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import gradio as gr
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae, torch_dtype=torch.float16, variant="fp16",
use_safetensors=True
)
def load_model(custom_model):
# This is where you load your trained weights
pipe.load_lora_weights(custom_model)
pipe.to("cuda")
return "Model loaded!"
def infer (prompt, inf_steps, guidance_scale, seed, lora_weigth, progress=gr.Progress(track_tqdm=True)):
generator = torch.Generator(device="cuda").manual_seed(seed)
image = pipe(
prompt=prompt,
num_inference_steps=inf_steps,
guidance_scale = float(guidance_scale),
generator=generator,
cross_attention_kwargs={"scale": float(lora_weight)}
).images[0]
return image
css = """
#col-container {max-width: 580px; margin-left: auto; margin-right: auto;}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("""
# SD-XL Custom Model Inference
""")
with gr.Row():
with gr.Column():
custom_model = gr.Textbox(label="Your custom model ID", placeholder="your_username/your_trained_model_name", info="Make sure your model is set to PUBLIC ")
model_status = gr.Textbox(label="model status", interactive=False)
load_model_btn = gr.Button("Load my model")
prompt_in = gr.Textbox(label="Prompt")
inf_steps = gr.Slider(
label="Inference steps",
minimum=12,
maximum=50,
step=1,
value=25
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.1,
maximum=0.9,
step=0.1,
value=7.5
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=500000,
step=1,
value=42
)
lora_weight = gr.Slider(
label="LoRa weigth",
minimum=0.0,
maximum=10.0,
step=0.01,
value=0.9
)
submit_btn = gr.Button("Submit")
image_out = gr.Image(label="Image output")
load_model_btn.click(
fn = load_model,
inputs=[custom_model],
outputs = [model_status]
)
submit_btn.click(
fn = infer,
inputs = [prompt_in, inf_steps, guidance_scale, seed, lora_weight],
outputs = [image_out]
)
demo.queue().launch() |