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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 cv2 | |
| import numpy | |
| from transforms import RGBTransform | |
| import random | |
| #import torch | |
| #from diffusers import AutoPipelineForImage2Image | |
| #from diffusers.utils import make_image_grid, load_image | |
| import uuid | |
| base_url=f'https://omnibus-top-20-img-img-tint.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") | |
| #pipeline.unet = torch.compile(pipeline.unet) | |
| grid_wide=10 | |
| def get_concat_h_cut_old(in1, in2, pos_x,pos_y): | |
| print(in1) | |
| print(in2) | |
| im1=Image.open(in1) | |
| im2=Image.open(in2) | |
| pos_x=int(pos_x) | |
| pos_y=int(pos_y) | |
| print(f'pos_x:: {pos_x}') | |
| print(f'pos_y:: {pos_y}') | |
| #im1=in1 | |
| #im2=in2 | |
| #dst = Image.new('RGB', (im1.width + im2.width, min(im1.height, im2.height))) | |
| width_tog=1 | |
| height_tog=0 | |
| width_var = im2.width | |
| height_var=0 | |
| if pos_y > 0 and pos_x==0: | |
| height_var = im2.height | |
| if pos_y > 0: | |
| width_var = 0 | |
| dst = Image.new('RGB', (im1.width + width_var, im1.height+height_var)) | |
| dst.paste(im1, (0, 0)) | |
| dst.paste(im2, (im1.width, im2.height *pos_y)) | |
| return dst | |
| def get_concat_h_cut(in1, in2): | |
| print(in1) | |
| print(in2) | |
| im1=Image.open(in1) | |
| im2=Image.open(in2) | |
| dst = Image.new('RGB', (im1.width + im2.width, min(im1.height,im2.height))) | |
| dst.paste(im1, (0, 0)) | |
| dst.paste(im2, (im1.width,0)) | |
| return dst | |
| def get_concat_v_cut(in1, in2): | |
| print(in1) | |
| print(in2) | |
| im1=Image.open(in1) | |
| im2=Image.open(in2) | |
| #im1=in1 | |
| #im2=in2 | |
| dst = Image.new( | |
| 'RGB', (min(im1.width, im2.width), im1.height + im2.height)) | |
| dst.paste(im1, (0, 0)) | |
| dst.paste(im2, (0, im1.height)) | |
| return dst | |
| def load_model(model_drop): | |
| pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32, use_safetensors=True) | |
| def run_dif_color(out_prompt,im_path,model_drop,tint,im_height,im_width): | |
| uid=uuid.uuid4() | |
| model=loaded_model[int(model_drop)] | |
| p_seed="" | |
| out_box=[] | |
| big_box=[] | |
| out_html="" | |
| im_height=int(im_height) | |
| im_width=int(im_width) | |
| #for i,ea in enumerate(im_path.root): | |
| cnt = 0 | |
| adj_h=0 | |
| for hh in range(int(im_height/grid_wide)): | |
| for b in range(int(im_width/grid_wide)): | |
| print(f'root::{im_path.root[cnt]}') | |
| #print(f'ea:: {ea}') | |
| #print(f'impath:: {im_path.path}') | |
| url = base_url+im_path.root[cnt].image.path | |
| print(url) | |
| myimg = cv2.imread(im_path.root[cnt].image.path) | |
| avg_color_per_row = numpy.average(myimg, axis=0) | |
| avg_color = numpy.average(avg_color_per_row, axis=0) | |
| r,g,b= avg_color | |
| color = (int(r),int(g),int(b)) | |
| print (color) | |
| rand=random.randint(1,500) | |
| for i in range(rand): | |
| p_seed+=" " | |
| try: | |
| #model=gr.load(f'models/{model[int(model_drop)]}') | |
| out_img=model(out_prompt+p_seed) | |
| #print(out_img) | |
| raw=Image.open(out_img) | |
| raw=raw.convert('RGB') | |
| colorize = RGBTransform().mix_with(color,factor=float(tint)).applied_to(raw) | |
