image-generation-and-separation-stable-diffusion.ipynb

{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"id":"trIJRTk85SUF"},"source":["## 1). Install requirements"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2023-05-29T17:14:24.653014Z","iopub.status.busy":"2023-05-29T17:14:24.652005Z","iopub.status.idle":"2023-05-29T17:14:37.791675Z","shell.execute_reply":"2023-05-29T17:14:37.790363Z","shell.execute_reply.started":"2023-05-29T17:14:24.652975Z"},"executionInfo":{"elapsed":15709,"status":"ok","timestamp":1685387837323,"user":{"displayName":"Walid LACHHAB","userId":"01214786590472354326"},"user_tz":-120},"id":"v0M8x7Ty5SUL","outputId":"21e1aaa8-4b4a-488d-f76a-50c0a7765b33","trusted":true},"outputs":[],"source":["!pip install torch==1.12.1 numpy opencv-python diffusers==0.14.0 daam==0.0.14 transformers==4.27.4 gradio==3.32.0"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"Uj6VpQa55SUO"},"source":["## 2). Run the code"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"JilUCHWP5SUO"},"source":["You can either use it in the notebook or use the public link for a full screen"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["d87a35ec0aac47e2876b672ab30be3de","74272e6a8bfc45d5a747f2b3b24d284e","69144aac2b244306aa86cbfeb864f2c1","939e6a529ee8485a9d9e57c3dc110499","3dc0e2d9b04a4fc38ca0c21e004ed219","52247d52855e46a8b46226770c7886c6","146030fd17a04975824a01b929d5fb80","da3802154a38441e92827c55b53c9b64","450f20d17bc6488c8ec9928e60e1f627","79983cb0caf04871a442fd974657ee80","056a94ff7c174d6182ce14951152f53b","2b3f9dec86b84f19931cf22787e25d16","eb7b6478f09343858cf6381e8a2d7e88","0fd48ce03e064d11a16684fee8d9eba2","ca732d4710f24fa994a0eb0f59a55ead","3663010550db4c6dbaa0f9ea26416805","fe20696c9e004cc7aaec4b9c07c057d0","1dc825e444014fb5af6154a9b57214a5","80cc036f19d34f63ac4352303a9c38b2","d7780138f8834bd69d12e33f7c69830e","5c1d4e50d3c549b9b3b670b8a479402c","81c61f6827b349158853a8608453e15f","ea492d2a768849639f9d6648be5132d8","ced04e26d5b141a99b34a6ca5f7375fa","57a3ceab61ad469bb2c52ff8e7ca3f1a","d838758f6fbe444cbc60f074f56737d4","defdbfa01b9a45e092b1b953c8875dbf","74e0c8607be14f21b842760a34e5a38e","a54d372dc9ba4bc197c5cd0bcc5f94df","84afc8512bc04f9485fc30c566524a82","09d81940d65a46c9846affaed766e84a","6a243ed0fed84ce8a029afa40505490f","e03513a0527e4b27b2dc323233ef4f23","158146c79ac44c42bb231d45e2d808e1","d1311cce275c4252924a74077961b9b4","525a12fa8acb43cebccceeb89567191b","91dc9df123ee495c96b2f340b949d31b","6ee401793da740fb8ccbe04bfd8b53a6","43edeae506904729bc69090af6a56093","b6f8a9540e054ca69beb87a37df5fb04","de44329cc8854a128d5f58c0ad1c38f4","e7349eb58819459497505bddf82cf491","dc6cd963ee634a75ae404394b30da5c2","803ebc5df3fb4ebcac68a5afae4bfa9a","084d63b3d3bb47b88726a974a110947b","e43c6de8bde84efb845dfce31c7b76d6","e6e5c17e10784dffa420fa89e5ab3982","3abab20d6a014b238a4408d80a9881f8","9c74aa528cea44b3aba0f46dd29b48a2","ec02a8a86d7c49639cce774fbb1b60c9","5220691002514e5b9d3b227993fa7b49","7fcad8d66fdb405ebe0255521277b87d","ab6d21265c7145e993b1d58c25fea2b4","5e62c51ec5b544b6960f85ee4e69c1b0","f9ea291da0c8469db4c29f435a85d9df","34d71584d03d47109d3e23be7e3c30c2","1a55778cb234468e90d36c676170f83e","0a762b78523f4fe4adbc872fdae0c0f8","3fedd6b0eef845ee81ca1e6a2d01709b","fd232a3fed824628831d94aaf6a4e195","801dd99db4744aeab6553f0936f61902","504e2f8a0de4417eb79a79014ae946c6","4a2416d29ff1484aa880d8e7e7e6d155","5d79966192714f43ad437b7f6d45ada9","e1c58bfcca1540389383a63328d5f831","5f1472471f7d43a2a0602cdc311af74b","cba4cdaa90ca4cf2ac3b1db42e8ac40c","2154aec30deb47c884f22de72c385c02","8ff97487227e46059b144a8ed1eee62f","ac3fef7ac18f4739b36a2de6c8799001","f34b07bb1a92463eb25b0f0363c2cbdb","7defb07bbbf34565ba0a39b9e90fb02c","1e07876aa305441892fb1577cebff5c8","a68e2620f4e84aadb9d0ea6c6e92e4f2","d1f4773ecd9e4238b534f879cc1ce137","ea431b1d28da4908a482c80c94b31c54","43a536725cf04d519583d7263240eb70","1e2f0d5b6de2482b8ed33234e710487d","286c97d0dcec47099735370faf312da5","2eb3b96b95514001a0e2360ecda91657","a51191