Create redux.py

redux.py

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+import gradio as gr
+import numpy as np
+import random
+
+import spaces
+import torch
+
+from diffusers import FluxPriorReduxPipeline, FluxPipeline
+from diffusers.utils import load_image
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Redux-dev",
+    torch_dtype=torch.bfloat16
+).to("cuda")
+
+pipe = FluxPipeline.from_pretrained(
+    "LPX55/FLUX.1-merged_uncensored" , 
+    text_encoder=None,
+    text_encoder_2=None,
+    torch_dtype=torch.bfloat16
+).to("cuda")
+
+@spaces.GPU
+def infer(control_image, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    pipe_prior_output = pipe_prior_redux(control_image)
+    images = pipe(
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=torch.Generator("cpu").manual_seed(seed),
+        **pipe_prior_output,
+    ).images[0]
+    return images, seed
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
+}
+"""
+
+with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# FLUX.1 Redux [dev]
+An adapter for FLUX [dev] to create image variations
+[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
+        """)
+        with gr.Row():
+            with gr.Column():
+                    input_image = gr.Image(label="Image to create variations", type="pil")
+                    run_button = gr.Button("Run")
+            result = gr.Image(label="Result", show_label=False)
+        
+        with gr.Accordion("Advanced Settings", open=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=1,
+                    maximum=15,
+                    step=0.1,
+                    value=3.5,
+                )
+  
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,
+                )
+
+    gr.on(
+        triggers=[run_button.click],
+        fn = infer,
+        inputs = [input_image, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
+    )
+
+demo.launch()