use customer diffuser

app.py

@@ -1,148 +1,8 @@
-# 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 sentencepiece
+import torch
 import spaces
 import gradio as gr
-from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
+from diffusers.pipelines.flux.pipeline_flux_controlnet_inpaint import FluxControlNetInpaintPipeline
 from diffusers.models.controlnet_flux import FluxControlNetModel
 from controlnet_aux import CannyDetector
 
@@ -153,12 +13,11 @@ 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(
+pipe = FluxControlNetInpaintPipeline.from_pretrained(
     base_model, controlnet=controlnet, torch_dtype=dtype
 ).to(device)
 
 pipe.enable_model_cpu_offload()
-# pipe.to("cuda")
 
 canny = CannyDetector()
 
@@ -202,6 +61,147 @@ iface = gr.Interface(
     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()
+
+    # 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()
+

requirements.txt

@@ -1,5 +1,5 @@
 torch
-git+https://github.com/huggingface/diffusers.git
+git+https://github.com/culda/flux-controlnet-inpaint.git
 transformers
 accelerate
 controlnet_aux