added sdxl

sdxl/.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
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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sdxl/README.md

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+---
+title: SD-XL + Control LoRas
+emoji: 🦀
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.44.4
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

sdxl/app_inference.py

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+import gradio as gr
+from huggingface_hub import login, HfFileSystem, HfApi, ModelCard
+import os
+import spaces
+import random
+import torch
+
+is_shared_ui = False
+
+hf_token = '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'
+login(token=hf_token)
+
+fs = HfFileSystem(token=hf_token)
+api = HfApi()
+
+device="cuda" if torch.cuda.is_available() else "cpu"
+
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
+from diffusers.utils import load_image
+from PIL import Image
+import torch
+import numpy as np
+import cv2
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+
+controlnet = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-canny-sdxl-1.0",
+    torch_dtype=torch.float16
+)
+
+def check_use_custom_or_no(value):
+    if value is True:
+        return gr.update(visible=True)
+    else:
+        return gr.update(visible=False)
+
+def get_files(file_paths):
+    last_files = {}  # Dictionary to store the last file for each path
+
+    for file_path in file_paths:
+        # Split the file path into directory and file components
+        directory, file_name = file_path.rsplit('/', 1)
+    
+        # Update the last file for the current path
+        last_files[directory] = file_name
+    
+    # Extract the last files from the dictionary
+    result = list(last_files.values())
+
+    return result
+
+def load_model(model_name):
+
+    if model_name == "":
+        gr.Warning("If you want to use a private model, you need to duplicate this space on your personal account.")
+        raise gr.Error("You forgot to define Model ID.")
+
+    # Get instance_prompt a.k.a trigger word
+    card = ModelCard.load(model_name)
+    repo_data = card.data.to_dict()
+    instance_prompt = repo_data.get("instance_prompt")
+
+    if instance_prompt is not None:
+        print(f"Trigger word: {instance_prompt}")
+    else:
+        instance_prompt = "no trigger word needed"
+        print(f"Trigger word: no trigger word needed")
+
+    # List all ".safetensors" files in repo
+    sfts_available_files = fs.glob(f"{model_name}/*safetensors")
+    sfts_available_files = get_files(sfts_available_files)
+
+    if sfts_available_files == []:
+        sfts_available_files = ["NO SAFETENSORS FILE"]
+
+    print(f"Safetensors available: {sfts_available_files}")
+
+    return model_name, "Model Ready", gr.update(choices=sfts_available_files, value=sfts_available_files[0], visible=True), gr.update(value=instance_prompt, visible=True)
+
+def custom_model_changed(model_name, previous_model):
+    if model_name == "" and previous_model == "" :
+        status_message = ""      
+    elif model_name != previous_model:
+        status_message = "model changed, please reload before any new run"
+    else:
+        status_message = "model ready"
+    return status_message
+
+def resize_image(input_path, output_path, target_height):
+    # Open the input image
+    img = Image.open(input_path)
+
+    # Calculate the aspect ratio of the original image
+    original_width, original_height = img.size
+    original_aspect_ratio = original_width / original_height
+
+    # Calculate the new width while maintaining the aspect ratio and the target height
+    new_width = int(target_height * original_aspect_ratio)
+
+    # Resize the image while maintaining the aspect ratio and fixing the height
+    img = img.resize((new_width, target_height), Image.LANCZOS)
+
+    # Save the resized image
+    img.save(output_path)
+
+    return output_path
+
+@spaces.GPU
+def infer(use_custom_model, model_name, weight_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed, progress=gr.Progress(track_tqdm=True)):
+
+    pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-base-1.0",
+        controlnet=controlnet,
+        vae=vae,
+        torch_dtype=torch.float16, 
+        variant="fp16",
+        use_safetensors=True
+    )
+    
+    pipe.to(device)
+    
+    prompt = prompt
+    negative_prompt = negative_prompt
+
+    if seed < 0 :
+        seed = random.randint(0, 423538377342)
+    
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    if image_in == None:
+        raise gr.Error("You forgot to upload a source image.")
+    
+    image_in = resize_image(image_in, "resized_input.jpg", 1024)
+    
+    if preprocessor == "canny":
+
+        image = load_image(image_in)
+
+        image = np.array(image)
+        image = cv2.Canny(image, 100, 200)
+        image = image[:, :, None]
+        image = np.concatenate([image, image, image], axis=2)
+        image = Image.fromarray(image)
+    
+    if use_custom_model:
+        
+        if model_name == "":
+            raise gr.Error("you forgot to set a custom model name.")
+        
+        custom_model = model_name
+
+        # This is where you load your trained weights
+        if weight_name == "NO SAFETENSORS FILE": 
+            pipe.load_lora_weights(
+                custom_model,     
+                low_cpu_mem_usage = True,
+                use_auth_token = True
+            )
+    
+        else:
+            pipe.load_lora_weights(
+                custom_model,
+                weight_name = weight_name,        
+                low_cpu_mem_usage = True,
+                use_auth_token = True
+            )
+    
+        lora_scale=custom_lora_weight
+
+        images = pipe(
+            prompt, 
+            negative_prompt=negative_prompt, 
+            image=image, 
+            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+            guidance_scale = float(guidance_scale),
+            num_inference_steps=inf_steps,
+            generator=generator,
+            cross_attention_kwargs={"scale": lora_scale}
+        ).images
+    else:
+        images = pipe(
+            prompt, 
+            negative_prompt=negative_prompt, 
+            image=image, 
+            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+            guidance_scale = float(guidance_scale),
+            num_inference_steps=inf_steps,
+            generator=generator,
+        ).images
+
+    images[0].save(f"result.png")
+
+    return f"result.png", seed
+
+css="""
+#col-container{
+    margin: 0 auto;
+    max-width: 720px;
+    text-align: left;
+}
+div#warning-duplicate {
+    background-color: #ebf5ff;
+    padding: 0 10px 5px;
+    margin: 20px 0;
+}
+div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
+    color: #0f4592!important;
+}
+div#warning-duplicate strong {
+    color: #0f4592;
+}
+p.actions {
+    display: flex;
+    align-items: center;
+    margin: 20px 0;
