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63a9842
1 Parent(s): 0596fa7

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  1. README.md +13 -12
  2. app.py +52 -0
  3. multit2i.py +180 -0
  4. requirements.txt +1 -0
README.md CHANGED
@@ -1,12 +1,13 @@
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- ---
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- title: Digiplay Liked Models
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- emoji: 👁
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- colorFrom: pink
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- colorTo: indigo
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- sdk: gradio
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- sdk_version: 4.39.0
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- app_file: app.py
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- pinned: false
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
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+ ---
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+ title: digiplay Liked Text-to-Image Models Playground
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+ emoji: 🖼️
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+ colorFrom: red
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+ colorTo: green
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+ sdk: gradio
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+ sdk_version: 4.39.0
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+ app_file: app.py
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+ pinned: false
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+ short_description: Text-to-Image
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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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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+
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+
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+ models = find_model_list("digiplay", [], "", "likes", 30)
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+ load_models(models, 10)
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+
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+
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+ css = """"""
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+
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+ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo:
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+ with gr.Column():
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+ model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
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+ model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]))
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+ image_num = gr.Slider(label="Number of Images", minimum=1, maximum=8, value=1, step=1)
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+ recom_prompt = gr.Checkbox(label="Recommended Prompt", value=True)
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+ prompt = gr.Text(label="Prompt", lines=1, max_lines=8, placeholder="1girl, solo, ...")
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+ run_button = gr.Button("Generate Image")
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+ results = gr.Gallery(label="Gallery", interactive=False, show_download_button=True, show_share_button=False,
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+ container=True, format="png", object_fit="contain")
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+ image_files = gr.Files(label="Download", interactive=False)
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+ clear_results = gr.Button("Clear Gallery and Download")
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+ gr.Markdown(
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+ f"""This demo was created in reference to the following demos.
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+ - [Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood).
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+ - [Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL).
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+ """
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+ )
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+ gr.DuplicateButton(value="Duplicate Space")
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+
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+ model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)
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+ gr.on(
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+ triggers=[run_button.click, prompt.submit],
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+ fn=infer_multi,
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+ inputs=[prompt, model_name, recom_prompt, image_num, results],
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+ outputs=[results],
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+ queue=True,
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+ show_progress="full",
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+ show_api=True,
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+ ).success(save_gallery_images, [results], [results, image_files], queue=False, show_api=False)
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+ clear_results.click(lambda: (None, None), None, [results, image_files], queue=False, show_api=False)
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+
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+ demo.queue()
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+ demo.launch()
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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+
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+
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+ loaded_models = {}
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+ model_info_dict = {}
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+
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+
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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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+
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+
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+ def list_uniq(l):
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+ return sorted(set(l), key=l.index)
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+
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+
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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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+
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+
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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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+
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+
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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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+
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+
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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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+
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+
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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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+ 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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+
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+
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+ async def async_load_models(models: list, limit: int=5):
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+ sem = asyncio.Semaphore(limit)
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+ async def async_load_model(model: str):
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+ async with sem:
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+ try:
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+ return load_model(model)
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+ except Exception as e:
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+ print(e)
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+ tasks = [asyncio.create_task(async_load_model(model)) for model in models]
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+ return await asyncio.wait(tasks)
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+
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+
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+ def load_models(models: list, limit: int=5):
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+ loop = asyncio.get_event_loop()
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+ try:
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+ loop.run_until_complete(async_load_models(models, limit))
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+ except Exception as e:
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+ print(e)
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+ pass
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+ loop.close()
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+
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+
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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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+
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+
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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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+
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+
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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.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.Image(), None)
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+ return (image, caption)
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+
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+
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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
requirements.txt ADDED
@@ -0,0 +1 @@
 
 
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+ huggingface_hub