feat: add optional model VAE select (#2867)

* Revert "fix: use LF as line breaks for Docker entrypoint.sh (#2843)" (#2865)

False alarm, worked as intended before. Sorry for the fuzz.
This reverts commit d16a54e.

* feat: add VAE select

* feat: use different default label, add translation

* fix: do not reload model when VAE stays the same

* refactor: code cleanup

* feat: add metadata handling

language/en.json

@@ -340,6 +340,8 @@
     "sgm_uniform": "sgm_uniform",
     "simple": "simple",
     "ddim_uniform": "ddim_uniform",
+    "VAE": "VAE",
+    "Default (model)": "Default (model)",
     "Forced Overwrite of Sampling Step": "Forced Overwrite of Sampling Step",
     "Set as -1 to disable. For developer debugging.": "Set as -1 to disable. For developer debugging.",
     "Forced Overwrite of Refiner Switch Step": "Forced Overwrite of Refiner Switch Step",

ldm_patched/modules/sd.py

@@ -427,12 +427,13 @@ class EmptyClass:
 
     return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
 
-def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
+def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, vae_filename_param=None):
     sd = ldm_patched.modules.utils.load_torch_file(ckpt_path)
     sd_keys = sd.keys()
     clip = None
     clipvision = None
     vae = None
+    vae_filename = None
     model = None
     model_patcher = None
     clip_target = None
@@ -462,8 +463,12 @@ class WeightsLoader(torch.nn.Module):
         model.load_model_weights(sd, "model.diffusion_model.")
 
     if output_vae:
-        vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
-        vae_sd = model_config.process_vae_state_dict(vae_sd)
+        if vae_filename_param is None:
+            vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
+            vae_sd = model_config.process_vae_state_dict(vae_sd)
+        else:
+            vae_sd = ldm_patched.modules.utils.load_torch_file(vae_filename_param)
+            vae_filename = vae_filename_param
         vae = VAE(sd=vae_sd)
 
     if output_clip:
@@ -485,7 +490,7 @@ class WeightsLoader(torch.nn.Module):
             print("loaded straight to GPU")
             model_management.load_model_gpu(model_patcher)
 
-    return (model_patcher, clip, vae, clipvision)
+    return model_patcher, clip, vae, vae_filename, clipvision
 
 
 def load_unet_state_dict(sd): #load unet in diffusers format

modules/async_worker.py

@@ -166,6 +166,7 @@ def handler(async_task):
         adaptive_cfg = args.pop()
         sampler_name = args.pop()
         scheduler_name = args.pop()
+        vae_name = args.pop()
         overwrite_step = args.pop()
         overwrite_switch = args.pop()
         overwrite_width = args.pop()
@@ -428,7 +429,7 @@ def handler(async_task):
             progressbar(async_task, 3, 'Loading models ...')
             pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
                                         loras=loras, base_model_additional_loras=base_model_additional_loras,
-                                        use_synthetic_refiner=use_synthetic_refiner)
+                                        use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
 
             progressbar(async_task, 3, 'Processing prompts ...')
             tasks = []
@@ -869,6 +870,7 @@ def callback(step, x0, x, total_steps, y):
 
                     d.append(('Sampler', 'sampler', sampler_name))
                     d.append(('Scheduler', 'scheduler', scheduler_name))
+                    d.append(('VAE', 'vae', vae_name))
                     d.append(('Seed', 'seed', str(task['task_seed'])))
 
                     if freeu_enabled:
@@ -883,7 +885,7 @@ def callback(step, x0, x, total_steps, y):
                         metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
                         metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
                                                  task['log_negative_prompt'], task['negative'],
-                                                 steps, base_model_name, refiner_model_name, loras)
+                                                 steps, base_model_name, refiner_model_name, loras, vae_name)
                     d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
                     d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
                     img_paths.append(log(x, d, metadata_parser, output_format))

modules/config.py

@@ -189,6 +189,7 @@ def get_dir_or_set_default(key, default_value, as_array=False, make_directory=Fa
 paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)
 path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/')
 path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/')
+path_vae = get_dir_or_set_default('path_vae', '../models/vae/')
 path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')
 path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/')
 path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/')
@@ -346,6 +347,11 @@ def init_temp_path(path: str | None, default_path: str) -> str:
     default_value='karras',
     validator=lambda x: x in modules.flags.scheduler_list
 )
+default_vae = get_config_item_or_set_default(
+    key='default_vae',
+    default_value=modules.flags.default_vae,
+    validator=lambda x: isinstance(x, str)
+)
 default_styles = get_config_item_or_set_default(
     key='default_styles',
     default_value=[
@@ -535,6 +541,7 @@ def add_ratio(x):
 
