commit inicial

app.py

@@ -1,146 +1,38 @@
+import os
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-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(
-        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
-
-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: 520px;
-}
-"""
-
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
+from gradio_client import Client, handle_file
+from gradio_imageslider import ImageSlider
+
+stable_diffusion_xl_refiner_10 = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-refiner-1.0"
+refiner_client = None
+
+def refine_image(image, prompt, negative_prompt, num_inference_steps, guidance_scale, seed):
+    global refiner_client
+    if refiner_client is None:
+        refiner_client = Client(stable_diffusion_xl_refiner_10)
+    job = refiner_client.submit(
+        inputs=image,
+        parameters={
+            "prompt":prompt,
+            "negative_prompt": negative_prompt,
+            "num_inference_steps": num_inference_steps,
+            "guidance_scale": guidance_scale,
+            "seed": seed,
+        }
+    )
+    return job.result()
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        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.Blocks() as demo:
+    image = gr.Image()
+    prompt = gr.Textbox(lines=3, label="Prompt")
+    negative_prompt = gr.Textbox(lines=3, label="Negative Prompt")
+    num_inference_steps = gr.Number(default=25, label="Number of Inference Steps")
+    guidance_scale = gr.Slider(minimum=3, maximum=30, default=12, label="Guidance Scale")
+    seed = gr.Number(default=-1, label="Seed")
+    refine_btn = gr.Button(text="Refine")
+    output = gr.Image()
 
-        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,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+refine_btn.click(refine_image, inputs=[image, prompt, negative_prompt, num_inference_steps, guidance_scale, seed], outpus=output)
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
-    )
+demo.launch()
 
-demo.queue().launch()

requirements.txt

@@ -1,6 +1,6 @@
-accelerate
-diffusers
-invisible_watermark
-torch
-transformers
-xformers
+# accelerate
+# diffusers
+# invisible_watermark
+# torch
+# transformers
+# xformers