Spaces:
Running
on
Zero
Running
on
Zero
eyal.benaroche
commited on
Commit
•
f88f754
1
Parent(s):
55c0372
add demo
Browse files- README.md +1 -1
- app.py +305 -0
- sdxl_lora.json +131 -0
README.md
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---
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title: Flash Lora
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emoji:
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colorFrom: blue
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colorTo: pink
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sdk: gradio
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---
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title: Flash Lora
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+
emoji: 🎨
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colorFrom: blue
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colorTo: pink
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sdk: gradio
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app.py
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import json
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import random
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import gradio as gr
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import numpy as np
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import spaces
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import torch
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from diffusers import DiffusionPipeline, LCMScheduler
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with open("sdxl_lora.json", "r") as file:
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data = json.load(file)
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sdxl_loras_raw = [
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{
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"image": item["image"],
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"title": item["title"],
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"repo": item["repo"],
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"trigger_word": item["trigger_word"],
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"weights": item["weights"],
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"is_pivotal": item.get("is_pivotal", False),
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"text_embedding_weights": item.get("text_embedding_weights", None),
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"likes": item.get("likes", 0),
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}
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for item in data
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]
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# Sort the loras by likes
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sdxl_loras_raw = sorted(sdxl_loras_raw, key=lambda x: x["likes"], reverse=True)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_id = "stabilityai/stable-diffusion-xl-base-1.0"
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if gr.NO_RELOAD:
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torch.cuda.max_memory_allocated(device=device)
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pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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pipe.load_lora_weights("jasperai/flash-sdxl", adapter_name="lora")
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pipe.to(device="cuda", dtype=torch.float16)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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def check_and_load_lora_user(user_lora_selector, user_lora_weight, gr_lora_loaded):
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flash_sdxl_id = "jasperai/flash-sdxl"
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global pipe
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if user_lora_selector == "" or user_lora_selector == "":
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raise gr.Error("Please select a LoRA before running the inference.")
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if gr_lora_loaded != user_lora_selector:
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gr.Info("Loading LoRA")
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pipe.unload_lora_weights()
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pipe.load_lora_weights(flash_sdxl_id, adapter_name="lora")
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pipe.load_lora_weights(user_lora_selector, adapter_name="user")
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pipe.set_adapters(["lora", "user"], adapter_weights=[1.0, user_lora_weight])
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gr.Info("LoRA Loaded")
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gr_lora_loaded = user_lora_selector
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return gr_lora_loaded
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def rescale_lora(user_lora_weight):
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global pipe
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pipe.set_adapters(["lora", "user"], adapter_weights=[1.0, user_lora_weight])
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def update_selection(
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selected_state: gr.SelectData,
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gr_sdxl_loras,
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):
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lora_id = gr_sdxl_loras[selected_state.index]["repo"]
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trigger_word = gr_sdxl_loras[selected_state.index]["trigger_word"]
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return lora_id, trigger_word
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@spaces.GPU
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def infer(
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pre_prompt,
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prompt,
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seed,
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randomize_seed,
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num_inference_steps,
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negative_prompt,
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guidance_scale,
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):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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if pre_prompt != "":
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prompt = f"{pre_prompt} {prompt}"
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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generator=generator,
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).images[0]
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return image
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css = """
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h1 {
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text-align: center;
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display:block;
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}
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p {
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text-align: justify;
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display:block;
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}
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"""
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if torch.cuda.is_available():
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power_device = "GPU"
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else:
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power_device = "CPU"
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with gr.Blocks(css=css) as demo:
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gr.Markdown(
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f"""
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# ⚡ FlashDiffusion: FlashLoRA ⚡
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This is an interactive demo of [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) **on top of** existing LoRAs.
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The distillation method proposed in [Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) *by Clément Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin* from Jasper Research.
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The LoRAs can be added **without** any retraining for similar results in most cases. Feel free to tweak the parameters and use your own LoRAs by giving a look at the [Github Repo](https://github.com/gojasper/flash-diffusion)
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"""
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)
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gr.Markdown(
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"If you enjoy the space, please also promote *open-source* by giving a ⭐ to our repo [![GitHub Stars](https://img.shields.io/github/stars/gojasper/flash-diffusion?style=social)](https://github.com/gojasper/flash-diffusion)"
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)
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# Index of selected LoRA
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gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
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# Serve as memory for currently loaded lora in pipe
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gr_lora_loaded = gr.State(value="")
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gr_lora_id = gr.State(value="")
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with gr.Row():
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with gr.Blocks():
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with gr.Column():
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user_lora_selector = gr.Textbox(
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label="Current Selected LoRA",
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max_lines=1,
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interactive=False,
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)
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user_lora_weight = gr.Slider(
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label="Selected LoRA Weight",
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minimum=0.5,
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maximum=3,
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step=0.1,
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value=1,
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)
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gallery = gr.Gallery(
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value=[(item["image"], item["title"]) for item in sdxl_loras_raw],
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label="SDXL LoRA Gallery",
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allow_preview=False,
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columns=3,
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elem_id="gallery",
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show_share_button=False,
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)
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with gr.Column():
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with gr.Row():
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter your prompt",
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container=False,
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scale=5,
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)
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run_button = gr.Button("Run", scale=1)
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result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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pre_prompt = gr.Text(
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label="Pre-Prompt",
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show_label=True,
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max_lines=1,
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placeholder="Pre Prompt from the LoRA config",
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container=True,
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scale=5,
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=4,
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maximum=8,
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step=1,
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value=4,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance Scale",
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minimum=1,
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maximum=6,
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step=0.5,
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value=1,
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)
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hint_negative = gr.Markdown(
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"""💡 _Hint : Negative Prompt will only work with Guidance > 1 but the model was
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trained to be used with guidance = 1 (ie. without guidance).
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Can degrade the results, use cautiously._"""
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)
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negative_prompt = gr.Text(
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label="Negative Prompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter a negative Prompt",
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container=False,
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)
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gr.on(
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[
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run_button.click,
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seed.change,
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randomize_seed.change,
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# prompt.change,
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prompt.submit,
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negative_prompt.change,
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negative_prompt.submit,
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guidance_scale.change,
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],
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fn=check_and_load_lora_user,
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inputs=[user_lora_selector, user_lora_weight, gr_lora_loaded],
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outputs=[gr_lora_loaded],
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).success(
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fn=infer,
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inputs=[
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pre_prompt,
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prompt,
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seed,
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randomize_seed,
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num_inference_steps,
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negative_prompt,
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guidance_scale,
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],
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outputs=[result],
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show_progress=True,
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)
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+
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user_lora_weight.change(
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fn=rescale_lora,
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inputs=[user_lora_weight],
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285 |
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outputs=[],
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show_progress="hidden",
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)
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288 |
+
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gallery.select(
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fn=update_selection,
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inputs=[gr_sdxl_loras],
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outputs=[
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user_lora_selector,
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pre_prompt,
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295 |
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],
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show_progress=False,
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)
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gr.Markdown("**Disclaimer:**")
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gr.Markdown(
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"This demo is only for research purpose. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards."
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)
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demo.queue().launch()
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sdxl_lora.json
ADDED
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