Update app.py

app.py

@@ -17,119 +17,141 @@ import re
 from gradio_client import Client
 
 
-def animate_img(encoded_string):
-    try:   
-        r = requests.post("https://stable-video-diffusion.com/api/upload", files={"file": open(encoded_string, 'rb')})
-        hash_ = r.json()['hash']
-        time.sleep(10)
-        c = 0
-        while c < 10:
-            r2 = requests.get(f"https://stable-video-diffusion.com/result?hash={hash_}")
-            source_string = r2.text
-            if "Generation has been in progress for" in source_string:
-                time.sleep(15)
-                c += 1
-                continue
-            if "Generation has been in progress for" not in source_string:
-                pattern = r'https://storage.stable-video-diffusion.com/([a-f0-9]{32})\.mp4'
-                matches = re.findall(pattern, source_string)
-                sd_video = []
-                for match in matches:
-                    sd_video.append(f"https://storage.stable-video-diffusion.com/{match}.mp4")
-                if len(sd_video) != 0:
-                    print("s_1")
-                    return sd_video[0]
-                else:
-                    _ = 1/0
-        print("f_1")
-    except:
-        print("2")
-        client1 = Client("https://emmadrex-stable-video-diffusion.hf.space")
-        result1 = client1.predict(encoded_string, api_name="/resize_image")        
-        client = Client("https://emmadrex-stable-video-diffusion.hf.space")
-        result = client.predict(result1, 0, True, 1, 15, api_name="/video")
-        res = result[0]['video']
-        print("s_2")
-        return res
-
+def animate_img(encoded_string, model):
+    if model == "Stable Video Diffusion":
+        try:   
+            r = requests.post("https://stable-video-diffusion.com/api/upload", files={"file": open(encoded_string, 'rb')})
+            hash_ = r.json()['hash']
+            time.sleep(10)
+            c = 0
+            while c < 10:
+                r2 = requests.get(f"https://stable-video-diffusion.com/result?hash={hash_}")
+                source_string = r2.text
+                if "Generation has been in progress for" in source_string:
+                    time.sleep(15)
+                    c += 1
+                    continue
+                if "Generation has been in progress for" not in source_string:
+                    pattern = r'https://storage.stable-video-diffusion.com/([a-f0-9]{32})\.mp4'
+                    matches = re.findall(pattern, source_string)
+                    sd_video = []
+                    for match in matches:
+                        sd_video.append(f"https://storage.stable-video-diffusion.com/{match}.mp4")
+                    if len(sd_video) != 0:
+                        print("s_1")
+                        return sd_video[0]
+                    else:
+                        _ = 1/0
+            print("f_1")
+        except:
+            print("2")
+            client1 = Client("https://emmadrex-stable-video-diffusion.hf.space")
+            result1 = client1.predict(encoded_string, api_name="/resize_image")        
+            client = Client("https://emmadrex-stable-video-diffusion.hf.space")
+            result = client.predict(result1, 0, True, 1, 15, api_name="/video")
+            res = result[0]['video']
+            print("s_2")
+            return res
 
-def create_video(prompt):
+    if model == "AnimateDiff":
+        client = Client("https://ap123-animateimage.hf.space/--replicas/zlwk6/")
+        result = client.predict(encoded_string, "zoom-out", api_name="/predict")
+        return result
+    
+    
+def create_video(prompt, model):
     url_sd3 = os.getenv("url_sd3")
     url_sd4 = os.getenv("url_sd4")
+    if model == "Stable Video Diffusion":
+        try:
+            with closing(create_connection(f"{url_sd3}", timeout=120)) as conn:
+                conn.send('{"fn_index":3,"session_hash":""}')
+                conn.send(f'{{"data":["{prompt}","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry",7.5,"(No style)"],"event_data":null,"fn_index":3,"session_hash":""}}')
+                c = 0
+                while c < 60:
+                    status = json.loads(conn.recv())['msg']
+                    if status == 'estimation':
+                        c += 1
+                        time.sleep(1)
+                        continue
+                    if status == 'process_starts':
+                        break
+                photo = json.loads(conn.recv())['output']['data'][0][0]
+                base64_string = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
+            
+                image_bytes = base64.b64decode(base64_string)
+                with tempfile.NamedTemporaryFile(delete=False) as temp:
+                    temp.write(image_bytes)
+                    temp_file_path = temp.name
+                    print("cs_1")
     
