bug fix

README.md

@@ -4,7 +4,7 @@ emoji: 🖼
 colorFrom: purple
 colorTo: red
 sdk: gradio
-sdk_version: 4.29
+sdk_version: 4.29.0
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -17,28 +17,29 @@ from diffusers import (
 from transformers import DPTFeatureExtractor, DPTForDepthEstimation, DPTImageProcessor
 from transformers import CLIPImageProcessor
 from diffusers.utils import load_image
- 
+from gradio_imageslider import ImageSlider
+
 device = "cuda"
 base_model_id = "SG161222/RealVisXL_V4.0"
 controlnet_model_id = "diffusers/controlnet-depth-sdxl-1.0"
 vae_model_id = "madebyollin/sdxl-vae-fp16-fix"
 
 
-# load pipe
-controlnet = ControlNetModel.from_pretrained(controlnet_model_id, variant="fp16", use_safetensors=True, torch_dtype=torch.float16)
-vae = AutoencoderKL.from_pretrained(vae_model_id, torch_dtype=torch.float16)
-pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-    base_model_id, 
-    controlnet=controlnet, 
-    vae=vae,
-    variant="fp16",
-    use_safetensors=True,
-    torch_dtype=torch.float16,
-)
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-pipe.enable_xformers_memory_efficient_attention()
-pipe.to(device)
+if torch.cuda.is_available():
+
+    # load pipe
+    controlnet = ControlNetModel.from_pretrained(controlnet_model_id, variant="fp16", use_safetensors=True, torch_dtype=torch.float16)
+    vae = AutoencoderKL.from_pretrained(vae_model_id, torch_dtype=torch.float16)
+    pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+        base_model_id, 
+        controlnet=controlnet, 
+        vae=vae,
+        variant="fp16",
+        use_safetensors=True,
+        torch_dtype=torch.float16,
+    )
+    pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+    pipe.to(device)
 
 depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
 feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
@@ -79,7 +80,7 @@ def get_depth_map(image):
 
 
 @spaces.GPU(enable_queue=True)
-def process(orginal_image, image_url, prompt, a_prompt, n_prompt, num_steps, guidance_scale, control_strength, seed):
+def process(orginal_image, image_url, prompt, n_prompt, num_steps, guidance_scale, control_strength, seed):
 
     if image_url:
         orginal_image = load_image(image_url)
@@ -117,7 +118,6 @@ with gr.Blocks() as demo:
                 control_strength = gr.Slider(label="Control Strength", minimum=0.1, maximum=4.0, value=0.8, step=0.1)
                 seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                 randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                 n_prompt = gr.Textbox(
                     label="Negative prompt",
                     value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
@@ -130,7 +130,6 @@ with gr.Blocks() as demo:
         image,
         image_url,
         prompt,
-        a_prompt,
         n_prompt,
         num_steps,
         guidance_scale,
@@ -149,6 +148,5 @@ with gr.Blocks() as demo:
             outputs=[result, logs],
             api_name=False
         )
-    return demo
 
 demo.queue().launch()