Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,630 Bytes
8fc365a 9c5b32a 8fc365a 9c5b32a 8fc365a 9c5b32a 8fc365a 9c5b32a 8fc365a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import gradio as gr
import numpy as np
import spaces
import torch
import random
import os
# from diffusers import QwenImageEditInpaintPipeline
from optimization import optimize_pipeline_
from qwenimage.pipeline_qwen_image_edit import QwenImageEditInpaintPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3
from PIL import Image
# Set environment variable for parallel loading
os.environ["HF_ENABLE_PARALLEL_LOADING"] = "YES"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# Initialize Qwen Image Edit pipeline
pipe = QwenImageEditInpaintPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=torch.bfloat16).to("cuda")
pipe.transformer.__class__ = QwenImageTransformer2DModel
pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
# --- Ahead-of-time compilation ---
optimize_pipeline_(pipe, image=Image.new("RGB", (1024, 1024)), prompt="prompt")
@spaces.GPU(duration=120)
def infer(edit_images, prompt, negative_prompt="", seed=42, randomize_seed=False, strength=1.0, num_inference_steps=35, true_cfg_scale=4.0, progress=gr.Progress(track_tqdm=True)):
image = edit_images["background"]
mask = edit_images["layers"][0]
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Generate image using Qwen pipeline
result_image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
mask_image=mask,
strength=strength,
num_inference_steps=num_inference_steps,
true_cfg_scale=true_cfg_scale,
generator=torch.Generator(device="cuda").manual_seed(seed)
).images[0]
return result_image, seed
examples = [
"change the hat to red",
"make the background a beautiful sunset",
"replace the object with a flower vase",
]
css="""
#col-container {
margin: 0 auto;
max-width: 1000px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML("""
<div id="logo-title">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_edit_logo.png" alt="Qwen-Image Edit Logo" width="400" style="display: block; margin: 0 auto;">
<h2 style="font-style: italic;color: #5b47d1;margin-top: -27px !important;margin-left: 133px;">Inapint</h2>
</div>
""")
gr.Markdown("""
Inpaint images with Qwen Image Edit. [Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series.
This demo uses the [Qwen-Image-Lightning](https://huggingface.co/lightx2v/Qwen-Image-Lightning) LoRA with AoT compilation and FA3 for accelerated 8-step inference.
Try on [Qwen Chat](https://chat.qwen.ai/), or [download model](https://huggingface.co/Qwen/Qwen-Image-Edit) to run locally with ComfyUI or diffusers.
""")
with gr.Row():
with gr.Column():
edit_image = gr.ImageEditor(
label='Upload and draw mask for inpainting',
type='pil',
sources=["upload", "webcam"],
image_mode='RGB',
layers=False,
brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"),
height=600
)
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt (e.g., 'change the hat to red')",
container=False,
)
negative_prompt = gr.Text(
label="Negative Prompt",
show_label=True,
max_lines=1,
placeholder="Enter what you don't want (optional)",
container=False,
value=""
)
run_button = gr.Button("Run")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
strength = gr.Slider(
label="Strength",
minimum=0.0,
maximum=2.0,
step=0.1,
value=1.0,
info="Controls how much the inpainted region should change"
)
true_cfg_scale = gr.Slider(
label="True CFG Scale",
minimum=1.0,
maximum=20.0,
step=0.5,
value=4.0,
info="Classifier-free guidance scale"
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=10,
maximum=100,
step=1,
value=35,
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [edit_image, prompt, negative_prompt, seed, randomize_seed, strength, num_inference_steps, true_cfg_scale],
outputs = [result, seed]
)
demo.launch() |