Update app.py

app.py

@@ -119,9 +119,7 @@ def xpath_finder(str,pattern):
     except:
         return ""
 
-@gpu(string(string,string),device=True,inline=True)
 def translate(text,lang):
-
     if text == None or lang == None:
         return ""       
     text = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', text)).lower().strip()
@@ -157,13 +155,12 @@ def translate(text,lang):
     print(ret)
     return ret
     
-@gpu(string(int),device=True,inline=True)
 def generate_random_string(length):
     characters = str(string.ascii_letters + string.digits)
     return ''.join(random.choice(characters) for _ in range(length))
     
-@gpu(void(),device=True,inline=True)
-def Piper():
+@gpu(void())
+def calc():
 
     global last_motion
     global ip_loaded
@@ -171,79 +168,72 @@ def Piper():
 
     x = grid(1)
 
-    if last_motion != pinp["motion"]:
+    if last_motion != inp["motion"]:
         pipe.unload_lora_weights()
-        if pinp["motion"] != "":
-            pipe.load_lora_weights(pinp["motion"], adapter_name="motion")
+        if inp["motion"] != "":
+            pipe.load_lora_weights(inp["motion"], adapter_name="motion")
             pipe.fuse_lora()
             pipe.set_adapters(["motion"], [0.7])
-        last_motion = pinp["motion"]
+        last_motion = inp["motion"]
 
     pipe.to(device,dtype)
 
-    if pinp["negative"]=="":
+    if inp["negative"]=="":
         out[x] = pipe(
-            prompt=pinp["positive"],
+            prompt=inp["positive"],
             height=height,
             width=width,
-            ip_adapter_image=pinp["image"].convert("RGB").resize((width,height)),
+            ip_adapter_image=inp["image"].convert("RGB").resize((width,height)),
             num_inference_steps=step,
             guidance_scale=accu,
             num_frames=(fps*time)
         )
     
     out[x] = pipe(
-        prompt=pinp["positive"],
-        negative_prompt=pinp["negative"],
+        prompt=inp["positive"],
+        negative_prompt=inp["negative"],
         height=height,
         width=width,
-        ip_adapter_image=pinp["image"].convert("RGB").resize((width,height)),
+        ip_adapter_image=inp["image"].convert("RGB").resize((width,height)),
         num_inference_steps=step,
         guidance_scale=accu,
         num_frames=(fps*time)
     )
 
-@gpu(void(),device=True,inline=True)
-def infer():
-    global pinp    
+def handle(*args):
+    global inp
     global out
-    
+
+    inp = args
+
     out = [""]
     out.remove("")
-    
-    p1 = str(pm["p"])
-    
-    neg = str(pm["n"])
-    if neg != "":
-        neg = f"{neg} where in the image"
 
-    _do = ['photographed', 'realistic', 'dynamic poze', 'deep field', 'reasonable', "natural", 'rough', 'best quality', 'focused', "highly detailed"]
-    if p1 != "":
-        _do.append(f"a new {p1} content in the image")
-    posi = ", ".join(_do)
+    inp[1] = translate(inp[1],"english")
+    inp[2] = translate(inp[2],"english")
 
-    if pm["i"] == None:
+    if inp[0] == None:
         return None
 
-    pinp={"image":pm["i"],"positive":posi,"negative":neg,"motion":pm["m"]}
+    if inp[2] != "":
+        inp[2] = f"{inp[2]} where in the image"
 
+    _do = ['photographed', 'realistic', 'dynamic poze', 'deep field', 'reasonable', "natural", 'rough', 'best quality', 'focused', "highly detailed"]
+    if inp[1] != "":
+        _do.append(f"a new {inp[1]} content in the image")
+    inp[1] = ", ".join(_do)
+    
     ln = len(result)
-    Piper[ln,32]()
+
+    calc[ln,32]()
+
     for i in range(ln):
         name = generate_random_string[1,32](12)+".png"
         export_to_gif(out[i].frames[0],name,fps=fps)
         out[i] = name
-
-@cpu(string[:](),cache=True,parallel=True)
-def handle():
-    global pm
-    p1_en = translate[1,32](p1,"english")
-    p2_en = translate[1,32](p2,"english")
-    pm = {"p":p1_en,"n":p2_en,"m":m,"i":i}
-    infer[1,32]()
+    
     return out
 
-@gpu(void(),device=True,inline=True)
 def ui():
     with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
         with gr.Column(elem_id="col-container"):
@@ -299,7 +289,7 @@ def ui():
                 result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
         demo.queue().launch()
 
-@gpu(void(),device=True,inline=True)
+@gpu(void())
 def pre():
     global pipe
     pipe = AnimateDiffPipeline.from_pretrained(base, vae=vae, motion_adapter=adapter, torch_dtype=dtype).to(device)
@@ -316,7 +306,7 @@ def pre():
     pipe.enable_vae_slicing()
     pipe.enable_free_init(method="butterworth", use_fast_sampling=fast)
 
-@gpu(void(),device=True,inline=True)
+@gpu(void())
 def events():
     gr.on(
         triggers=[
@@ -325,18 +315,18 @@ def events():
             prompt2.submit
         ],
         fn=handle,
+        input=[img,prompt,prompt2,motion]
         output=result
     )
 
-@cpu(void(),cache=True,parallel=True)
 def entry():
     os.chdir(os.path.abspath(os.path.dirname(__file__)))
     pre[1,32]()
-    ui[1,32]()
+    ui()
     events[1,32]()
 
 # entry
 
-entry[1,32]()
+entry()
 
 # end