Duplicate from multimodalart/dreambooth-training

Co-authored-by: Multimodal AI art <multimodalart@users.noreply.huggingface.co>

.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
+*.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
+xformers-0.0.14.dev0-cp38-cp38-linux_x86_64_t4.whl filter=lfs diff=lfs merge=lfs -text
+xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl filter=lfs diff=lfs merge=lfs -text

.gitignore

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+Mix
+output_model
+instance_images
+__pycache__
+intraining.lock
+hastrained.success
+diffusers_model.tar

README.md

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+---
+title: Dreambooth Training
+emoji: ☁️
+colorFrom: pink
+colorTo: red
+sdk: gradio
+sdk_version: 3.11
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: multimodalart/dreambooth-training
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+from pathlib import Path
+import argparse
+import shutil
+from train_dreambooth import run_training
+from convertosd import convert
+from PIL import Image
+from slugify import slugify
+import requests
+import torch
+import zipfile
+import tarfile
+import urllib.parse
+import gc
+from diffusers import StableDiffusionPipeline
+from huggingface_hub import snapshot_download
+
+
+is_spaces = True if "SPACE_ID" in os.environ else False
+is_shared_ui = True if "IS_SHARED_UI" in os.environ else False
+is_gpu_associated = torch.cuda.is_available()
+
+css = '''
+    .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
+    .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
+    #component-4, #component-3, #component-10{min-height: 0}
+    .duplicate-button img{margin: 0}
+'''
+maximum_concepts = 3
+
+#Pre download the files
+if(is_gpu_associated):
+    model_v1 = snapshot_download(repo_id="multimodalart/sd-fine-tunable")
+    model_v2 = snapshot_download(repo_id="stabilityai/stable-diffusion-2")
+    model_v2_512 = snapshot_download(repo_id="stabilityai/stable-diffusion-2-base")
+    safety_checker = snapshot_download(repo_id="multimodalart/sd-sc")
+    model_to_load = model_v1
+
+with zipfile.ZipFile("mix.zip", 'r') as zip_ref:
+    zip_ref.extractall(".")
+
+def swap_text(option, base):
+    resize_width = 768 if base == "v2-768" else 512
+    mandatory_liability = "You must have the right to do so and you are liable for the images you use, example:"
+    if(option == "object"):
+        instance_prompt_example = "cttoy"
+        freeze_for = 30
+        return [f"You are going to train `object`(s), upload 5-10 images of each object you are planning on training on from different angles/perspectives. You can use services like <a style='text-decoration: underline' target='_blank' href='https://www.birme.net/?target_width={resize_width}&target_height={resize_width}'>birme</a> for smart cropping. {mandatory_liability}:", '''<img src="file/cat-toy.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}.", freeze_for, gr.update(visible=False)]
+    elif(option == "person"):
+       instance_prompt_example = "julcto"
+       freeze_for = 70
+       #show_prior_preservation = True if base != "v2-768" else False
+       show_prior_preservation=False
+       if(show_prior_preservation):
+           prior_preservation_box_update = gr.update(visible=show_prior_preservation)
+       else: 
+           prior_preservation_box_update = gr.update(visible=show_prior_preservation, value=False)
+       return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. You can use services like <a style='text-decoration: underline' target='_blank' href='https://www.birme.net/?target_width={resize_width}&target_height={resize_width}'>birme</a> for smart cropping. {mandatory_liability}:", '''<img src="file/person.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}.", freeze_for, prior_preservation_box_update]
+    elif(option == "style"):
+        instance_prompt_example = "trsldamrl"
+        freeze_for = 10
+        return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. You can use services like <a style='text-decoration: underline' target='_blank' href='https://www.birme.net/?target_width={resize_width}&target_height={resize_width}'>birme</a> for smart cropping. Name the files with the words you would like  {mandatory_liability}:", '''<img src="file/trsl_style.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to {resize_width}x{resize_width}", freeze_for, gr.update(visible=False)]
+
+def swap_base_model(selected_model):
+    if(is_gpu_associated):
+        global model_to_load
+        if(selected_model == "v1-5"):
+            model_to_load = model_v1
+        elif(selected_model == "v2-768"):
+            model_to_load = model_v2
+        else:
+            model_to_load = model_v2_512
+
+def count_files(*inputs):
+    file_counter = 0
+    concept_counter = 0
+    for i, input in enumerate(inputs):
+        if(i < maximum_concepts-1):
+            files = inputs[i]
+            if(files):
+                concept_counter+=1
+                file_counter+=len(files)
+    uses_custom = inputs[-1] 
+    type_of_thing = inputs[-4]
+    selected_model = inputs[-5]
+    experimental_faces = inputs[-6]
+    if(uses_custom):
+        Training_Steps = int(inputs[-3])
+    else:
+        Training_Steps = file_counter*150
+        if(type_of_thing == "person" and Training_Steps > 2400):
+            Training_Steps = 2400 #Avoid overfitting on person faces
+    if(is_spaces):
+        if(selected_model == "v1-5"):
+            its = 1.1
+            if(experimental_faces):
+                its = 1
+        elif(selected_model == "v2-512"):
+            its = 0.8
+            if(experimental_faces):
+                its = 0.7
+        elif(selected_model == "v2-768"):
+            its = 0.5
+        summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps. The training should take around {round(Training_Steps/its, 2)} seconds, or {round((Training_Steps/its)/60, 2)} minutes.
