Upload app.py

app.py

@@ -129,12 +129,12 @@ def load_pipeline(repo_id: str, cn_on: bool, model_type: str, task: str, dtype_s
             #transformer, text_encoder_2 = load_quantized_control(control_repo, dtype, hf_token)
             pipe = pipeline.from_pretrained(models_dev[0], transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=dtype, token=hf_token)
             pipe_i2i = pipeline_i2i.from_pipe(pipe, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=dtype)
-        elif ".safetensors" in repo_id: # from single file #  or ".gguf" in repo_id
+        elif ".safetensors" in repo_id or ".gguf" in repo_id: # from single file
             file_url = repo_id.replace("/resolve/main/", "/blob/main/").replace("?download=true", "")
-            #if ".gguf" in file_url: transformer = transformer_model.from_single_file(file_url, subfolder="transformer",
-            #    quantization_config=GGUFQuantizationConfig(compute_dtype=dtype), torch_dtype=dtype, config=single_file_base_model)
-            #else:
-            transformer = transformer_model.from_single_file(file_url, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model)
+            if ".gguf" in file_url: transformer = transformer_model.from_single_file(file_url, subfolder="transformer",
+                quantization_config=GGUFQuantizationConfig(compute_dtype=dtype), torch_dtype=dtype, config=single_file_base_model)
+            else: transformer = transformer_model.from_single_file(file_url, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model)
+            if not transformer: raise Exception(f"URL not found. {file_url}")
             pipe = pipeline.from_pretrained(single_file_base_model, transformer=transformer, torch_dtype=dtype, token=hf_token, **kwargs)
             pipe_i2i = pipeline_i2i.from_pretrained(single_file_base_model, vae=pipe.vae, transformer=pipe.transformer,
                 text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2,