+}
+div#warning-duplicate .actions a {
+    display: inline-block;
+    margin-right: 10px;
+}
+button#load_model_btn{
+    height: 46px;
+}
+#status_info{
+    font-size: 0.9em;
+}
+"""
+def create_inference_demo() -> gr.Blocks:
+  
+  with gr.Blocks(css=css) as demo:
+      with gr.Column(elem_id="col-container"):
+          if is_shared_ui:
+              top_description = gr.HTML(f'''
+                  <div class="gr-prose">
+                      <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
+                      Note: you might want to use a <strong>private</strong> custom LoRa model</h2>
+                      <p class="main-message">
+                          To do so, <strong>duplicate the Space</strong> and run it on your own profile using <strong>your own access token</strong> and eventually a GPU (T4-small or A10G-small) for faster inference without waiting in the queue.<br />
+                      </p>
+                      <p class="actions">
+                          <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true">
+                              <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" />
+                          </a>
+                          to start using private models and skip the queue
+                      </p>
+                  </div>
+              ''', elem_id="warning-duplicate")
+          gr.HTML("""
+  <h2 style="text-align: center;">SD-XL Control LoRas</h2>
+  <p style="text-align: center;">Use StableDiffusion XL with <a href="https://huggingface.co/collections/diffusers/sdxl-controlnets-64f9c35846f3f06f5abe351f">Diffusers' SDXL ControlNets</a></p>
+
+          """)
+
+          use_custom_model = gr.Checkbox(label="Use a custom pre-trained LoRa model ? (optional)", value=False, info="To use a private model, you'll need to duplicate the space with your own access token.")
+          
+          with gr.Box(visible=False) as custom_model_box:
+              with gr.Row():
+                  with gr.Column():
+                      if not is_shared_ui:
+                          your_username = api.whoami()["name"]
+                          my_models = api.list_models(author=your_username, filter=["diffusers", "stable-diffusion-xl", 'lora'])
+                          model_names = [item.modelId for item in my_models]
+      
+                      if not is_shared_ui:
+                          custom_model = gr.Dropdown(
+                              label = "Your custom model ID",
+                              info="You can pick one of your private models",
+                              choices = model_names,
+                              allow_custom_value = True
+                              #placeholder = "username/model_id"
+                          )
+                      else:
+                          custom_model = gr.Textbox(
+                              label="Your custom model ID", 
+                              placeholder="your_username/your_trained_model_name", 
+                              info="Make sure your model is set to PUBLIC"
+                          )
+                      
+                      weight_name = gr.Dropdown(
+                          label="Safetensors file", 
+                          #value="pytorch_lora_weights.safetensors", 
+                          info="specify which one if model has several .safetensors files",
+                          allow_custom_value=True,
+                          visible = False
+                      )
+                  with gr.Column():
+                      with gr.Group():
+                          load_model_btn = gr.Button("Load my model", elem_id="load_model_btn")
+                          previous_model = gr.Textbox(
+                              visible = False
+                          )
+                          model_status = gr.Textbox(
+                              label = "model status",
+                              show_label = False,
+                              elem_id = "status_info"
+                          )
+                      trigger_word = gr.Textbox(label="Trigger word", interactive=False, visible=False)
+          
+          image_in = gr.Image(source="upload", type="filepath")
+          
+          with gr.Row():
+              
+              with gr.Column():
+                  with gr.Group():
+                      prompt = gr.Textbox(label="Prompt")
+                      negative_prompt = gr.Textbox(label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured")
+                  with gr.Group():
+                      guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5)
+                      inf_steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25)
+                      custom_lora_weight = gr.Slider(label="Custom model weights", minimum=0.1, maximum=0.9, step=0.1, value=0.9)
+
+              with gr.Column():
+                  with gr.Group():
+                      preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False, info="For the moment, only canny is available")  
+                      controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.5)
+                  with gr.Group():
+                      seed = gr.Slider(
+                          label="Seed",
+                          info = "-1 denotes a random seed",
+                          minimum=-1,
+                          maximum=423538377342,
+                          step=1,
+                          value=-1
+                      )
+                      last_used_seed = gr.Number(
+                          label = "Last used seed",
+                          info = "the seed used in the last generation",
+                      )
+          
+          
+          submit_btn = gr.Button("Submit")
+          
+          result = gr.Image(label="Result")
+      
+      use_custom_model.change(
+          fn = check_use_custom_or_no,
+          inputs =[use_custom_model],
+          outputs = [custom_model_box],
+          queue = False
+      )
+      custom_model.blur(
+          fn=custom_model_changed,
+          inputs = [custom_model, previous_model],
+          outputs = [model_status],
+          queue = False
+      )
+      load_model_btn.click(
+          fn = load_model,
+          inputs=[custom_model],
+          outputs = [previous_model, model_status, weight_name, trigger_word],
+          queue = False
+      )
+      submit_btn.click(
+          fn = infer,
+          inputs = [use_custom_model, custom_model, weight_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed],
+          outputs = [result, last_used_seed]
+      )
+
+  return demo 
+  
+
+#demo.queue(max_size=12).launch(share=True)

sdxl/requirements.txt

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+torch==2.0.1
+torchvision==0.15.2
+invisible_watermark
+accelerate
+transformers
+safetensors
+opencv-python
+git+https://github.com/huggingface/diffusers.git