Update diffusers_image_outpaint_jupyter.ipynb

diffusers_image_outpaint_jupyter.ipynb

@@ -1,108 +1,237 @@
-%cd /content
-!git clone -b dev https://github.com/camenduru/diffusers-image-outpaint-hf
-%cd /content/diffusers-image-outpaint-hf
-!pip install transformers accelerate diffusers
-
-!apt -y install -qq aria2
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/model_index.json -d /content/model/lightning -o model_index.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/scheduler/scheduler_config.json -d /content/model/lightning/scheduler -o scheduler_config.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/text_encoder/config.json -d /content/model/lightning/text_encoder -o config.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/resolve/main/lightning/text_encoder/model.fp16.safetensors -d /content/model/lightning/text_encoder -o model.fp16.safetensors
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/text_encoder_2/config.json -d /content/model/lightning/text_encoder_2 -o config.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/resolve/main/lightning/text_encoder_2/model.fp16.safetensors -d /content/model/lightning/text_encoder_2 -o model.fp16.safetensors
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/tokenizer/merges.txt -d /content/model/lightning/tokenizer -o merges.txt
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/tokenizer/special_tokens_map.json -d /content/model/lightning/tokenizer -o special_tokens_map.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/tokenizer/tokenizer_config.json -d /content/model/lightning/tokenizer -o tokenizer_config.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/tokenizer/vocab.json -d /content/model/lightning/tokenizer -o vocab.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/tokenizer_2/merges.txt -d /content/model/lightning/tokenizer_2 -o merges.txt
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/tokenizer_2/special_tokens_map.json -d /content/model/lightning/tokenizer_2 -o special_tokens_map.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/tokenizer_2/tokenizer_config.json -d /content/model/lightning/tokenizer_2 -o tokenizer_config.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/tokenizer_2/vocab.json -d /content/model/lightning/tokenizer_2 -o vocab.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/unet/config.json -d /content/model/lightning/unet -o config.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/resolve/main/lightning/unet/diffusion_pytorch_model.fp16.safetensors -d /content/model/lightning/unet -o diffusion_pytorch_model.fp16.safetensors
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/unet/diffusion_pytorch_model.safetensors.index.json -d /content/model/lightning/unet -o diffusion_pytorch_model.safetensors.index.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/lightning/vae/config.json -d /content/model/lightning/vae -o config.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/resolve/main/lightning/vae/diffusion_pytorch_model.fp16.safetensors -d /content/model/lightning/vae -o diffusion_pytorch_model.fp16.safetensors
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/vae-fix/config.json -d /content/model/vae-fix -o config.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/resolve/main/vae-fix/diffusion_pytorch_model.safetensors -d /content/model/vae-fix -o diffusion_pytorch_model.safetensors
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/raw/main/union/config_promax.json -d /content/model/union -o config_promax.json
-!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/camenduru/outpaint/resolve/main/union/diffusion_pytorch_model_promax.safetensors -d /content/model/union -o diffusion_pytorch_model_promax.safetensors
-
-
-    import torch
-    from diffusers import AutoencoderKL
-    from diffusers.models.model_loading_utils import load_state_dict
-    from controlnet_union import ControlNetModel_Union
-    from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline
-    from PIL import Image, ImageDraw
-
-    config = ControlNetModel_Union.load_config("/content/model/union/config_promax.json")
-    controlnet_model = ControlNetModel_Union.from_config(config)
-    state_dict = load_state_dict("/content/model/union/diffusion_pytorch_model_promax.safetensors")
-    model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model(controlnet_model, state_dict, "/content/model/union/diffusion_pytorch_model_promax.safetensors", "/content/model/union")
-    model.to(device="cuda", dtype=torch.float16)
-    vae = AutoencoderKL.from_pretrained("/content/model/vae-fix", torch_dtype=torch.float16).to("cuda")
-    pipe = StableDiffusionXLFillPipeline.from_pretrained("/content/model/lightning", torch_dtype=torch.float16, vae=vae, controlnet=model, variant="fp16").to("cuda")
-
-    def infer(image, width, height, overlap_width, num_inference_steps, prompt_input=None):
-        source = image
-        target_size = (width, height)
-        overlap = overlap_width
-
-        if source.width < target_size[0] and source.height < target_size[1]:
-            scale_factor = min(target_size[0] / source.width, target_size[1] / source.height)
