feat img2video

app.py

@@ -2,13 +2,35 @@ from PIL import Image
 import torch
 import re
 import gradio as gr
+import ramdon
 from diffusers import AutoPipelineForText2Image
 from diffusers import AutoPipelineForImage2Image
-from diffusers.utils import load_image, make_image_grid
+from diffusers.utils import load_image, export_to_video
+from diffusers import StableVideoDiffusionPipeline
+
+
+pipelineVideo = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt",).to("cuda")
 pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
 pipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
 pipeline_text2image = pipeline_text2image.to("cuda")
 
+def image2video(image,seed="",fps=7,outfile="")
+    if seed=="":
+        seed=random.randint(0, 5000)
+    
+    else:
+        try:
+            seed=int(seed)
+        except:
+            seed=random.randint(0, 5000)
+    if outfile=="":
+        outfile=str(seed)+".mp4"
+    image = load_image(image)
+    image = image.resize((1024, 576))
+    generator = torch.manual_seed(seed)
+    frames = pipeline(image, decode_chunk_size=8, generator=generator).frames[0]
+    export_to_video(frames, outfile, fps=fps)
+    return outfile
 
 def text2img(prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe.",guidance_scale=0.0, num_inference_steps=1):
     image = pipeline_text2image(prompt=prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
@@ -36,13 +58,23 @@ gradio_app_img2img = gr.Interface(
         gr.Image(type='filepath'),
         gr.Text(),
         gr.Slider(0.0, 10.0, value=1,step=0.1),
-        gr.Slider(0.0, 100.0, value=1,step=1),
-        gr.Slider(0.0, 1.0, value=0.5,step=0.05)
+        gr.Text()
     ],
     outputs="image",
 )
 
-demo = gr.TabbedInterface([gradio_app_text2img,gradio_app_img2img], ["text2img","img2img"])
+gradio_app_img2video = gr.Interface(
+    fn=img2img,
+    inputs=[
+        gr.Image(type='filepath'),
+        gr.Text(),
+        gr.Slider(0.0, 40.0, value=9,step=1),
+        gr.Text()
+    ],
+    outputs="video",
+)
+
+demo = gr.TabbedInterface([gradio_app_text2img,gradio_app_img2img,gradio_app_img2video], ["text2img","img2img","img2video"])
 
 if __name__ == "__main__":
     demo.launch()