Update app.py

app.py

@@ -10,7 +10,7 @@ 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='
+base_url=f'https://omnibus-top-20-img-img-basic.hf.space/file='
 loaded_model=[]
 for i,model in enumerate(models):
     try:
@@ -26,117 +26,16 @@ 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
+    for i in range(cnt):
+        yield out_box,f"Working on {i} of {cnt}"
+        url = base_url+im_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
+    yield out_box,"Complete"
 
 
 css="""
@@ -150,36 +49,6 @@ 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():
@@ -199,9 +68,9 @@ with gr.Blocks(css=css) as app:
         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])
+    #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()