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import gradio as gr
from huggingface_hub import login
import os
hf_token = os.environ.get("HF_TOKEN")
login(token=hf_token)
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers.utils import load_image
from PIL import Image
import torch
import numpy as np
import cv2
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
torch_dtype=torch.float16,
)
custom_model = "fffiloni/eugene_jour_general"
# This is where you load your trained weights
pipe.load_lora_weights(custom_model, use_auth_token=True)
#pipe.to("cuda")
pipe.enable_model_cpu_offload()
def infer(image_in, prompt):
prompt = prompt
negative_prompt = ""
image = load_image(image_in)
controlnet_conditioning_scale = 0.5 # recommended for good generalization
image = np.array(image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)
images = pipe(
prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,
).images
images[0].save(f"hug_lab.png")
return f"hug_lab.png"
with gr.Blocks() as demo:
with gr.Column():
image_in = gr.Image(source="upload", type="filepath")
prompt = gr.Textbox(label="Prompt")
submit_btn = gr.Button("Submit")
result = gr.Image(label="Result")
submit_btn.click(
fn = infer,
inputs = [image_in, prompt],
outputs = [result]
)
demo.queue().launch()
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