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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.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", torch_dtype=torch.float32, use_safetensors=True) | |
| 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): | |
| print(f'im_path:: {im_path}') | |
| print(f'im_path0:: {im_path.root[0]}') | |
| print(f'im_path0.image.path:: {im_path.root[0].image.path}') | |
| out_box=[] | |
| for i,ea in enumerate(im_path.root): | |
| print(f'root::{im_path.root[i]}') | |
| #print(f'ea:: {ea}') | |
| #print(f'impath:: {im_path.path}') | |
| url = base_url+im_path.root[i].image.path | |
| print(url) | |
| #init_image = load_image(url) | |
| init_image=load_image(url) | |
| #prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" | |
| # pass prompt and image to pipeline | |
| #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] | |
| #make_image_grid([init_image, image], rows=1, cols=2) | |
| out_box.append(image) | |
| yield out_box,"" | |
| def run_dif_old(out_prompt,model_drop,cnt): | |
| p_seed="" | |
| out_box=[] | |
| out_html="" | |
| #for i,ea in enumerate(loaded_model): | |
| for i in range(int(cnt)): | |
| p_seed+=" " | |
| try: | |
| model=loaded_model[int(model_drop)] | |
| out_img=model(out_prompt+p_seed) | |
| print(out_img) | |
| out_box.append(out_img) | |
| except Exception as e: | |
| print(e) | |
| out_html=str(e) | |
| pass | |
| yield out_box,out_html | |
| def run_dif_og(out_prompt,model_drop,cnt): | |
| out_box=[] | |
| out_html="" | |
| #for i,ea in enumerate(loaded_model): | |
| for i in range(cnt): | |
| try: | |
| #print (ea) | |
| model=loaded_model[int(model_drop)] | |
| out_img=model(out_prompt) | |
| print(out_img) | |
| url=f'https://omnibus-top-20.hf.space/file={out_img}' | |
| print(url) | |
| uid = uuid.uuid4() | |
| #urllib.request.urlretrieve(image, 'tmp.png') | |
| #out=Image.open('tmp.png') | |
| r = requests.get(url, stream=True) | |
| if r.status_code == 200: | |
| img_buffer = io.BytesIO(r.content) | |
| print (f'bytes:: {io.BytesIO(r.content)}') | |
| str_equivalent_image = base64.b64encode(img_buffer.getvalue()).decode() | |
| img_tag = "<img src='data:image/png;base64," + str_equivalent_image + "'/>" | |
| out_html+=f"<div class='img_class'><a href='https://huggingface.co/models/{models[i]}'>{models[i]}</a><br>"+img_tag+"</div>" | |
| out = Image.open(io.BytesIO(r.content)) | |
| out_box.append(out) | |
| html_out = "<div class='grid_class'>"+out_html+"</div>" | |
| yield out_box,html_out | |
| except Exception as e: | |
| out_html+=str(e) | |
| html_out = "<div class='grid_class'>"+out_html+"</div>" | |
| yield out_box,html_out | |
| def thread_dif(out_prompt,mod): | |
| out_box=[] | |
| out_html="" | |
| #for i,ea in enumerate(loaded_model): | |
| try: | |
| print (ea) | |
| model=loaded_model[int(mod)] | |
| out_img=model(out_prompt) | |
| print(out_img) | |
| url=f'https://omnibus-top-20.hf.space/file={out_img}' | |
| print(url) | |
| uid = uuid.uuid4() | |
| #urllib.request.urlretrieve(image, 'tmp.png') | |
| #out=Image.open('tmp.png') | |
| r = requests.get(url, stream=True) | |
| if r.status_code == 200: | |
| img_buffer = io.BytesIO(r.content) | |
| print (f'bytes:: {io.BytesIO(r.content)}') | |
| str_equivalent_image = base64.b64encode(img_buffer.getvalue()).decode() | |
| img_tag = "<img src='data:image/png;base64," + str_equivalent_image + "'/>" | |
| #out_html+=f"<div class='img_class'><a href='https://huggingface.co/models/{models[i]}'>{models[i]}</a><br>"+img_tag+"</div>" | |
| out = Image.open(io.BytesIO(r.content)) | |
| out_box.append(out) | |
| else: | |
| out_html=r.status_code | |
| html_out = "<div class='grid_class'>"+out_html+"</div>" | |
| return out_box,html_out | |
| except Exception as e: | |
| out_html=str(e) | |
| #out_html+=str(e) | |
| html_out = "<div class='grid_class'>"+out_html+"</div>" | |
| return out_box,html_out | |
| css=""" | |
| .grid_class{ | |
| display:flex; | |
| height:100%; | |
| } | |
| .img_class{ | |
| min-width:200px; | |
| } | |
| """ | |
| def load_im(img): | |
| im_box=[] | |
| im = Image.open(img) | |
| width, height = im.size | |
| new_w=int(width/10) | |
| new_h=new_w | |
| w=0 | |
| h=0 | |
| newsize=(512,512) | |
| for i in range(int(height/new_h)): | |
| print(i) | |
| for b in range(10): | |
| print(b) | |
| # Setting the points for cropped image | |
| left = w | |
| top = h | |
| right = left+new_w | |
| bottom = top+new_h | |
| # Cropped image of above dimension | |
| # (It will not change original image) | |
| im1 = im.crop((left, top, right, bottom)) | |
| im1 = im1.resize(newsize) | |
| im_box.append(im1) | |
| w+=new_w | |
| yield im_box,[] | |
| h+=new_h | |
| w=0 | |
| yield im_box,im_box | |
| 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,outp,model_drop,cnt,strength,guidance,infer],[fingal,out_html]) | |
| stop_btn.click(None,None,None,cancels=[go_btn]) | |
| app.queue().launch() |