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Delete app.py

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  1. app.py +0 -224
app.py DELETED
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- import os
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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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- import random
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- from PIL import Image
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- from typing import Iterable
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- from gradio.themes import Soft
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- from gradio.themes.utils import colors, fonts, sizes
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-
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- colors.steel_blue = colors.Color(
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- name="steel_blue",
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- c50="#EBF3F8",
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- c100="#D3E5F0",
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- c200="#A8CCE1",
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- c300="#7DB3D2",
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- c400="#529AC3",
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- c500="#4682B4",
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- c600="#3E72A0",
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- c700="#36638C",
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- c800="#2E5378",
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- c900="#264364",
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- c950="#1E3450",
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- )
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-
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- class SteelBlueTheme(Soft):
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- def __init__(
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- self,
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- *,
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- primary_hue: colors.Color | str = colors.gray,
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- secondary_hue: colors.Color | str = colors.steel_blue,
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- neutral_hue: colors.Color | str = colors.slate,
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- text_size: sizes.Size | str = sizes.text_lg,
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- font: fonts.Font | str | Iterable[fonts.Font | str] = (
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- fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
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- ),
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- font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
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- fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
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- ),
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- ):
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- super().__init__(
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- primary_hue=primary_hue,
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- secondary_hue=secondary_hue,
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- neutral_hue=neutral_hue,
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- text_size=text_size,
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- font=font,
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- font_mono=font_mono,
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- )
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- super().set(
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- background_fill_primary="*primary_50",
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- background_fill_primary_dark="*primary_900",
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- body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
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- body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
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- button_primary_text_color="white",
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- button_primary_text_color_hover="white",
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- button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
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- button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
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- slider_color="*secondary_500",
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- slider_color_dark="*secondary_600",
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- block_title_text_weight="600",
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- block_border_width="3px",
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- block_shadow="*shadow_drop_lg",
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- )
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-
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- steel_blue_theme = SteelBlueTheme()
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-
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- from diffusers import FlowMatchEulerDiscreteScheduler
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- # from optimization import optimize_pipeline_ # Assuming this is a custom file, if not available, comment out the call
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- from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
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- from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
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- from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
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-
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- dtype = torch.bfloat16
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- device = "cuda" if torch.cuda.is_available() else "cpu"
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-
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- pipe = QwenImageEditPlusPipeline.from_pretrained(
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- "Qwen/Qwen-Image-Edit-2509",
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- transformer=QwenImageTransformer2DModel.from_pretrained(
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- "linoyts/Qwen-Image-Edit-Rapid-AIO",
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- subfolder='transformer',
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- torch_dtype=dtype,
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- device_map='cuda'
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- ),
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- torch_dtype=dtype
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- ).to(device)
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-
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- pipe.load_lora_weights("autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime",
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- weight_name="Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors",
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- adapter_name="anime")
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- pipe.load_lora_weights("dx8152/Qwen-Edit-2509-Multiple-angles",
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- weight_name="镜头转换.safetensors",
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- adapter_name="multiple-angles")
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- pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Light_restoration",
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- weight_name="移除光影.safetensors",
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- adapter_name="light-restoration")
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- pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Relight",
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- weight_name="Qwen-Edit-Relight.safetensors",
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- adapter_name="relight")
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-
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- pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
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- MAX_SEED = np.iinfo(np.int32).max
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-
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- @spaces.GPU
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- def infer(
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- input_image,
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- prompt,
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- lora_adapter,
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- seed,
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- randomize_seed,
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- guidance_scale,
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- steps,
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- progress=gr.Progress(track_tqdm=True)
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- ):
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- if input_image is None:
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- raise gr.Error("Please upload an image to edit.")
