app.py

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch
#from controlnet_aux import OpenposeDetector
#from diffusers.utils import load_image
import gradio as gr


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

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

#pipe = StableDiffusionXLPipeline.from_pretrained(
#    model_base, vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
#)

pipe = StableDiffusionXLPipeline.from_single_file(
    "https://huggingface.co/Krebzonide/Colossus_Project_XL/blob/main/colossusProjectXLSFW_v202BakedVAE.safetensors",
    torch_dtype = torch.float16,
    variant = "fp16",
    vae = vae,
    use_safetensors = True,
    scheduler_type = "ddim"
)
pipe = pipe.to("cuda")

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=1,
        #generator=torch.manual_seed(97),
    ).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=1100)
        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)