fa83cc4b279e8778cbf01d8158","1ace79b573954a0499eaaa3fcddab8b7","29827f8d22f14180a0aeeebfccd37cec","99b6ea295328425b9eef9d9009345562","f28bc88afa3f4b41887f52f2b4a81073","47672bcf994f4d4ca1257aaf0aea80d7","d6ea0d56b9854572aedf79fdd31faf4b","ea97ae6648c946738d05e2d38c42e9fe","12ac874a275e46ea81378b051ecebfd3","560b51fdd0a4433f8464026604f921dc","e727123362e34988892b3a9a567be42b","5bb3f3ebebfa452f9087da5f2e3c9c32","6306fe2e2d464447a01b137584d68e6e","373bfe3be2124fb994cc3e16807c1968","8fea2fda77df44eb82ddde89f2ad2478","51c71fb0c8584f6aabdb42aab7986812","0fc5d3ebdf39457893f82c7bc8438416","a8e34b9ffefa41029b23aefb2c3a7505","2ed42cc17e4d4fb49ee84ab973208781","bfa2887635c54031a1d149bd3fe8bef3","d7b6ff4a60eb44389f798fb23b3ad0f1","e5da5bdf74c84da88aafac131ea6b991","21d1c8e132d54499b6c95199b4f21d92","5c7d18acb8cf4bd6a1a41e8e8f4c1ad8","ca54a4a52d7147bfb02d5b0bdfb69bce","4822554c54a047348b2f513e9207bae3","fc4a654568cd4c948f0a694e27deaa40","6884f612c43640f6a7dc273418651bcd","267060bfec234bf8851143c5d298706d","621edba88e6049a393836edb878a80a5","b22eeadeb62b4f6a92b2590e215ea60e","1ff6e9af719f40a4af310448a5a0ba98","730cdeca6ade410eb8140b6ed389f29f","b1822587eadf4137b9531b7cc0f10365","90231a2312b349a981bc4d0a8463f5fe","8ddc2b6d24ae49da8c6c71754e69446c","e4b93b8ae629437a8dff829d46c51a5f","8375b95c8ab14704ae600c62a48da6a7","1657e345d4484ad9ac51bf5a31a85148","36f2e580ecd74e9aa4b522eccc7360ec","b730ea8abdcc4520b005c25f0ae82c40","4fe18119e3af4deba0cde9e3f990d534","1647b03d43334602b199c848fbb0adae","2dd7115f0b044fad8b00a1e825546bc8","310a282818c744b0bb3fae874c3b0cbf","16dd8f1982c449bb87b41534d9946e85","4d59732cdd1549af83c0d95d728f8db9","881f1593e6564d569b9efbea3a833e4a","6d16afb42ad4403a96bc9d21b5d626b3","87708638a6f44c7a85ce04759859a829","6a554e9162ca41f5996f176e746c45c6","a217ea0dc8cc48468fa2aea04c83f2eb","53d7ce575d9f4358880a38f50229acfa","99dc2a014d4c4a3583f3c7098f367194","64d21954191c4087b750ec650d35e775","add83e0ec1034b32b19c9fa738ede899","522b2f00a93c4e989e2899887fb2cf1f","fc6ffe1ef2c74549b19098a7dc683421","c1b2f5b6a1dc46dc96cb46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LACHHAB","userId":"01214786590472354326"},"user_tz":-120},"id":"XuUA2Bbc5SUP","outputId":"d4fecc9d-c7f8-4737-ef2c-cc9983ff1ba4","trusted":true},"outputs":[],"source":["# %%\n","import numpy as np\n","import torch\n","import cv2\n","import gradio as gr\n","from diffusers import StableDiffusionInpaintPipeline\n","from daam import trace, set_seed\n","\n","# %%\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","pipe = StableDiffusionInpaintPipeline.from_pretrained(\n","    \"runwayml/stable-diffusion-inpainting\",\n",").to(device)\n","\n","# %%\n","daam_heat_map = [0, 0]\n","prompts = [\"\"]\n","\n","# %%\n","def diffusion(img, mask, prompt, neg_prompt, steps, guidance, update_attention_map, text_to_image):\n","\n","    if text_to_image:\n","        mask = np.ones(shape=(512, 512), dtype=\"uint8\")\n","        image = np.ones(shape=(512, 512, 3), dtype=\"uint8\")\n","\n","    else: # inpainting\n","        if type(img) != type(None):\n","            mask = mask[..., 0]\n","            image = img[\"image\"]\n","        else:\n","            return None, prompts[0]\n","    \n","    # Diffusion\n","    with trace(pipe) as tc:\n","        image = pipe(prompt=prompt, image=image, mask_image=mask, negative_prompt=neg_prompt, num_inference_steps=steps, guidance_scale=guidance, ).images[0]\n","        # Damm attention weights\n","        heat_map = tc.compute_global_heat_map()\n","    if update_attention_map:\n","        daam_heat_map[0] = heat_map\n","        prompts[0] = prompt\n","    return image, prompts[0]\n","\n","# %%\n","def update_mask(img, invert, blur, load_attention_map, attention):\n","\n","    if load_attention_map:\n","        if type(attention) != type(None):\n","            mask = 255-attention[..., 0] if invert else attention[..., 0]\n","            mask = cv2.GaussianBlur(mask, (0, 0), blur) if blur else mask\n","            