test gradio

app.py

@@ -1,36 +1,72 @@
+import torch
+from diffusers.models import MotionAdapter
+from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler
+from diffusers.utils import export_to_gif
+
 import gradio as gr
 from huggingface_hub import login
 import os
-import spaces
+import spaces,tempfile
+import torch
+from diffusers import StableDiffusionXLPipeline
+from PIL import Image
+import torch
+from diffusers import AutoPipelineForText2Image, DDIMScheduler
 from diffusers import AutoPipelineForText2Image
 from diffusers.utils import load_image
 import torch
-import tempfile
+from diffusers.models import MotionAdapter
+from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler
+from diffusers.utils import export_to_gif
 
 token = os.getenv("HF_TOKEN")
 login(token=token)
 
 
-pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16).to("cuda")
-pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-sdxl-beta", torch_dtype=torch.float16)
+
+model_id = "stabilityai/sdxl-turbo"
+scheduler = DDIMScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    beta_schedule="linear",
+    steps_offset=1,
+)
+pipe = AnimateDiffSDXLPipeline.from_pretrained(
+    model_id,
+    motion_adapter=adapter,
+    scheduler=scheduler,
+    torch_dtype=torch.float16,
+    variant="fp16",
+).to("cuda")
+pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
 
+# enable memory savings
+pipe.enable_vae_slicing()
+pipe.enable_vae_tiling()
+pipeline = pipe
 
 
 @spaces.GPU
-def generate_image(prompt, reference_image, controlnet_conditioning_scale):
+def generate_image(prompt, reference_image, controlnet_conditioning_scale,num_frames):
     style_images = [load_image(f.name) for f in reference_image]
 
     pipeline.set_ip_adapter_scale(controlnet_conditioning_scale)
 
-    image = pipeline(
+    output = pipeline(
         prompt=prompt,
         ip_adapter_image=[style_images],
         negative_prompt="",
         guidance_scale=5,
         num_inference_steps=30,
-    ).images[0]
+        num_frames=num_frames,
+    )
+    frames = output.frames[0]
+    export_to_gif(frames, "animation.gif")
 
-    return image
+    return "animation.gif"
 
 # Set up Gradio interface
 interface = gr.Interface(
@@ -38,8 +74,10 @@ interface = gr.Interface(
     inputs=[
         gr.Textbox(label="Prompt"),
         # gr.Image( type= "filepath",label="Reference Image (Style)"),
-        gr.File(file_count="multiple",label="Reference Image (Style)"),
+        gr.File(type="file",file_count="multiple",label="Reference Image (Style)"),
         gr.Slider(label="Control Net Conditioning Scale", minimum=0, maximum=1.0, step=0.1, value=1.0),
+        gr.Slider(label="Number of frames", minimum=0, maximum=1.0, step=0.1, value=1.0),
+
     ],
     outputs="image",
     title="Image Generation with Stable Diffusion 3 medium and ControlNet",
@@ -48,3 +86,6 @@ interface = gr.Interface(
 )
 
 interface.launch()
+
+
+

app_image_style.py

@@ -2,14 +2,10 @@ import gradio as gr
 from huggingface_hub import login
 import os
 import spaces
-import torch
-from diffusers import StableDiffusionXLPipeline
-from PIL import Image
-import torch
-from diffusers import AutoPipelineForText2Image, DDIMScheduler
 from diffusers import AutoPipelineForText2Image
 from diffusers.utils import load_image
 import torch
+import tempfile
 
 token = os.getenv("HF_TOKEN")
 login(token=token)
@@ -22,7 +18,7 @@ pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name=
 
 @spaces.GPU
 def generate_image(prompt, reference_image, controlnet_conditioning_scale):
-    style_images = [load_image(f.file.name) for f in reference_image]
+    style_images = [load_image(f.name) for f in reference_image]
 
     pipeline.set_ip_adapter_scale(controlnet_conditioning_scale)