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4179614 | 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 | import spaces
import torch
import gradio as gr
from diffusers import StableDiffusionXLPipeline
MODEL_REPO = "BinaryLight1011/Imageflow"
pipe = StableDiffusionXLPipeline.from_pretrained(
MODEL_REPO,
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.to("cuda")
@spaces.GPU(duration=60)
def generate(prompt, negative_prompt, width, height, guidance_scale, steps, seed):
generator = torch.Generator(device="cuda").manual_seed(int(seed))
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt or None,
width=int(width),
height=int(height),
guidance_scale=float(guidance_scale),
num_inference_steps=int(steps),
generator=generator,
).images[0]
return image
with gr.Blocks(title="Imageflow") as demo:
gr.Markdown("# 🖼️ Imageflow — Text-to-Image (SDXL)")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", lines=4, placeholder="Descreva a imagem...")
negative_prompt = gr.Textbox(label="Negative prompt (opcional)", lines=2)
with gr.Row():
width = gr.Slider(512, 1536, value=1024, step=64, label="Largura")
height = gr.Slider(512, 1536, value=1024, step=64, label="Altura")
guidance_scale = gr.Slider(1.0, 15.0, value=7.0, step=0.5, label="Guidance scale")
steps = gr.Slider(10, 100, value=30, step=5, label="Inference steps")
seed = gr.Number(value=42, label="Seed")
btn = gr.Button("Gerar imagem", variant="primary")
with gr.Column():
output_image = gr.Image(label="Resultado")
btn.click(
fn=generate,
inputs=[prompt, negative_prompt, width, height, guidance_scale, steps, seed],
outputs=output_image,
)
demo.launch() |