 model_filenames = []
 lora_filenames = []
+vae_filenames = []
 wildcard_filenames = []
 
 sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
@@ -546,15 +553,20 @@ def get_model_filenames(folder_paths, extensions=None, name_filter=None):
     if extensions is None:
         extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
     files = []
+
+    if not isinstance(folder_paths, list):
+        folder_paths = [folder_paths]
     for folder in folder_paths:
         files += get_files_from_folder(folder, extensions, name_filter)
+
     return files
 
 
 def update_files():
-    global model_filenames, lora_filenames, wildcard_filenames, available_presets
+    global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets
     model_filenames = get_model_filenames(paths_checkpoints)
     lora_filenames = get_model_filenames(paths_loras)
+    vae_filenames = get_model_filenames(path_vae)
     wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
     available_presets = get_presets()
     return

modules/core.py

@@ -35,12 +35,13 @@
 
 
 class StableDiffusionModel:
-    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None):
+    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_filename=None):
         self.unet = unet
         self.vae = vae
         self.clip = clip
         self.clip_vision = clip_vision
         self.filename = filename
+        self.vae_filename = vae_filename
         self.unet_with_lora = unet
         self.clip_with_lora = clip
         self.visited_loras = ''
@@ -142,9 +143,10 @@ def apply_controlnet(positive, negative, control_net, image, strength, start_per
 
 @torch.no_grad()
 @torch.inference_mode()
-def load_model(ckpt_filename):
-    unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings)
-    return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename)
+def load_model(ckpt_filename, vae_filename=None):
+    unet, clip, vae, vae_filename, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings,
+                                                                vae_filename_param=vae_filename)
+    return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename, vae_filename=vae_filename)
 
 
 @torch.no_grad()

modules/default_pipeline.py

@@ -3,6 +3,7 @@
 import torch
 import modules.patch
 import modules.config
+import modules.flags
 import ldm_patched.modules.model_management
 import ldm_patched.modules.latent_formats
 import modules.inpaint_worker
@@ -58,17 +59,21 @@ def assert_model_integrity():
 
 @torch.no_grad()
 @torch.inference_mode()
-def refresh_base_model(name):
+def refresh_base_model(name, vae_name=None):
     global model_base
 
     filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
 
-    if model_base.filename == filename:
+    vae_filename = None
+    if vae_name is not None and vae_name != modules.flags.default_vae:
+        vae_filename = get_file_from_folder_list(vae_name, modules.config.path_vae)
+
+    if model_base.filename == filename and model_base.vae_filename == vae_filename:
         return
 
-    model_base = core.StableDiffusionModel()
-    model_base = core.load_model(filename)
+    model_base = core.load_model(filename, vae_filename)
     print(f'Base model loaded: {model_base.filename}')
+    print(f'VAE loaded: {model_base.vae_filename}')
     return
 
 
@@ -216,7 +221,7 @@ def prepare_text_encoder(async_call=True):
 @torch.no_grad()
 @torch.inference_mode()
 def refresh_everything(refiner_model_name, base_model_name, loras,
-                       base_model_additional_loras=None, use_synthetic_refiner=False):
+                       base_model_additional_loras=None, use_synthetic_refiner=False, vae_name=None):
     global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion
 
     final_unet = None
@@ -227,11 +232,11 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 
     if use_synthetic_refiner and refiner_model_name == 'None':
         print('Synthetic Refiner Activated')
-        refresh_base_model(base_model_name)
+        refresh_base_model(base_model_name, vae_name)
         synthesize_refiner_model()
     else:
         refresh_refiner_model(refiner_model_name)
-        refresh_base_model(base_model_name)
+        refresh_base_model(base_model_name, vae_name)
 
     refresh_loras(loras, base_model_additional_loras=base_model_additional_loras)
     assert_model_integrity()
@@ -254,7 +259,8 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 refresh_everything(
     refiner_model_name=modules.config.default_refiner_model_name,
     base_model_name=modules.config.default_base_model_name,
-    loras=get_enabled_loras(modules.config.default_loras)
+    loras=get_enabled_loras(modules.config.default_loras),
+    vae_name=modules.config.default_vae,
 )
 
 

modules/flags.py

@@ -53,6 +53,8 @@
 sampler_list = SAMPLER_NAMES
 scheduler_list = SCHEDULER_NAMES
 
