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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| from diffusers.utils import numpy_to_pil | |
| from diffusers import ( | |
| T2IAdapter, | |
| StableDiffusionXLAdapterPipeline, | |
| AutoencoderKL, | |
| EulerAncestralDiscreteScheduler | |
| ) | |
| from controlnet_aux import PidiNetDetector | |
| # Global variable to store the pipeline | |
| pipe = None | |
| def load_pipe(): | |
| global pipe | |
| if pipe is None: | |
| model_id = "stabilityai/stable-diffusion-xl-base-1.0" | |
| adapter = T2IAdapter.from_pretrained( | |
| "Adapter/t2iadapter", | |
| subfolder="sketch_sdxl_1.0", | |
| torch_dtype=torch.float16, | |
| adapter_type="full_adapter_xl") | |
| euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| pipe = StableDiffusionXLAdapterPipeline.from_pretrained( | |
| model_id, | |
| adapter=adapter, | |
| vae=vae, | |
| scheduler=euler_a, | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| ).to("cuda") | |
| pipe.enable_xformers_memory_efficient_attention() | |
| def preprocess_image(uploaded_file): | |
| if uploaded_file is None: | |
| return None, "Please upload an image." | |
| img_upload = Image.open(uploaded_file) | |
| preprocessor = PidiNetDetector.from_pretrained("lllyasviel/Annotators").to("cuda") | |
| img_preprocessed = preprocessor( | |
| img_upload, | |
| detect_resolution=1024, | |
| image_resolution=1024, | |
| apply_filter=True).convert("L") | |
| return img_preprocessed, "" | |
| def generate(prompt, uploaded_file, prompt_addition, negative_prompt, num_images, num_steps, guidance_scale, adapter_conditioning_scale, adapter_conditioning_factor): | |
| global pipe | |
| load_pipe() # Ensure the model is loaded | |
| img_preprocessed, error_message = preprocess_image(uploaded_file) | |
| if error_message: | |
| return error_message | |
| num_images = int(num_images) | |
| params = { | |
| "image": img_preprocessed, | |
| "num_inference_steps": num_steps, | |
| "prompt": f"{prompt},{prompt_addition}" if prompt_addition.strip() else prompt, | |
| "negative_prompt": negative_prompt, | |
| "guidance_scale": guidance_scale, | |
| "adapter_conditioning_scale": adapter_conditioning_scale / 100, | |
| "adapter_conditioning_factor": adapter_conditioning_factor / 100, | |
| "num_images_per_prompt": num_images | |
| } | |
| generated_images = pipe(**params).images | |
| return generated_images # Returning PIL images directly | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(label="Prompt", value="a robot elephant", placeholder="Enter a description for the image you want to generate") | |
| prompt_addition = gr.Textbox(label="Prompt addition", value="in outer space, in the style of picasso, highly detailed") | |
| negative_prompt = gr.Textbox(label="Negative prompt", value="disfigured, wrong number of digits, cropped, low quality") | |
| num_images = gr.Slider(minimum=1, maximum=10, value=1, label="Number of images to generate") | |
| num_steps = gr.Slider(minimum=1, maximum=100, value=20, label="Number of steps") | |
| guidance_scale = gr.Slider(minimum=6, maximum=10, value=7, label="Guidance scale") | |
| adapter_conditioning_scale = gr.Slider(minimum=0, maximum=100, value=90, label="Adapter conditioning scale") | |
| adapter_conditioning_factor = gr.Slider(minimum=0, maximum=100, value=90, label="Adapter conditioning factor") | |
| uploaded_file = gr.File(label="Upload image", type='filepath') | |
| with gr.Column(): | |
| output_gallery = gr.Gallery(label="Generated images") | |
| generate_button = gr.Button("Generate") | |
| generate_button.click( | |
| generate, | |
| inputs=[prompt, uploaded_file, prompt_addition, negative_prompt, num_images, num_steps, guidance_scale, adapter_conditioning_scale, adapter_conditioning_factor], | |
| outputs=[output_gallery] | |
| ) | |
| demo.launch() | |