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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, | |
| variant="fp16", | |
| use_safetensors=True | |
| ) | |
| pipe.to("cuda") | |
| generator = torch.Generator(device="cuda") | |
| #pipe.enable_model_cpu_offload() | |
| def infer(model_name, image_in, prompt, controlnet_conditioning_scale, guidance_scale, seed): | |
| custom_model = model_name | |
| # This is where you load your trained weights | |
| pipe.load_lora_weights(custom_model, weight_name="pytorch_lora_weights.safetensors", use_auth_token=True) | |
| prompt = prompt | |
| negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured" | |
| image = load_image(image_in) | |
| #controlnet_conditioning_scale = 0.25 # 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) | |
| lora_scale= 0.9 | |
| images = pipe( | |
| prompt, | |
| negative_prompt=negative_prompt, | |
| image=image, | |
| controlnet_conditioning_scale=controlnet_conditioning_scale, | |
| guidance_scale = guidance_scale, | |
| num_inference_steps=50, | |
| generator=generator.manual_seed(seed), | |
| cross_attention_kwargs={"scale": lora_scale} | |
| ).images | |
| images[0].save(f"hug_lab.png") | |
| return f"hug_lab.png" | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| model_name = gr.Textbox(label="Model to use", placeholder="username/my_model") | |
| image_in = gr.Image(source="upload", type="filepath") | |
| prompt = gr.Textbox(label="Prompt") | |
| guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5, type="float") | |
| controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5, type="float") | |
| seed = gr.Slider(label="seed", minimum=0, maximum=500000, step=1, value=42) | |
| submit_btn = gr.Button("Submit") | |
| result = gr.Image(label="Result") | |
| submit_btn.click( | |
| fn = infer, | |
| inputs = [model_name, image_in, prompt, controlnet_conditioning_scale, guidance_scale, seed], | |
| outputs = [result] | |
| ) | |
| demo.queue().launch() | |