+            The setup, compression and uploading the model can take up to 20 minutes.<br>As the T4-Small GPU costs US$0.60 for 1h, <span style="font-size: 120%"><b>the estimated cost for this training is below US${round((((Training_Steps/its)/3600)+0.3+0.1)*0.60, 2)}.</b></span><br><br>
+            If you check the box below the GPU attribution will automatically removed after training is done and the model is uploaded. If not, don't forget to come back here and swap the hardware back to CPU.<br><br>'''
+    else:
+        summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps.<br><br>'''
+        
+    return([gr.update(visible=True), gr.update(visible=True, value=summary_sentence)])
+
+def update_steps(*files_list):
+    file_counter = 0
+    for i, files in enumerate(files_list):
+        if(files):
+            file_counter+=len(files)
+    return(gr.update(value=file_counter*200))
+
+def pad_image(image):
+    w, h = image.size
+    if w == h:
+        return image
+    elif w > h:
+        new_image = Image.new(image.mode, (w, w), (0, 0, 0))
+        new_image.paste(image, (0, (w - h) // 2))
+        return new_image
+    else:
+        new_image = Image.new(image.mode, (h, h), (0, 0, 0))
+        new_image.paste(image, ((h - w) // 2, 0))
+        return new_image
+
+def train(*inputs):
+    if is_shared_ui:
+        raise gr.Error("This Space only works in duplicated instances")
+    if not is_gpu_associated:
+        raise gr.Error("Please associate a T4 GPU for this Space")
+    torch.cuda.empty_cache()
+    if 'pipe' in globals():
+        global pipe, pipe_is_set
+        del pipe
+        pipe_is_set = False
+        gc.collect()
+        
+    if os.path.exists("output_model"): shutil.rmtree('output_model')
+    if os.path.exists("instance_images"): shutil.rmtree('instance_images')
+    if os.path.exists("diffusers_model.tar"): os.remove("diffusers_model.tar")
+    if os.path.exists("model.ckpt"): os.remove("model.ckpt")
+    if os.path.exists("hastrained.success"): os.remove("hastrained.success")
+    file_counter = 0
+    which_model = inputs[-10]
+    resolution = 512 if which_model != "v2-768" else 768
+    for i, input in enumerate(inputs):
+        if(i < maximum_concepts-1):
+            if(input):
+                os.makedirs('instance_images',exist_ok=True)
+                files = inputs[i+(maximum_concepts*2)]
+                prompt = inputs[i+maximum_concepts]
+                if(prompt == "" or prompt == None):
+                    raise gr.Error("You forgot to define your concept prompt")
+                for j, file_temp in enumerate(files):
+                    file = Image.open(file_temp.name)
+                    image = pad_image(file)
+                    image = image.resize((resolution, resolution))
+                    extension = file_temp.name.split(".")[1]
+                    image = image.convert('RGB')
+                    image.save(f'instance_images/{prompt}_({j+1}).jpg', format="JPEG", quality = 100)
+                    file_counter += 1
+    
+    os.makedirs('output_model',exist_ok=True)
+    uses_custom = inputs[-1] 
+    type_of_thing = inputs[-4]
+    remove_attribution_after = inputs[-6]
+    experimental_face_improvement = inputs[-9]
+    
+    if(uses_custom):
+        Training_Steps = int(inputs[-3])
+        Train_text_encoder_for = int(inputs[-2])
+    else:
+        if(type_of_thing == "object"):
+            Train_text_encoder_for=30
+            
+        elif(type_of_thing == "style"):
+            Train_text_encoder_for=15
+            
+        elif(type_of_thing == "person"):
+            Train_text_encoder_for=70
+        
+        Training_Steps = file_counter*150
+        if(type_of_thing == "person" and Training_Steps > 2600):
+            Training_Steps = 2600 #Avoid overfitting on people's faces
+    stptxt = int((Training_Steps*Train_text_encoder_for)/100)
+    gradient_checkpointing = True if (experimental_face_improvement or which_model != "v1-5") else False 
+    cache_latents = True if which_model != "v1-5" else False
+    if (type_of_thing == "object" or type_of_thing == "style" or (type_of_thing == "person" and not experimental_face_improvement)):
+        args_general = argparse.Namespace(
+            image_captions_filename = True,
+            train_text_encoder = True if stptxt > 0 else False,
+            stop_text_encoder_training = stptxt,
+            save_n_steps = 0,
+            pretrained_model_name_or_path = model_to_load,
+            instance_data_dir="instance_images",
+            class_data_dir=None,
+            output_dir="output_model",
+            instance_prompt="",
+            seed=42,
+            resolution=resolution,
+            mixed_precision="fp16",
+            train_batch_size=1,
+            gradient_accumulation_steps=1,
+            use_8bit_adam=True,
+            learning_rate=2e-6,
+            lr_scheduler="polynomial",
+            lr_warmup_steps = 0,
+            max_train_steps=Training_Steps,     
+            gradient_checkpointing=gradient_checkpointing,
+            cache_latents=cache_latents,
+        )
+        print("Starting single training...")
