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| import gradio as gr | |
| from models import models | |
| from PIL import Image | |
| import requests | |
| import uuid | |
| import io | |
| import base64 | |
| import torch | |
| from diffusers import AutoPipelineForImage2Image | |
| from diffusers.utils import make_image_grid, load_image | |
| base_url=f'https://omnibus-top-20-img-img-basic.hf.space/file=' | |
| loaded_model=[] | |
| for i,model in enumerate(models): | |
| try: | |
| loaded_model.append(gr.load(f'models/{model}')) | |
| except Exception as e: | |
| print(e) | |
| pass | |
| print (loaded_model) | |
| pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None, variant="fp16", use_safetensors=True).to("cpu") | |
| def load_model(model_drop): | |
| pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32, use_safetensors=True) | |
| def run_dif(prompt,im_path,model_drop,cnt,strength,guidance,infer): | |
| out_box=[] | |
| for i in range(int(cnt)): | |
| yield out_box,f"Working on {i} of {int(cnt)}" | |
| url = base_url+im_path | |
| print(url) | |
| init_image=load_image(url) | |
| #image = pipeline(prompt, image=init_image, strength=0.8,guidance_scale=8.0,negative_prompt=negative_prompt,num_inference_steps=50).images[0] | |
| image = pipeline(prompt, image=init_image, strength=float(strength),guidance_scale=float(guidance),num_inference_steps=int(infer)).images[0] | |
| out_box.append(image) | |
| yield out_box,"Complete" | |
| css=""" | |
| .grid_class{ | |
| display:flex; | |
| height:100%; | |
| } | |
| .img_class{ | |
| min-width:200px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as app: | |
| with gr.Row(): | |
| with gr.Column(): | |
| inp=gr.Textbox(label="Prompt") | |
| strength=gr.Slider(label="Strength",minimum=0,maximum=1,step=0.1,value=0.2) | |
| guidance=gr.Slider(label="Guidance",minimum=0,maximum=10,step=0.1,value=8.0) | |
| infer=gr.Slider(label="Inference Steps",minimum=0,maximum=50,step=1,value=10) | |
| with gr.Row(): | |
| btn=gr.Button() | |
| stop_btn=gr.Button("Stop") | |
| with gr.Column(): | |
| inp_im=gr.Image(type='filepath') | |
| im_btn=gr.Button("Image Grid") | |
| with gr.Row(): | |
| model_drop=gr.Dropdown(label="Models", choices=models, type='index', value=models[0]) | |
| cnt = gr.Number(value=1) | |
| out_html=gr.HTML() | |
| outp=gr.Gallery(columns=10) | |
| #fingal=gr.Gallery(columns=10) | |
| #im_list=gr.Textbox() | |
| #im_btn.click(load_im,inp_im,[outp,im_list]) | |
| go_btn = btn.click(run_dif,[inp,inp_im,model_drop,cnt,strength,guidance,infer],[outp,out_html]) | |
| stop_btn.click(None,None,None,cancels=[go_btn]) | |
| app.queue().launch() |