Update README.md

README.md

@@ -4,9 +4,9 @@ emoji: 🏆
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 4.39.0
+sdk_version: 5.14.0
 app_file: app.py
 pinned: true
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,106 +1,57 @@
-import spaces
-
 import gradio as gr
 import numpy as np
 import os
+import spaces
 import random
 import json
+# from image_gen_aux import DepthPreprocessor
 from PIL import Image
 import torch
 from torchvision import transforms
-import zipfile
 
 from diffusers import FluxFillPipeline, AutoencoderKL
 from PIL import Image
-# from samgeo.text_sam import LangSAM
+
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-# sam = LangSAM(model_type="sam2-hiera-large").to(device)
-
 pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
+# pipe.load_lora_weights("Himanshu806/testLora")
+# pipe.enable_lora()
 
-# with open("lora_models.json", "r") as f:
-#     lora_models = json.load(f)
-
-# def download_model(model_name, model_path):
-#     print(f"Downloading model: {model_name} from {model_path}")
-#     try:
-#         pipe.load_lora_weights(model_path)
-#         print(f"Successfully downloaded model: {model_name}")
-#     except Exception as e:
-#         print(f"Failed to download model: {model_name}. Error: {e}")
-
-# # Iterate through the models and download each one
-# for model_name, model_path in lora_models.items():
-#     download_model(model_name, model_path)
-
-# lora_models["None"] = None
+with open("lora_models.json", "r") as f:
+    lora_models = json.load(f)
 
-# def calculate_optimal_dimensions(image: Image.Image):
-#     # Extract the original dimensions
-#     original_width, original_height = image.size
+def download_model(model_name, model_path):
+    print(f"Downloading model: {model_name} from {model_path}")
+    try:
+        pipe.load_lora_weights(model_path)
+        print(f"Successfully downloaded model: {model_name}")
+    except Exception as e:
+        print(f"Failed to download model: {model_name}. Error: {e}")
 
-#     # Set constants
-#     MIN_ASPECT_RATIO = 9 / 16
-#     MAX_ASPECT_RATIO = 16 / 9
-#     FIXED_DIMENSION = 1024
+# Iterate through the models and download each one
+for model_name, model_path in lora_models.items():
+    download_model(model_name, model_path)
 
-#     # Calculate the aspect ratio of the original image
-#     original_aspect_ratio = original_width / original_height
-
-#     # Determine which dimension to fix
-#     if original_aspect_ratio > 1:  # Wider than tall
-#         width = FIXED_DIMENSION
-#         height = round(FIXED_DIMENSION / original_aspect_ratio)
-#     else:  # Taller than wide
-#         height = FIXED_DIMENSION
-#         width = round(FIXED_DIMENSION * original_aspect_ratio)
-
-#     # Ensure dimensions are multiples of 8
-#     width = (width // 8) * 8
-#     height = (height // 8) * 8
-
-#     # Enforce aspect ratio limits
-#     calculated_aspect_ratio = width / height
-#     if calculated_aspect_ratio > MAX_ASPECT_RATIO:
-#         width = (height * MAX_ASPECT_RATIO // 8) * 8
-#     elif calculated_aspect_ratio < MIN_ASPECT_RATIO:
-#         height = (width / MIN_ASPECT_RATIO // 8) * 8
-
-#     # Ensure width and height remain above the minimum dimensions
-#     width = max(width, 576) if width == FIXED_DIMENSION else width
-#     height = max(height, 576) if height == FIXED_DIMENSION else height
-
-#     return width, height
+lora_models["None"] = None
 
 @spaces.GPU(durations=300)
-# def infer(edit_images, prompt, width, height, lora_model, strength, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-def infer(edit_images, prompt, width, height, strength, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(edit_images, prompt, width, height, lora_model, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     # pipe.enable_xformers_memory_efficient_attention()
-    gr.Info("Infering")
-
-    # if lora_model != "None":
-    #     pipe.load_lora_weights(lora_models[lora_model])
-    #     pipe.enable_lora()
 
-    gr.Info("starting checks")
+    if lora_model != "None":
+        pipe.load_lora_weights(lora_models[lora_model])
+        pipe.enable_lora()
 
     image = edit_images["background"]
-    mask = edit_images["layers"][0]
-
-    if not image:
-        gr.Info("Please upload an image.")
-        return None, None
-
     # width, height = calculate_optimal_dimensions(image)
+    mask = edit_images["layers"][0]
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
     # controlImage = processor(image)
-    gr.Info("generating image")
     image = pipe(
         # mask_image_latent=vae.encode(controlImage),
         prompt=prompt,
@@ -110,10 +61,8 @@ def infer(edit_images, prompt, width, height, strength, seed=42, randomize_seed=
         height=height,
         width=width,
         guidance_scale=guidance_scale,
-        # strength=strength,
         num_inference_steps=num_inference_steps,
         generator=torch.Generator(device='cuda').manual_seed(seed),
-        # generator=torch.Generator().manual_seed(seed),
         # lora_scale=0.75 // not supported in this version
     ).images[0]
 
@@ -123,55 +72,6 @@ def infer(edit_images, prompt, width, height, strength, seed=42, randomize_seed=
     return output_image_jpg, seed
     # return image, seed
 
