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Upload 2 files
Browse files- app.py +10 -157
- multit2i.py +180 -0
app.py
CHANGED
@@ -1,164 +1,17 @@
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import gradio as gr
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from
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def list_uniq(l):
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return sorted(set(l), key=l.index)
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def is_repo_name(s):
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import re
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return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
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def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30):
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from huggingface_hub import HfApi
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api = HfApi()
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default_tags = ["diffusers"]
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models = []
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try:
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model_infos = api.list_models(author=author, task="text-to-image", pipeline_tag="text-to-image",
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tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit * 5)
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except Exception as e:
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print(f"Error: Failed to list models.")
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print(e)
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return models
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for model in model_infos:
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if not model.private and not model.gated:
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if not_tag and not_tag in model.tags: continue
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models.append(model.id)
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if len(models) == limit: break
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return models
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models = find_model_list("John6666", ["pony"])
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def get_t2i_model_info_dict(repo_id: str):
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from huggingface_hub import HfApi
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api = HfApi()
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info = {"md": "None"}
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try:
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if not is_repo_name(repo_id) or not api.repo_exists(repo_id=repo_id): return info
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model = api.model_info(repo_id=repo_id)
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except Exception as e:
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print(f"Error: Failed to get {repo_id}'s info.")
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print(e)
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return info
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if model.private or model.gated: return info
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try:
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tags = model.tags
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except Exception:
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return info
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if not 'diffusers' in model.tags: return info
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if 'diffusers:StableDiffusionXLPipeline' in tags: info["ver"] = "SDXL"
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elif 'diffusers:StableDiffusionPipeline' in tags: info["ver"] = "SD1.5"
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elif 'diffusers:StableDiffusion3Pipeline' in tags: info["ver"] = "SD3"
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else: info["ver"] = "Other"
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info["url"] = f"https://huggingface.co/{repo_id}/"
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if model.card_data and model.card_data.tags:
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info["tags"] = model.card_data.tags
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info["downloads"] = model.downloads
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info["likes"] = model.likes
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info["last_modified"] = model.last_modified.strftime("lastmod: %Y-%m-%d")
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un_tags = ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']
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descs = [info["ver"]] + list_sub(info["tags"], un_tags) + [f'DLs: {info["downloads"]}'] + [f'❤: {info["likes"]}'] + [info["last_modified"]]
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info["md"] = f'Model Info: {", ".join(descs)} [Model Repo]({info["url"]})'
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return info
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def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
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from datetime import datetime, timezone, timedelta
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progress(0, desc="Updating gallery...")
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dt_now = datetime.now(timezone(timedelta(hours=9)))
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basename = dt_now.strftime('%Y%m%d_%H%M%S_')
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i = 1
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if not images: return images
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output_images = []
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output_paths = []
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for image in images:
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filename = f'{image[1]}_{basename}{str(i)}.png'
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i += 1
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oldpath = Path(image[0])
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newpath = oldpath
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try:
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if oldpath.stem == "image" and oldpath.exists():
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newpath = oldpath.resolve().rename(Path(filename).resolve())
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except Exception as e:
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print(e)
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pass
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finally:
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output_paths.append(str(newpath))
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output_images.append((str(newpath), str(filename)))
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progress(1, desc="Gallery updated.")
