Create _app.py

_app.py

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+# Copyright Volkan Sah! Do not steal code if its for free! Respect creators!
+# You can use it for free a star or follow will be greate!
+import gradio as gr
+import numpy as np
+import random
+import os
+import spaces
+import time
+from diffusers import AutoPipelineForText2Image
+import torch
+from huggingface_hub import login
+
+login(os.environ.get("HF_TOKEN"))
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+pipe = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.load_lora_weights('enhanceaiteam/Flux-uncensored', weight_name='lora.safetensors')
+pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+def dev_pipeline_test(prompt, width, height, steps):
+    """Simulates pipeline tests without actual image generation"""
+    time.sleep(0.1)  # Simulate processing
+    estimated_memory = (width * height * 3 * 4) / (1024 * 1024)  # MB
+    return {
+        'success': True,
+        'memory_required': f"{estimated_memory:.2f}MB",
+        'compute_units': steps * (width * height) / 1024**2,
+        'would_generate': True
+    }
+
+@spaces.GPU
+def infer(
+    prompt,
+    seed,
+    randomize_seed,
+    width,
+    height,
+    guidance_scale,
+    num_inference_steps,
+    dev_mode,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if dev_mode:
+        result = dev_pipeline_test(prompt, width, height, num_inference_steps)
+        # Create a black test image with debug info
+        debug_image = np.zeros((height, width, 3), dtype=np.uint8)
+        return debug_image, seed, f"DEV MODE: {result}"
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator,
+    ).images[0]
+    return image, seed, "Production generation completed"
+
+examples = [
+    "Tiger in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a pink 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("""# [FLUX.1-dev](https://blackforestlabs.ai/)
+Generate any type of image with Flux-Dev (Lora: Flux-uncensored). Note: This script works well, but please use min. ZeroGPU
+""")
+        
+        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)
+        status_text = gr.Text(label="Status", show_label=True)
+        
+        with gr.Accordion("Advanced Settings", open=False):
+            dev_mode = gr.Checkbox(label="Developer Mode (No actual generation)", value=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,
+                )
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+            
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=3.5,
+                )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=2,
+                )
+        
+        gr.Examples(examples=examples, inputs=[prompt])
+        
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            dev_mode,
+        ],
+        outputs=[result, seed, status_text],
+    )
+
+if __name__ == "__main__":
+    demo.launch()