Update app.py

app.py

@@ -16,7 +16,7 @@ import warnings
 #import spaces
 import torch
 import gradio as gr
-from numpy import array
+from numpy import asarray as array
 from lxml.html import fromstring
 #from transformers import pipeline
 #from diffusers.pipelines.flux import FluxPipeline
@@ -30,6 +30,9 @@ from diffusers import DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, Eul
 from numba import cuda, njit as cpu, void, int64 as int, float64 as float, boolean as bool
 from numba.cuda import jit as gpu, grid
 from numba.types import unicode_type as string
+from PIL.Image import fromarray as array2image
+import numpy as np
+
 # logging
 
 warnings.filterwarnings("ignore")
@@ -46,11 +49,13 @@ formatter = logging.Formatter('\n >>> [%(levelname)s] %(asctime)s %(name)s: %(me
 handler2.setFormatter(formatter)
 root.addHandler(handler2)
 
-# data
+# output data
 
-out=array([""])
-inp=[]
+out_pipe=array([""])
 last_motion=array([""])
+
+# constant data
+
 dtype = torch.float16
 device = "cuda"
 #repo = "ByteDance/AnimateDiff-Lightning"
@@ -61,6 +66,8 @@ vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(device, dtyp
 #unet = UNet2DConditionModel.from_config("emilianJR/epiCRealism",subfolder="unet").to(device, dtype).load_state_dict(load_file(hf_hub_download("emilianJR/epiCRealism", "unet/diffusion_pytorch_model.safetensors"), device=device), strict=False)
 adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3", torch_dtype=dtype, device=device)
 
+# precision data
+
 fast=True
 fps=10
 time=1
@@ -69,6 +76,8 @@ height=768
 step=40
 accu=10
 
+# ui data
+
 css="""
 input, input::placeholder {
     text-align: center !important;
@@ -162,53 +171,46 @@ def generate_random_string(length):
     return ''.join(random.choice(characters) for _ in range(length))
     
 @gpu(void())
-def calc():
-    global inp
-    global out
+def calc(img,p1,p2,motion):
+    global out_pipe
     global last_motion
 
     x = grid(1)
 
-    if last_motion[0] != inp[3]:
+    if last_motion[0] != motion:
         pipe.unload_lora_weights()
         if inp[3] != "":
-            pipe.load_lora_weights(inp[3], adapter_name="motion")
+            pipe.load_lora_weights(motion, adapter_name="motion")
             pipe.fuse_lora()
-            pipe.set_adapters([3], [0.7])
-        last_motion[0] = inp[3]
+            pipe.set_adapters("motion", [0.7])
+        last_motion[0] = motion
 
     pipe.to(device,dtype)
 
-    if inp[2]=="":
-        out[x] = pipe(
-            prompt=inp[1],
+    if p2=="":
+        out_pipe[x] = pipe(
+            prompt=p1,
             height=height,
             width=width,
-            ip_adapter_image=inp[0].convert("RGB").resize((width,height)),
+            ip_adapter_image=array2image(img).convert("RGB").resize((width,height)),
             num_inference_steps=step,
             guidance_scale=accu,
             num_frames=(fps*time)
         )
     
-    out[x] = pipe(
-        prompt=inp[1],
-        negative_prompt=inp[2],
+    out_pipe[x] = pipe(
+        prompt=p1,
+        negative_prompt=p2,
         height=height,
         width=width,
-        ip_adapter_image=inp[0].convert("RGB").resize((width,height)),
+        ip_adapter_image=array2image(img).convert("RGB").resize((width,height)),
         num_inference_steps=step,
         guidance_scale=accu,
         num_frames=(fps*time)
     )
 
-def handle(*args):
-    global inp
-    global out
-
-    inp = args
-
-    out = array([],dtype=string)
-
+def handle(*inp):
+    
     inp[1] = translate(inp[1],"english")
     inp[2] = translate(inp[2],"english")
 
@@ -225,20 +227,24 @@ def handle(*args):
     
     ln = len(result)
 
-    calc[ln,32]()
+    inp[0] = array(inp[0])
+    inp[1] = array(inp[1])
+    inp[2] = array(inp[2])
+    inp[3] = array(inp[3])
+    calc[ln,32](*inp)
 
     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
+        name = generate_random_string(12)+".png"
+        export_to_gif(out_pipe[i].frames[0],name,fps=fps)
+        out_pipe[i] = name
     
-    return out
+    return out_pipe
 
 def ui():
     with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
         with gr.Column(elem_id="col-container"):
             gr.Markdown(f"""
-                # MULTI-LANGUAGE IMAGE GENERATOR
+                # MULTI-LANGUAGE GIF CREATOR
             """)
             with gr.Row():
                 global img