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all_models.py

@@ -896,3 +896,16 @@ models = [
     "CompVis/stable-diffusion-v1-2", #208
     "CompVis/stable-diffusion-v1-1", #209
 ]
+
+
+from externalmod import find_model_list
+
+#models = find_model_list("Yntec", [], "", "last_modified", 20)
+
+# Examples:
+#models = ['yodayo-ai/kivotos-xl-2.0', 'yodayo-ai/holodayo-xl-2.1'] # specific models
+#models = find_model_list("Yntec", [], "", "last_modified", 20) # Yntec's latest 20 models
+#models = find_model_list("Yntec", ["anime"], "", "last_modified", 20) # Yntec's latest 20 models with 'anime' tag
+#models = find_model_list("Yntec", [], "anime", "last_modified", 20) # Yntec's latest 20 models without 'anime' tag
+#models = find_model_list("", [], "", "last_modified", 20) # latest 20 text-to-image models of huggingface
+#models = find_model_list("", [], "", "downloads", 20) # monthly most downloaded 20 text-to-image models of huggingface

app.py

@@ -40,6 +40,12 @@ def update_imgbox(choices):
     return [gr.Image(None, label = m, visible = (m != 'NA')) for m in choices_plus]
 
 
+def random_choices():
+    import random
+    random.seed()
+    return random.choices(models, k = num_models)
+
+
 # https://huggingface.co/docs/api-inference/detailed_parameters
 # https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
 async def infer(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, timeout=inference_timeout):
@@ -114,7 +120,8 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
                     steps = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                     cfg = gr.Number(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
             with gr.Row():
-                gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', scale=2)
+                gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', scale=3)
+                random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
                 stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
                 gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
             gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide")
@@ -143,6 +150,7 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
                 model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
                 model_choice.change(update_imgbox, model_choice, output)
                 model_choice.change(extend_choices, model_choice, current_models)
+                random_button.click(random_choices, None, model_choice)
 
     with gr.Tab('Single model'):
         with gr.Column(scale=2):

externalmod.py

@@ -532,3 +532,49 @@ def gr_Interface_load(
     **kwargs,
 ) -> Blocks:
     return load_blocks_from_repo(name, src, hf_token, alias)
+
+
+def list_uniq(l):
+    return sorted(set(l), key=l.index)
+
+
+def get_status(model_name: str):
+    from huggingface_hub import InferenceClient
+    client = InferenceClient(timeout=10)
+    return client.get_model_status(model_name)
+
+
+def is_loadable(model_name: str, force_gpu: bool = False):
+    try:
+        status = get_status(model_name)
+    except Exception as e:
+        print(e)
+        print(f"Couldn't load {model_name}.")
+        return False
+    gpu_state = isinstance(status.compute_type, dict) and "gpu" in status.compute_type.keys()
+    if status is None or status.state not in ["Loadable", "Loaded"] or (force_gpu and not gpu_state):
+        print(f"Couldn't load {model_name}. Model state:'{status.state}', GPU:{gpu_state}")
+    return status is not None and status.state in ["Loadable", "Loaded"] and (not force_gpu or gpu_state)
+
+
+def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30, force_gpu=False, check_status=False):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    default_tags = ["diffusers"]
+    if not sort: sort = "last_modified"
+    limit = limit * 20 if check_status and force_gpu else limit * 5
+    models = []
+    try:
+        model_infos = api.list_models(author=author, task="text-to-image",
+                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
+    except Exception as e:
+        print(f"Error: Failed to list models.")
+        print(e)
+        return models
+    for model in model_infos:
+        if not model.private and not model.gated:
+           loadable = is_loadable(model.id, force_gpu) if check_status else True
+           if not_tag and not_tag in model.tags or not loadable: continue
+           models.append(model.id)
+           if len(models) == limit: break
+    return models