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from diffusers import StableDiffusionXLPipeline | |
import torch | |
from transformers.pipelines.image_to_text import Image | |
import gradio as gr | |
pipe = StableDiffusionXLPipeline.from_pretrained( | |
"segmind/SSD-1B", | |
torch_dtype = torch.float32, | |
use_safetensors=True, | |
# for cuda -- variant = "fp16", | |
) | |
#pipe.to("cuda") | |
prompt = "astronaut riding a green horse" | |
neg_prompt = "ugly, blurry, poor quality" | |
def generate_image(prompt, neg_prompt): | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=neg_prompt | |
).images[0] | |
return image | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt :", | |
container=False | |
) | |
neg_prompt = gr.Text( | |
label="Negative Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your negative prompt", | |
container=False | |
) | |
iface = gr.Interface( | |
fn=generate_image, | |
inputs=[prompt, neg_prompt], | |
outputs="image", | |
title="Text to Image Generation", | |
examples=[ | |
["a painting of a cute cat sitting on a chair","ugly, blurry"], | |
["an assortment riding a horse on mars","poorly down"] | |
], | |
allow_flagging=False | |
) | |
iface.launch(share=True) | |
#image = pipe(prompt=prompt, negative_prompt = neg_prompt).images[0] | |
# print(image) | |