+        lock_file = open("intraining.lock", "w")
+        lock_file.close()
+        run_training(args_general)
+    else:
+        args_general = argparse.Namespace(
+            image_captions_filename = True,
+            train_text_encoder = True if stptxt > 0 else False,
+            stop_text_encoder_training = stptxt,
+            save_n_steps = 0,
+            pretrained_model_name_or_path = model_to_load,
+            instance_data_dir="instance_images",
+            class_data_dir="Mix",
+            output_dir="output_model",
+            with_prior_preservation=True,
+            prior_loss_weight=1.0,
+            instance_prompt="",
+            seed=42,
+            resolution=resolution,
+            mixed_precision="fp16",
+            train_batch_size=1,
+            gradient_accumulation_steps=1,
+            use_8bit_adam=True,
+            learning_rate=2e-6,
+            lr_scheduler="polynomial",
+            lr_warmup_steps = 0,
+            max_train_steps=Training_Steps,
+            num_class_images=200,     
+            gradient_checkpointing=gradient_checkpointing,
+            cache_latents=cache_latents,
+        )
+        print("Starting multi-training...")
+        lock_file = open("intraining.lock", "w")
+        lock_file.close()
+        run_training(args_general)
+    gc.collect()
+    torch.cuda.empty_cache()
+    if(which_model == "v1-5"):
+        print("Adding Safety Checker to the model...")
+        shutil.copytree(f"{safety_checker}/feature_extractor", "output_model/feature_extractor")
+        shutil.copytree(f"{safety_checker}/safety_checker", "output_model/safety_checker")
+        shutil.copy(f"model_index.json", "output_model/model_index.json")
+    
+    if(not remove_attribution_after):
+        print("Archiving model file...")
+        with tarfile.open("diffusers_model.tar", "w") as tar:
+            tar.add("output_model", arcname=os.path.basename("output_model"))
+        if os.path.exists("intraining.lock"): os.remove("intraining.lock")
+        trained_file = open("hastrained.success", "w")
+        trained_file.close()
+        print("Training completed!")
+        return [
+            gr.update(visible=True, value=["diffusers_model.tar"]), #result
+            gr.update(visible=True), #try_your_model
+            gr.update(visible=True), #push_to_hub
+            gr.update(visible=True), #convert_button
+            gr.update(visible=False), #training_ongoing
+            gr.update(visible=True) #completed_training
+        ]
+    else:
+        hf_token = inputs[-5]
+        model_name = inputs[-7]
+        where_to_upload = inputs[-8]
+        push(model_name, where_to_upload, hf_token, which_model, True)
+        hardware_url = f"https://huggingface.co/spaces/{os.environ['SPACE_ID']}/hardware"
+        headers = { "authorization" : f"Bearer {hf_token}"}
+        body = {'flavor': 'cpu-basic'}
+        requests.post(hardware_url, json = body, headers=headers)
+
+pipe_is_set = False
+def generate(prompt, steps):
+    torch.cuda.empty_cache()
+    from diffusers import StableDiffusionPipeline
+    global pipe_is_set
+    if(not pipe_is_set):
+        global pipe
+        pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
+        pipe = pipe.to("cuda")
+        pipe_is_set = True
+        
+    image = pipe(prompt, num_inference_steps=steps).images[0]  
+    return(image)
+    
+def push(model_name, where_to_upload, hf_token, which_model, comes_from_automated=False):
+    if(not os.path.exists("model.ckpt")):
+        convert("output_model", "model.ckpt")
+    from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
+    from huggingface_hub import create_repo
+    model_name_slug = slugify(model_name)
+    api = HfApi()
+    your_username = api.whoami(token=hf_token)["name"]
+    if(where_to_upload == "My personal profile"):    
+        model_id = f"{your_username}/{model_name_slug}"
+    else:
+        model_id = f"sd-dreambooth-library/{model_name_slug}"
+        headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"}
+        response = requests.post("https://huggingface.co/organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", headers=headers)
+    
+    images_upload = os.listdir("instance_images")
+    image_string = ""
+    instance_prompt_list = []
+    previous_instance_prompt = ''
+    for i, image in enumerate(images_upload):
+        instance_prompt = image.split("_")[0]
+        if(instance_prompt != previous_instance_prompt):
+            title_instance_prompt_string = instance_prompt
