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Browse files- app.py +11 -5
- multit2i.py +57 -27
- requirements.txt +2 -1
app.py
CHANGED
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@@ -3,7 +3,7 @@ from model import models
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from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery,
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change_model, warm_model, get_model_info_md, loaded_models,
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get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
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get_recom_prompt_type, set_recom_prompt_preset, get_tag_type)
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max_images = 6
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MAX_SEED = 2**32-1
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@@ -19,24 +19,27 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
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with gr.Row():
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with gr.Column(scale=10):
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with gr.Group():
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clear_prompt = gr.Button(value="Clear Prompt ποΈ", variant="secondary", size="sm", scale=1)
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prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
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with gr.Accordion("Advanced options", open=False):
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neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
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with gr.Row():
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width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
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height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
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with gr.Row():
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steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
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cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
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seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
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recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
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with gr.Row():
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positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
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positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
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negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
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negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
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-
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with gr.Row():
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run_button = gr.Button("Generate Image", variant="primary", scale=6)
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random_button = gr.Button("Random Model π²", variant="secondary", scale=3)
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@@ -78,7 +81,7 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
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#gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False)
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model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)\
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.success(warm_model, [model_name], None, queue=
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for i, o in enumerate(output):
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img_i = gr.Number(i, visible=False)
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image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=False)
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@@ -99,6 +102,9 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
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clear_results.click(lambda: (None, None), None, [results, image_files], queue=False, show_api=False)
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recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
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[positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=False, show_api=False)
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demo.queue(default_concurrency_limit=200, max_size=200)
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demo.launch(max_threads=400)
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from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery,
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change_model, warm_model, get_model_info_md, loaded_models,
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get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
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get_recom_prompt_type, set_recom_prompt_preset, get_tag_type, randomize_seed, translate_to_en)
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max_images = 6
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MAX_SEED = 2**32-1
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with gr.Row():
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with gr.Column(scale=10):
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with gr.Group():
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prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
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with gr.Accordion("Advanced options", open=False):
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neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
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with gr.Row():
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width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
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height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
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steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
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with gr.Row():
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cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
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seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
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seed_rand = gr.Button("Randomize Seed π²", size="sm", variant="secondary")
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recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
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with gr.Row():
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positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
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positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
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negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
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negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
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with gr.Row():
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image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2)
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trans_prompt = gr.Button(value="Translate π", variant="secondary", size="sm", scale=2)
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clear_prompt = gr.Button(value="Clear ποΈ", variant="secondary", size="sm", scale=1)
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with gr.Row():
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run_button = gr.Button("Generate Image", variant="primary", scale=6)
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random_button = gr.Button("Random Model π²", variant="secondary", scale=3)
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#gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False)
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model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)\
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.success(warm_model, [model_name], None, queue=False, show_api=False)
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for i, o in enumerate(output):
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img_i = gr.Number(i, visible=False)
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image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, 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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recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
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[positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=False, show_api=False)
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seed_rand.click(randomize_seed, None, [seed], queue=False, show_api=False)
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trans_prompt.click(translate_to_en, [prompt], [prompt], queue=False, show_api=False)\
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.then(translate_to_en, [neg_prompt], [neg_prompt], queue=False, show_api=False)
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demo.queue(default_concurrency_limit=200, max_size=200)
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demo.launch(max_threads=400)
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multit2i.py
CHANGED
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@@ -60,7 +60,7 @@ def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="l
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limit = limit * 20 if check_status and force_gpu else limit * 5
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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",
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tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
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except Exception as e:
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print(f"Error: Failed to list models.")
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@@ -110,7 +110,7 @@ def get_t2i_model_info_dict(repo_id: str):
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def rename_image(image_path: str | None, model_name: str, save_path: str | None = None):
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from datetime import datetime, timezone, timedelta
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if image_path is None: return None
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dt_now = datetime.now(timezone(timedelta(hours=9)))
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@@ -118,7 +118,7 @@ def rename_image(image_path: str | None, model_name: str, save_path: str | None
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try:
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if Path(image_path).exists():
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png_path = "image.png"
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if save_path is not None:
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new_path = str(Path(png_path).resolve().rename(Path(save_path).resolve()))
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else:
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@@ -363,16 +363,16 @@ def warm_model(model_name: str):
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# https://huggingface.co/docs/api-inference/detailed_parameters
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# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
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def infer_body(client: InferenceClient | gr.Interface | object, prompt: str, neg_prompt: str
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height: int
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steps: int | None = None, cfg: int | None = None, seed: int = -1):
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png_path = "image.png"
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kwargs = {}
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if height
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if width
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if steps
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if cfg
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if seed
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try:
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if isinstance(client, InferenceClient):
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image = client.text_to_image(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
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@@ -380,26 +380,18 @@ def infer_body(client: InferenceClient | gr.Interface | object, prompt: str, neg
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image = client.fn(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
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else: return None
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if isinstance(image, tuple): return None
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image
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return str(Path(png_path).resolve())
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except Exception as e:
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print(e)
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raise Exception() from e
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async def infer(model_name: str, prompt: str, neg_prompt: str
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steps: int | None = None, cfg: int | None = None, seed: int = -1,
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save_path: str | None = None, timeout: float = inference_timeout):
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import random
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noise = ""
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if seed < 0:
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rand = random.randint(1, 500)
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for i in range(rand):
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noise += " "
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model = load_model(model_name)
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if not model: return None
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task = asyncio.create_task(asyncio.to_thread(infer_body, model,
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height, width, steps, cfg, seed))
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await asyncio.sleep(0)
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try:
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# https://github.com/aio-libs/pytest-aiohttp/issues/8 # also AsyncInferenceClient is buggy.