-    try:
-        with closing(create_connection(f"{url_sd3}", timeout=120)) as conn:
-            conn.send('{"fn_index":3,"session_hash":""}')
-            conn.send(f'{{"data":["{prompt}","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry",7.5,"(No style)"],"event_data":null,"fn_index":3,"session_hash":""}}')
+                
+        except:
+            print("c_2")
+            with closing(create_connection(f"{url_sd4}", timeout=120)) as conn:
+                conn.send('{"fn_index":0,"session_hash":""}')
+                conn.send(f'{{"data":["{prompt}","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry","dreamshaperXL10_alpha2.safetensors [c8afe2ef]",30,"DPM++ 2M Karras",7,1024,1024,-1],"event_data":null,"fn_index":0,"session_hash":""}}')
+                conn.recv()
+                conn.recv()
+                conn.recv()
+                conn.recv()
+                photo = json.loads(conn.recv())['output']['data'][0]
+                base64_string = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
+            
+                image_bytes = base64.b64decode(base64_string)
+                with tempfile.NamedTemporaryFile(delete=False) as temp:
+                    temp.write(image_bytes)
+                    temp_file_path = temp.name
+                    print("cs_2")
+    
+        try:
+            r = requests.post("https://stable-video-diffusion.com/api/upload", files={"file": open(temp_file_path, 'rb')})
+            print(r.text)
+            hash_ = r.json()['hash']
+            time.sleep(10)
             c = 0
-            while c < 60:
-                status = json.loads(conn.recv())['msg']
-                if status == 'estimation':
+            while c < 10:
+                r2 = requests.get(f"https://stable-video-diffusion.com/result?hash={hash_}")
+                source_string = r2.text
+                if "Generation has been in progress for" in source_string:
+                    time.sleep(15)
                     c += 1
-                    time.sleep(1)
                     continue
-                if status == 'process_starts':
-                    break
-            photo = json.loads(conn.recv())['output']['data'][0][0]
-            base64_string = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
+                if "Generation has been in progress for" not in source_string:
+                    pattern = r'https://storage.stable-video-diffusion.com/([a-f0-9]{32})\.mp4'
+                    matches = re.findall(pattern, source_string)
+                    sd_video = []
+                    for match in matches:
+                        sd_video.append(f"https://storage.stable-video-diffusion.com/{match}.mp4")
+                    print(sd_video[0])
+                    if len(sd_video) != 0:
+                        return sd_video[0]
+                    else:
+                        _ = 1/0
+        except:
+            client1 = Client("https://emmadrex-stable-video-diffusion.hf.space")
+            result1 = client1.predict(encoded_string, api_name="/resize_image")        
+            client = Client("https://emmadrex-stable-video-diffusion.hf.space")
+            result = client.predict(result1, 0, True, 1, 15, api_name="/video")
+            return result[0]['video']
         
-            image_bytes = base64.b64decode(base64_string)
-            with tempfile.NamedTemporaryFile(delete=False) as temp:
-                temp.write(image_bytes)
-                temp_file_path = temp.name
-                print("cs_1")
-
             
-    except:
-        print("c_2")
-        with closing(create_connection(f"{url_sd4}", timeout=120)) as conn:
-            conn.send('{"fn_index":0,"session_hash":""}')
-            conn.send(f'{{"data":["{prompt}","[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry","dreamshaperXL10_alpha2.safetensors [c8afe2ef]",30,"DPM++ 2M Karras",7,1024,1024,-1],"event_data":null,"fn_index":0,"session_hash":""}}')
-            conn.recv()
-            conn.recv()
-            conn.recv()
-            conn.recv()
-            photo = json.loads(conn.recv())['output']['data'][0]
-            base64_string = photo.replace('data:image/jpeg;base64,', '').replace('data:image/png;base64,', '')
-        
-            image_bytes = base64.b64decode(base64_string)
-            with tempfile.NamedTemporaryFile(delete=False) as temp:
-                temp.write(image_bytes)
-                temp_file_path = temp.name
-                print("cs_2")
 