-            new_width = int(source.width * scale_factor)
-            new_height = int(source.height * scale_factor)
-            source = source.resize((new_width, new_height), Image.LANCZOS)
-
-        if source.width > target_size[0] or source.height > target_size[1]:
-            scale_factor = min(target_size[0] / source.width, target_size[1] / source.height)
-            new_width = int(source.width * scale_factor)
-            new_height = int(source.height * scale_factor)
-            source = source.resize((new_width, new_height), Image.LANCZOS)
-
-        margin_x = (target_size[0] - source.width) // 2
-        margin_y = (target_size[1] - source.height) // 2
-
-        background = Image.new('RGB', target_size, (255, 255, 255))
-        background.paste(source, (margin_x, margin_y))
-
-        mask = Image.new('L', target_size, 255)
-        mask_draw = ImageDraw.Draw(mask)
-        mask_draw.rectangle([
-            (margin_x + overlap, margin_y + overlap),
-            (margin_x + source.width - overlap, margin_y + source.height - overlap)
-        ], fill=0)
-
-        cnet_image = background.copy()
-        cnet_image.paste(0, (0, 0), mask)
-
-        final_prompt = "high quality"
-        if prompt_input and prompt_input.strip():
-            final_prompt += ", " + prompt_input
-
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = pipe.encode_prompt(final_prompt, "cuda", True)
-
-        results = []
-        
-        for image in pipe(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            image=cnet_image,
-            num_inference_steps=num_inference_steps
-        ):
-            results.append((cnet_image, image))
-
-        image = image.convert("RGBA")
-        cnet_image.paste(image, (0, 0), mask)
-
-        results.append((background, cnet_image))
-
-        return results
+import gradio as gr
+import torch
+from diffusers import AutoencoderKL, TCDScheduler
+from diffusers.models.model_loading_utils import load_state_dict
+from gradio_imageslider import ImageSlider
+from huggingface_hub import hf_hub_download
+
+from controlnet_union import ControlNetModel_Union
+from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline
+
+from PIL import Image, ImageDraw
+import numpy as np
+
+MODELS = {
+    "RealVisXL V5.0 Lightning": "SG161222/RealVisXL_V5.0_Lightning",
+}
+
+config_file = hf_hub_download(
+    "xinsir/controlnet-union-sdxl-1.0",
+    filename="config_promax.json",
+)
+
+config = ControlNetModel_Union.load_config(config_file)
+controlnet_model = ControlNetModel_Union.from_config(config)
+model_file = hf_hub_download(
+    "xinsir/controlnet-union-sdxl-1.0",
+    filename="diffusion_pytorch_model_promax.safetensors",
+)
+state_dict = load_state_dict(model_file)
+model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model(
+    controlnet_model, state_dict, model_file, "xinsir/controlnet-union-sdxl-1.0"
+)
+model.to(device="cuda", dtype=torch.float16)
+
+vae = AutoencoderKL.from_pretrained(
+    "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
+).to("cuda")
+
+pipe = StableDiffusionXLFillPipeline.from_pretrained(
+    "SG161222/RealVisXL_V5.0_Lightning",
+    torch_dtype=torch.float16,
+    vae=vae,
+    controlnet=model,
+    variant="fp16",
+).to("cuda")
+
+pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+
+
+def infer(image, model_selection, width, height, overlap_width, num_inference_steps, prompt_input=None):
+    source = image
+    target_size = (width, height)
+    target_ratio = (width, height)  # Calculate aspect ratio from width and height
+    overlap = overlap_width
+
+    # Upscale if source is smaller than target in both dimensions
+    if source.width < target_size[0] and source.height < target_size[1]:
+        scale_factor = min(target_size[0] / source.width, target_size[1] / source.height)
+        new_width = int(source.width * scale_factor)
+        new_height = int(source.height * scale_factor)
+        source = source.resize((new_width, new_height), Image.LANCZOS)
+
+    if source.width > target_size[0] or source.height > target_size[1]:
+        scale_factor = min(target_size[0] / source.width, target_size[1] / source.height)
+        new_width = int(source.width * scale_factor)
+        new_height = int(source.height * scale_factor)
+        source = source.resize((new_width, new_height), Image.LANCZOS)
+
+    margin_x = (target_size[0] - source.width) // 2
+    margin_y = (target_size[1] - source.height) // 2
+
+    background = Image.new('RGB', target_size, (255, 255, 255))
+    background.paste(source, (margin_x, margin_y))
+
+    mask = Image.new('L', target_size, 255)
+    mask_draw = ImageDraw.Draw(mask)
+    mask_draw.rectangle([
+        (margin_x + overlap, margin_y + overlap),
+        (margin_x + source.width - overlap, margin_y + source.height - overlap)
+    ], fill=0)
+
+    cnet_image = background.copy()
+    cnet_image.paste(0, (0, 0), mask)
+
+    final_prompt = "high quality"
+    if prompt_input.strip() != "":
+        final_prompt += ", " + prompt_input
+
+    (
+        prompt_embeds,
