use spaces decorator

app.py

@@ -1,204 +1,206 @@
-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;
-}
-"""
-
-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)]
-        """)
-        
-        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)
-        
-        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.Examples(
-            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]
-    )
-
-demo.launch()
-# import torch
 # import gradio as gr
-# from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
-# from diffusers.models.controlnet_flux import FluxControlNetModel
-# from controlnet_aux import CannyDetector
+# 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"
 #
-# base_model = "black-forest-labs/FLUX.1-schnell"
-# controlnet_model = "YishaoAI/flux-dev-controlnet-canny-kid-clothes"
-#
-# controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=dtype)
-# pipe = FluxPipeline.from_pretrained(
-#     base_model, controlnet=controlnet, torch_dtype=dtype
-# ).to(device)
+# 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()
 #
-# pipe.enable_model_cpu_offload()
-# # pipe.to("cuda")
+# MAX_SEED = np.iinfo(np.int32).max
+# MAX_IMAGE_SIZE = 2048
 #
-# canny = CannyDetector()
+# # 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",
+# ]
 #
-# def inpaint(
-#     image,
-#     mask,
-#     prompt,
-#     strength,
-#     num_inference_steps,
-#     guidance_scale,
-#     controlnet_conditioning_scale,
-# ):
-#     canny_image = canny(image)
+# css="""
+# #col-container {
+#     margin: 0 auto;
+#     max-width: 520px;
+# }
+# """
 #
-#     image_res = pipe(
-#         prompt,
-#         image=image,
-#         control_image=canny_image,
-#         controlnet_conditioning_scale=controlnet_conditioning_scale,
-#         mask_image=mask,
-#         strength=strength,
-#         num_inference_steps=num_inference_steps,
-#         guidance_scale=guidance_scale,
-#     ).images[0]
+# 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)]
+#         """)
+#         
+#         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)
+#         
+#         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():
 #
-#     return image_res
+#                 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.Examples(
+#             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]
+#     )
 #
-# 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.",
-# )
-#
-# iface.launch()
+# demo.launch()
+
+import torch
+import spaces
+import gradio as gr
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
+from diffusers.models.controlnet_flux import FluxControlNetModel
+from controlnet_aux import CannyDetector
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+base_model = "black-forest-labs/FLUX.1-schnell"
+controlnet_model = "YishaoAI/flux-dev-controlnet-canny-kid-clothes"
+
+controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=dtype)
+pipe = FluxPipeline.from_pretrained(
+    base_model, controlnet=controlnet, torch_dtype=dtype
+).to(device)
+
+pipe.enable_model_cpu_offload()
+# pipe.to("cuda")
+
+canny = CannyDetector()
+
+@spaces.GPU(duration=75)
+def inpaint(
+    image,
+    mask,
+    prompt,
+    strength,
+    num_inference_steps,
+    guidance_scale,
+    controlnet_conditioning_scale,
+):
+    canny_image = canny(image)
+
+    image_res = pipe(
+        prompt,
+        image=image,
+        control_image=canny_image,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        mask_image=mask,
+        strength=strength,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+    ).images[0]
+
+    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.",
+)
+
+iface.launch()

requirements.txt

@@ -4,5 +4,5 @@ transformers
 accelerate
 controlnet_aux
 gradio
-sentencepiece
 tokenizers
+spaces