Merge branch 'main' into feat/import-with-vae

ldm/generate.py

@@ -146,7 +146,7 @@ class Generate:
             gfpgan=None,
             codeformer=None,
             esrgan=None,
-            free_gpu_mem=False,
+            free_gpu_mem: bool=False,
             safety_checker:bool=False,
             max_loaded_models:int=2,
             # these are deprecated; if present they override values in the conf file
@@ -460,10 +460,13 @@ class Generate:
         init_image = None
         mask_image = None
 
-
-        if self.free_gpu_mem and self.model.cond_stage_model.device != self.model.device:
-            self.model.cond_stage_model.device = self.model.device
-            self.model.cond_stage_model.to(self.model.device)
+        try:
+            if self.free_gpu_mem and self.model.cond_stage_model.device != self.model.device:
+                self.model.cond_stage_model.device = self.model.device
+                self.model.cond_stage_model.to(self.model.device)
+        except AttributeError:
+            print(">> Warning: '--free_gpu_mem' is not yet supported when generating image using model based on HuggingFace Diffuser.")
+            pass
 
         try:
             uc, c, extra_conditioning_info = get_uc_and_c_and_ec(
@@ -531,6 +534,7 @@ class Generate:
                 inpaint_height = inpaint_height,
                 inpaint_width = inpaint_width,
                 enable_image_debugging = enable_image_debugging,
+                free_gpu_mem=self.free_gpu_mem,
             )
 
             if init_color:

ldm/invoke/ckpt_generator/base.py

@@ -56,9 +56,11 @@ class CkptGenerator():
                  image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
                  safety_checker:dict=None,
                  attention_maps_callback = None,
+                 free_gpu_mem: bool=False,
                  **kwargs):
         scope = choose_autocast(self.precision)
         self.safety_checker = safety_checker
+        self.free_gpu_mem = free_gpu_mem
         attention_maps_images = []
         attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
         make_image = self.get_make_image(

ldm/invoke/generator/base.py

@@ -62,9 +62,11 @@ class Generator:
     def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
                  image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
                  safety_checker:dict=None,
+                 free_gpu_mem: bool=False,
                  **kwargs):
         scope = nullcontext
         self.safety_checker = safety_checker
+        self.free_gpu_mem = free_gpu_mem
         attention_maps_images = []
         attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
         make_image = self.get_make_image(

ldm/invoke/globals.py

@@ -29,6 +29,7 @@ else:
 
 # Where to look for the initialization file
 Globals.initfile = 'invokeai.init'
+Globals.models_file = 'models.yaml'
 Globals.models_dir = 'models'
 Globals.config_dir = 'configs'
 Globals.autoscan_dir = 'weights'
@@ -49,6 +50,9 @@ Globals.disable_xformers = False
 # whether we are forcing full precision
 Globals.full_precision = False
 
+def global_config_file()->Path:
+    return Path(Globals.root, Globals.config_dir, Globals.models_file)
+
 def global_config_dir()->Path:
     return Path(Globals.root, Globals.config_dir)
 

ldm/invoke/merge_diffusers.py

@@ -0,0 +1,62 @@
+'''
+ldm.invoke.merge_diffusers exports a single function call merge_diffusion_models()
+used to merge 2-3 models together and create a new InvokeAI-registered diffusion model.
+'''
+import os
+from typing import List
+from diffusers import DiffusionPipeline
+from ldm.invoke.globals import global_config_file, global_models_dir, global_cache_dir
+from ldm.invoke.model_manager import ModelManager
+from omegaconf import OmegaConf
+
+def merge_diffusion_models(models:List['str'],
+                           merged_model_name:str,
+                           alpha:float=0.5,
+                           interp:str=None,
+                           force:bool=False,
+                           **kwargs):
+    '''
+    models - up to three models, designated by their InvokeAI models.yaml model name
+    merged_model_name = name for new model
+    alpha  - The interpolation parameter. Ranges from 0 to 1.  It affects the ratio in which the checkpoints are merged. A 0.8 alpha
+               would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
+    interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_difference" and None.
+               Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_difference" is supported.
+    force  - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
+
+    **kwargs - the default DiffusionPipeline.get_config_dict kwargs:
+         cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
+    '''
+    config_file = global_config_file()
+    model_manager = ModelManager(OmegaConf.load(config_file))
+    for mod in models:
+        assert (mod in model_manager.model_names()), f'** Unknown model "{mod}"'
+        assert (model_manager.model_info(mod).get('format',None) == 'diffusers'), f'** {mod} is not a diffusers model. It must be optimized before merging.'
+    model_ids_or_paths = [model_manager.model_name_or_path(x) for x in models]
+
+    pipe = DiffusionPipeline.from_pretrained(model_ids_or_paths[0],
+                                             cache_dir=kwargs.get('cache_dir',global_cache_dir()),
+                                             custom_pipeline='checkpoint_merger')
+    merged_pipe = pipe.merge(pretrained_model_name_or_path_list=model_ids_or_paths,
+                             alpha=alpha,
+                             interp=interp,
+                             force=force,
+                             **kwargs)
+    dump_path = global_models_dir() / 'merged_diffusers'
+    os.makedirs(dump_path,exist_ok=True)
+    dump_path = dump_path / merged_model_name
+    merged_pipe.save_pretrained (
+        dump_path,
+        safe_serialization=1
+    )
+    model_manager.import_diffuser_model(
+        dump_path,
+        model_name = merged_model_name,
+        description = f'Merge of models {", ".join(models)}'
+    )
+    print('REMINDER: When PR 2369 is merged, replace merge_diffusers.py line 56 with vae= argument to impormodel()')
+    if vae := model_manager.config[models[0]].get('vae',None):
+        print(f'>> Using configured VAE assigned to {models[0]}')
+        model_manager.config[merged_model_name]['vae'] = vae
+
+    model_manager.commit(config_file)

ldm/invoke/model_manager.py

@@ -42,7 +42,11 @@ VAE_TO_REPO_ID = { # hack, see note in convert_and_import()
     }
 
 class ModelManager(object):
-    def __init__(self, config:OmegaConf, device_type:str, precision:str, max_loaded_models=DEFAULT_MAX_MODELS):
+    def __init__(self,
+                 config:OmegaConf,
+                 device_type:str='cpu',
+                 precision:str='float16',
+                 max_loaded_models=DEFAULT_MAX_MODELS):
         '''
         Initialize with the path to the models.yaml config file,
         the torch device type, and precision. The optional
@@ -565,7 +569,7 @@ class ModelManager(object):
             format='diffusers',
         )
         if isinstance(repo_or_path,Path) and repo_or_path.exists():
-            new_config.update(path=repo_or_path)
+            new_config.update(path=str(repo_or_path))
         else:
             new_config.update(repo_id=repo_or_path)