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# Ref: https://huggingface.co./spaces/multimodalart/cosxl
import gradio as gr
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import spaces
import torch
import os
from huggingface_hub import hf_hub_download
model_id = "aipicasso/emi-2"
token=os.environ["TOKEN"]
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id,subfolder="scheduler",token=token)
pipe_normal = StableDiffusionXLPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.bfloat16,token=token)
negative_ti_file = hf_hub_download(repo_id="Aikimi/unaestheticXL_Negative_TI", filename="unaestheticXLv31.safetensors")
state_dict = load_file(negative_ti_file)
pipe.load_textual_inversion(state_dict["clip_g"], token="unaestheticXLv31", text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
pipe.load_textual_inversion(state_dict["clip_l"], token="unaestheticXLv31", text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
pipe_normal.to("cuda")
@spaces.GPU
def run_normal(prompt, negative_prompt="", guidance_scale=7.5, progress=gr.Progress(track_tqdm=True)):
return pipe_normal(prompt, negative_prompt="unaestheticXLv31"+negative_prompt, guidance_scale=guidance_scale, num_inference_steps=20).images[0]
css = '''
.gradio-container{
max-width: 768px !important;
margin: 0 auto;
}
'''
normal_examples = ["1girl, face, brown bob short hair, brown eyes, looking at viewer"]
with gr.Blocks(css=css) as demo:
gr.Markdown('''# Emi 2
Official demo for Emi 2
''')
with gr.Group():
with gr.Row():
prompt_normal = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt, e.g.: 1girl, face, brown bob short hair, brown eyes, looking at viewer")
button_normal = gr.Button("Generate", min_width=120)
output_normal = gr.Image(label="Your result image", interactive=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt_normal = gr.Textbox(label="Negative Prompt")
guidance_scale_normal = gr.Number(label="Guidance Scale", value=7)
gr.Examples(examples=normal_examples, fn=run_normal, inputs=[prompt_normal], outputs=[output_normal], cache_examples=True)
gr.on(
triggers=[
button_normal.click,
prompt_normal.submit
],
fn=run_normal,
inputs=[prompt_normal, negative_prompt_normal, guidance_scale_normal],
outputs=[output_normal],
)
if __name__ == "__main__":
demo.launch(share=True) |