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def infer_fn(model_name: str, prompt: str, neg_prompt: str
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pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
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if model_name == 'NA':
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return None
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@@ -446,8 +438,8 @@ def infer_fn(model_name: str, prompt: str, neg_prompt: str | None = None, height
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return result
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def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str
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pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
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import random
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if model_name_dummy == 'NA':
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@@ -470,3 +462,41 @@ def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str | None = N
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finally:
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loop.close()
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return result
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limit = limit * 20 if check_status and force_gpu else limit * 5
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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",
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tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
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except Exception as e:
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print(f"Error: Failed to list models.")
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def rename_image(image_path: str | None, model_name: str, save_path: str | None = None):
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import shutil
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from datetime import datetime, timezone, timedelta
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if image_path is None: return None
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dt_now = datetime.now(timezone(timedelta(hours=9)))
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try:
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if Path(image_path).exists():
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png_path = "image.png"
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if str(Path(image_path).resolve()) != str(Path(png_path).resolve()): shutil.copy(image_path, png_path)
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if save_path is not None:
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new_path = str(Path(png_path).resolve().rename(Path(save_path).resolve()))
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else:
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# https://huggingface.co/docs/api-inference/detailed_parameters
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# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
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def infer_body(client: InferenceClient | gr.Interface | object, model_str: str, prompt: str, neg_prompt: str = "",
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height: int = 0, width: int = 0, steps: int = 0, cfg: int = 0, seed: int = -1):
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png_path = "image.png"
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kwargs = {}
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if height > 0: kwargs["height"] = height
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if width > 0: kwargs["width"] = width
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if steps > 0: kwargs["num_inference_steps"] = steps
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if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
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if seed == -1: kwargs["seed"] = randomize_seed()
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else: kwargs["seed"] = seed
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try:
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if isinstance(client, InferenceClient):
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image = client.text_to_image(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
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image = client.fn(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
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else: return None
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if isinstance(image, tuple): return None
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return save_image(image, png_path, model_str, prompt, neg_prompt, height, width, steps, cfg, seed)
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except Exception as e:
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print(e)
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raise Exception() from e
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async def infer(model_name: str, prompt: str, neg_prompt: str ="", height: int = 0, width: int = 0,
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steps: int = 0, cfg: int = 0, seed: int = -1,
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save_path: str | None = None, timeout: float = inference_timeout):
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model = load_model(model_name)
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if not model: return None
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task = asyncio.create_task(asyncio.to_thread(infer_body, model, model_name, prompt, neg_prompt,
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height, width, steps, cfg, seed))
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await asyncio.sleep(0)
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try:
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# https://github.com/aio-libs/pytest-aiohttp/issues/8 # also AsyncInferenceClient is buggy.
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def infer_fn(model_name: str, prompt: str, neg_prompt: str = "", height: int = 0, width: int = 0,
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steps: int = 0, cfg: int = 0, seed: int = -1,
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pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
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if model_name == 'NA':
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return None
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return result
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def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str = "", height: int = 0, width: int = 0,
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steps: int = 0, cfg: int = 0, seed: int = -1,
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pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
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import random
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if model_name_dummy == 'NA':
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finally:
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loop.close()
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return result
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def save_image(image, savefile, modelname, prompt, nprompt, height=0, width=0, steps=0, cfg=0, seed=-1):
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from PIL import Image, PngImagePlugin
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import json
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try:
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metadata = {"prompt": prompt, "negative_prompt": nprompt, "Model": {"Model": modelname.split("/")[-1]}}
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if steps > 0: metadata["num_inference_steps"] = steps
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if cfg > 0: metadata["guidance_scale"] = cfg
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if seed != -1: metadata["seed"] = seed
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if width > 0 and height > 0: metadata["resolution"] = f"{width} x {height}"
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metadata_str = json.dumps(metadata)
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info = PngImagePlugin.PngInfo()
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info.add_text("metadata", metadata_str)
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image.save(savefile, "PNG", pnginfo=info)
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return str(Path(savefile).resolve())
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except Exception as e:
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print(f"Failed to save image file: {e}")
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raise Exception(f"Failed to save image file:") from e
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def randomize_seed():
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| 487 |
+
from random import seed, randint
|
| 488 |
+
MAX_SEED = 2**32-1
|
| 489 |
+
seed()
|
| 490 |
+
rseed = randint(0, MAX_SEED)
|
| 491 |
+
return rseed
|
| 492 |
+
|
| 493 |
+
|
| 494 |
+
from translatepy import Translator
|
| 495 |
+
translator = Translator()
|
| 496 |
+
def translate_to_en(input: str):
|
| 497 |
+
try:
|
| 498 |
+
output = str(translator.translate(input, 'English'))
|
| 499 |
+
except Exception as e:
|
| 500 |
+
output = input
|
| 501 |
+
print(e)
|
| 502 |
+
return output
|
requirements.txt
CHANGED
|
@@ -1 +1,2 @@
|
|
| 1 |
-
huggingface_hub
|
|
|
|
|
|
| 1 |
+
huggingface_hub
|
| 2 |
+
translatepy
|