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Browse files- README.md +1 -1
- app.py +14 -12
- multit2i.py +13 -11
README.md
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@@ -4,7 +4,7 @@ emoji: ππ
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 4.
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app_file: app.py
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short_description: Text-to-Image
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license: mit
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 4.42.0
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app_file: app.py
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short_description: Text-to-Image
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license: mit
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app.py
CHANGED
@@ -1,13 +1,12 @@
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import gradio as gr
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from model import models
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from multit2i import (
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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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)
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max_images = 8
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load_models(models)
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css = """
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@@ -26,10 +25,13 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
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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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neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
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with gr.Accordion("Advanced options", open=False):
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-
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-
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with gr.Accordion("Recommended Prompt", open=False):
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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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@@ -81,13 +83,13 @@ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
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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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gen_event = gr.on(triggers=[run_button.click, prompt.submit],
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fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4) if (i < n) else None,
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inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg,
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positive_prefix, positive_suffix, negative_prefix, negative_suffix],
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outputs=[o], queue=True, show_api=False)
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gen_event2 = gr.on(triggers=[random_button.click],
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fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, l1, l2, l3, l4) if (i < n) else None,
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inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg,
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positive_prefix, positive_suffix, negative_prefix, negative_suffix],
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outputs=[o], queue=True, show_api=False)
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o.change(save_gallery, [o, results], [results, image_files], show_api=False)
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import gradio as gr
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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 = 8
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MAX_SEED = 2**32-1
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load_models(models)
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css = """
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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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neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
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with gr.Accordion("Advanced options", open=False):
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with gr.Row():
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width = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
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height = gr.Number(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.Number(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.Number(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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with gr.Accordion("Recommended Prompt", open=False):
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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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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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gen_event = gr.on(triggers=[run_button.click, prompt.submit],
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fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
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inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
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positive_prefix, positive_suffix, negative_prefix, negative_suffix],
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outputs=[o], queue=True, show_api=False)
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gen_event2 = gr.on(triggers=[random_button.click],
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fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
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inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
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positive_prefix, positive_suffix, negative_prefix, negative_suffix],
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outputs=[o], queue=True, show_api=False)
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o.change(save_gallery, [o, results], [results, image_files], show_api=False)
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multit2i.py
CHANGED
@@ -6,7 +6,7 @@ from huggingface_hub import InferenceClient
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import os
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HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None
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server_timeout = 600
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inference_timeout = 300
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@@ -354,13 +354,14 @@ def warm_model(model_name: str):
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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, prompt: str, neg_prompt: str | None = None,
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height: int | None = None, width: int | None = None,
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steps: int | None = None, cfg: int | None = None):
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png_path = "image.png"
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kwargs = {}
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if height is not None and height >= 256: kwargs["height"] = height
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if width is not None and width >= 256: kwargs["width"] = width
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if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
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if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
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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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async def infer(model_name: str, prompt: str, neg_prompt: str | None = None,
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height: int | None = None, width: int | None = None,
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steps: int | None = None, cfg: int | None = None,
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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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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, f"{prompt} {noise}", neg_prompt,
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height, width, steps, cfg))
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await asyncio.sleep(0)
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try:
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result = await asyncio.wait_for(task, timeout=timeout)
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def infer_fn(model_name: str, prompt: str, neg_prompt: str | None = None, height: int | None = None,
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width: int | None = None, steps: int | None = None, cfg: int | None = None,
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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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prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
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loop = asyncio.new_event_loop()
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result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
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steps, cfg, save_path, inference_timeout))
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except (Exception, asyncio.CancelledError) as e:
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print(e)
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print(f"Task aborted: {model_name}")
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def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str | None = None, height: int | None = None,
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width: int | None = None, steps: int | None = None, cfg: int | None = None,
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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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prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
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loop = asyncio.new_event_loop()
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result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
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steps, cfg, save_path, inference_timeout))
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except (Exception, asyncio.CancelledError) as e:
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print(e)
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print(f"Task aborted: {model_name}")
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import os
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HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
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server_timeout = 600
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inference_timeout = 300
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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, prompt: str, neg_prompt: str | None = None,
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height: int | None = None, width: int | None = None,
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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 is not None and height >= 256: kwargs["height"] = height
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if width is not None and width >= 256: kwargs["width"] = width
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if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
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if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
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if seed >= 0: 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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async def infer(model_name: str, prompt: str, neg_prompt: str | None = None,
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height: int | None = None, width: int | None = None,
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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, f"{prompt} {noise}", 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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result = await asyncio.wait_for(task, timeout=timeout)
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def infer_fn(model_name: str, prompt: str, neg_prompt: str | None = None, height: int | None = None,
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width: int | None = None, steps: int | None = None, cfg: int | None = None, 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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prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
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loop = asyncio.new_event_loop()
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result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
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steps, cfg, seed, save_path, inference_timeout))
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except (Exception, asyncio.CancelledError) as e:
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print(e)
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print(f"Task aborted: {model_name}")
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def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str | None = None, height: int | None = None,
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width: int | None = None, steps: int | None = None, cfg: int | None = None, 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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prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
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loop = asyncio.new_event_loop()
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result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
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steps, cfg, seed, save_path, inference_timeout))
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except (Exception, asyncio.CancelledError) as e:
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print(e)
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print(f"Task aborted: {model_name}")
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