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-
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- if lora_adapter == "Photo-to-Anime":
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- pipe.set_adapters(["anime"], adapter_weights=[1.0])
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- elif lora_adapter == "Multiple-Angles":
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- pipe.set_adapters(["multiple-angles"], adapter_weights=[1.0])
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- elif lora_adapter == "Light-Restoration":
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- pipe.set_adapters(["light-restoration"], adapter_weights=[1.0])
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- elif lora_adapter == "Relight":
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- pipe.set_adapters(["relight"], adapter_weights=[1.0])
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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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-
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- generator = torch.Generator(device=device).manual_seed(seed)
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- negative_prompt = "worst quality, low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry"
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-
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- original_image = input_image.convert("RGB")
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- width, height = original_image.size
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-
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- result = pipe(
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- image=original_image,
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- prompt=prompt,
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- negative_prompt=negative_prompt,
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- height=height,
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- width=width,
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- num_inference_steps=steps,
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- generator=generator,
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- true_cfg_scale=guidance_scale,
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- ).images[0]
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-
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- return result, seed
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-
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- @spaces.GPU
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- def infer_example(input_image, prompt, lora_adapter):
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- input_pil = input_image.convert("RGB")
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- guidance_scale = 1.0
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- steps = 4
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- result, seed = infer(input_pil, prompt, lora_adapter, 0, True, guidance_scale, steps)
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- return result, seed
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-
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-
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- css="""
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- #col-container {
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- margin: 0 auto;
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- max-width: 960px;
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- }
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- #main-title h1 {font-size: 2.1em !important;}
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- """
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-
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- with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
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- with gr.Column(elem_id="col-container"):
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- gr.Markdown("# **Qwen-Image-Edit-2509-LoRAs-Fast**", elem_id="main-title")
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- gr.Markdown("Perform diverse image edits using specialized LoRA adapters for the Qwen-Image-Edit model.")
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-
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- with gr.Row(equal_height=True):
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- with gr.Column():
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- input_image = gr.Image(label="Upload Image", type="pil")
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-
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- prompt = gr.Text(
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- label="Edit Prompt",
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- show_label=True,
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- placeholder="e.g., transform into anime",
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- )
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-
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- run_button = gr.Button("Run", variant="primary")
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-
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- with gr.Column():
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- output_image = gr.Image(label="Output Image", interactive=False, format="png", height=290)
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-
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- with gr.Row():
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- lora_adapter = gr.Dropdown(
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- label="Choose Editing Style",
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- choices=["Photo-to-Anime", "Multiple-Angles", "Light-Restoration", "Relight"],
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- value="Photo-to-Anime"
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- )
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- with gr.Accordion("⚙️ Advanced Settings", open=False):
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- seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
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- randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
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- guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
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- steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4)
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-
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- gr.Examples(
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- examples=[
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- ["examples/1.jpg", "Transform into anime.", "Photo-to-Anime"],
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- ["examples/5.jpg", "Remove shadows and relight the image using soft lighting.", "Light-Restoration"],
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- ["examples/4.jpg", "Relight the image using soft, diffused lighting that simulates sunlight filtering.", "Relight"],
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- ["examples/2.jpeg", "Move the camera left.", "Multiple-Angles"],
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- ["examples/2.jpeg", "Move the camera right.", "Multiple-Angles"],
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- ["examples/2.jpeg", "Rotate the camera 45 degrees to the left.", "Multiple-Angles"],
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- ["examples/3.jpg", "Rotate the camera 45 degrees to the right.", "Multiple-Angles"],
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- ["examples/3.jpg", "Switch the camera to a top-down view.", "Multiple-Angles"],
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- ["examples/3.jpg", "Switch the camera to a wide-angle lens.", "Multiple-Angles"],
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- ["examples/3.jpg", "Switch the camera to a close-up lens.", "Multiple-Angles"],
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- ],
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- inputs=[input_image, prompt, lora_adapter],
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- outputs=[output_image, seed],
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- fn=infer_example,
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- cache_examples=False,
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- label="Examples"
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- )
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-
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- run_button.click(
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- fn=infer,
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- inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
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- outputs=[output_image, seed]
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- )
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-
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- demo.launch(mcp_server=True, ssr_mode=False, show_error=True)