Duplicate from ioclab/illumination_composition_controlnet

.fqapp copy

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+from PIL import Image
+import gradio as gr
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+import torch
+torch.backends.cuda.matmul.allow_tf32 = True
+import gc
+
+controlnet = [ControlNetModel.from_pretrained("ioclab/connow", torch_dtype=torch.float16, use_safetensors=True),ControlNetModel.from_pretrained( "lllyasviel/control_v11p_sd15_seg" , torch_dtype=torch.float16),] 
+
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "andite/anything-v4.0",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+    safety_checker=None,
+)
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# pipe.enable_xformers_memory_efficient_attention()
+# pipe.enable_model_cpu_offload()
+# pipe.enable_attention_slicing()
+
+def infer(
+        prompt,
+        negative_prompt,
+        conditioning_image,
+        seg_image,
+        num_inference_steps=30,
+        size=768,
+        guidance_scale=7.0,
+        seed=1234,
+        ill=0.6,
+        seg=1
+
+):
+
+    conditioning_image = Image.fromarray(conditioning_image)
+    # conditioning_image = conditioning_image_raw.convert('L')
+    seg_image= Image.fromarray(seg_image)
+    g_cpu = torch.Generator()
+
+    if seed == -1:
+        generator = g_cpu.manual_seed(g_cpu.seed())
+    else:
+        generator = g_cpu.manual_seed(seed)
+    isa = [conditioning_image,seg_image]
+    output_image = pipe(
+        prompt,
+        isa,
+        height=size,
+        width=size,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        controlnet_conditioning_scale=[ill,seg],
+        
+    ).images[0]
+
+    del conditioning_image, conditioning_image_raw,seg_image
+    gc.collect()
+
+    return output_image
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    # ControlNet on Brightness
+
+    This is a demo on ControlNet based on brightness.
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+            )
+            conditioning_image = gr.Image(
+                label="Conditioning Image",
+            )
+            seg_image = gr.Image(
+                label="(Optional)seg Image",
+            )
+            with gr.Accordion('Advanced options', open=False):
+                with gr.Row():
+                    num_inference_steps = gr.Slider(
+                        10, 40, 20,
+                        step=1,
+                        label="Steps",
+                    )
+                    size = gr.Slider(
+                        256, 768, 512,
+                        step=128,
+                        label="Size",
+                    )
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label='Guidance Scale',
+                        minimum=0.1,
+                        maximum=30.0,
+                        value=7.0,
+                        step=0.1
+                    )
+                    seed = gr.Slider(
+                        label='Seed',
+                        value=-1,
+                        minimum=-1,
+                        maximum=2147483647,
+                        step=1,
+                        # randomize=True
+                    )
+                with gr.Row():
+                    ill = gr.Slider(
+                        label='controlnet_ILL_scale',
+                        minimum=0,
+                        maximum=1,
+                        value=0.6,
+                        step=0.05
+                    )
+                    seg = gr.Slider(
+                        label='controlnet_SEG_scale',
+                        value=1,
+                        minimum=0,
+                        maximum=1,
+                        step=0.1,
+                        # randomize=True
+                    )
+            submit_btn = gr.Button(
+                value="Submit",
+                variant="primary"
+            )
+        with gr.Column(min_width=300):
+            output = gr.Image(
+                label="Result",
+            )
+
+    submit_btn.click(
+        fn=infer,
+        inputs=[
+            prompt, negative_prompt, conditioning_image,seg_image, num_inference_steps, size, guidance_scale, seed,ill,seg
+        ],
+        outputs=output
+    )
+    gr.Markdown(
+        """
+    * [Dataset](https://huggingface.co/datasets/ioclab/grayscale_image_aesthetic_3M) Note that this was handled extra, and a preview version of the processing is here
+      [Anime Dataset](https://huggingface.co/datasets/ioclab/lighttestout)  [Nature Dataset] (https://huggingface.co/datasets/ioclab/light)
+    * [Diffusers model](https://huggingface.co/ioclab/connow/tree/main), [Web UI model](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui)
+    * [Training Report](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui), [Doc(Chinese)](https://aigc.ioclab.com/sd-showcase/light_controlnet.html)
+    """)
+
+demo.launch()