+            instance_prompt_list.append(instance_prompt)
+        else:
+            title_instance_prompt_string = ''
+        previous_instance_prompt = instance_prompt
+        image_string = f'''{title_instance_prompt_string} {"(use that on your prompt)" if title_instance_prompt_string != "" else ""} 
+{image_string}![{instance_prompt} {i}](https://huggingface.co/{model_id}/resolve/main/concept_images/{urllib.parse.quote(image)})'''
+    readme_text = f'''---
+license: creativeml-openrail-m
+tags:
+- text-to-image
+widget:
+- text: {instance_prompt_list[0]}
+---
+### {model_name} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the {which_model} base model
+
+You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! 
+
+Sample pictures of:
+{image_string}
+'''
+    #Save the readme to a file
+    readme_file = open("model.README.md", "w")
+    readme_file.write(readme_text)
+    readme_file.close()
+    #Save the token identifier to a file
+    text_file = open("token_identifier.txt", "w")
+    text_file.write(', '.join(instance_prompt_list))
+    text_file.close()
+    try:
+        create_repo(model_id,private=True, token=hf_token)
+    except:
+        import time
+        epoch_time = str(int(time.time()))
+        create_repo(f"{model_id}-{epoch_time}", private=True,token=hf_token)
+    operations = [
+        CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),
+        CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="model.README.md"),
+        CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt")
+    ]
+    api.create_commit(
+    repo_id=model_id,
+    operations=operations,
+    commit_message=f"Upload the model {model_name}",
+    token=hf_token
+    )
+    api.upload_folder(
+    folder_path="output_model",
+    repo_id=model_id,
+    token=hf_token
+    )
+    api.upload_folder(
+    folder_path="instance_images",
+    path_in_repo="concept_images",
+    repo_id=model_id,
+    token=hf_token
+    )
+    if is_spaces:
+        if(not comes_from_automated):
+            extra_message = "Don't forget to remove the GPU attribution after you play with it."
+        else:
+            extra_message = "The GPU has been removed automatically as requested, and you can try the model via the model page"
+        api.create_discussion(repo_id=os.environ['SPACE_ID'], title=f"Your model {model_name} has finished trained from the Dreambooth Train Spaces!", description=f"Your model has been successfully uploaded to: https://huggingface.co/{model_id}. {extra_message}",repo_type="space", token=hf_token)
+
+    return [gr.update(visible=True, value=f"Successfully uploaded your model. Access it [here](https://huggingface.co/{model_id})"), gr.update(visible=True, value=["diffusers_model.tar", "model.ckpt"])]
+
+def convert_to_ckpt():
+    if 'pipe' in globals():
+        global pipe, pipe_is_set
+        del pipe
+        pipe_is_set = False
+        gc.collect()
+    convert("output_model", "model.ckpt")
+    return gr.update(visible=True, value=["diffusers_model.tar", "model.ckpt"])
+
+def check_status(top_description):
+    if os.path.exists("hastrained.success"):
+        if is_spaces:
+            update_top_tag = gr.update(value=f'''
+            <div class="gr-prose" style="max-width: 80%">
+                <h2>Your model has finished training ✅</h2>
+                <p>Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub). Once you are done, your model is safe, and you don't want to train a new one, go to the <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}" target="_blank">settings page</a> and downgrade your Space to a CPU Basic</p> 
+            </div>
+            ''')
+        else:
+            update_top_tag = gr.update(value=f'''
+            <div class="gr-prose" style="max-width: 80%">
+                <h2>Your model has finished training ✅</h2>
+                <p>Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub).</p> 
+            </div>
+            ''')
+        show_outputs = True
+    elif os.path.exists("intraining.lock"):
+        update_top_tag = gr.update(value='''
+        <div class="gr-prose" style="max-width: 80%">
+            <h2>Don't worry, your model is still training! ⌛</h2>
+            <p>You closed the tab while your model was training, but it's all good! It is still training right now. You can click the "Open logs" button above here to check the training status. Once training is done, reload this tab to interact with your model</p> 
+        </div>
+        ''')
+        show_outputs = False
+    else:
+        update_top_tag = gr.update(value=top_description)