return mask\n","        else:\n","            return None\n","\n","    else:\n","        if type(img) != type(None):\n","            # Preprocessing\n","            mask = 255-img[\"mask\"][..., 0] if invert else img[\"mask\"][..., 0]\n","            mask = cv2.GaussianBlur(mask, (0, 0), blur) if blur else mask\n","            return mask\n","        else:\n","            return None\n","\n","# %%\n","def get_daam_map(word):\n","    heat_map = daam_heat_map[0].compute_word_heat_map(word)\n","    word_segment = heat_map.heatmap.cpu().numpy()\n","    word_segment /= word_segment.max()\n","    word_segment = (word_segment*255.0).astype(\"uint8\")\n","    word_segment = cv2.resize(word_segment, (512, 512))\n","    word_segment = (word_segment > 127).astype(\"uint8\")*255\n","    daam_heat_map[1] = word_segment\n","    return word_segment\n","\n","# %%\n","def load_attention(attention):\n","    if type(attention) != type(None):\n","        return attention\n","    else:\n","        return None\n","\n","# %%\n","with gr.Blocks() as demo:\n","\n","    with gr.Tab(label=\"Inpainting\"):\n","        with gr.Row():\n","            with gr.Column():\n","                input_image = gr.Image(label=\"Input image\", shape=(512, 512), tool=\"sketch\", source=\"upload\")\n","                text_to_image = gr.Checkbox(label=\"Text to image\")\n","                prompt = gr.Textbox(label=\"Prompt\", value=\"<write what you want>, high resolution, realistic, high quality, high definition, high details, natural colors\")\n","                neg_prompt = gr.Textbox(label=\"Negative prompt\", value=\"bad details, ugly, low resolution, low quality, unatural colors, bad anatomy, blurry, pixelated, obscure, poor lighting, unclear\")\n","                steps = gr.Slider(label=\"Inference steps\", minimum=20, maximum=300, step=1, value=30)\n","                guidance = gr.Slider(label=\"Guidance scale\", minimum=1, maximum=20, step=0.5, value=7.5)\n","            with gr.Column():\n","                mask_image = gr.Image(label=\"Input mask\", shape=(512, 512))\n","                invert = gr.Checkbox(label=\"Invert mask\")\n","                blur = gr.Slider(label=\"Blur strengh\", minimum=0, maximum=30, step=1, value=10)\n","                load_attention_map = gr.Checkbox(label=\"Load attention map\")\n","                update_attention_map = gr.Checkbox(label=\"Update attention map\")\n","            output_image = gr.Image(label=\"Output image\", shape=(512, 512))\n","\n","        submit = gr.Button(value=\"Run\")\n","\n","    with gr.Tab(label=\"Daam attention maps\"):\n","        with gr.Row():\n","            with gr.Column():\n","                attention = gr.Image(label=\"Attention mask\", shape=(512, 512))\n","            with gr.Column():\n","                label = gr.Label(label=\"Your prompt\")\n","                word = gr.Textbox(label=\"Word to visualize\")\n","\n","        get_map = gr.Button(value=\"Run\")\n","\n","    # Update mask\n","    input_image.edit(fn=update_mask, inputs=[input_image, invert, blur, load_attention_map, attention], outputs=[mask_image])\n","    invert.select(fn=update_mask, inputs=[input_image, invert, blur, load_attention_map, attention], outputs=[mask_image])\n","    blur.release(fn=update_mask, inputs=[input_image, invert, blur, load_attention_map, attention], outputs=[mask_image])\n","    # Run diffusion\n","    submit.click(fn=diffusion, inputs=[input_image, mask_image, prompt, neg_prompt, steps, guidance, update_attention_map, text_to_image], outputs=[output_image, label])\n","    # Attention maps\n","    get_map.click(fn=get_daam_map, inputs=[word], outputs=[attention])\n","    load_attention_map.select(fn=load_attention, inputs=[attention], outputs=[mask_image])\n","\n","demo.launch(share=True)\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"FPV7lHfU5SUS"},"source":["You can shut down the server by this 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