Update app.py

app.py

@@ -1,73 +1,77 @@
+import spaces
 import gradio as gr
 import numpy as np
+import PIL.Image
+from PIL import Image
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from diffusers import ControlNetModel, StableDiffusionXLPipeline, AutoencoderKL
+from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
+import cv2
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+#vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+#pipe = StableDiffusionXLPipeline.from_pretrained(
+#    #"yodayo-ai/clandestine-xl-1.0", 
+#    torch_dtype=torch.float16, 
+#    use_safetensors=True,
+#    custom_pipeline="lpw_stable_diffusion_xl",
+#    add_watermarker=False #,
+#    #variant="fp16"
+#)
+pipe = StableDiffusionXLPipeline.from_single_file(
+    #"https://huggingface.co/Laxhar/noob_sdxl_beta/noob_hercules4/fp16/checkpoint-e0_s10000.safetensors/checkpoint-e0_s10000.safetensors",
+    "https://huggingface.co/bluepen5805/illustrious_pencil-XL/resolve/main/illustrious_pencil-XL-v1.2.1.safetensors",
+    use_safetensors=True,
+    torch_dtype=torch.float16,
+)
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
+MAX_IMAGE_SIZE = 1216
+
+    
+@spaces.GPU
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
     generator = torch.Generator().manual_seed(seed)
 
-    image = pipe(
+    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,
+        generator=generator
     ).images[0]
 
-    return image, seed
-
+    return output_image
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
 
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 520px;
 }
 """
 
 with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
 
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("""
+         Text-to-Image Demo
+        using [illustrious_pencil-XL](https://huggingface.co/bluepen5805/illustrious_pencil-XL)
+        """)
+        #yodayo-ai/clandestine-xl-1.0　
+        #yodayo-ai/holodayo-xl-2.1
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -77,16 +81,17 @@ with gr.Blocks(css=css) as demo:
                 container=False,
             )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+            run_button = gr.Button("Run", scale=0)
 
         result = gr.Image(label="Result", show_label=False)
-
+        
         with gr.Accordion("Advanced Settings", open=False):
+
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
             )
 
             seed = gr.Slider(
@@ -105,7 +110,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,#832,
                 )
 
                 height = gr.Slider(
@@ -113,42 +118,30 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,#1216,
                 )
 
             with gr.Row():
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
-                    maximum=10.0,
+                    maximum=20.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=4,
                 )
 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=28,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=28,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
+    run_button.click(#lambda x: None, inputs=None, outputs=result).then(
         fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.queue().launch()