+        show_outputs = False
+    if os.path.exists("diffusers_model.tar"):
+       update_files_tag = gr.update(visible=show_outputs, value=["diffusers_model.tar"])
+    else:
+       update_files_tag = gr.update(visible=show_outputs)
+    return [
+        update_top_tag, #top_description
+        gr.update(visible=show_outputs), #try_your_model
+        gr.update(visible=show_outputs), #push_to_hub
+        update_files_tag, #result
+        gr.update(visible=show_outputs), #convert_button
+    ]
+
+def checkbox_swap(checkbox):
+    return [gr.update(visible=checkbox), gr.update(visible=checkbox), gr.update(visible=checkbox), gr.update(visible=checkbox)]
+
+with gr.Blocks(css=css) as demo:
+    with gr.Box():
+        if is_shared_ui:
+            top_description = gr.HTML(f'''
+                <div class="gr-prose" style="max-width: 80%">
+                <h2>Attention - This Space doesn't work in this shared UI</h2>
+                <p>For it to work, you can either run locally or duplicate the Space and run it on your own profile using a (paid) private T4 GPU for training. As each T4 costs US$0.60/h, it should cost < US$1 to train most models using default settings!&nbsp;&nbsp;<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
+                <img class="instruction" src="file/duplicate.png"> 
+                <img class="arrow" src="file/arrow.png" />
+                </div>
+            ''')
+        elif(is_spaces):
+            if(is_gpu_associated):
+                top_description = gr.HTML(f'''
+                        <div class="gr-prose" style="max-width: 80%">
+                        <h2>You have successfully associated a GPU to the Dreambooth Training Space 🎉</h2>
+                        <p>Certify that you got a T4. You can now train your model! You will be billed by the minute from when you activated the GPU until when it is turned it off.</p> 
+                        </div>
+                ''')
+            else:
+                top_description = gr.HTML(f'''
+                        <div class="gr-prose" style="max-width: 80%">
+                        <h2>You have successfully duplicated the Dreambooth Training Space 🎉</h2>
+                        <p>There's only one step left before you can train your model: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a <b>T4 GPU</b> to it (via the Settings tab)</a> and run the training below. Other GPUs are not compatible for now. You will be billed by the minute from when you activate the GPU until when it is turned it off.</p> 
+                        </div>
+                ''')
+        else:
+            top_description = gr.HTML(f'''
+                    <div class="gr-prose" style="max-width: 80%">
+                    <h2>You have successfully cloned the Dreambooth Training Space locally 🎉</h2>
+                    <p>Do a <code>pip install requirements-local.txt</code></p> 
+                    </div>
+                ''')
+    gr.Markdown("# Dreambooth Training UI 💭")
+    gr.Markdown("Customize Stable Diffusion v1 or v2 (ⁿᵉʷ!) by giving it a few examples of a concept. Based on the [🧨 diffusers](https://github.com/huggingface/diffusers) implementation, additional techniques from [TheLastBen](https://github.com/TheLastBen/diffusers) and [ShivamShrirao](https://github.com/ShivamShrirao/diffusers)")
+    
+    with gr.Row() as what_are_you_training:
+        type_of_thing = gr.Dropdown(label="What would you like to train?", choices=["object", "person", "style"], value="object", interactive=True)
+        base_model_to_use = gr.Dropdown(label="Which base model would you like to use?", choices=["v1-5", "v2-512", "v2-768"], value="v1-5", interactive=True)
+    
+    #Very hacky approach to emulate dynamically created Gradio components   
+    with gr.Row() as upload_your_concept:
+        with gr.Column():
+            thing_description = gr.Markdown("You are going to train an `object`, please upload 5-10 images of the object you are planning on training on from different angles/perspectives. You must have the right to do so and you are liable for the images you use, example")
+            thing_experimental = gr.Checkbox(label="Improve faces (prior preservation) - can take longer training but can improve faces", visible=False, value=False)
+            thing_image_example = gr.HTML('''<img src="file/cat-toy.png" />''')
+            things_naming = gr.Markdown("You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `cttoy` here). Images will be automatically cropped to 512x512.")
