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae") - with FormRow(): discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights") - with gr.Row(): modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary') diff --git a/webui.sh b/webui.sh index 5e4df3f5b..2abf44a67 100755 --- a/webui.sh +++ b/webui.sh @@ -81,10 +81,15 @@ else exit 1 fi -if [[ "$@" == *"--use-ipex"* ]] +#Set OneAPI environmet if it's not set by the user +if [[ "$@" == *"--use-ipex"* ]] && ! [ -x "$(command -v sycl-ls)" ] then - echo "Setting OneAPI enviroment" - source /opt/intel/oneapi/setvars.sh + echo "Setting OneAPI environment" + if [[ -z "$ONEAPI_ROOT" ]] + then + ONEAPI_ROOT=/opt/intel/oneapi + fi + source $ONEAPI_ROOT/setvars.sh fi if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]