hf_demo.py

# Authors: Hui Ren (rhfeiyang.github.io)
import spaces
import os
import gradio as gr
from diffusers import DiffusionPipeline
import matplotlib.pyplot as plt
import torch
from PIL import Image



device = "cuda" if torch.cuda.is_available() else "cpu"
dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16
print(f"Using {device} device, dtype={dtype}")
pipe = DiffusionPipeline.from_pretrained("rhfeiyang/art-free-diffusion-v1",
                                         torch_dtype=dtype).to(device)

from inference import get_lora_network, inference, get_validation_dataloader
lora_map = {
    "None": "None",
    "Andre Derain": "andre-derain_subset1",
    "Vincent van Gogh": "van_gogh_subset1",
    "Andy Warhol": "andy_subset1",
    "Walter Battiss": "walter-battiss_subset2",
    "Camille Corot": "camille-corot_subset1",
    "Claude Monet": "monet_subset2",
    "Pablo Picasso": "picasso_subset1",
    "Jackson Pollock": "jackson-pollock_subset1",
    "Gerhard Richter": "gerhard-richter_subset1",
    "M.C. Escher": "m.c.-escher_subset1",
    "Albert Gleizes": "albert-gleizes_subset1",
    "Hokusai": "katsushika-hokusai_subset1",
    "Wassily Kandinsky": "kandinsky_subset1",
    "Gustav Klimt": "klimt_subset3",
    "Roy Lichtenstein": "roy-lichtenstein_subset1",
    "Henri Matisse": "henri-matisse_subset1",
    "Joan Miro": "joan-miro_subset2",
}
@spaces.GPU
def demo_inference_gen(adapter_choice:str, prompt:str, samples:int=1,seed:int=0, steps=50, guidance_scale=7.5):
    adapter_path = lora_map[adapter_choice]
    if adapter_path not in [None, "None"]:
        adapter_path = f"data/Art_adapters/{adapter_path}/adapter_alpha1.0_rank1_all_up_1000steps.pt"

    prompts = [prompt]*samples
    infer_loader = get_validation_dataloader(prompts,num_workers=0)
    network = get_lora_network(pipe.unet, adapter_path)["network"]
    pred_images = inference(network, pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet, pipe.scheduler, infer_loader,
                            height=512, width=512, scales=[1.0],
                            save_dir=None, seed=seed,steps=steps, guidance_scale=guidance_scale,
                            start_noise=-1, show=False, style_prompt="sks art", no_load=True,
                            from_scratch=True, device=device)[0][1.0]
    return pred_images
@spaces.GPU
def demo_inference_stylization(adapter_path:str, prompts:list, image:list, start_noise=800,seed:int=0):
    infer_loader = get_validation_dataloader(prompts, image,num_workers=0)
    network = get_lora_network(pipe.unet, adapter_path,"all_up")["network"]
    pred_images = inference(network, pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet, pipe.scheduler, infer_loader,
                            height=512, width=512, scales=[0.,1.],
                            save_dir=None, seed=seed,steps=20, guidance_scale=7.5,
                            start_noise=start_noise, show=True, style_prompt="sks art", no_load=True,
                            from_scratch=False, device=device)
    return pred_images

# def infer(prompt, samples, steps, scale, seed):
#     generator = torch.Generator(device=device).manual_seed(seed)
#     images_list = pipe(  # type: ignore
#         [prompt] * samples,
#         num_inference_steps=steps,
#         guidance_scale=scale,
#         generator=generator,
#     )
#     images = []
#     safe_image = Image.open(r"data/unsafe.png")
#     print(images_list)
#     for i, image in enumerate(images_list["images"]):  # type: ignore
#         if images_list["nsfw_content_detected"][i]:  # type: ignore
#             images.append(safe_image)
#         else:
#             images.append(image)
#     return images




block = gr.Blocks()
# Direct infer
with block:
    with gr.Group():
        gr.Markdown(" # Art-Free Diffusion Demo")
        with gr.Row():
            text = gr.Textbox(
                label="Enter your prompt",
                max_lines=2,
                placeholder="Enter your prompt",
                container=False,
                value="Park with cherry blossom trees, picnicker’s and a clear blue pond.",
            )



            btn = gr.Button("Run", scale=0)
        gallery = gr.Gallery(
            label="Generated images",
            show_label=False,
            elem_id="gallery",
            columns=[2],
        )

        advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")

        with gr.Row(elem_id="advanced-options"):
            adapter_choice = gr.Dropdown(
                label="Choose adapter",
                choices=["None", "Andre Derain","Vincent van Gogh","Andy Warhol", "Walter Battiss",
                         "Camille Corot", "Claude Monet", "Pablo Picasso",
                         "Jackson Pollock", "Gerhard Richter", "M.C. Escher",
                         "Albert Gleizes", "Hokusai", "Wassily Kandinsky", "Gustav Klimt", "Roy Lichtenstein",
                         "Henri Matisse", "Joan Miro"
                         ],
                value="None"
            )
            # print(adapter_choice[0])
            # lora_path = lora_map[adapter_choice.value]
            # if lora_path is not None:
            #     lora_path = f"data/Art_adapters/{lora_path}/adapter_alpha1.0_rank1_all_up_1000steps.pt"

            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=1, step=1)
            steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=20, step=1)
            scale = gr.Slider(
                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
            )
            print(scale)
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=2147483647,
                step=1,
                randomize=True,
            )

        gr.on([text.submit, btn.click], demo_inference_gen, inputs=[adapter_choice, text, samples, seed, steps, scale], outputs=gallery)
        advanced_button.click(
            None,
            [],
            text,
        )



block.launch()