add max_load_models parameter for model cache control

- ldm.generate.Generator() now takes an argument named `max_load_models`.
  This is an integer that limits the model cache size. When the cache
  reaches the limit, it will start purging older models from cache.

- CLI takes an argument --max_load_models, default to 2. This will keep
  one model in GPU and the other in CPU and switch back and forth
  quickly.

- To not cache models at all, pass --max_load_models=1

ldm/generate.py

@@ -57,7 +57,7 @@ torch.bernoulli = fix_func(torch.bernoulli)
 torch.multinomial = fix_func(torch.multinomial)
 
 # this is fallback model in case no default is defined
-FALLBACK_MODEL_NAME='stable-diffusion-1.4'
+FALLBACK_MODEL_NAME='stable-diffusion-1.5'
 
 """Simplified text to image API for stable diffusion/latent diffusion
 
@@ -146,6 +146,7 @@ class Generate:
             esrgan=None,
             free_gpu_mem=False,
             safety_checker:bool=False,
+            max_loaded_models:int=3,
             # these are deprecated; if present they override values in the conf file
             weights = None,
             config = None,
@@ -177,6 +178,7 @@ class Generate:
         self.codeformer = codeformer
         self.esrgan = esrgan
         self.free_gpu_mem = free_gpu_mem
+        self.max_loaded_models = max_loaded_models,
         self.size_matters = True  # used to warn once about large image sizes and VRAM
         self.txt2mask = None
         self.safety_checker = None
@@ -198,7 +200,7 @@ class Generate:
             self.precision = choose_precision(self.device)
 
         # model caching system for fast switching
-        self.model_cache = ModelCache(mconfig,self.device,self.precision)
+        self.model_cache = ModelCache(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
         self.model_name  = model or self.model_cache.default_model() or FALLBACK_MODEL_NAME
 
         # for VRAM usage statistics