+        negative_prompt_embeds,
+        pooled_prompt_embeds,
+        negative_pooled_prompt_embeds,
+    ) = pipe.encode_prompt(final_prompt, "cuda", True)
+
+    for image in pipe(
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=negative_prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+        image=cnet_image,
+        num_inference_steps=num_inference_steps
+    ):
+        yield cnet_image, image
+
+    image = image.convert("RGBA")
+    cnet_image.paste(image, (0, 0), mask)
+
+    yield background, cnet_image
+
+def preload_presets(target_ratio):
+    if target_ratio == "9:16":
+        changed_width = 720
+        changed_height = 1280
+        return changed_width, changed_height, gr.update(open=False)
+    elif target_ratio == "16:9":
+        changed_width = 1280
+        changed_height = 720
+        return changed_width, changed_height, gr.update(open=False)
+    elif target_ratio == "Custom":
+        return 720, 1280, gr.update(open=True)
+
+def clear_result():
+    return gr.update(value=None)
+
+
+css = """
+.gradio-container {
+    width: 1200px !important;
+}
+"""
+
+
+title = """<h1 align="center">Diffusers Image Outpaint</h1>
+<div align="center">Drop an image you would like to extend, pick your expected ratio and hit Generate.</div>
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column():
+        gr.HTML(title)
+
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(
+                    type="pil",
+                    label="Input Image",
+                    sources=["upload"],
+                    height = 300
+                )
+                
+                prompt_input = gr.Textbox(label="Prompt (Optional)")
+                
+                with gr.Row():
+                    target_ratio = gr.Radio(
+                        label = "Expected Ratio",
+                        choices = ["9:16", "16:9", "Custom"],
+                        value = "9:16",
+                        scale = 2
+                    )
+                    
+                    run_button = gr.Button("Generate", scale=1)
+
+                with gr.Accordion(label="Advanced settings", open=False) as settings_panel:
+                    with gr.Column(): 
+                        with gr.Row():
+                            width_slider = gr.Slider(
+                                label="Width",
+                                minimum=720,
+                                maximum=1440,
+                                step=8,
+                                value=720,  # Set a default value
+                            )
+                            height_slider = gr.Slider(
+                                label="Height",
+                                minimum=720,
+                                maximum=1440,
+                                step=8,
+                                value=1280,  # Set a default value
+                            )
+                        with gr.Row():
+                            model_selection = gr.Dropdown(
+                                choices=list(MODELS.keys()),
+                                value="RealVisXL V5.0 Lightning",
+                                label="Model",
+                            )
+                            num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8 )
+
+                        overlap_width = gr.Slider(
+                            label="Mask overlap width",
+                            minimum=1,
+                            maximum=50,
+                            value=42,
+                            step=1
+                        )
+
+                gr.Examples(
+                    examples=[
+                        ["./examples/example_1.webp", "RealVisXL V5.0 Lightning", 1280, 720],  
+                        ["./examples/example_2.jpg", "RealVisXL V5.0 Lightning", 720, 1280],  
+                        ["./examples/example_3.jpg", "RealVisXL V5.0 Lightning", 1024, 1024],  
+                    ],
+                    inputs=[input_image, model_selection, width_slider, height_slider],
+                )
+
+            with gr.Column():
+                result = ImageSlider(
+                    interactive=False,
+                    label="Generated Image",
+                )
+
+    target_ratio.change(
+        fn = preload_presets,
+        inputs = [target_ratio],
+        outputs = [width_slider, height_slider, settings_panel],
+        queue = False
+    )
+    run_button.click(
+        fn=clear_result,
+        inputs=None,
+        outputs=result,
+    ).then(
+        fn=infer,
+        inputs=[input_image, model_selection, width_slider, height_slider, overlap_width, num_inference_steps, prompt_input],
+        outputs=result,
+    )
+
+    prompt_input.submit(
+        fn=clear_result,
+        inputs=None,
+        outputs=result,
+    ).then(
+        fn=infer,
+        inputs=[input_image, model_selection, width_slider, height_slider, overlap_width, num_inference_steps, prompt_input],
+        outputs=result,
+    )
+
+demo.queue().launch(share=True, show_error=True, show_api=True, inline=False)