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Parent(s):
42015e6
Update app.py
Browse files
app.py
CHANGED
@@ -49,6 +49,10 @@ with open("sdxl_loras.json", "r") as file:
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for item in data
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]
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device = "cuda"
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state_dicts = {}
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@@ -131,49 +135,20 @@ button.addEventListener('click', function() {
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element.classList.add('selected');
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});
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'''
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def update_selection(selected_state: gr.SelectData, sdxl_loras, is_new=False):
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lora_repo = sdxl_loras[selected_state.index]["repo"]
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new_placeholder = "Type a prompt. This LoRA applies for all prompts, no need for a trigger word" if instance_prompt == "" else "Type a prompt to use your selected LoRA"
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weight_name = sdxl_loras[selected_state.index]["weights"]
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updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) β¨ {'(non-commercial LoRA, `cc-by-nc`)' if sdxl_loras[selected_state.index]['is_nc'] else '' }"
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is_compatible = sdxl_loras[selected_state.index]["is_compatible"]
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is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
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use_with_diffusers = f'''
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## Using [`{lora_repo}`](https://huggingface.co/{lora_repo})
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## Use it with diffusers:
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'''
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if is_compatible:
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use_with_diffusers += f'''
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from diffusers import StableDiffusionXLPipeline
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import torch
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model_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
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pipe.to("cuda")
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pipe.load_lora_weights("{lora_repo}", weight_name="{weight_name}")
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prompt = "{instance_prompt}..."
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lora_scale= 0.9
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image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, cross_attention_kwargs={{"scale": lora_scale}}).images[0]
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image.save("image.png")
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'''
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elif not is_pivotal:
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use_with_diffusers += "This LoRA is not compatible with diffusers natively yet. But you can still use it on diffusers with `bmaltais/kohya_ss` LoRA class, check out this [Google Colab](https://colab.research.google.com/drive/14aEJsKdEQ9_kyfsiV6JDok799kxPul0j )"
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else:
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use_with_diffusers += f"This LoRA is not compatible with diffusers natively yet. But you can still use it on diffusers with sdxl-cog `TokenEmbeddingsHandler` class, check out the [model repo](https://huggingface.co/{lora_repo}#inference-with-π§¨-diffusers)"
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use_with_uis = f'''
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## Use it with Comfy UI, Invoke AI, SD.Next, AUTO1111:
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- [ComfyUI guide](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
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- [Invoke AI guide](https://invoke-ai.github.io/InvokeAI/features/CONCEPTS/?h=lora#using-loras)
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- [SD.Next guide](https://github.com/vladmandic/automatic)
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- [AUTOMATIC1111 guide](https://stable-diffusion-art.com/lora/)
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'''
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if(is_new):
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if(selected_state.index == 0):
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selected_state.index = -9999
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@@ -182,24 +157,23 @@ def update_selection(selected_state: gr.SelectData, sdxl_loras, is_new=False):
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return (
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updated_text,
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instance_prompt,
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gr.update(placeholder=new_placeholder),
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)
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def center_crop_image_as_square(img):
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square_size = min(img.size)
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# Calculate the coordinates of the crop box
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left = (img.width - square_size) / 2
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top = (img.height - square_size) / 2
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right = (img.width + square_size) / 2
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bottom = (img.height + square_size) / 2
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# Perform the crop
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img_cropped = img.crop((left, top, right, bottom))
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return img_cropped
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@@ -230,13 +204,21 @@ def merge_incompatible_lora(full_path_lora, lora_scale):
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del lora_model
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gc.collect()
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def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras,
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global last_lora, last_merged, last_fused, pipe
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face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
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face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
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face_emb = face_info['embedding']
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face_kps = draw_kps(face_image, face_info['kps'])
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#prepare face zoe
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with torch.no_grad():
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image_zoe = zoe(face_image)
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@@ -245,12 +227,12 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
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images = [face_kps, image_zoe.resize((height, width))]
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if(selected_state.index < 0):
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sdxl_loras = sdxl_loras_new
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print("Selected State: ", selected_state.index)
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print(sdxl_loras[selected_state.index]["repo"])
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if negative == "":
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@@ -342,7 +324,11 @@ with gr.Blocks(css="custom.css") as demo:
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photo = gr.Image(label="Upload a picture of yourself", interactive=True, type="pil")
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selected_loras = gr.Gallery(label="Selected LoRAs", height=80, show_share_button=False, visible=False, elem_id="gallery_selected", )
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order_gallery = gr.Radio(choices=["random", "likes"], value="random", label="Order by", elem_id="order_radio")
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new_gallery = gr.Gallery(