+default_vae = 'Default (model)'
+
 refiner_swap_method = 'joint'
 
 cn_ip = "ImagePrompt"

modules/meta_parser.py

@@ -46,6 +46,7 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
     get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
     get_str('sampler', 'Sampler', loaded_parameter_dict, results)
     get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
+    get_str('vae', 'VAE', loaded_parameter_dict, results)
     get_seed('seed', 'Seed', loaded_parameter_dict, results)
 
     if is_generating:
@@ -253,6 +254,7 @@ def __init__(self):
         self.refiner_model_name: str = ''
         self.refiner_model_hash: str = ''
         self.loras: list = []
+        self.vae_name: str = ''
 
     @abstractmethod
     def get_scheme(self) -> MetadataScheme:
@@ -267,7 +269,7 @@ def parse_string(self, metadata: dict) -> str:
         raise NotImplementedError
 
     def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
-                 refiner_model_name, loras):
+                 refiner_model_name, loras, vae_name):
         self.raw_prompt = raw_prompt
         self.full_prompt = full_prompt
         self.raw_negative_prompt = raw_negative_prompt
@@ -289,6 +291,7 @@ def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_p
                 lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
                 lora_hash = get_sha256(lora_path)
                 self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
+        self.vae_name = Path(vae_name).stem
 
     @staticmethod
     def remove_special_loras(lora_filenames):
@@ -310,6 +313,7 @@ def get_scheme(self) -> MetadataScheme:
         'steps': 'Steps',
         'sampler': 'Sampler',
         'scheduler': 'Scheduler',
+        'vae': 'VAE',
         'guidance_scale': 'CFG scale',
         'seed': 'Seed',
         'resolution': 'Size',
@@ -397,13 +401,12 @@ def parse_json(self, metadata: str) -> dict:
                     data['sampler'] = k
                     break
 
-        for key in ['base_model', 'refiner_model']:
+        for key in ['base_model', 'refiner_model', 'vae']:
             if key in data:
-                for filename in modules.config.model_filenames:
-                    path = Path(filename)
-                    if data[key] == path.stem:
-                        data[key] = filename
-                        break
+                if key == 'vae':
+                    self.add_extension_to_filename(data, modules.config.vae_filenames, 'vae')
+                else:
+                    self.add_extension_to_filename(data, modules.config.model_filenames, key)
 
         lora_data = ''
         if 'lora_weights' in data and data['lora_weights'] != '':
@@ -433,6 +436,7 @@ def parse_string(self, metadata: dict) -> str:
 
         sampler = data['sampler']
         scheduler = data['scheduler']
+
         if sampler in SAMPLERS and SAMPLERS[sampler] != '':
             sampler = SAMPLERS[sampler]
             if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
@@ -451,6 +455,7 @@ def parse_string(self, metadata: dict) -> str:
 
             self.fooocus_to_a1111['performance']: data['performance'],
             self.fooocus_to_a1111['scheduler']: scheduler,
+            self.fooocus_to_a1111['vae']: Path(data['vae']).stem,
             # workaround for multiline prompts
             self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
             self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
@@ -491,6 +496,14 @@ def parse_string(self, metadata: dict) -> str:
         negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
         return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
 
+    @staticmethod
+    def add_extension_to_filename(data, filenames, key):
+        for filename in filenames:
+            path = Path(filename)
+            if data[key] == path.stem:
+                data[key] = filename
+                break
+
 
 class FooocusMetadataParser(MetadataParser):
     def get_scheme(self) -> MetadataScheme:
@@ -499,6 +512,7 @@ def get_scheme(self) -> MetadataScheme:
     def parse_json(self, metadata: dict) -> dict:
         model_filenames = modules.config.model_filenames.copy()
         lora_filenames = modules.config.lora_filenames.copy()
+        vae_filenames = modules.config.vae_filenames.copy()
         self.remove_special_loras(lora_filenames)
         for key, value in metadata.items():
             if value in ['', 'None']:
@@ -507,6 +521,8 @@ def parse_json(self, metadata: dict) -> dict:
                 metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
             elif key.startswith('lora_combined_'):
                 metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
+            elif key == 'vae':
+                metadata[key] = self.replace_value_with_filename(key, value, vae_filenames)
             else:
                 continue
 
@@ -533,6 +549,7 @@ def parse_string(self, metadata: list) -> str:
             res['refiner_model'] = self.refiner_model_name
             res['refiner_model_hash'] = self.refiner_model_hash
 
+        res['vae'] = self.vae_name
         res['loras'] = self.loras
 
         if modules.config.metadata_created_by != '':

modules/util.py

@@ -371,6 +371,9 @@ def is_json(data: str) -> bool:
 
 
 def get_file_from_folder_list(name, folders):
+    if not isinstance(folders, list):
+        folders = [folders]
+
     for folder in folders:
         filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))
         if os.path.isfile(filename):