-    try:
-        r = requests.post("https://stable-video-diffusion.com/api/upload", files={"file": open(temp_file_path, 'rb')})
-        print(r.text)
-        hash_ = r.json()['hash']
-        time.sleep(10)
+
+    if model == "AnimateDiff":
+        data = {"prompt": prompt, "negative_prompt": "EasyNegative"}
+        r = requests.post("https://sd.cuilutech.com/sdapi/async/txt2gif", json=data)
         c = 0
-        while c < 10:
-            r2 = requests.get(f"https://stable-video-diffusion.com/result?hash={hash_}")
-            source_string = r2.text
-            if "Generation has been in progress for" in source_string:
-                time.sleep(15)
-                c += 1
-                continue
-            if "Generation has been in progress for" not in source_string:
-                pattern = r'https://storage.stable-video-diffusion.com/([a-f0-9]{32})\.mp4'
-                matches = re.findall(pattern, source_string)
-                sd_video = []
-                for match in matches:
-                    sd_video.append(f"https://storage.stable-video-diffusion.com/{match}.mp4")
-                print(sd_video[0])
-                if len(sd_video) != 0:
-                    return sd_video[0]
-                else:
-                    _ = 1/0
-    except:
-        client1 = Client("https://emmadrex-stable-video-diffusion.hf.space")
-        result1 = client1.predict(encoded_string, api_name="/resize_image")        
-        client = Client("https://emmadrex-stable-video-diffusion.hf.space")
-        result = client.predict(result1, 0, True, 1, 15, api_name="/video")
-        return result[0]['video']
-        
-            
+        while c < 60:
+            r2 = requests.post("https://sd.cuilutech.com/sdapi/get_task_info", json={'task_id': r.json()['data']['task_id']})
+            time.sleep(2)
+            if r2.json()['data']:
+                photo = r2.json()['data']['image_urls'][0]
+                break
+            c += 1
+        return photo
+
+
 def flip_text1(prompt, motion):
     try:
         language = detect(prompt)
@@ -269,25 +291,27 @@ with gr.Blocks(css=css) as demo:
     with gr.Tab("Сгенерировать видео"):
         with gr.Column():
             prompt = gr.Textbox(placeholder="Введите описание видео...", show_label=True, label='Описание:', lines=3)
-            motion1 = gr.Dropdown(value="Приближение →←", interactive=True, show_label=True, label="Движение камеры:", choices=[
-                    "Приближение →←", "Отдаление ←→", "Вверх ↑", "Вниз ↓", "Влево ←", "Вправо →", "По часовой стрелке ⟳", "Против часовой стрелки ⟲"])
+            # motion1 = gr.Dropdown(value="Приближение →←", interactive=True, show_label=True, label="Движение камеры:", choices=["Приближение →←", "Отдаление ←→", "Вверх ↑", "Вниз ↓", "Влево ←", "Вправо →", "По часовой стрелке ⟳", "Против часовой стрелки ⟲"])
+            model = gr.Radio(interactive=True, value="Stable Video Diffusion", show_label=True, 
+                             label="Модель нейросети:", choices=['Stable Video Diffusion', 'AnimateDiff'])
         with gr.Column():
             text_button = gr.Button("Сгенерировать видео", variant='primary', elem_id="generate")
         with gr.Column():
             video_output = gr.Video(show_label=True, label='Результат:', type="file")
-            text_button.click(create_video, inputs=[prompt], outputs=video_output)
+            text_button.click(create_video, inputs=[prompt, model], outputs=video_output)
             
     with gr.Tab("Анимировать изображение"):
         with gr.Column():
             prompt2 = gr.Image(show_label=True, interactive=True, type='filepath', label='Исходное изображение:')
             prompt12 = gr.Textbox(placeholder="Введите описание видео...", show_label=True, label='Описание видео (опционально):', lines=3)
-            motion2 = gr.Dropdown(value="Приближение →←", interactive=True, show_label=True, label="Движение камеры:", choices=[
-                    "Приближение →←", "Отдаление ←→", "Вверх ↑", "Вниз ↓", "Влево ←", "Вправо →", "По часовой стрелке ⟳", "Против часовой стрелки ⟲"])
+            # motion2 = gr.Dropdown(value="Приближение →←", interactive=True, show_label=True, label="Движение камеры:", choices=["Приближение →←", "Отдаление ←→", "Вверх ↑", "Вниз ↓", "Влево ←", "Вправо →", "По часовой стрелке ⟳", "Против часовой стрелки ⟲"])
+            model2 = gr.Radio(interactive=True, value="Stable Video Diffusion", show_label=True, 
+                             label="Модель нейросети:", choices=['Stable Video Diffusion', 'AnimateDiff'])
         with gr.Column():
             text_button2 = gr.Button("Анимировать изображение", variant='primary', elem_id="generate")
         with gr.Column():
             video_output2 = gr.Video(show_label=True, label='Результат:', type="file")
-            text_button2.click(animate_img, inputs=[prompt2], outputs=video_output2)
+            text_button2.click(animate_img, inputs=[prompt2, model2], outputs=video_output2)
     
 demo.queue(concurrency_count=12)
 demo.launch()