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return gr.update(value=output_images), gr.update(value=output_paths)
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def load_model(model_name: str):
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if model_name in loaded_models.keys(): return loaded_models[model_name]
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try:
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loaded_models[model_name] = gr.load(f'models/{model_name}')
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print(f"Loaded: {model_name}")
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except Exception as e:
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if model_name in loaded_models.keys(): del loaded_models[model_name]
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print(f"Failed to load: {model_name}")
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print(e)
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return None
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try:
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model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
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except Exception as e:
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if model_name in model_info_dict.keys(): del model_info_dict[model_name]
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print(e)
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return loaded_models[model_name]
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for model in models:
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load_model(model)
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def get_model_info_md(model_name: str):
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if model_name in model_info_dict.keys(): return model_info_dict[model_name].get("md", "")
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def change_model(model_name: str):
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load_model(model_name)
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return get_model_info_md(model_name)
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def infer(prompt: str, model_name: str, recom_prompt: bool, progress=gr.Progress(track_tqdm=True)):
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from PIL import Image
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import random
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seed = ""
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rand = random.randint(1, 500)
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for i in range(rand):
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seed += " "
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rprompt = ", highly detailed, masterpiece, best quality, very aesthetic, absurdres, " if recom_prompt else ""
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caption = model_name.split("/")[-1]
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try:
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model = load_model(model_name)
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if not model: return (Image(), None)
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image_path = model(prompt + rprompt + seed)
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image = Image.open(image_path).convert('RGB')
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except Exception as e:
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print(e)
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return (Image(), None)
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return (image, caption)
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def infer_multi(prompt: str, model_name: str, recom_prompt: bool, image_num: float, results: list, progress=gr.Progress(track_tqdm=True)):
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image_num = int(image_num)
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images = results if results else []
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for i in range(image_num):
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images.append(infer(prompt, model_name, recom_prompt))
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yield images
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css = """"""
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import gradio as gr
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from multit2i import (
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load_models,
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find_model_list,
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infer_multi,
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save_gallery_images,
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change_model,
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get_model_info_md,
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loaded_models,
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)
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models = find_model_list("John6666", ["pony"])
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load_models(models, 10)
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css = """"""
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multit2i.py
ADDED
@@ -0,0 +1,180 @@
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import gradio as gr
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import asyncio
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from pathlib import Path
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loaded_models = {}
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model_info_dict = {}
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def list_sub(a, b):
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return [e for e in a if e not in b]
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def list_uniq(l):
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return sorted(set(l), key=l.index)
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def is_repo_name(s):
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import re
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return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
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def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30):
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from huggingface_hub import HfApi
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api = HfApi()
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default_tags = ["diffusers"]
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if not sort: sort = "last_modified"
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models = []
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try:
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model_infos = api.list_models(author=author, task="text-to-image", pipeline_tag="text-to-image",
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tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit * 5)
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except Exception as e:
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print(f"Error: Failed to list models.")
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print(e)
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return models
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for model in model_infos:
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if not model.private and not model.gated:
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if not_tag and not_tag in model.tags: continue
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models.append(model.id)
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if len(models) == limit: break
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return models
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def get_t2i_model_info_dict(repo_id: str):
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from huggingface_hub import HfApi
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api = HfApi()
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info = {"md": "None"}
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try:
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if not is_repo_name(repo_id) or not api.repo_exists(repo_id=repo_id): return info
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model = api.model_info(repo_id=repo_id)
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except Exception as e:
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print(f"Error: Failed to get {repo_id}'s info.")
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print(e)
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return info
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if model.private or model.gated: return info
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try:
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tags = model.tags
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except Exception:
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return info
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if not 'diffusers' in model.tags: return info
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if 'diffusers:StableDiffusionXLPipeline' in tags: info["ver"] = "SDXL"
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elif 'diffusers:StableDiffusionPipeline' in tags: info["ver"] = "SD1.5"
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elif 'diffusers:StableDiffusion3Pipeline' in tags: info["ver"] = "SD3"
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else: info["ver"] = "Other"
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info["url"] = f"https://huggingface.co/{repo_id}/"
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if model.card_data and model.card_data.tags:
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info["tags"] = model.card_data.tags
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info["downloads"] = model.downloads
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info["likes"] = model.likes
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info["last_modified"] = model.last_modified.strftime("lastmod: %Y-%m-%d")
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un_tags = ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']
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descs = [info["ver"]] + list_sub(info["tags"], un_tags) + [f'DLs: {info["downloads"]}'] + [f'❤: {info["likes"]}'] + [info["last_modified"]]
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info["md"] = f'Model Info: {", ".join(descs)} [Model Repo]({info["url"]})'
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return info
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def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
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from datetime import datetime, timezone, timedelta
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progress(0, desc="Updating gallery...")
