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Running
on
Zero
Running
on
Zero
import spaces | |
import gradio as gr | |
import torch | |
import modin.pandas as pd | |
import numpy as np | |
from diffusers import DiffusionPipeline, DPMSolverSinglestepScheduler, ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL | |
import requests | |
import cv2 | |
pipe = DiffusionPipeline.from_pretrained("mann-e/Mann-E_Dreams", torch_dtype=torch.float16).to("cuda") | |
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True) | |
pipe.enable_xformers_memory_efficient_attention() | |
pipe.enable_vae_slicing() | |
torch.cuda.empty_cache() | |
def genie (prompt, negative_prompt, width, height, steps, seed): | |
generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed) | |
int_image = pipe(prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, generator=generator, num_inference_steps=steps, guidance_scale=3.0).images[0] | |
return int_image | |
gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 75 Token Limit.'), | |
gr.Textbox(label='What you DO NOT want the AI to generate. 75 Token Limit.'), | |
gr.Slider(576, maximum=1280, value=768, step=16, label='Width (can go up to 1280, but for square images maximum is 1024x1024)'), | |
gr.Slider(576, maximum=1280, value=768, step=16, label='Height (can go up to 1280, but for square images maximum is 1024x1024)'), | |
gr.Slider(1, maximum=8, value=6, step=1, label='Number of Iterations'), | |
gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=True, label="Seed"), | |
], | |
outputs='image', | |
title="Mann-E Dreams", | |
description="Mann-E Dreams <br><br><b>WARNING: This model is capable of producing NSFW (Softcore) images.</b>", | |
article = "").launch(debug=True, max_threads=80, show_error=True) | |