testy

app.py

@@ -1,142 +1,145 @@
-import gradio as gr
-import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-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
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, 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)):
+# import gradio as gr
+# import numpy as np
+# import random
+# #import spaces #[uncomment to use ZeroGPU]
+# from diffusers import DiffusionPipeline
+# 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
+#
+# pipe = DiffusionPipeline.from_pretrained(model_repo_id, 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(f"""
+#         # 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)
+#
+#         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]
+#     )
+#
+# demo.queue().launch()
 
-    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(f"""
-        # 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)
-        
-        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]
-    )
-
-demo.queue().launch()
+import gradio as gr
+gr.load("models/nerijs/dark-fantasy-illustration-flux").launch()