Himanshu-AT commited on
Commit
e08bb94
·
1 Parent(s): fceb263
Files changed (3) hide show
  1. app.py +18 -6
  2. lora_models.json +2 -1
  3. requirements.txt +2 -1
app.py CHANGED
@@ -1,24 +1,24 @@
 
 
1
  import gradio as gr
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  import numpy as np
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  import os
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- import spaces
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  import random
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  import json
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- # from image_gen_aux import DepthPreprocessor
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  from PIL import Image
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  import torch
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  from torchvision import transforms
11
 
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  from diffusers import FluxFillPipeline, AutoencoderKL
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  from PIL import Image
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-
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  MAX_SEED = np.iinfo(np.int32).max
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  MAX_IMAGE_SIZE = 2048
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  pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
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- # pipe.load_lora_weights("Himanshu806/testLora")
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- # pipe.enable_lora()
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  with open("lora_models.json", "r") as f:
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  lora_models = json.load(f)
@@ -119,6 +119,11 @@ def download_image(image):
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  def set_image_as_inpaint(image):
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  return image
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  examples = [
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  "photography of a young woman, accent lighting, (front view:1.4), "
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  # "a tiny astronaut hatching from an egg on the moon",
@@ -237,6 +242,12 @@ with gr.Blocks(css=css) as demo:
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  outputs=[edit_image]
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  )
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  # demo.launch()
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  PASSWORD = os.getenv("GRADIO_PASSWORD")
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  USERNAME = os.getenv("GRADIO_USERNAME")
@@ -249,7 +260,8 @@ def authenticate(username, password):
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  return False
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  # Launch the app with authentication
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- demo.launch(auth=authenticate)
 
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  # import gradio as gr
 
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+ import spaces
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+
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  import gradio as gr
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  import numpy as np
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  import os
 
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  import random
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  import json
 
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  from PIL import Image
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  import torch
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  from torchvision import transforms
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  from diffusers import FluxFillPipeline, AutoencoderKL
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  from PIL import Image
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+ from samgeo.text_sam import LangSAM
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  MAX_SEED = np.iinfo(np.int32).max
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  MAX_IMAGE_SIZE = 2048
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+ sam = LangSAM(model_type="sam2-hiera-large")
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+
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  pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
 
 
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  with open("lora_models.json", "r") as f:
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  lora_models = json.load(f)
 
119
  def set_image_as_inpaint(image):
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  return image
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+ def generate_mask(image, click_x, click_y):
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+ text_prompt = "face"
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+ mask = sam.predict(image, text_prompt, box_threshold=0.24, text_threshold=0.24)
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+ return mask
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+
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  examples = [
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  "photography of a young woman, accent lighting, (front view:1.4), "
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  # "a tiny astronaut hatching from an egg on the moon",
 
242
  outputs=[edit_image]
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  )
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+ edit_image.select(
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+ fn=generate_mask,
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+ inputs=[edit_image, gr.Number(), gr.Number()],
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+ outputs=[edit_image]
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+ )
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+
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  # demo.launch()
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  PASSWORD = os.getenv("GRADIO_PASSWORD")
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  USERNAME = os.getenv("GRADIO_USERNAME")
 
260
  return False
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  # Launch the app with authentication
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+ demo.launch(debug=True, auth=authenticate)
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+ # demo.launch()
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  # import gradio as gr
lora_models.json CHANGED
@@ -1,5 +1,6 @@
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  {
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  "RahulFineTuned (qwertyui)": "Himanshu806/testLora",
 
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  "KodaRealistic (fmlft style)": "alvdansen/flux-koda",
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- "femaleIndian (indmodelf)": "Himanshu806/ind-f-model"
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  }
 
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  {
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  "RahulFineTuned (qwertyui)": "Himanshu806/testLora",
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+ "femaleIndian (indmodelf)": "Himanshu806/ind-f-model",
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  "KodaRealistic (fmlft style)": "alvdansen/flux-koda",
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+ "superRealism (Super Realism)": "strangerzonehf/Flux-Super-Realism-LoRA"
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  }
requirements.txt CHANGED
@@ -8,4 +8,5 @@ peft
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  xformers
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  torchvision
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  torch
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- opencv-python
 
 
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  xformers
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  torchvision
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  torch
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+ opencv-python
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+ segment-geospatial