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import socketserver
socketserver.TCPServer.allow_reuse_address = True
import gradio as gr
import torch
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16)
# load the patched VQ-VAE
patched_decoder_ckpt = "checkpoint_000.pth"
if patched_decoder_ckpt is not None:
sd2 = torch.load(patched_decoder_ckpt)['ldm_decoder']
#print("patching keys for first_stage_model: ", sd2.keys())
msg = pipe.vae.load_state_dict(sd2, strict=False)
print(f"loaded LDM decoder state_dict with message\n{msg}")
print("you should check that the decoder keys are correctly matched")
pipe = pipe.to("cuda")
prompt = "sailing ship in storm by Rembrandt"
def generate(prompt):
output = pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")
return output.images[0]
iface = gr.Interface(fn=generate, inputs=[gr.Textbox(label="Prompt", value=prompt)], outputs=[gr.Image(type="pil")])
iface.launch(server_name="0.0.0.0")
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