allow loading of sd models from safetensors without online lookups using local config files (#5019)

finish config_files implementation

Author: vladmandic

scripts/convert_original_stable_diffusion_to_diffusers.py

@@ -154,6 +154,7 @@
     pipe = download_from_original_stable_diffusion_ckpt(
         checkpoint_path_or_dict=args.checkpoint_path,
         original_config_file=args.original_config_file,
+        config_files=args.config_files,
         image_size=args.image_size,
         prediction_type=args.prediction_type,
         model_type=args.pipeline_type,

src/diffusers/loaders.py

@@ -2098,6 +2098,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
         from .pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
 
         original_config_file = kwargs.pop("original_config_file", None)
+        config_files = kwargs.pop("config_files", None)
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
         resume_download = kwargs.pop("resume_download", False)
         force_download = kwargs.pop("force_download", False)
@@ -2215,6 +2216,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
             vae=vae,
             tokenizer=tokenizer,
             original_config_file=original_config_file,
+            config_files=config_files,
         )
 
         if torch_dtype is not None:

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -1256,25 +1256,37 @@ def download_from_original_stable_diffusion_ckpt(
         key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         key_name_sd_xl_base = "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias"
         key_name_sd_xl_refiner = "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"
+        config_url = None
 
         # model_type = "v1"
-        config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
+        if config_files is not None and "v1" in config_files:
+            original_config_file = config_files["v1"]
+        else:
+            config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
 
         if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:
             # model_type = "v2"
-            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
-
+            if config_files is not None and "v2" in config_files:
+                original_config_file = config_files["v2"]
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
             if global_step == 110000:
                 # v2.1 needs to upcast attention
                 upcast_attention = True
         elif key_name_sd_xl_base in checkpoint:
             # only base xl has two text embedders
-            config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
+            if config_files is not None and "xl" in config_files:
+                original_config_file = config_files["xl"]
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
         elif key_name_sd_xl_refiner in checkpoint:
             # only refiner xl has embedder and one text embedders
-            config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml"
-
-        original_config_file = BytesIO(requests.get(config_url).content)
+            if config_files is not None and "xl_refiner" in config_files:
+                original_config_file = config_files["xl_refiner"]
+            else:
+                config_url = "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml"
+        if config_url is not None:
+            original_config_file = BytesIO(requests.get(config_url).content)
 
     original_config = OmegaConf.load(original_config_file)