Update app.py

app.py

@@ -1,32 +1,155 @@
-import os
-import torch
+
+# from huggingface_hub import login
+# # Login com token via Secrets
+# login(os.environ["HF_TOKEN"])
 import gradio as gr
-from diffusers import StableDiffusion3Pipeline
-from huggingface_hub import login
+import numpy as np
+import random
+
+import spaces
+from diffusers import DiffusionPipeline
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_repo_id = "stabilityai/stable-diffusion-3.5-medium"
+
+if torch.cuda.is_available():
+    torch_dtype = torch.bfloat16
+else:
+    torch_dtype = torch.float32
 
-# Login com token via Secrets
-login(os.environ["HF_TOKEN"])
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
 
-# Carregar pipeline (sem variant)
-pipe = StableDiffusion3Pipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3.5-medium",
-    torch_dtype=torch.float16
-).to("cuda")
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1440
+
+@spaces.GPU(duration=65)
+def infer(
+    prompt,
+    negative_prompt="",
+    seed=42,
+    randomize_seed=False,
+    width=1024,
+    height=1024,
+    guidance_scale=5,
+    num_inference_steps=40,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
 
-def gerar_imagem(prompt):
     image = pipe(
-        prompt,
-        num_inference_steps=30,
-        guidance_scale=4.5
+        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
 
-demo = gr.Interface(
-    fn=gerar_imagem,
-    inputs=gr.Textbox(label="Prompt"),
-    outputs=gr.Image(label="Imagem"),
-    title="Stable Diffusion 3.5 Medium",
-    description="Gere imagens com SD3.5 diretamente no Hugging Face"
-)
+    return image, seed
+
+
+examples = [
+        "A capybara wearing a suit holding a sign that reads Hello World",
+]
+
+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(" # [Stable Diffusion 3.5 Medium (2.6B)](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium)")
+        gr.Markdown("[Learn more](https://stability.ai/news/introducing-stable-diffusion-3-5) about the Stable Diffusion 3.5 series. Try on [Stability AI API](https://platform.stability.ai/docs/api-reference#tag/Generate/paths/~1v2beta~1stable-image~1generate~1sd3/post), or [download model](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium) to run locally with ComfyUI or diffusers.")
+        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=512,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024, 
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=512,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=7.5,
+                    step=0.1,
+                    value=4.5,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=40, 
+                )
+
+        gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True, cache_mode="lazy")
+    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.launch(share=True)
+if __name__ == "__main__":
+    demo.launch()