uploaded app with image upload

app.py

@@ -0,0 +1,205 @@
+import gradio as gr
+import numpy as np
+import random
+from PIL import Image
+
+import spaces
+from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, StableDiffusionImg2ImgPipeline
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_repo_id = "tensorart/stable-diffusion-3.5-large-TurboX"
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+# For text-to-image
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_repo_id, subfolder="scheduler", shift=5)
+pipe = pipe.to(device)
+
+# For image-to-image
+img2img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    model_repo_id,
+    torch_dtype=torch_dtype
+)
+img2img_pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_repo_id, subfolder="scheduler", shift=5)
+img2img_pipe = img2img_pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+
+@spaces.GPU(duration=65)
+def infer(
+        prompt,
+        negative_prompt="",
+        seed=42,
+        randomize_seed=False,
+        width=1024,
+        height=1024,
+        guidance_scale=1.5,
+        num_inference_steps=8,
+        input_image=None,
+        strength=0.8,
+        progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    # Text-to-image if no input image is provided
+    if input_image is None:
+        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]
+    # Image-to-image if an input image is provided
+    else:
+        # Convert to PIL Image if it's a numpy array
+        if isinstance(input_image, np.ndarray):
+            input_image = Image.fromarray(input_image)
+
+        # Resize image to match requested dimensions
+        input_image = input_image.resize((width, height), Image.LANCZOS)
+
+        image = img2img_pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=input_image,
+            strength=strength,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+        ).images[0]
+
+    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(" # TensorArt Stable Diffusion 3.5 Large TurboX")
+        gr.Markdown(
+            "[8-step distilled turbo model](https://huggingface.co/tensorart/stable-diffusion-3.5-large-TurboX)")
+        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")
+
+        # Add image upload component
+        input_image = gr.Image(
+            label="Input Image (Optional)",
+            type="pil",
+            sources=["upload", "clipboard"],
+        )
+
+        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",
+            )
+
+            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=1.5,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=8,
+                )
+
+            # Add strength parameter for image-to-image
+            strength = gr.Slider(
+                label="Strength (for image-to-image)",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.8,
+                info="How much to transform the reference image. 1.0 means complete transformation."
+            )
+
+        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,
+            input_image,
+            strength,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+accelerate
+diffusers
+torch
+transformers
+git+https://github.com/huggingface/diffusers.git
+sentencepiece
+gradio
+spaces