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import gradio as gr
import torch
import requests
from torchvision import transforms
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler, EulerDiscreteScheduler
model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def generate(inp):
torch.cuda.empty_cache()
print(f"Is CUDA available: {torch.cuda.is_available()}")
pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
#pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
#another comment
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to("cuda")
image = pipeline(inp, height=512, width=512).images[0]
return image
def run():
demo = gr.Interface(
fn=generate,
inputs=gr.inputs.Textbox(label="Prompt"),
outputs=gr.outputs.Image(type="pil"),
)
demo.launch(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
run() |