Update app.py

app.py

@@ -3,33 +3,16 @@ import numpy as np
 import PIL.Image
 from PIL import Image
 import random
-from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, StableDiffusionXLPipeline, AutoencoderKL
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
 from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
-#from diffusers.utils import load_image
-
 import cv2
 import torch
 import spaces
 
-
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-controlnet = ControlNetModel.from_pretrained(
-    #"2vXpSwA7/test_controlnet2/CN-anytest_v4-marged_am_dim256.safetensors",
-    "xinsir/controlnet-scribble-sdxl-1.0",
-    torch_dtype=torch.float16
-    #from_tf=False,  
-    #variant="safetensors"
-)
-
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 
-#pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-#    "yodayo-ai/holodayo-xl-2.1",
-#    controlnet=controlnet,
-#    vae=vae,
-#    torch_dtype=torch.float16,
-#)
 pipe = StableDiffusionXLPipeline.from_pretrained(
     "yodayo-ai/holodayo-xl-2.1",
     vae=vae,
@@ -44,48 +27,22 @@ MAX_IMAGE_SIZE = 1216
 
     
 @spaces.GPU
-#def infer(use_image, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, image: PIL.Image.Image = None) -> PIL.Image.Image:
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-    # Check if the input image is a valid PIL Image and is not empty
-    use_image = False
-    #image = None
-    
-    #if use_image :# and image is not None :
-    #    width, height = image['composite'].size
-    #    ratio = np.sqrt(1024. * 1024. / (width * height))
-    #    new_width, new_height = int(width * ratio), int(height * ratio)
-    #    image = image['composite'].resize((new_width, new_height))
-    #    print(image)
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
     generator = torch.Generator().manual_seed(seed)
 
-    if use_image:
-        print("use_image")
-        #output_image = pipe(
-        #    prompt=prompt + ", masterpiece, best quality, very aesthetic, absurdres",
-        #    negative_prompt=negative_prompt,
-        #    image=image,
-        #    controlnet_conditioning_scale=1.0,
-        #    guidance_scale=guidance_scale,
-        #    num_inference_steps=num_inference_steps,
-        #    width=new_width,
-        #    height=new_height,
-        #    generator=generator
-        #).images[0]
-    else:
-        # If no valid image is provided, generate an image based only on the text prompt
-        output_image = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
-            generator=generator
-        ).images[0]
+    output_image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator
+    ).images[0]
 
     return output_image
 
@@ -116,10 +73,7 @@ with gr.Blocks(css=css) as demo:
 
             run_button = gr.Button("Run", scale=0)
 
-        #image = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512))
         result = gr.Image(label="Result", show_label=False)
-
-        #use_image = gr.Checkbox(label="Use image", value=True)
         
         with gr.Accordion("Advanced Settings", open=False):
 
@@ -176,9 +130,8 @@ with gr.Blocks(css=css) as demo:
 
     run_button.click(#lambda x: None, inputs=None, outputs=result).then(
         fn=infer,
-        #inputs=[use_image, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,image],
         inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result]
     )
 
-demo.queue().launch(show_error=True)
+demo.queue().launch()