|
|
import os
|
|
|
import gradio as gr
|
|
|
import numpy as np
|
|
|
import spaces
|
|
|
import torch
|
|
|
import random
|
|
|
from PIL import Image
|
|
|
from typing import Iterable
|
|
|
from gradio.themes import Soft
|
|
|
from gradio.themes.utils import colors, fonts, sizes
|
|
|
|
|
|
colors.steel_blue = colors.Color(
|
|
|
name="steel_blue",
|
|
|
c50="#EBF3F8",
|
|
|
c100="#D3E5F0",
|
|
|
c200="#A8CCE1",
|
|
|
c300="#7DB3D2",
|
|
|
c400="#529AC3",
|
|
|
c500="#4682B4",
|
|
|
c600="#3E72A0",
|
|
|
c700="#36638C",
|
|
|
c800="#2E5378",
|
|
|
c900="#264364",
|
|
|
c950="#1E3450",
|
|
|
)
|
|
|
|
|
|
class SteelBlueTheme(Soft):
|
|
|
def __init__(
|
|
|
self,
|
|
|
*,
|
|
|
primary_hue: colors.Color | str = colors.gray,
|
|
|
secondary_hue: colors.Color | str = colors.steel_blue,
|
|
|
neutral_hue: colors.Color | str = colors.slate,
|
|
|
text_size: sizes.Size | str = sizes.text_lg,
|
|
|
font: fonts.Font | str | Iterable[fonts.Font | str] = (
|
|
|
fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
|
|
|
),
|
|
|
font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
|
|
|
fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
|
|
|
),
|
|
|
):
|
|
|
super().__init__(
|
|
|
primary_hue=primary_hue,
|
|
|
secondary_hue=secondary_hue,
|
|
|
neutral_hue=neutral_hue,
|
|
|
text_size=text_size,
|
|
|
font=font,
|
|
|
font_mono=font_mono,
|
|
|
)
|
|
|
super().set(
|
|
|
background_fill_primary="*primary_50",
|
|
|
background_fill_primary_dark="*primary_900",
|
|
|
body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
|
|
|
body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
|
|
|
button_primary_text_color="white",
|
|
|
button_primary_text_color_hover="white",
|
|
|
button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
|
|
|
button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
|
|
|
slider_color="*secondary_500",
|
|
|
slider_color_dark="*secondary_600",
|
|
|
block_title_text_weight="600",
|
|
|
block_border_width="3px",
|
|
|
block_shadow="*shadow_drop_lg",
|
|
|
)
|
|
|
|
|
|
steel_blue_theme = SteelBlueTheme()
|
|
|
|
|
|
from diffusers import FlowMatchEulerDiscreteScheduler
|
|
|
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
|
|
|
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
|
|
|
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
|
|
|
|
|
|
dtype = torch.bfloat16
|
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
|
|
|
|
pipe = QwenImageEditPlusPipeline.from_pretrained(
|
|
|
"Qwen/Qwen-Image-Edit-2509",
|
|
|
transformer=QwenImageTransformer2DModel.from_pretrained(
|
|
|
"linoyts/Qwen-Image-Edit-Rapid-AIO",
|
|
|
subfolder='transformer',
|
|
|
torch_dtype=dtype,
|
|
|
device_map='cuda'
|
|
|
),
|
|
|
torch_dtype=dtype
|
|
|
).to(device)
|
|
|
|
|
|
pipe.load_lora_weights("autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime",
|
|
|
weight_name="Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors",
|
|
|
adapter_name="anime")
|
|
|
pipe.load_lora_weights("dx8152/Qwen-Edit-2509-Multiple-angles",
|
|
|
weight_name="镜头转换.safetensors",
|
|
|
adapter_name="multiple-angles")
|
|
|
pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Light_restoration",
|
|
|
weight_name="移除光影.safetensors",
|
|
|
adapter_name="light-restoration")
|
|
|
pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Relight",
|
|
|
weight_name="Qwen-Edit-Relight.safetensors",
|
|
|
adapter_name="relight")
|
|
|
|
|
|
pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
|
|
|
MAX_SEED = np.iinfo(np.int32).max
|
|
|
|
|
|
@spaces.GPU
|
|
|
def infer(
|
|
|
input_image,
|
|
|
prompt,
|
|
|
lora_adapter,
|
|
|
seed,
|
|
|
randomize_seed,
|
|
|
guidance_scale,
|
|
|
steps,
|
|
|
progress=gr.Progress(track_tqdm=True)
|
|
|
):
|
|
|
if input_image is None:
|
|
|
raise gr.Error("Please upload an image to edit.")
