app.py

import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from diffusers import EulerAncestralDiscreteScheduler
from compel import Compel, ReturnedEmbeddingsType
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained("John6666/mala-anime-mix-nsfw-pony-xl-v6-sdxl", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained("John6666/mala-anime-mix-nsfw-pony-xl-v6-sdxl", use_safetensors=True)
    pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024



pipe.safety_checker = None
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

compel = Compel(
  tokenizer=[pipe.tokenizer, pipe.tokenizer_2] ,
  text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
  returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
  requires_pooled=[False, True]
)

pipe.load_lora_weights("xenov2/pony", weight_name="style_cogecha_pony_1.safetensors", adapter_name="chochega")

def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_weight):

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)

    conditioning, pooled = compel(prompt)
    negative_conditioning, negative_pooled = compel(negative_prompt)

    [conditioning, negative_conditioning] = compel.pad_conditioning_tensors_to_same_length([conditioning, negative_conditioning])

    
    image = pipe(
        # prompt = prompt, 
        # negative_prompt = negative_prompt,
        prompt_embeds=conditioning,
        pooled_prompt_embeds=pooled,
        negative_propmt_embeds=negative_conditioning,
        negative_pooled_prompt_embeds=negative_pooled,
        guidance_scale = guidance_scale, 
        cross_attention_kwargs={"scale": lora_weight},
        num_inference_steps = num_inference_steps, 
        width = width, 
        height = height,
        generator = generator
    ).images[0] 
    
    return image

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Text-to-Image Gradio Template
        Currently running on {power_device}.
        """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            lora_weight = gr.Slider(
                label="Lora weight",
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=0.0,
            )
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.0,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=80,
                    step=1,
                    value=20,
                )
        
        gr.Examples(
            examples = examples,
            inputs = [prompt]
        )

    run_button.click(
        fn = infer,
        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_weight],
        outputs = [result]
    )

demo.queue().launch()