Update app.py

app.py

@@ -1,154 +1,79 @@
 import gradio as gr
 import numpy as np
-import random
-
+import random 
 # import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
+from diffusers import StableDiffusionPipeline
+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():
+model_path = "https://huggingface.co/DreamingOracle/Quagmaform_alpha-1/resolve/main/DPS_Quagmaform_AlphaV1.safetensors" # Your custom model file URL
+if torch.cuda.is_available(): 
     torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+else: 
+    torch_dtype = torch.float32 
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+pipe = StableDiffusionPipeline.from_single_file(model_path, torch_dtype=torch_dtype) 
+pipe = 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),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    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 image, seed
-
-
-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;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        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,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+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),): 
+    if randomize_seed: 
+        seed = random.randint(0, MAX_SEED) 
+    generator = torch.Generator().manual_seed(seed) 
+    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 image, seed 
+
+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;}""" 
+
+with gr.Blocks(css=css) as demo: 
+    with gr.Column(elem_id="col-container"): 
+        gr.Markdown(" # Text-to-Image Gradio Template") 
+        with gr.Row(): 
+            prompt = gr.Text( 
+                label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, 
+            ) 
+            run_button = gr.Button("Run", scale=0, variant="primary") 
+        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, 
+            ) 
+            seed = gr.Slider( 
+                label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, 
+            ) 
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True) 
+            with gr.Row(): 
+                width = gr.Slider( 
+                    label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, # Replace with defaults that work for your model 
+                ) 
+                height = gr.Slider( 
+                    label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, # Replace with defaults that work for your model 
+                ) 
+            with gr.Row(): 
+                guidance_scale = gr.Slider( 
+                    label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5, # Replace with defaults that work for your model 
+                ) 
+                num_inference_steps = gr.Slider( 
+                    label="Number of inference steps", minimum=1, maximum=50, step=1, value=50, # Replace with defaults that work for your model 
+                ) 
+        gr.Examples(examples=examples, inputs=[prompt]) 
+        gr.on( 
+            triggers=[run_button.click, prompt.submit], 
+            fn=infer, 
+            inputs=[ 
+                prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, 
+            ], 
+            outputs=[result, seed], 
+        ) 
+if __name__ == "__main__": 
+    demo.launch()