Upload 2 files

app.py

@@ -1,164 +1,17 @@
 import gradio as gr
-from pathlib import Path
-
-
-loaded_models = {}
-model_info_dict = {}
-
-
-def list_sub(a, b):
-    return [e for e in a if e not in b]
-
-
-def list_uniq(l):
-        return sorted(set(l), key=l.index)
-
-
-def is_repo_name(s):
-    import re
-    return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
-
-
-def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30):
-    from huggingface_hub import HfApi
-    api = HfApi()
-    default_tags = ["diffusers"]
-    models = []
-    try:
-        model_infos = api.list_models(author=author, task="text-to-image", pipeline_tag="text-to-image",
-                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit * 5)
-    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:
-           if not_tag and not_tag in model.tags: continue
-           models.append(model.id)
-           if len(models) == limit: break
-    return models
+from multit2i import (
+    load_models,
+    find_model_list,
+    infer_multi,
+    save_gallery_images,
+    change_model,
+    get_model_info_md,
+    loaded_models,
+)
 
 
 models = find_model_list("John6666", ["anime"])
-
-
-def get_t2i_model_info_dict(repo_id: str):
-    from huggingface_hub import HfApi
-    api = HfApi()
-    info = {"md": "None"}
-    try:
-        if not is_repo_name(repo_id) or not api.repo_exists(repo_id=repo_id): return info
-        model = api.model_info(repo_id=repo_id)
-    except Exception as e:
-        print(f"Error: Failed to get {repo_id}'s info.")
-        print(e)
-        return info
-    if model.private or model.gated: return info
-    try:
-        tags = model.tags
-    except Exception:
-        return info
-    if not 'diffusers' in model.tags: return info
-    if 'diffusers:StableDiffusionXLPipeline' in tags: info["ver"] = "SDXL"
-    elif 'diffusers:StableDiffusionPipeline' in tags: info["ver"] = "SD1.5"
-    elif 'diffusers:StableDiffusion3Pipeline' in tags: info["ver"] = "SD3"
-    else: info["ver"] = "Other"
-    info["url"] = f"https://huggingface.co/{repo_id}/"
-    if model.card_data and model.card_data.tags:
-        info["tags"] = model.card_data.tags
-    info["downloads"] = model.downloads
-    info["likes"] = model.likes
-    info["last_modified"] = model.last_modified.strftime("lastmod: %Y-%m-%d")
-    un_tags = ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']
-    descs = [info["ver"]] + list_sub(info["tags"], un_tags) + [f'DLs: {info["downloads"]}'] + [f'❤: {info["likes"]}'] + [info["last_modified"]]
-    info["md"] = f'Model Info: {", ".join(descs)} [Model Repo]({info["url"]})'
-    return info
-
-
-def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
-    from datetime import datetime, timezone, timedelta
-    progress(0, desc="Updating gallery...")
-    dt_now = datetime.now(timezone(timedelta(hours=9)))
-    basename = dt_now.strftime('%Y%m%d_%H%M%S_')
-    i = 1
-    if not images: return images
-    output_images = []
-    output_paths = []
-    for image in images:
-        filename = f'{image[1]}_{basename}{str(i)}.png'
-        i += 1
-        oldpath = Path(image[0])
-        newpath = oldpath
-        try:
-            if oldpath.stem == "image" and oldpath.exists():
-                newpath = oldpath.resolve().rename(Path(filename).resolve())
-        except Exception as e:
-           print(e)
-           pass
-        finally:
-            output_paths.append(str(newpath))
-            output_images.append((str(newpath), str(filename)))
-    progress(1, desc="Gallery updated.")
-    return gr.update(value=output_images), gr.update(value=output_paths)
-
-
-def load_model(model_name: str):
-    if model_name in loaded_models.keys(): return loaded_models[model_name]
-    try:
-        loaded_models[model_name] = gr.load(f'models/{model_name}')
-        print(f"Loaded: {model_name}")
-    except Exception as e:
-        if model_name in loaded_models.keys(): del loaded_models[model_name]
-        print(f"Failed to load: {model_name}")
-        print(e)
-        return None
-    try:
-        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
-    except Exception as e:
-        if model_name in model_info_dict.keys(): del model_info_dict[model_name]
-        print(e)
-    return loaded_models[model_name]
-
-
-for model in models:
-    load_model(model)
-
-
-def get_model_info_md(model_name: str):
-    if model_name in model_info_dict.keys(): return model_info_dict[model_name].get("md", "")
-
-
-def change_model(model_name: str):
-    load_model(model_name)
-    return get_model_info_md(model_name)
-
-
-def infer(prompt: str, model_name: str, recom_prompt: bool, progress=gr.Progress(track_tqdm=True)):
-    from PIL import Image
-    import random
-    seed = ""
-    rand = random.randint(1, 500)
-    for i in range(rand):
-        seed += " "
-    rprompt = ", highly detailed, masterpiece, best quality, very aesthetic, absurdres, " if recom_prompt else ""
-    caption = model_name.split("/")[-1]
-    try:
-        model = load_model(model_name)
-        if not model: return (Image(), None)
-        image_path = model(prompt + rprompt + seed)
-        image = Image.open(image_path).convert('RGB')
-    except Exception as e:
-        print(e)
-        return (Image(), None)
-    return (image, caption)
-
-
-def infer_multi(prompt: str, model_name: str, recom_prompt: bool, image_num: float, results: list, progress=gr.Progress(track_tqdm=True)):
-    image_num = int(image_num)
-    images = results if results else []
-    for i in range(image_num):
-        images.append(infer(prompt, model_name, recom_prompt))
-        yield images
+load_models(models, 10)
 
 
 css = """"""

