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import gradio as gr
import torch
from diffusers import StableDiffusionXLImg2ImgPipeline
from diffusers.utils import load_image
from PIL import Image
import requests
#from diffusers import DiffusionPipeline
'''
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe = pipe.to("cpu")
url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
def run_fn(img_url):
init_image = load_image(url).convert("RGB")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt, image=init_image).images
return image
'''
device = "cuda" if torch.cuda.is_available() else "cpu"
#pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
pipe = pipe.to(device)
def resize(value,img):
img = Image.open(requests.get(img, stream=True).raw)
img.save("tmp_im.png")
img = Image.open("tmp_im.png")
#img = img.resize((value,value))
return img
def infer(source_img, prompt, negative_prompt, guide, steps, seed, Strength):
#source_img = load_image(source_img).convert("RGB")
generator = torch.Generator(device).manual_seed(seed)
source_image = resize(768, source_img)
source_image.save('source.png')
image = pipe(prompt, negative_prompt=negative_prompt, image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps).images[0]
return image
gr.Interface(fn=infer, inputs=[gr.Textbox(), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), gr.Textbox(label='What you Do Not want the AI to generate.'),
gr.Slider(2, 15, value = 7, label = 'Guidance Scale'),
gr.Slider(1, 25, value = 10, step = 1, label = 'Number of Iterations'),
gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True),
gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)],
outputs='image').launch()
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