From MP4 to GIF

app_pose.py

@@ -27,7 +27,8 @@ def create_demo(model: Model):
                 prompt = gr.Textbox(label='Prompt')
                 run_button = gr.Button(label='Run')
             with gr.Column():
-                result = gr.Video(label="Generated Video")
+                # result = gr.Video(label="Generated Video")
+                result = gr.Image(label="Generated Video")
 
         input_video_path.change(on_video_path_update, None, pose_sequence_selector)
         gallery_pose_sequence.select(pose_gallery_callback, None, input_video_path)

model.py

@@ -186,7 +186,8 @@ class Model:
                                 split_to_chunks=True,
                                 chunk_size=8,
                                 )
-        return utils.create_video(result, fps)
+        return utils.create_gif(result, fps)
+        # return utils.create_video(result, fps)
 
     def process_controlnet_canny_db(self,
                                     db_path,
@@ -255,46 +256,6 @@ class Model:
                                 )
         return utils.create_video(result, fps)
 
-    # def process_text2video(self, prompt, resolution=512, seed=24, num_frames=8, fps=4, t0=881, t1=941,
-    #                        use_cf_attn=True, use_motion_field=True, use_foreground_motion_field=False,
-    #                        smooth_bg=False, smooth_bg_strength=0.4, motion_field_strength=12):
-        
-    #     if self.model_type != ModelType.Text2Video:
-    #         unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
-    #         self.set_model(ModelType.Text2Video, model_id="runwayml/stable-diffusion-v1-5", unet=unet)
-    #         self.pipe.scheduler = DDIMScheduler.from_config(self.pipe.scheduler.config)
-    #         self.pipe.unet.set_attn_processor(processor=self.text2video_attn_proc)
-    #         self.generator.manual_seed(seed)
-        
-
-    #     added_prompt = "high quality, HD, 8K, trending on artstation, high focus, dramatic lighting"
-    #     self.generator.manual_seed(seed)
-
-    #     prompt = prompt.rstrip()
-    #     if len(prompt) > 0 and (prompt[-1] == "," or prompt[-1]  == "."):
-    #         prompt = prompt.rstrip()[:-1]
-    #     prompt = prompt.rstrip()
-    #     prompt = prompt + ", "+added_prompt
-
-    #     result = self.inference(prompt=[prompt],
-    #                             video_length=num_frames,
-    #                             height=resolution,
-    #                             width=resolution,
-    #                             num_inference_steps=50,
-    #                             guidance_scale=7.5,
-    #                             guidance_stop_step=1.0,
-    #                             t0=t0,
-    #                             t1=t1,
-    #                             use_foreground_motion_field=use_foreground_motion_field,
-    #                             motion_field_strength=motion_field_strength,
-    #                             use_motion_field=use_motion_field,
-    #                             smooth_bg=smooth_bg,
-    #                             smooth_bg_strength=smooth_bg_strength,
-    #                             seed=seed,
-    #                             output_type='numpy',
-    #                             )
-    #     return utils.create_video(result, fps)
-
     def process_text2video(self, prompt, motion_field_strength_x=12,motion_field_strength_y=12, n_prompt="", resolution=512, seed=24, num_frames=8, fps=4, t0=881, t1=941,
                            use_cf_attn=True, use_motion_field=True, 
                            smooth_bg=False, smooth_bg_strength=0.4 ):