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README.md

@@ -4,7 +4,7 @@ emoji: 😻🏷️🔍
 colorFrom: indigo
 colorTo: pink
 sdk: gradio
-sdk_version: 5.12.0
+sdk_version: 5.13.1
 app_file: app.py
 pinned: false
 ---

hft2i.py

@@ -212,31 +212,29 @@ def load_model(model_name: str):
 def load_model_api(model_name: str):
     global loaded_models
     global model_info_dict
-    if model_name in loaded_models.keys(): return loaded_models[model_name]
     try:
-        client = InferenceClient(timeout=5)
-        status = client.get_model_status(model_name, token=HF_TOKEN)
-        if status is None or status.framework != "diffusers" or status.state not in ["Loadable", "Loaded"]:
+        loaded = False
+        client = InferenceClient(timeout=5, token=HF_TOKEN)
+        status = client.get_model_status(model_name)
+        if status is None or status.framework != "diffusers" or not status.loaded or status.state not in ["Loadable", "Loaded"]:
             print(f"Failed to load by API: {model_name}")
-            return None
         else:
-            kwargs = {}
-            if HF_TOKEN is not None: kwargs["token"] = HF_TOKEN
-            loaded_models[model_name] = InferenceClient(model_name, timeout=server_timeout, **kwargs)
+            loaded_models[model_name] = InferenceClient(model_name, timeout=server_timeout)
+            loaded = True
             print(f"Loaded by API: {model_name}")
     except Exception as e:
-        if model_name in loaded_models.keys(): del loaded_models[model_name]
         print(f"Failed to load by API: {model_name}")
         print(e)
-        return None
+        loaded = False
     try:
-        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
-        print(f"Assigned by API: {model_name}")
+        if loaded:
+            model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
+            print(f"Assigned by API: {model_name}")
     except Exception as e:
         if model_name in model_info_dict.keys(): del model_info_dict[model_name]
         print(f"Failed to assigned by API: {model_name}")
         print(e)
-    return loaded_models[model_name]
+    return loaded_models[model_name] if model_name in loaded_models.keys() else None
 
 
 def load_models(models: list):
@@ -358,6 +356,11 @@ def warm_model(model_name: str):
             print(e)
 
 
+def warm_models(models: list[str]):
+    for model in models:
+        asyncio.new_event_loop().run_in_executor(None, warm_model, model)
+
+
 # https://huggingface.co/docs/api-inference/detailed_parameters
 # https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
 def infer_body(client: InferenceClient | gr.Interface | object, model_str: str, prompt: str, neg_prompt: str = "",
@@ -370,24 +373,24 @@ def infer_body(client: InferenceClient | gr.Interface | object, model_str: str,
     if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
     if seed == -1: kwargs["seed"] = randomize_seed()
     else: kwargs["seed"] = seed
-    if HF_TOKEN is not None: kwargs["token"] = HF_TOKEN
     try:
         if isinstance(client, InferenceClient):
             image = client.text_to_image(prompt=prompt, negative_prompt=neg_prompt, **kwargs)
         elif isinstance(client, gr.Interface):
+            if HF_TOKEN is not None: kwargs["token"] = HF_TOKEN
             image = client.fn(prompt=prompt, negative_prompt=neg_prompt, **kwargs)
         else: return None
         if isinstance(image, tuple): return None
         return save_image(image, png_path, model_str, prompt, neg_prompt, height, width, steps, cfg, seed)
     except Exception as e:
         print(e)
-        raise Exception() from e
+        raise Exception(e) from e
 
 
 async def infer(model_name: str, prompt: str, neg_prompt: str ="", height: int = 0, width: int = 0,
                steps: int = 0, cfg: int = 0, seed: int = -1,
                save_path: str | None = None, timeout: float = inference_timeout):
-    model = load_model(model_name)
+    model = load_model_api(model_name)
     if not model: return None
     task = asyncio.create_task(asyncio.to_thread(infer_body, model, model_name, prompt, neg_prompt,
                                                  height, width, steps, cfg, seed))
@@ -404,7 +407,7 @@ async def infer(model_name: str, prompt: str, neg_prompt: str ="", height: int =
         print(e)
         if not task.done(): task.cancel()
         result = None
-        raise Exception() from e
+        raise Exception(e) from e
     if task.done() and result is not None:
         with lock:
             image = rename_image(result, model_name, save_path)
@@ -511,11 +514,6 @@ def get_def_model(i: int):
     else: return ""
 
 
-def warm_models(models: list[str]):
-    for model in models:
-        asyncio.new_event_loop().run_in_executor(None, warm_model, model)
-
-
 def gen_image(model_name: str, prompt: str, neg_prompt: str = "", height: int = 0, width: int = 0,
              steps: int = 0, cfg: int = 0, seed: int = -1, recom_mode = "None"):
     if model_name in ["NA", ""]: return gr.update()

model.py

@@ -1,7 +1,8 @@
 from hft2i import find_model_list
 
 
-models_animagine = ["cagliostrolab/animagine-xl-3.1", "yodayo-ai/kivotos-xl-2.0"]
+models_animagine4 = ["cagliostrolab/animagine-xl-4.0"]
+models_animagine = ["cagliostrolab/animagine-xl-4.0", "cagliostrolab/animagine-xl-3.1", "yodayo-ai/kivotos-xl-2.0", "yodayo-ai/holodayo-xl-2.1", "Raelina/Rae-Diffusion-XL-V2", "Raelina/Raemu-XL-V4", "kayfahaarukku/UrangDiffusion-2.0"]
 models_noob = ["John6666/noobai-xl-nai-xl-epsilonpred11version-sdxl"]
 models_ill = ["Raelina/Raehoshi-illust-XL-3", "John6666/illustrious-xl-early-release-v0-sdxl"]
 
@@ -9,8 +10,6 @@ models_ill = ["Raelina/Raehoshi-illust-XL-3", "John6666/illustrious-xl-early-rel
 models = [
     "yodayo-ai/clandestine-xl-1.0",
     "yodayo-ai/kivotos-xl-2.0",
-    "yodayo-ai/holodayo-xl-2.1",
-    "cagliostrolab/animagine-xl-3.1",
     "votepurchase/ponyDiffusionV6XL",
     "eienmojiki/Anything-XL",
     "eienmojiki/Starry-XL-v5.2",
@@ -21,10 +20,8 @@ models = [
     "Meina/MeinaMix_V11",
     "KBlueLeaf/Kohaku-XL-Epsilon-rev3",
     "KBlueLeaf/Kohaku-XL-Zeta",
-    "kayfahaarukku/UrangDiffusion-1.4",
     "Eugeoter/artiwaifu-diffusion-2.0",
-    "Raelina/Rae-Diffusion-XL-V2",
-    "Raelina/Raemu-XL-V4",
+    
 ]