-def download_image(image):
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-    image.save("output.png", "PNG")
-    return "output.png"
-
-def save_details(result, edit_image, prompt, strength, seed, guidance_scale, num_inference_steps):
-    image = edit_image["background"]
-    mask = edit_image["layers"][0]
-
-    if isinstance(result, np.ndarray):
-        result = Image.fromarray(result)
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-    if isinstance(mask, np.ndarray):
-        mask = Image.fromarray(mask)
-
-    result.save("saved_result.png", "PNG")
-    image.save("saved_image.png", "PNG")
-    mask.save("saved_mask.png", "PNG")
-
-    details = {
-        "prompt": prompt,
-        "strength": strength,
-        "seed": seed,
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": num_inference_steps
-    }
-
-    with open("details.json", "w") as f:
-        json.dump(details, f)
-
-    # Create a ZIP file
-    with zipfile.ZipFile("output.zip", "w") as zipf:
-        zipf.write("saved_result.png")
-        zipf.write("saved_image.png")
-        zipf.write("saved_mask.png")
-        zipf.write("details.json")
-
-    return "output.zip"
-
-def set_image_as_inpaint(image):
-    return image
-
-# def generate_mask(image, click_x, click_y):
-#     text_prompt = "face"
-#     mask = sam.predict(image, text_prompt, box_threshold=0.24, text_threshold=0.24)
-#     return mask
-
 examples = [
     "photography of a young woman,  accent lighting,  (front view:1.4),  "
     # "a tiny astronaut hatching from an egg on the moon",
@@ -210,11 +110,11 @@ with gr.Blocks(css=css) as demo:
                     container=False,
                 )
 
-                # lora_model = gr.Dropdown(
-                #     label="Select LoRA Model",
-                #     choices=list(lora_models.keys()),
-                #     value="None",
-                # )
+                lora_model = gr.Dropdown(
+                    label="Select LoRA Model",
+                    choices=list(lora_models.keys()),
+                    value="None",
+                )
 
                 run_button = gr.Button("Run")
 
@@ -250,16 +150,6 @@ with gr.Blocks(css=css) as demo:
                     value=28,
                 )
 
-            with gr.Row():
-
-                strength = gr.Slider(
-                    label="Strength",
-                    minimum=0,
-                    maximum=1,
-                    step=0.01,
-                    value=0.85,
-                )
-
             with gr.Row():
 
                 width = gr.Slider(
@@ -281,38 +171,10 @@ with gr.Blocks(css=css) as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
-        inputs = [edit_image, prompt, width, height, strength, seed, randomize_seed, guidance_scale, num_inference_steps],
+        inputs = [edit_image, prompt, width, height, lora_model, seed, randomize_seed, guidance_scale, num_inference_steps],
         outputs = [result, seed]
     )
 
-    download_button = gr.Button("Download Image as PNG")
-    set_inpaint_button = gr.Button("Set Image as Inpaint")
-    save_button = gr.Button("Save Details")
-
-    download_button.click(
-            fn=download_image,
-            inputs=[result],
-            outputs=gr.File(label="Download Image")
-        )
-
-    set_inpaint_button.click(
-            fn=set_image_as_inpaint,
-            inputs=[result],
-            outputs=[edit_image]
-    )
-
-    save_button.click(
-            fn=save_details,
-            inputs=[result, edit_image, prompt, strength, seed, guidance_scale, num_inference_steps],
-            outputs=gr.File(label="Download/Save Status")
-    )
-
-    # edit_image.select(
-    #     fn=generate_mask,
-    #     inputs=[edit_image, gr.Number(), gr.Number()],
-    #     outputs=[edit_image]
-    # )
-
 # demo.launch()
 PASSWORD = os.getenv("GRADIO_PASSWORD")
 USERNAME = os.getenv("GRADIO_USERNAME")
@@ -325,4 +187,4 @@ def authenticate(username, password):
         return False
 # Launch the app with authentication
 
-demo.launch(debug=True, auth=authenticate)
+demo.launch(auth=authenticate)

lora_models.json

@@ -1,9 +1,4 @@
 {
-  "RahulFineTuned (qwertyui)": "Himanshu806/testLora",
-  "femaleIndian (indmodelf)": "Himanshu806/ind-f-model",
-  "KodaRealistic (flmft style)": "alvdansen/flux-koda",
-  "superRealism (Super Realism)": "strangerzonehf/Flux-Super-Realism-LoRA",
-  "ThirdMarch blue (blueThirdMarchDress)": "Himanshu806/bluethirdmarchdress",
-  "manosouthf (manoSouthf)": "Himanshu806/manosouthf",
-  "greenDress (onlyGreenDress)": "Himanshu806/onlygreendress"
+  "RahulFineTuned": "Himanshu806/testLora",
+  "KodaRealistic": "alvdansen/flux-koda"
 }

readme.md

@@ -1,10 +1,10 @@
 ---
-title: Inpainting Test UI
+title: Inpainting
 emoji: 🏆
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 5.8.0
+sdk_version: 4.39.0
 app_file: app.py
 pinned: true
 ---

requirements.txt

@@ -1,5 +1,5 @@
 git+https://github.com/asomoza/image_gen_aux.git
-git+https://github.com/huggingface/diffusers
+git+https://github.com/huggingface/diffusers.git
 transformers
 accelerate
 safetensors
@@ -8,6 +8,3 @@ peft
 xformers
 torchvision
 torch
-opencv-python
-segment-geospatial
-groundingdino-py