|
|
|
|
|
|
if lora_adapter == "Photo-to-Anime":
|
|
|
pipe.set_adapters(["anime"], adapter_weights=[1.0])
|
|
|
elif lora_adapter == "Multiple-Angles":
|
|
|
pipe.set_adapters(["multiple-angles"], adapter_weights=[1.0])
|
|
|
elif lora_adapter == "Light-Restoration":
|
|
|
pipe.set_adapters(["light-restoration"], adapter_weights=[1.0])
|
|
|
elif lora_adapter == "Relight":
|
|
|
pipe.set_adapters(["relight"], adapter_weights=[1.0])
|
|
|
|
|
|
if randomize_seed:
|
|
|
seed = random.randint(0, MAX_SEED)
|
|
|
|
|
|
generator = torch.Generator(device=device).manual_seed(seed)
|
|
|
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"
|
|
|
|
|
|
original_image = input_image.convert("RGB")
|
|
|
width, height = original_image.size
|
|
|
|
|
|
result = pipe(
|
|
|
image=original_image,
|
|
|
prompt=prompt,
|
|
|
negative_prompt=negative_prompt,
|
|
|
height=height,
|
|
|
width=width,
|
|
|
num_inference_steps=steps,
|
|
|
generator=generator,
|
|
|
true_cfg_scale=guidance_scale,
|
|
|
).images[0]
|
|
|
|
|
|
return result, seed
|
|
|
|
|
|
@spaces.GPU
|
|
|
def infer_example(input_image, prompt, lora_adapter):
|
|
|
input_pil = input_image.convert("RGB")
|
|
|
guidance_scale = 1.0
|
|
|
steps = 4
|
|
|
result, seed = infer(input_pil, prompt, lora_adapter, 0, True, guidance_scale, steps)
|
|
|
return result, seed
|
|
|
|
|
|
|
|
|
css="""
|
|
|
#col-container {
|
|
|
margin: 0 auto;
|
|
|
max-width: 960px;
|
|
|
}
|
|
|
#main-title h1 {font-size: 2.1em !important;}
|
|
|
"""
|
|
|
|
|
|
with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
|
|
|
with gr.Column(elem_id="col-container"):
|
|
|
gr.Markdown("# **Qwen-Image-Edit-2509-LoRAs-Fast**", elem_id="main-title")
|
|
|
gr.Markdown("Perform diverse image edits using specialized LoRA adapters for the Qwen-Image-Edit model.")
|
|
|
|
|
|
with gr.Row(equal_height=True):
|
|
|
with gr.Column():
|
|
|
input_image = gr.Image(label="Upload Image", type="pil")
|
|
|
|
|
|
prompt = gr.Text(
|
|
|
label="Edit Prompt",
|
|
|
show_label=True,
|
|
|
placeholder="e.g., transform into anime",
|
|
|
)
|
|
|
|
|
|
run_button = gr.Button("Run", variant="primary")
|
|
|
|
|
|
with gr.Column():
|
|
|
output_image = gr.Image(label="Output Image", interactive=False, format="png", height=290)
|
|
|
|
|
|
with gr.Row():
|
|
|
lora_adapter = gr.Dropdown(
|
|
|
label="Choose Editing Style",
|
|
|
choices=["Photo-to-Anime", "Multiple-Angles", "Light-Restoration", "Relight"],
|
|
|
value="Photo-to-Anime"
|
|
|
)
|
|
|
with gr.Accordion("⚙️ Advanced Settings", open=False):
|
|
|
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
|
|
|
randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
|
|
|
guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)
|
|
|
steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4)
|
|
|
|
|
|
gr.Examples(
|
|
|
examples=[
|
|
|
["examples/1.jpg", "Transform into anime.", "Photo-to-Anime"],
|
|
|
["examples/5.jpg", "Remove shadows and relight the image using soft lighting.", "Light-Restoration"],
|
|
|
["examples/4.jpg", "Relight the image using soft, diffused lighting that simulates sunlight filtering.", "Relight"],
|
|
|
["examples/2.jpeg", "Move the camera left.", "Multiple-Angles"],
|
|
|
["examples/2.jpeg", "Move the camera right.", "Multiple-Angles"],
|
|
|
["examples/2.jpeg", "Rotate the camera 45 degrees to the left.", "Multiple-Angles"],
|
|
|
["examples/3.jpg", "Rotate the camera 45 degrees to the right.", "Multiple-Angles"],
|
|
|
["examples/3.jpg", "Switch the camera to a top-down view.", "Multiple-Angles"],
|
|
|
["examples/3.jpg", "Switch the camera to a wide-angle lens.", "Multiple-Angles"],
|
|
|
["examples/3.jpg", "Switch the camera to a close-up lens.", "Multiple-Angles"],
|
|
|
],
|
|
|
inputs=[input_image, prompt, lora_adapter],
|
|
|
outputs=[output_image, seed],
|
|
|
fn=infer_example,
|
|
|
cache_examples=False,
|
|
|
label="Examples"
|
|
|
)
|
|
|
|
|
|
run_button.click(
|
|
|
fn=infer,
|
|
|
inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
|
|
|
outputs=[output_image, seed]
|
|
|
)
|
|
|
|
|
|
demo.launch(mcp_server=True, ssr_mode=False, show_error=True) |
