Create app.py

app.py

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+from diffusers import DiffusionPipeline
+import gradio as gr
+import torch
+import cv2
+import os
+
+MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
+
+device="cuda"
+
+pipe = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    use_auth_token=MY_SECRET_TOKEN,
+    revision='fp16',
+    torch_dtype=torch.float16,
+    safety_checker=None,  # Very important for videos...lots of false positives while interpolating
+    custom_pipeline="interpolate_stable_diffusion",
+).to(device)
+pipe.enable_attention_slicing()
+
+def run(prompt1, seed1, prompt2, seed2, prompt3, seed3):
+    
+    frame_filepaths = pipe3.walk(
+        prompts=[prompt1, prompt2, prompt3],
+        seeds=[seed1, seed2, seed3],
+        num_interpolation_steps=16,
+        output_dir='./dreams',
+        batch_size=4,
+        height=512,
+        width=512,
+        guidance_scale=8.5,
+        num_inference_steps=50,
+    )
+    print(frame_filepaths)
+    
+    frame = cv2.imread(frame_filepaths[0])
+    height, width, layers = frame.shape
+    fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
+    video = cv2.VideoWriter("out.mp4", fourcc, 24, (width,height))
+    for image in frame_filepaths:
+        #print(image)
+        video.write(cv2.imread(image))
+    
+    video.release()
+    cv2.destroyAllWindows()
+    #print(video)
+    
+    return "out.mp4", frame_filepaths
+
+with gr.Blocks() as demo:
+  with gr.Column():
+    gr.HTML('''
+        <h1>
+            Stable Diffusion Interpolation • Community pipeline
+        </h1>
+        <p style='text-align: center;'>
+            This community pipeline returns a list of images saved under the folder as defined in output_dir. <br />
+            You can use these images to create videos of stable diffusion.
+        </p>
+        
+        <p style='text-align: center;'>
+            This demo can be run on a GPU of at least 8GB VRAM and should take approximately 5 minutes.<br />
+            —         
+        </p>
+
+    ''')
+    with gr.Row():
+      with gr.Column():
+        with gr.Column():
+          with gr.Row():
+            intpol_prompt_1 = gr.Textbox(lines=1, label="prompt 1")
+            seed1 = gr.Slider(label = "Seed 1", minimum = 0, maximum = 2147483647, step = 1, randomize = True)
+          with gr.Row():
+            intpol_prompt_2 = gr.Textbox(lines=1, label="prompt 2")
+            seed2 = gr.Slider(label = "Seed 2", minimum = 0, maximum = 2147483647, step = 1, randomize = True)
+          with gr.Row():
+            intpol_prompt_3 = gr.Textbox(lines=1, label="prompt 3")
+            seed3 = gr.Slider(label = "Seed 3", minimum = 0, maximum = 2147483647, step = 1, randomize = True)
+          intpol_run = gr.Button("Run Interpolation")
+      
+      with gr.Column():
+        video_output = gr.Video(label="Generated video", show_label=True)
+        gallery_output = gr.Gallery(label="Generated images", show_label=False).style(grid=2, height="auto")
+    
+    intpol_run.click(run, inputs=[intpol_prompt_1, seed1, intpol_prompt_2, seed2, intpol_prompt_3, seed3], outputs=[gallery_output, video_output])
+    
+demo.launch()