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import random | |
import matplotlib.pyplot as plt | |
from PIL import Image | |
import torch | |
from torch import autocast | |
from diffusers import StableDiffusionPipeline, DDIMScheduler | |
import gradio | |
from gradio.components import Textbox, Image | |
import torch | |
from torch import autocast | |
from diffusers import StableDiffusionPipeline, DDIMScheduler | |
pipe = StableDiffusionPipeline.from_pretrained("taltaf9133/finetuned-stable-diffusion-log", torch_dtype=torch.float32) #.to('cuda') | |
#pipe.enable_xformers_memory_efficient_attention() | |
prompt = "tv with sofa, realistic, hd, vivid" | |
negative_prompt = "bad anatomy, ugly, deformed, desfigured, distorted, blurry, low quality, low definition, lowres, out of frame, out of image, cropped, cut off, signature, watermark" | |
num_samples = 1 | |
guidance_scale = 7.5 | |
num_inference_steps = 5 | |
height = 512 | |
width = 512 | |
#seed = random.randint(0, 2147483647) | |
#print("Seed: {}".format(str(seed))) | |
#generator = torch.Generator(device='cuda').manual_seed(seed) | |
def predict(prompt, negative_prompt): | |
#with autocast("cuda"), torch.inference_mode(): | |
img = pipe( | |
prompt, | |
negative_prompt=negative_prompt, | |
height=height, width=width, | |
num_images_per_prompt=num_samples, | |
num_inference_steps=num_inference_steps, | |
guidance_scale=guidance_scale, | |
#generator=generator | |
).images[0] | |
return img | |
title = "Stable Diffusion Demo" | |
description = "Stable diffusion demo" | |
# Input from user | |
neg_p = "bad anatomy, ugly, deformed, desfigured, distorted, blurry, low quality, low definition, lowres, out of frame, out of image, cropped, cut off, signature, watermark" | |
in_prompt = gradio.inputs.Textbox(lines=5, placeholder=None, default="ldg with scn style", label='Enter prompt') | |
in_neg_prompt = gradio.inputs.Textbox(lines=5, placeholder=None, default=neg_p, label='Enter negative prompt') | |
# Output response | |
out_response = Image(label="Generated image:") | |
# Create the Gradio demo | |
demo = gradio.Interface(fn=predict, # mapping function from input to output | |
inputs=[in_prompt, in_neg_prompt], | |
outputs=gradio.Image(), | |
title=title, | |
description=description,) | |
# Launch the demo! | |
demo.launch(debug = True) | |