oauth

README.md

@@ -6,6 +6,7 @@ colorTo: gray
 sdk: gradio
 sdk_version: 4.44.0
 app_file: app.py
+hf_oauth: true
 pinned: false
 ---
 

app.py

@@ -3,13 +3,16 @@ import torch
 import spaces
 import gradio as gr
 import os
-from diffusers.pipelines.flux.pipeline_flux_controlnet_inpaint import FluxControlNetInpaintPipeline
+from diffusers.pipelines.flux.pipeline_flux_controlnet_inpaint import (
+    FluxControlNetInpaintPipeline,
+)
 from diffusers.models.controlnet_flux import FluxControlNetModel
 from controlnet_aux import CannyDetector
 
 # login hf token
 HF_TOKEN = os.getenv("HF_TOKEN")
 from huggingface_hub import login
+
 login()
 
 dtype = torch.bfloat16
@@ -27,6 +30,7 @@ pipe.enable_model_cpu_offload()
 
 canny = CannyDetector()
 
+
 @spaces.GPU(duration=75)
 def inpaint(
     image,
@@ -53,161 +57,162 @@ def inpaint(
     return image_res
 
 
-iface = gr.Interface(
-    fn=inpaint,
-    inputs=[
-        gr.Image(type="pil", label="Input Image"),
-        gr.Image(type="pil", label="Mask Image"),
-        gr.Textbox(label="Prompt"),
-        gr.Slider(0, 1, value=0.95, label="Strength"),
-        gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps"),
-        gr.Slider(0, 20, value=5, label="Guidance Scale"),
-        gr.Slider(0, 1, value=0.5, label="ControlNet Conditioning Scale"),
-    ],
-    outputs=gr.Image(type="pil", label="Output Image"),
-    title="Flux Inpaint AI Model",
-    description="Upload an image and a mask, then provide a prompt to generate an inpainted image.",
+with gr.Blocks() as demo:
+    gr.LoginButton()
+    gr.Interface(
+        fn=inpaint,
+        inputs=[
+            gr.Image(type="pil", label="Input Image"),
+            gr.Image(type="pil", label="Mask Image"),
+            gr.Textbox(label="Prompt"),
+            gr.Slider(0, 1, value=0.95, label="Strength"),
+            gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps"),
+            gr.Slider(0, 20, value=5, label="Guidance Scale"),
+            gr.Slider(0, 1, value=0.5, label="ControlNet Conditioning Scale"),
+        ],
+        outputs=gr.Image(type="pil", label="Output Image"),
+        title="Flux Inpaint AI Model",
+        description="Upload an image and a mask, then provide a prompt to generate an inpainted image.",
     )
 
-iface.launch()
+demo.launch()
 
-    # import gradio as gr
-    # import numpy as np
-    # import random
-    # # import spaces
-    # import torch
-    # from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
-    # from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
-    # # from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
-    #
-    # dtype = torch.bfloat16
-    # device = "cuda" if torch.cuda.is_available() else "cpu"
-    #
-    # taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-    # good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-schnell", subfolder="vae", torch_dtype=dtype).to(device)
-    # pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype, vae=taef1).to(device)
-    # torch.cuda.empty_cache()
-    #
-    # MAX_SEED = np.iinfo(np.int32).max
-    # MAX_IMAGE_SIZE = 2048
-    #
-    # # pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-    #
-    # # @spaces.GPU(duration=75)
-    # def infer(prompt, 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)
-    #     generator = torch.Generator().manual_seed(seed)
-    #     
-    #     for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-        #             prompt=prompt,
-        #             guidance_scale=guidance_scale,
-        #             num_inference_steps=num_inference_steps,
-        #             width=width,
-        #             height=height,
-        #             generator=generator,
-        #             output_type="pil",
-        #             good_vae=good_vae,
-        #         ):
-    #             yield img, seed
-#     
+# import gradio as gr
+# import numpy as np
+# import random
+# # import spaces
+# import torch
+# from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
+# from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+# # from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+#
+# dtype = torch.bfloat16
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+#
+# taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+# good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-schnell", subfolder="vae", torch_dtype=dtype).to(device)
+# pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype, vae=taef1).to(device)
+# torch.cuda.empty_cache()
+#
+# MAX_SEED = np.iinfo(np.int32).max
+# MAX_IMAGE_SIZE = 2048
+#
+# # pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
+#
+# # @spaces.GPU(duration=75)
+# def infer(prompt, 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)
+#     generator = torch.Generator().manual_seed(seed)
+#
+#     for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+#             prompt=prompt,
+#             guidance_scale=guidance_scale,
+#             num_inference_steps=num_inference_steps,
+#             width=width,
+#             height=height,
+#             generator=generator,
+#             output_type="pil",
+#             good_vae=good_vae,
+#         ):
+#             yield img, seed
+#
 # examples = [
-        #     "a tiny astronaut hatching from an egg on the moon",
-        #     "a cat holding a sign that says hello world",
-        #     "an anime illustration of a wiener schnitzel",
-        # ]
-#
-    # css="""
-    # #col-container {
-            #     margin: 0 auto;
-            #     max-width: 520px;
-            # }
+#     "a tiny astronaut hatching from an egg on the moon",
+#     "a cat holding a sign that says hello world",
+#     "an anime illustration of a wiener schnitzel",
+# ]
+#
+# css="""
+# #col-container {
+#     margin: 0 auto;
+#     max-width: 520px;
+# }
 # """
 #
 # with gr.Blocks(css=css) as demo:
-#     
+#
 #     with gr.Column(elem_id="col-container"):
 #         gr.Markdown(f"""# FLUX.1 [dev]
-                      # 12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-                      # [[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)]
-                      #         """)
-#         
+# 12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
+# [[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():
-#             
+#
 #             prompt = gr.Text(
-        #                 label="Prompt",
-        #                 show_label=False,
-        #                 max_lines=1,
-        #                 placeholder="Enter your prompt",
-        #                 container=False,
-        #             )
-#             
+#                 label="Prompt",
+#                 show_label=False,
+#                 max_lines=1,
+#                 placeholder="Enter your prompt",
+#                 container=False,
+#             )
+#
 #             run_button = gr.Button("Run", scale=0)
-#         
+#
 #         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,
-        #             )
-#             
+#                 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,
-        #                 )
-#                 
+#                     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,
-        #                 )
-#             
+#                     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,
-        #                 )
-#   
+#                     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,
-        #                 )
-#         
+#                     label="Number of inference steps",
+#                     minimum=1,
+#                     maximum=50,
+#                     step=1,
+#                     value=28,
+#                 )
+#
 #         gr.Examples(
-        #             examples = examples,
-        #             fn = infer,
-        #             inputs = [prompt],
-        #             outputs = [result, seed],
-        #             cache_examples="lazy"
-        #         )
+#             examples = examples,
+#             fn = infer,
+#             inputs = [prompt],
+#             outputs = [result, seed],
+#             cache_examples="lazy"
+#         )
 #
 #     gr.on(
-        #         triggers=[run_button.click, prompt.submit],
-        #         fn = infer,
-        #         inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        #         outputs = [result, seed]
-        #     )
+#         triggers=[run_button.click, prompt.submit],
+#         fn = infer,
+#         inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+#         outputs = [result, seed]
+#     )
 #
 # demo.launch()
-