+            
+        with gr.Column():
+            file_collection = []
+            concept_collection = []
+            buttons_collection = []
+            delete_collection = []
+            is_visible = []
+
+            row = [None] * maximum_concepts
+            for x in range(maximum_concepts):
+                ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4])
+                if(x == 0):
+                    visible = True
+                    is_visible.append(gr.State(value=True))
+                else:
+                    visible = False
+                    is_visible.append(gr.State(value=False))
+
+                file_collection.append(gr.File(label=f'''Upload the images for your {ordinal(x+1) if (x>0) else ""} concept''', file_count="multiple", interactive=True, visible=visible))
+                with gr.Column(visible=visible) as row[x]:
+                    concept_collection.append(gr.Textbox(label=f'''{ordinal(x+1) if (x>0) else ""} concept prompt - use a unique, made up word to avoid collisions'''))  
+                    with gr.Row():
+                        if(x < maximum_concepts-1):
+                            buttons_collection.append(gr.Button(value="Add +1 concept", visible=visible))
+                        if(x > 0):
+                            delete_collection.append(gr.Button(value=f"Delete {ordinal(x+1)} concept"))
+            
+            counter_add = 1
+            for button in buttons_collection:
+                if(counter_add < len(buttons_collection)):
+                    button.click(lambda:
+                    [gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), True, None],
+                    None, 
+                    [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], buttons_collection[counter_add], is_visible[counter_add], file_collection[counter_add]], queue=False)
+                else:
+                    button.click(lambda:[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), True], None, [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], is_visible[counter_add]], queue=False)
+                counter_add += 1
+            
+            counter_delete = 1
+            for delete_button in delete_collection:
+                if(counter_delete < len(delete_collection)+1):
+                    delete_button.click(lambda:[gr.update(visible=False),gr.update(visible=False), gr.update(visible=True), False], None, [file_collection[counter_delete], row[counter_delete], buttons_collection[counter_delete-1], is_visible[counter_delete]], queue=False)
+                counter_delete += 1
+                  
+    with gr.Accordion("Custom Settings", open=False):
+        swap_auto_calculated = gr.Checkbox(label="Use custom settings")
+        gr.Markdown("If not checked, the % of frozen encoder will be tuned automatically to whether you are training an `object`, `person` or `style`. The text-encoder is frozen after 10% of the steps for a style, 30% of the steps for an object and 75% trained for persons. The number of steps varies between 1400 and 2400 depending on how many images uploaded. If you see too many artifacts in your output, it means it may have overfit and you need less steps. If your results aren't really what you wanted, it may be underfitting and you need more steps.")
+        steps = gr.Number(label="How many steps", value=2400)
+        perc_txt_encoder = gr.Number(label="Percentage of the training steps the text-encoder should be trained as well", value=30)
+        
+    with gr.Box(visible=False) as training_summary:
+        training_summary_text = gr.HTML("", visible=True, label="Training Summary")
+        is_advanced_visible = True if is_spaces else False
+        training_summary_checkbox = gr.Checkbox(label="Automatically remove paid GPU attribution and upload model to the Hugging Face Hub after training", value=True, visible=is_advanced_visible)
+        training_summary_model_name = gr.Textbox(label="Name of your model", visible=True)
+        training_summary_where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], value="My personal profile", label="Upload to", visible=True)
+        training_summary_token_message = gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.", visible=True)            
+        training_summary_token = gr.Textbox(label="Hugging Face Write Token", type="password", visible=True)
+        
+    train_btn = gr.Button("Start Training")
+    if(is_shared_ui):
+        training_ongoing = gr.Markdown("## This Space only works in duplicated instances. Please duplicate it and try again!", visible=False)
+    elif(not is_gpu_associated):
+        training_ongoing = gr.Markdown("## Oops, you haven't associated your T4 GPU to this Space. Visit the Settings tab, associate and try again.", visible=False)
+    else:
+        training_ongoing = gr.Markdown("## Training is ongoing ⌛... You can close this tab if you like or just wait. If you did not check the `Remove GPU After training`, you can come back here to try your model and upload it after training. Don't forget to remove the GPU attribution after you are done. ", visible=False)
+    
+    #Post-training UI
+    completed_training = gr.Markdown('''# ✅ Training completed. 
+    ### Don't forget to remove the GPU attribution after you are done trying and uploading your model''', visible=False)
+    
+    with gr.Row():
+        with gr.Box(visible=False) as try_your_model:
+            gr.Markdown("## Try your model")
+            prompt = gr.Textbox(label="Type your prompt")
+            result_image = gr.Image()
+            inference_steps = gr.Slider(minimum=1, maximum=150, value=50, step=1)
+            generate_button = gr.Button("Generate Image")
+        
+        with gr.Box(visible=False) as push_to_hub:
+            gr.Markdown("## Push to Hugging Face Hub")
+            model_name = gr.Textbox(label="Name of your model", placeholder="Tarsila do Amaral Style")
+            where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to")
+            gr.Markdown("[A Hugging Face write access token](https://huggingface.co/settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.")