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gallery = gr.Gallery(
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#value=[(item["image"], item["title"]) for item in sdxl_loras],
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label="SDXL LoRA Gallery",
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@@ -359,7 +345,7 @@ with gr.Blocks(css="custom.css") as demo:
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elem_id="selected_lora",
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)
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="
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button = gr.Button("Run", elem_id="run_button")
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with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
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community_icon = gr.HTML(community_icon_html)
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weight = gr.Slider(0, 10, value=0.9, step=0.1, label="LoRA weight")
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guidance_scale = gr.Slider(0, 50, value=7, step=0.1, label="Guidance Scale")
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depth_control_scale = gr.Slider(0, 1, value=0.8, step=0.01, label="Zoe Depth ControlNet strenght")
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with gr.Accordion(
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"Use it with: 𧨠diffusers, ComfyUI, Invoke AI, SD.Next, AUTO1111",
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open=False,
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elem_id="accordion",
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):
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with gr.Row():
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use_diffusers = gr.Markdown("""## Select a LoRA first π€""")
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use_uis = gr.Markdown()
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with gr.Accordion("Submit a LoRA! π₯", open=False):
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submit_title = gr.Markdown(
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"### Streamlined submission coming soon! Until then [suggest your LoRA in the community tab](https://huggingface.co/spaces/multimodalart/LoraTheExplorer/discussions) π€"
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)
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with gr.Group(elem_id="soon"):
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submit_source = gr.Radio(
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["Hugging Face", "CivitAI"],
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label="LoRA source",
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value="Hugging Face",
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)
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with gr.Row():
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submit_source_hf = gr.Textbox(
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label="Hugging Face Model Repo",
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info="In the format `username/model_id`",
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)
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submit_safetensors_hf = gr.Textbox(
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label="Safetensors filename",
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info="The filename `*.safetensors` in the model repo",
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)
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with gr.Row():
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submit_trigger_word_hf = gr.Textbox(label="Trigger word")
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submit_image = gr.Image(
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label="Example image (optional if the repo already contains images)"
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)
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submit_button = gr.Button("Submit!")
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submit_disclaimer = gr.Markdown(
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"This is a curated gallery by me, [apolinΓ‘rio (multimodal.art)](https://twitter.com/multimodalart). I'll try to include as many cool LoRAs as they are submitted! You can [duplicate this Space](https://huggingface.co/spaces/multimodalart/LoraTheExplorer?duplicate=true) to use it privately, and add your own LoRAs by editing `sdxl_loras.json` in the Files tab of your private space."
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)
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order_gallery.change(
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fn=swap_gallery,
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inputs=[order_gallery, gr_sdxl_loras],
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)
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gallery.select(
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fn=update_selection,
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inputs=[gr_sdxl_loras],
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outputs=[prompt_title, prompt,
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queue=False,
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show_progress=False
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)
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new_gallery.select(
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fn=update_selection,
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inputs=[gr_sdxl_loras_new, gr.State(True)],
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outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis, gallery],
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queue=False,
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show_progress=False
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)
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prompt.submit(
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fn=check_selected,
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inputs=[selected_state],
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show_progress=False,
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).success(
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fn=run_lora,
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inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras
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outputs=[result, share_group],
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)
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button.click(
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show_progress=False,
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).success(
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fn=run_lora,
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inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras
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outputs=[result, share_group],
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)
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share_button.click(None, [], [], js=share_js)
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for item in data
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]
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with open("defaults_data.json", "r") as file:
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lora_defaults = json.load(file)
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device = "cuda"
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state_dicts = {}
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element.classList.add('selected');
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});
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'''
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def update_selection(selected_state: gr.SelectData, sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative, is_new=False):
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lora_repo = sdxl_loras[selected_state.index]["repo"]
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new_placeholder = "Type a prompt to use your selected LoRA"
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weight_name = sdxl_loras[selected_state.index]["weights"]
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updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) β¨ {'(non-commercial LoRA, `cc-by-nc`)' if sdxl_loras[selected_state.index]['is_nc'] else '' }"
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for lora_list in lora_defaults:
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if lora_list["model"] == sdxl_loras[selected_state.index]["repo"]:
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face_strength = lora_list.get("face_strength", face_strength)
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image_strength = lora_list.get("image_strength", image_strength)
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weight = lora_list.get("weight", weight)
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depth_control_scale = lora_list.get("depth_control_scale", depth_control_scale)
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negative = lora_list.get("negative", negative)
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if(is_new):
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if(selected_state.index == 0):
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selected_state.index = -9999
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return (
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updated_text,
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gr.update(placeholder=new_placeholder),
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face_strength,
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image_strength,
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weight,
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depth_control_scale,
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negative,
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selected_state
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)
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def center_crop_image_as_square(img):
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square_size = min(img.size)
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left = (img.width - square_size) / 2
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top = (img.height - square_size) / 2
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right = (img.width + square_size) / 2
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bottom = (img.height + square_size) / 2
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img_cropped = img.crop((left, top, right, bottom))
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return img_cropped
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del lora_model
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gc.collect()
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def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, progress=gr.Progress(track_tqdm=True)):
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global last_lora, last_merged, last_fused, pipe
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face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
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face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
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face_emb = face_info['embedding']
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face_kps = draw_kps(face_image, face_info['kps'])
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for lora_list in lora_defaults:
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if lora_list["model"] == sdxl_loras[selected_state.index]["repo"]:
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prompt_full = lora_list["model"].get("prompt", None)
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if(prompt_full):
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prompt = prompt_full.replace("<subject>", prompt)
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print("Prompt:", prompt)
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#prepare face zoe
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with torch.no_grad():
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image_zoe = zoe(face_image)
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images = [face_kps, image_zoe.resize((height, width))]
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#if(selected_state.index < 0):
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# if(selected_state.index == -9999):
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# selected_state.index = 0
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# else:
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# selected_state.index *= -1
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#sdxl_loras = sdxl_loras_new
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print("Selected State: ", selected_state.index)
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print(sdxl_loras[selected_state.index]["repo"])
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if negative == "":
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photo = gr.Image(label="Upload a picture of yourself", interactive=True, type="pil")
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selected_loras = gr.Gallery(label="Selected LoRAs", height=80, show_share_button=False, visible=False, elem_id="gallery_selected", )
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order_gallery = gr.Radio(choices=["random", "likes"], value="random", label="Order by", elem_id="order_radio")
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#new_gallery = gr.Gallery(
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# label="New LoRAs",
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# elem_id="gallery_new",
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# columns=3,
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# value=[(item["image"], item["title"]) for item in sdxl_loras_raw_new], allow_preview=False, show_share_button=False)
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gallery = gr.Gallery(
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#value=[(item["image"], item["title"]) for item in sdxl_loras],
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label="SDXL LoRA Gallery",
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elem_id="selected_lora",
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)
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="A person",, elem_id="prompt")
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button = gr.Button("Run", elem_id="run_button")
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with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
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community_icon = gr.HTML(community_icon_html)
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weight = gr.Slider(0, 10, value=0.9, step=0.1, label="LoRA weight")
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guidance_scale = gr.Slider(0, 50, value=7, step=0.1, label="Guidance Scale")
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depth_control_scale = gr.Slider(0, 1, value=0.8, step=0.01, label="Zoe Depth ControlNet strenght")
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order_gallery.change(
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fn=swap_gallery,
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inputs=[order_gallery, gr_sdxl_loras],
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)
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gallery.select(
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fn=update_selection,
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inputs=[gr_sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative],
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outputs=[prompt_title, prompt, face_strength, image_strength, weight, depth_control_scale, negative, selected_state],
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queue=False,
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show_progress=False
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)
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#new_gallery.select(
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# fn=update_selection,
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380 |
+
# inputs=[gr_sdxl_loras_new, gr.State(True)],
|
381 |
+
# outputs=[prompt_title, prompt, prompt, selected_state, gallery],
|
382 |
+
# queue=False,
|
383 |
+
# show_progress=False
|
384 |
+
#)
|
385 |
prompt.submit(
|
386 |
fn=check_selected,
|
387 |
inputs=[selected_state],
|
|
|
395 |
show_progress=False,
|
396 |
).success(
|
397 |
fn=run_lora,
|
398 |
+
inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras],
|
399 |
outputs=[result, share_group],
|
400 |
)
|
401 |
button.click(
|
|
|
411 |
show_progress=False,
|
412 |
).success(
|
413 |
fn=run_lora,
|
414 |
+
inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras],
|
415 |
outputs=[result, share_group],
|
416 |
)
|
417 |
share_button.click(None, [], [], js=share_js)
|