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

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+
+.idea
+
+venv
+.venv
+
+gradio_cached_examples

README.md

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+---
+title: Brightness ControlNet
+emoji: 💻
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: 3.27.0
+app_file: app.py
+pinned: false
+tags:
+- jax-diffusers-event
+duplicated_from: ioclab/illumination_composition_controlnet
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from PIL import Image
+import gradio as gr
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+import torch
+torch.backends.cuda.matmul.allow_tf32 = True
+import gc
+
+controlnet = ControlNetModel.from_pretrained("ioclab/connow", torch_dtype=torch.float16, use_safetensors=True)
+
+pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    "andite/anything-v4.0",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+    safety_checker=None,
+)
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+pipe.enable_xformers_memory_efficient_attention()
+pipe.enable_model_cpu_offload()
+pipe.enable_attention_slicing()
+
+def infer(
+        prompt,
+        negative_prompt,
+        conditioning_image,
+        num_inference_steps=30,
+        size=768,
+        guidance_scale=7.0,
+        seed=1234,
+        ill=0.6,
+):
+
+    conditioning_image = Image.fromarray(conditioning_image)
+    # conditioning_image = conditioning_image_raw.convert('L')
+    g_cpu = torch.Generator()
+
+    if seed == -1:
+        generator = g_cpu.manual_seed(g_cpu.seed())
+    else:
+        generator = g_cpu.manual_seed(seed)
+
+    output_image = pipe(
+        prompt,
+        conditioning_image,
+        height=size,
+        width=size,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        controlnet_conditioning_scale=ill,
+        
+    ).images[0]
+
+    del conditioning_image, conditioning_image_raw,seg_image
+    gc.collect()
+
+    return output_image
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+    # ControlNet on Brightness
+
+    This is a demo on ControlNet based on brightness.
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+            )
+            conditioning_image = gr.Image(
+                label="Conditioning Image",
+            )
+            with gr.Accordion('Advanced options', open=False):
+                with gr.Row():
+                    num_inference_steps = gr.Slider(
+                        10, 40, 20,
+                        step=1,
+                        label="Steps",
+                    )
+                    size = gr.Slider(
+                        256, 768, 512,
+                        step=128,
+                        label="Size",
+                    )
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label='Guidance Scale',
+                        minimum=0.1,
+                        maximum=30.0,
+                        value=7.0,
+                        step=0.1
+                    )
+                    seed = gr.Slider(
+                        label='Seed',
+                        value=-1,
+                        minimum=-1,
+                        maximum=2147483647,
+                        step=1,
+                        # randomize=True
+                    )
+                with gr.Row():
+                    ill = gr.Slider(
+                        label='controlnet_ILL_scale',
+                        minimum=0,
+                        maximum=1,
+                        value=0.6,
+                        step=0.05
+                    )
+                    
+            submit_btn = gr.Button(
+                value="Submit",
+                variant="primary"
+            )
+        with gr.Column(min_width=300):
+            output = gr.Image(
+                label="Result",
+            )
+
+    submit_btn.click(
+        fn=infer,
+        inputs=[
+            prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed,ill,
+        ],
+        outputs=output
+    )
+    gr.Markdown(
+        """
+    * [Dataset](https://huggingface.co/datasets/ioclab/grayscale_image_aesthetic_3M) Note that this was handled extra, and a preview version of the processing is here
+      [Anime Dataset](https://huggingface.co/datasets/ioclab/lighttestout)  [Nature Dataset] (https://huggingface.co/datasets/ioclab/light)
+    * [Diffusers model](https://huggingface.co/ioclab/connow/tree/main), [Web UI model](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui)
+    * [Training Report](https://huggingface.co/ioclab/control_v1u_sd15_illumination_webui), [Doc(Chinese)](https://aigc.ioclab.com/sd-showcase/light_controlnet.html)
+    """)
+
+demo.launch()

requirements.txt

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+
+accelerate
+diffusers
+transformers
+torch
+xformers
+safetensors