import gradio as gr import torch import numpy as np import modin.pandas as pd from PIL import Image from huggingface_hub import hf_hub_download from diffusers import StableDiffusion3Pipeline device = 'cuda' #if torch.cuda.is_available() else 'cpu' torch.cuda.max_memory_allocated(device=device) torch.cuda.empty_cache() SD3 = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16) SD3.enable_xformers_memory_efficient_attention() SD3 = SD3.to(device) def genie (Prompt, negative_prompt, height, width, scale, steps, d_steps, seed): generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed) torch.cuda.empty_cache() image=SD3( prompt=Prompt, height=height, width=width, negative_prompt=negative_prompt, guidance_scale=scale, num_images_per_prompt=1, num_inference_steps=steps).images[0] return image gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'), gr.Slider(512, 1536, 1024, step=128, label='Height'), gr.Slider(512, 1536, 1024, step=128, label='Width'), gr.Slider(.5, maximum=10, value=3, step=.25, label='Guidance Scale'), gr.Slider(10, maximum=40, value=20, step=5, label='Number of Prior Iterations'), gr.Slider(5, maximum=20, value=10, step=5, label="Number of Decoder Iterations"), gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random')], outputs=gr.Image(label='Generated Image'), title="Manju Dream Booth V2.2 with Stable-Cascade - GPU", description="

Warning: This Demo is capable of producing NSFW content.", article = "If You Enjoyed this Demo and would like to Donate, you can send any amount to any of these Wallets.

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Code Monkey: Manjushri").launch(debug=True)