+            hf_token = gr.Textbox(label="Hugging Face Write Token", type="password")
+            
+            push_button = gr.Button("Push to the Hub")
+    
+    result = gr.File(label="Download the uploaded models in the diffusers format", visible=True)
+    success_message_upload = gr.Markdown(visible=False)
+    convert_button = gr.Button("Convert to CKPT", visible=False)
+    
+    #Swap the examples and the % of text encoder trained depending if it is an object, person or style
+    type_of_thing.change(fn=swap_text, inputs=[type_of_thing, base_model_to_use], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder, thing_experimental], queue=False, show_progress=False)
+    
+    #Swap the base model
+    base_model_to_use.change(fn=swap_text, inputs=[type_of_thing, base_model_to_use], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder, thing_experimental], queue=False, show_progress=False)
+    base_model_to_use.change(fn=swap_base_model, inputs=base_model_to_use, outputs=[])
+
+    #Update the summary box below the UI according to how many images are uploaded and whether users are using custom settings or not 
+    for file in file_collection:
+        #file.change(fn=update_steps,inputs=file_collection, outputs=steps)
+        file.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+    
+    thing_experimental.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+    base_model_to_use.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+    steps.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+    perc_txt_encoder.change(fn=count_files, inputs=file_collection+[thing_experimental]+[base_model_to_use]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
+    
+    #Give more options if the user wants to finish everything after training
+    if(is_spaces):
+        training_summary_checkbox.change(fn=checkbox_swap, inputs=training_summary_checkbox, outputs=[training_summary_token_message, training_summary_token, training_summary_model_name, training_summary_where_to_upload],queue=False, show_progress=False)
+    #Add a message for while it is in training
+    train_btn.click(lambda:gr.update(visible=True), inputs=None, outputs=training_ongoing)
+    
+    #The main train function
+    train_btn.click(fn=train, inputs=is_visible+concept_collection+file_collection+[base_model_to_use]+[thing_experimental]+[training_summary_where_to_upload]+[training_summary_model_name]+[training_summary_checkbox]+[training_summary_token]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button, training_ongoing, completed_training], queue=False)
+    
+    #Button to generate an image from your trained model after training
+    generate_button.click(fn=generate, inputs=[prompt, inference_steps], outputs=result_image, queue=False)
+    #Button to push the model to the Hugging Face Hub
+    push_button.click(fn=push, inputs=[model_name, where_to_upload, hf_token, base_model_to_use], outputs=[success_message_upload, result], queue=False)
+    #Button to convert the model to ckpt format 
+    convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result, queue=False)
+    
+    #Checks if the training is running
+    demo.load(fn=check_status, inputs=top_description, outputs=[top_description, try_your_model, push_to_hub, result, convert_button], queue=False, show_progress=False)
+
+demo.queue(default_enabled=False).launch(debug=True)

convertosd.py

@@ -0,0 +1,226 @@
+# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
+# *Only* converts the UNet, VAE, and Text Encoder.
+# Does not convert optimizer state or any other thing.
+# Written by jachiam
+
+import argparse
+import os.path as osp
+
+import torch
+import gc 
+
+# =================#
+# UNet Conversion #
+# =================#
+
+unet_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
+    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
+    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
+    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
+    ("input_blocks.0.0.weight", "conv_in.weight"),
+    ("input_blocks.0.0.bias", "conv_in.bias"),
+    ("out.0.weight", "conv_norm_out.weight"),
+    ("out.0.bias", "conv_norm_out.bias"),
+    ("out.2.weight", "conv_out.weight"),
+    ("out.2.bias", "conv_out.bias"),
+]
+
+unet_conversion_map_resnet = [
+    # (stable-diffusion, HF Diffusers)
+    ("in_layers.0", "norm1"),
+    ("in_layers.2", "conv1"),
+    ("out_layers.0", "norm2"),
+    ("out_layers.3", "conv2"),
+    ("emb_layers.1", "time_emb_proj"),
+    ("skip_connection", "conv_shortcut"),
+]
+
+unet_conversion_map_layer = []
+# hardcoded number of downblocks and resnets/attentions...
+# would need smarter logic for other networks.
+for i in range(4):
+    # loop over downblocks/upblocks
+
+    for j in range(2):
+        # loop over resnets/attentions for downblocks
+        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
+        sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
+        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
+
+        if i < 3:
+            # no attention layers in down_blocks.3
+            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
+            sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
+            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
+
+    for j in range(3):
+        # loop over resnets/attentions for upblocks
+        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
+        sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
+        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
+
+        if i > 0:
+            # no attention layers in up_blocks.0
+            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
+            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
+            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
+
+    if i < 3:
+        # no downsample in down_blocks.3
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
+        sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
+        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        # no upsample in up_blocks.3
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
+        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
+
+hf_mid_atn_prefix = "mid_block.attentions.0."