| print (colorize) | |
| colorize.save(f'tmp-{cnt}-{uid}.png') | |
| #out_box.append(f'tmp-{uid}.png') | |
| out_box.append(f'tmp-{cnt}-{uid}.png') | |
| print(f'out_box:: {out_box}') | |
| if out_box: | |
| if len(out_box)>1: | |
| #im_roll = get_concat_v_cut(f'{out_box[0]}',f'{out_box[1]}') | |
| #im_roll.save(f'comb-{uid}-tmp.png') | |
| #for i in range(2,len(out_box)): | |
| #im_roll = get_concat_h_cut(f'comb-{uid}-tmp.png',f'{out_box[cnt]}') | |
| im_roll = get_concat_h_cut(f'comb-{uid}-{hh}-tmp.png',f'tmp-{cnt}-{uid}.png') | |
| im_roll.save(f'comb-{uid}-{hh}-tmp.png') | |
| out = f'comb-{uid}-{hh}-tmp.png' | |
| yield gr.Image(out),out_html | |
| else: | |
| tmp_im = Image.open(out_box[0]) | |
| #tmp_im = out_box[0] | |
| tmp_im.save(f'comb-{uid}-{hh}-tmp.png') | |
| out = f'comb-{uid}-{hh}-tmp.png' | |
| yield gr.Image(out),out_html | |
| except Exception as e: | |
| print(e) | |
| out_html=str(e) | |
| pass | |
| cnt+=1 | |
| big_box.append(f'comb-{uid}-{hh}-tmp.png') | |
| if big_box: | |
| print(f'big_box:: {big_box}') | |
| if len(big_box)>1: | |
| im_roll2 = get_concat_v_cut(big_box[hh],big_box[hh-1]) | |
| im_roll2.save(f'comb-{uid}-{hh}-out.png') | |
| out=[] | |
| yield gr.Image(f'comb-{uid}-{hh}-out.png'),out_html | |
| adj_h+=1 | |
| yield gr.Image(out),out_html | |
| def run_dif(prompt,im_path,model_drop,cnt,strength,guidance,infer,im_height,im_width): | |
| uid=uuid.uuid4() | |
| 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=[] | |
| im_height=int(im_height) | |
| im_width=int(im_width) | |
| for i,ea in enumerate(im_path.root): | |
| for hh in range(int(im_height/grid_wide)): | |
| for b in range(int(im_width/grid_wide)): | |
| 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) | |
| if out_box: | |
| if len(out_box)>1: | |
| im_roll = get_concat_v_cut(f'{out_box[0]}',f'{out_box[1]}') | |
| im_roll.save(f'comb-{uid}-tmp.png') | |
| for i in range(2,len(out_box)): | |
| im_roll = get_concat_v_cut(f'comb-{uid}-tmp.png',f'{out_box[i]}') | |
| im_roll.save(f'comb-{uid}-tmp.png') | |
| out = f'comb-{uid}-tmp.png' | |
| else: | |
| #tmp_im = Image.open(out_box[0]) | |
| tmp_im = out_box[0] | |
| tmp_im.save(f'comb-{uid}-tmp.png') | |
| out = f'comb-{uid}-tmp.png' | |
| yield out,"" | |
| 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/grid_wide) | |
| 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(grid_wide): | |
| 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,height,width | |
| 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) | |
| tint = gr.Slider(label="Tint Strength", minimum=0, maximum=1, step=0.01, value=0.30) | |
| 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=grid_wide) | |
| #fingal=gr.Gallery(columns=grid_wide) | |
| fin=gr.Image() | |
| im_height=gr.Number() | |
| im_width=gr.Number() | |
| im_list=gr.Textbox(visible=False) | |
| im_btn.click(load_im,inp_im,[outp,im_list,im_height,im_width]) | |
| go_btn=btn.click(run_dif_color,[inp,outp,model_drop,tint,im_height,im_width],[fin,out_html]) | |
| #go_btn = btn.click(run_dif_color,[inp,outp,model_drop,cnt,strength,guidance,infer,im_height,im_width],[fin,out_html]) | |
| stop_btn.click(None,None,None,cancels=[go_btn]) | |
| app.queue().launch() |