multit2i.py

@@ -0,0 +1,180 @@
+import gradio as gr
+import asyncio
+from pathlib import Path
+
+
+loaded_models = {}
+model_info_dict = {}
+
+
+def list_sub(a, b):
+    return [e for e in a if e not in b]
+
+
+def list_uniq(l):
+        return sorted(set(l), key=l.index)
+
+
+def is_repo_name(s):
+    import re
+    return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
+
+
+def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    default_tags = ["diffusers"]
+    if not sort: sort = "last_modified"
+    models = []
+    try:
+        model_infos = api.list_models(author=author, task="text-to-image", pipeline_tag="text-to-image",
+                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit * 5)
+    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:
+           if not_tag and not_tag in model.tags: continue
+           models.append(model.id)
+           if len(models) == limit: break
+    return models
+
+
+def get_t2i_model_info_dict(repo_id: str):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    info = {"md": "None"}
+    try:
+        if not is_repo_name(repo_id) or not api.repo_exists(repo_id=repo_id): return info
+        model = api.model_info(repo_id=repo_id)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info.")
+        print(e)
+        return info
+    if model.private or model.gated: return info
+    try:
+        tags = model.tags
+    except Exception:
+        return info
+    if not 'diffusers' in model.tags: return info
+    if 'diffusers:StableDiffusionXLPipeline' in tags: info["ver"] = "SDXL"
+    elif 'diffusers:StableDiffusionPipeline' in tags: info["ver"] = "SD1.5"
+    elif 'diffusers:StableDiffusion3Pipeline' in tags: info["ver"] = "SD3"
+    else: info["ver"] = "Other"
+    info["url"] = f"https://huggingface.co/{repo_id}/"
+    if model.card_data and model.card_data.tags:
+        info["tags"] = model.card_data.tags
+    info["downloads"] = model.downloads
+    info["likes"] = model.likes
+    info["last_modified"] = model.last_modified.strftime("lastmod: %Y-%m-%d")
+    un_tags = ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']
+    descs = [info["ver"]] + list_sub(info["tags"], un_tags) + [f'DLs: {info["downloads"]}'] + [f'❤: {info["likes"]}'] + [info["last_modified"]]
+    info["md"] = f'Model Info: {", ".join(descs)} [Model Repo]({info["url"]})'
+    return info
+
+
+def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
+    from datetime import datetime, timezone, timedelta
+    progress(0, desc="Updating gallery...")
+    dt_now = datetime.now(timezone(timedelta(hours=9)))
+    basename = dt_now.strftime('%Y%m%d_%H%M%S_')
+    i = 1
+    if not images: return images
+    output_images = []
+    output_paths = []
+    for image in images:
+        filename = f'{image[1]}_{basename}{str(i)}.png'
+        i += 1
+        oldpath = Path(image[0])
+        newpath = oldpath
+        try:
+            if oldpath.stem == "image" and oldpath.exists():
+                newpath = oldpath.resolve().rename(Path(filename).resolve())
+        except Exception as e:
+           print(e)
+           pass
+        finally:
+            output_paths.append(str(newpath))
+            output_images.append((str(newpath), str(filename)))
+    progress(1, desc="Gallery updated.")
+    return gr.update(value=output_images), gr.update(value=output_paths)
+
+
+def load_model(model_name: str):
+    global loaded_models
+    global model_info_dict
+    if model_name in loaded_models.keys(): return loaded_models[model_name]
+    try:
+        loaded_models[model_name] = gr.load(f'models/{model_name}')
+        print(f"Loaded: {model_name}")
+    except Exception as e:
+        if model_name in loaded_models.keys(): del loaded_models[model_name]
+        print(f"Failed to load: {model_name}")
+        print(e)
+        return None
+    try:
+        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
+    except Exception as e:
+        if model_name in model_info_dict.keys(): del model_info_dict[model_name]
+        print(e)
+    return loaded_models[model_name]
+
+
+async def async_load_models(models: list, limit: int=5):
+    sem = asyncio.Semaphore(limit)
+    async def async_load_model(model: str):
+        async with sem:
+           try:
+               return load_model(model)
+           except Exception as e:
+               print(e)
+    tasks = [asyncio.create_task(async_load_model(model)) for model in models]
+    return await asyncio.wait(tasks)
+
+
+def load_models(models: list, limit: int=5):
+    loop = asyncio.get_event_loop()
+    try:
+        loop.run_until_complete(async_load_models(models, limit))
+    except Exception as e:
+        print(e)
+        pass
+    loop.close()
+
+
+def get_model_info_md(model_name: str):
+    if model_name in model_info_dict.keys(): return model_info_dict[model_name].get("md", "")
+
+
+def change_model(model_name: str):
+    load_model(model_name)
+    return get_model_info_md(model_name)
+
+
+def infer(prompt: str, model_name: str, recom_prompt: bool, progress=gr.Progress(track_tqdm=True)):
+    from PIL import Image
+    import random
+    seed = ""
+    rand = random.randint(1, 500)
+    for i in range(rand):
+        seed += " "
+    rprompt = ", highly detailed, masterpiece, best quality, very aesthetic, absurdres, " if recom_prompt else ""
+    caption = model_name.split("/")[-1]
+    try:
+        model = load_model(model_name)
+        if not model: return (Image.Image(), None)
+        image_path = model(prompt + rprompt + seed)
+        image = Image.open(image_path).convert('RGB')
+    except Exception as e:
+        print(e)
+        return (Image.Image(), None)
+    return (image, caption)
+
+
+def infer_multi(prompt: str, model_name: str, recom_prompt: bool, image_num: float, results: list, progress=gr.Progress(track_tqdm=True)):
+    image_num = int(image_num)
+    images = results if results else []
+    for i in range(image_num):
+        images.append(infer(prompt, model_name, recom_prompt))
+        yield images