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dt_now = datetime.now(timezone(timedelta(hours=9)))
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basename = dt_now.strftime('%Y%m%d_%H%M%S_')
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i = 1
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if not images: return images
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output_images = []
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output_paths = []
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for image in images:
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filename = f'{image[1]}_{basename}{str(i)}.png'
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i += 1
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oldpath = Path(image[0])
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newpath = oldpath
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try:
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if oldpath.stem == "image" and oldpath.exists():
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newpath = oldpath.resolve().rename(Path(filename).resolve())
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except Exception as e:
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print(e)
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pass
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finally:
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output_paths.append(str(newpath))
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output_images.append((str(newpath), str(filename)))
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progress(1, desc="Gallery updated.")
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return gr.update(value=output_images), gr.update(value=output_paths)
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def load_model(model_name: str):
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global loaded_models
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global model_info_dict
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107 |
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if model_name in loaded_models.keys(): return loaded_models[model_name]
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108 |
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try:
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109 |
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loaded_models[model_name] = gr.load(f'models/{model_name}')
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110 |
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print(f"Loaded: {model_name}")
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111 |
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except Exception as e:
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if model_name in loaded_models.keys(): del loaded_models[model_name]
|
113 |
+
print(f"Failed to load: {model_name}")
|
114 |
+
print(e)
|
115 |
+
return None
|
116 |
+
try:
|
117 |
+
model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
|
118 |
+
except Exception as e:
|
119 |
+
if model_name in model_info_dict.keys(): del model_info_dict[model_name]
|
120 |
+
print(e)
|
121 |
+
return loaded_models[model_name]
|
122 |
+
|
123 |
+
|
124 |
+
async def async_load_models(models: list, limit: int=5):
|
125 |
+
sem = asyncio.Semaphore(limit)
|
126 |
+
async def async_load_model(model: str):
|
127 |
+
async with sem:
|
128 |
+
try:
|
129 |
+
return load_model(model)
|
130 |
+
except Exception as e:
|
131 |
+
print(e)
|
132 |
+
tasks = [asyncio.create_task(async_load_model(model)) for model in models]
|
133 |
+
return await asyncio.wait(tasks)
|
134 |
+
|
135 |
+
|
136 |
+
def load_models(models: list, limit: int=5):
|
137 |
+
loop = asyncio.get_event_loop()
|
138 |
+
try:
|
139 |
+
loop.run_until_complete(async_load_models(models, limit))
|
140 |
+
except Exception as e:
|
141 |
+
print(e)
|
142 |
+
pass
|
143 |
+
loop.close()
|
144 |
+
|
145 |
+
|
146 |
+
def get_model_info_md(model_name: str):
|
147 |
+
if model_name in model_info_dict.keys(): return model_info_dict[model_name].get("md", "")
|
148 |
+
|
149 |
+
|
150 |
+
def change_model(model_name: str):
|
151 |
+
load_model(model_name)
|
152 |
+
return get_model_info_md(model_name)
|
153 |
+
|
154 |
+
|
155 |
+
def infer(prompt: str, model_name: str, recom_prompt: bool, progress=gr.Progress(track_tqdm=True)):
|
156 |
+
from PIL import Image
|
157 |
+
import random
|
158 |
+
seed = ""
|
159 |
+
rand = random.randint(1, 500)
|
160 |
+
for i in range(rand):
|
161 |
+
seed += " "
|
162 |
+
rprompt = ", highly detailed, masterpiece, best quality, very aesthetic, absurdres, " if recom_prompt else ""
|
163 |
+
caption = model_name.split("/")[-1]
|
164 |
+
try:
|
165 |
+
model = load_model(model_name)
|
166 |
+
if not model: return (Image.Image(), None)
|
167 |
+
image_path = model(prompt + rprompt + seed)
|
168 |
+
image = Image.open(image_path).convert('RGB')
|
169 |
+
except Exception as e:
|
170 |
+
print(e)
|
171 |
+
return (Image.Image(), None)
|
172 |
+
return (image, caption)
|
173 |
+
|
174 |
+
|
175 |
+
def infer_multi(prompt: str, model_name: str, recom_prompt: bool, image_num: float, results: list, progress=gr.Progress(track_tqdm=True)):
|
176 |
+
image_num = int(image_num)
|
177 |
+
images = results if results else []
|
178 |
+
for i in range(image_num):
|
179 |
+
images.append(infer(prompt, model_name, recom_prompt))
|
180 |
+
yield images
|