app.py

from diffusers import AutoencoderKL, StableDiffusionXLControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
from controlnet_aux import OpenposeDetector
from diffusers.utils import load_image
import gradio as gr

#sd1.5 bases
#model_base = "SG161222/Realistic_Vision_V5.1_noVAE" #fantasy people
#model_base = "Justin-Choo/epiCRealism-Natural_Sin_RC1_VAE" #cartoon people
#model_base = "Lykon/DreamShaper" #unrealistic people
#model_base = "runwayml/stable-diffusion-v1-5" #base
#model_base = "Krebzonide/LazyMixPlus" #nsfw people
#model_base = "Krebzonide/Humans" #boring people
#model_base = "aufahr/unofficial_aom3" #anime people

#lora_model_path = "Krebzonide/LoRA-CH-0" #mecjh - Corey H, traind on epiCRealism
#lora_model_path = "Krebzonide/LoRA-CH-1" #mecjh - Corey H, traind on epiCRealism
#lora_model_path = "Krebzonide/LoRA-EM1" #exgfem - Emily M, trained on LizyMixPlus
#lora_model_path = "Krebzonide/LoRA-EM-2-0" #exgfem - Emily M, trained on Humans
#lora_model_path = "Krebzonide/LoRA-YX1" #uwspyx - Professor Xing, trained on Realistic_Vision

#pipe = StableDiffusionPipeline.from_pretrained(model_base, torch_dtype=torch.float16, use_safetensors=True, use_auth_token="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")
#pipe.unet.load_attn_procs(lora_model_path, use_auth_token="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")
#pipe.to("cuda")


model_base = "stabilityai/stable-diffusion-xl-base-1.0"

openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")

#image is a random guy. openpose_image is the pose of that guy.
image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
)
openpose_image = openpose(image)

controlnet = ControlNetModel.from_pretrained("thibaud/controlnet-openpose-sdxl-1.0", torch_dtype=torch.float16)

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    model_base, controlnet=controlnet, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

css = """
.btn-green {
  background-image: linear-gradient(to bottom right, #6dd178, #00a613) !important;
  border-color: #22c55e !important;
  color: #166534 !important;
}
.btn-green:hover {
  background-image: linear-gradient(to bottom right, #6dd178, #6dd178) !important;
}
"""

def generate(prompt, neg_prompt, samp_steps, guide_scale, lora_scale, progress=gr.Progress(track_tqdm=True)):
    images = pipe(
        prompt,
        negative_prompt=neg_prompt,
        num_inference_steps=samp_steps,
        guidance_scale=guide_scale,
        #cross_attention_kwargs={"scale": lora_scale},
        num_images_per_prompt=4,
        #generator=torch.manual_seed(97),
        image=openpose_image.resize((1024, 1024)), #THIS IS THE OPENPOSE IMAGE
    ).images
    return [(img, f"Image {i+1}") for i, img in enumerate(images)]
        

with gr.Blocks(css=css) as demo:
    with gr.Column():
        prompt = gr.Textbox(label="Prompt")
        negative_prompt = gr.Textbox(label="Negative Prompt", value="lowres, bad anatomy, bad hands, cropped, worst quality, disfigured, deformed, extra limbs, asian, filter, render")
        submit_btn = gr.Button("Generate", elem_classes="btn-green")
        gallery = gr.Gallery(label="Generated images", height=700)
        with gr.Row():
            samp_steps = gr.Slider(1, 100, value=25, step=1, label="Sampling steps")
            guide_scale = gr.Slider(1, 10, value=6, step=0.5, label="Guidance scale")
            lora_scale = gr.Slider(0, 1, value=0.5, step=0.01, label="LoRA power")

    submit_btn.click(generate, [prompt, negative_prompt, samp_steps, guide_scale, lora_scale], [gallery], queue=True)

demo.queue(1)
demo.launch(debug=True)