Update

app.py

@@ -11,53 +11,66 @@ from safetensors.torch import load_file
 from PIL import Image
 
 # Constants
-base = "frankjoshua/toonyou_beta6"
-loaded = None
+bases = {
+    "ToonYou": "frankjoshua/toonyou_beta6",
+    "epiCRealism": "emilianJR/epiCRealism"
+}
+step_loaded = None
+base_loaded = "ToonYou"
 
 # Ensure model and scheduler are initialized in GPU-enabled function
-if torch.cuda.is_available():
-    device = "cuda"
-    dtype = torch.float16
-    adapter = MotionAdapter().to(device, dtype)
-    pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
-    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
-else:
+if not torch.cuda.is_available():
     raise NotImplementedError("No GPU detected!")
 
+device = "cuda"
+dtype = torch.float16
+pipe = AnimateDiffPipeline.from_pretrained(bases[base_loaded], torch_dtype=dtype).to(device)
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
+
 # Function 
 @spaces.GPU(enable_queue=True)
-def generate_image(prompt, step):
+def generate_image(prompt, base, step):
     global loaded
     print(prompt, step)
 
-    if loaded != step:
+    if step_loaded != step:
         repo = "ByteDance/AnimateDiff-Lightning"
         ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
         pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
-        loaded = step
+        step_loaded = step
+
+    if base_loaded != base:
+        pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False)
+        base_loaded = base
 
     output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=step)
-    
     name = str(uuid.uuid4()).replace("-", "")
     path = f"/tmp/{name}.mp4"
     export_to_video(output.frames[0], path, fps=10)
     return path
 
 
-
 # Gradio Interface
-
 with gr.Blocks(css="style.css") as demo:
     gr.HTML("<h1><center>AnimateDiff-Lightning ⚡</center></h1>")
     gr.HTML("<p><center>Lightning-fast text-to-video generation</center></p><p><center><a href='https://huggingface.co/ByteDance/AnimateDiff-Lightning'>https://huggingface.co/ByteDance/AnimateDiff-Lightning</a></center></p>")
     with gr.Group():
         with gr.Row():
             prompt = gr.Textbox(
-                label='Enter your prompt (English)',
+                label='Prompt (English)',
                 scale=8
             )
-            ckpt = gr.Dropdown(
-                label='Select inference steps',
+            select_base = gr.Dropdown(
+                label='Base model',
+                choices=[
+                    "ToonYou", 
+                    "epiCRealism",
+                ],
+                value=base_loaded,
+                interactive=True
+            )
+            select_step = gr.Dropdown(
+                label='Inference steps',
                 choices=[
                     ('1-Step', 1), 
                     ('2-Step', 2),
@@ -77,12 +90,12 @@ with gr.Blocks(css="style.css") as demo:
 
     prompt.submit(
         fn=generate_image,
-        inputs=[prompt, ckpt],
+        inputs=[prompt, select_base, select_step],
         outputs=video,
     )
     submit.click(
         fn=generate_image,
-        inputs=[prompt, ckpt],
+        inputs=[prompt, select_base, select_step],
         outputs=video,
     )