Update app.py

app.py

@@ -1,154 +1,174 @@
+import spaces
 import gradio as gr
-import numpy as np
-import random
+import re
+from PIL import Image
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import os
+import numpy as np
 import torch
+from diffusers import FluxImg2ImgPipeline
 
+dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    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, seed
-
-
-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 {
+
+pipe = FluxImg2ImgPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(device)
+
+
+
+def sanitize_prompt(prompt):
+  # Allow only alphanumeric characters, spaces, and basic punctuation
+  allowed_chars = re.compile(r"[^a-zA-Z0-9\s.,!?-]")
+  sanitized_prompt = allowed_chars.sub("", prompt)
+  return sanitized_prompt
+
+def convert_to_fit_size(original_width_and_height, maximum_size = 2048):
+    width, height =original_width_and_height
+    if width <= maximum_size and height <= maximum_size:
+        return width,height
+    
+    if width > height:
+        scaling_factor = maximum_size / width
+    else:
+        scaling_factor = maximum_size / height
+
+    new_width = int(width * scaling_factor)
+    new_height = int(height * scaling_factor)
+    return new_width, new_height
+
+def adjust_to_multiple_of_32(width: int, height: int):
+    width = width - (width % 32)
+    height = height - (height % 32)
+    return width, height
+
+
+
+
+@spaces.GPU(duration=120)
+def process_images(image,prompt="a girl",strength=0.75,seed=0,inference_step=4,progress=gr.Progress(track_tqdm=True)):
+    #print("start process_images")
+    progress(0, desc="Starting")
+
+
+    def process_img2img(image,prompt="a person",strength=0.75,seed=0,num_inference_steps=4):
+        #print("start  process_img2img")
+        if image == None:
+            print("empty input image returned")
+            return None
+
+        generators = []
+        generator = torch.Generator(device).manual_seed(seed)
+        generators.append(generator)
+        fit_width,fit_height = convert_to_fit_size(image.size)
+        #print(f"fit {width}x{height}")
+        width,height = adjust_to_multiple_of_32(fit_width,fit_height)
+        #print(f"multiple {width}x{height}")
+        image = image.resize((width, height), Image.LANCZOS)
+        #mask_image = mask_image.resize((width, height), Image.NEAREST)
+
+        # more parameter see https://huggingface.co/docs/diffusers/api/pipelines/flux#diffusers.FluxInpaintPipeline
+        #print(prompt)
+        output = pipe(prompt=prompt, image=image,generator=generator,strength=strength,width=width,height=height
+                    ,guidance_scale=0,num_inference_steps=num_inference_steps,max_sequence_length=256)
+
+        pil_image = output.images[0]#Image.fromarray()
+        new_width,new_height = pil_image.size
+
+        # resize back multiple of 32
+        if (new_width!=fit_width) or (new_height!=fit_height):
+            resized_image= pil_image.resize((fit_width,fit_height),Image.LANCZOS)
+            return resized_image
+        
+        return pil_image
+
+    output = process_img2img(image,prompt,strength,seed,inference_step)
+   
+    #print("end process_images")
+    return output
+    
+
+def read_file(path: str) -> str:
+    with open(path, 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    return content
+
+
+css="""
+#col-left {
+    margin: 0 auto;
+    max-width: 640px;
+}
+#col-right {
     margin: 0 auto;
     max-width: 640px;
 }
+.grid-container {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap:10px
+}
+
+.image {
+  width: 128px; 
+  height: 128px; 
+  object-fit: cover; 
+}
+
+.text {
+  font-size: 16px;
+}
+
 """
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        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=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
+with gr.Blocks(css=css, elem_id="demo-container") as demo:
+    with gr.Column():
+        gr.HTML(read_file("demo_header.html"))
+        gr.HTML(read_file("demo_tools.html"))
+    with gr.Row():
+                with gr.Column():
+                    image = gr.Image(height=800,sources=['upload','clipboard'],image_mode='RGB', elem_id="image_upload", type="pil", label="Upload")
+                    with gr.Row(elem_id="prompt-container",  equal_height=False):
+                        with gr.Row():
+                            prompt = gr.Textbox(label="Prompt",value="a women",placeholder="Your prompt (what you want in place of what is erased)", elem_id="prompt")
+                            
+                    btn = gr.Button("Img2Img", elem_id="run_button",variant="primary")
+                    
+                    with gr.Accordion(label="Advanced Settings", open=False):
+                        with gr.Row( equal_height=True):
+                            strength = gr.Number(value=0.75, minimum=0, maximum=0.75, step=0.01, label="strength")
+                            seed = gr.Number(value=100, minimum=0, step=1, label="seed")
+                            inference_step = gr.Number(value=4, minimum=1, step=4, label="inference_step")
+                        id_input=gr.Text(label="Name", visible=False)
+                            
+                with gr.Column():
+                    image_out = gr.Image(height=800,sources=[],label="Output", elem_id="output-img",format="jpg")
+                   
+
+                    
+            
+
+    gr.Examples(
+               examples=[
+                    ["examples/draw_input.jpg", "examples/draw_output.jpg","a women ,eyes closed,mouth opened"],
+                    ["examples/draw-gimp_input.jpg", "examples/draw-gimp_output.jpg","a women ,eyes closed,mouth opened"],
+                    ["examples/gimp_input.jpg", "examples/gimp_output.jpg","a women ,hand on neck"],
+                    ["examples/inpaint_input.jpg", "examples/inpaint_output.jpg","a women ,hand on neck"]
+                         ]
+,
+                inputs=[image,image_out,prompt],
+    )
+    gr.HTML(
+       gr.HTML(read_file("demo_footer.html"))
+    )
     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],
+        triggers=[btn.click, prompt.submit],
+        fn = process_images,
+        inputs = [image,prompt,strength,seed,inference_step],
+        outputs = [image_out]
     )
 
 if __name__ == "__main__":
     demo.launch()
+
+