+sd_mid_atn_prefix = "middle_block.1."
+unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
+
+for j in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{j}."
+    sd_mid_res_prefix = f"middle_block.{2*j}."
+    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+def convert_unet_state_dict(unet_state_dict):
+    # buyer beware: this is a *brittle* function,
+    # and correct output requires that all of these pieces interact in
+    # the exact order in which I have arranged them.
+    mapping = {k: k for k in unet_state_dict.keys()}
+    for sd_name, hf_name in unet_conversion_map:
+        mapping[hf_name] = sd_name
+    for k, v in mapping.items():
+        if "resnets" in k:
+            for sd_part, hf_part in unet_conversion_map_resnet:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    for k, v in mapping.items():
+        for sd_part, hf_part in unet_conversion_map_layer:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
+    return new_state_dict
+
+
+# ================#
+# VAE Conversion #
+# ================#
+
+vae_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("nin_shortcut", "conv_shortcut"),
+    ("norm_out", "conv_norm_out"),
+    ("mid.attn_1.", "mid_block.attentions.0."),
+]
+
+for i in range(4):
+    # down_blocks have two resnets
+    for j in range(2):
+        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
+        sd_down_prefix = f"encoder.down.{i}.block.{j}."
+        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
+
+    if i < 3:
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
+        sd_downsample_prefix = f"down.{i}.downsample."
+        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"up.{3-i}.upsample."
+        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
+
+    # up_blocks have three resnets
+    # also, up blocks in hf are numbered in reverse from sd
+    for j in range(3):
+        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
+        sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
+        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
+
+# this part accounts for mid blocks in both the encoder and the decoder
+for i in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{i}."
+    sd_mid_res_prefix = f"mid.block_{i+1}."
+    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+vae_conversion_map_attn = [
+    # (stable-diffusion, HF Diffusers)
+    ("norm.", "group_norm."),
+    ("q.", "query."),
+    ("k.", "key."),
+    ("v.", "value."),
+    ("proj_out.", "proj_attn."),
+]
+
+
+def reshape_weight_for_sd(w):
+    # convert HF linear weights to SD conv2d weights
+    return w.reshape(*w.shape, 1, 1)
+
+
+def convert_vae_state_dict(vae_state_dict):
+    mapping = {k: k for k in vae_state_dict.keys()}
+    for k, v in mapping.items():
+        for sd_part, hf_part in vae_conversion_map:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    for k, v in mapping.items():
+        if "attentions" in k:
+            for sd_part, hf_part in vae_conversion_map_attn:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
+    weights_to_convert = ["q", "k", "v", "proj_out"]
+    print("Converting to CKPT ...")    
+    for k, v in new_state_dict.items():
+        for weight_name in weights_to_convert:
+            if f"mid.attn_1.{weight_name}.weight" in k:
+                new_state_dict[k] = reshape_weight_for_sd(v)
+    return new_state_dict
+
+
+# =========================#
+# Text Encoder Conversion #
+# =========================#
+# pretty much a no-op
+
+
+def convert_text_enc_state_dict(text_enc_dict):
+    return text_enc_dict
+
+
+def convert(model_path, checkpoint_path):    
+    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
+    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
+    text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
+
+    # Convert the UNet model
+    unet_state_dict = torch.load(unet_path, map_location='cpu')
+    unet_state_dict = convert_unet_state_dict(unet_state_dict)
+    unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
+
+    # Convert the VAE model
+    vae_state_dict = torch.load(vae_path, map_location='cpu')
+    vae_state_dict = convert_vae_state_dict(vae_state_dict)
+    vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
+
+    # Convert the text encoder model
+    text_enc_dict = torch.load(text_enc_path, map_location='cpu')
+    text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
+    text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
+
+    # Put together new checkpoint
+    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
+    
+    state_dict = {k:v.half() for k,v in state_dict.items()}
+    state_dict = {"state_dict": state_dict}
+    torch.save(state_dict, checkpoint_path)
+    del state_dict, text_enc_dict, vae_state_dict, unet_state_dict
+    torch.cuda.empty_cache()
+    gc.collect()

mix.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09207c4e95fcf5296eb0ff708fdc672da960aeb2864d298810db5094b072a0d4
+size 28022653

model_index.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.6.0",
+  "feature_extractor": [
+    "transformers",
+    "CLIPFeatureExtractor"
+  ],
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

requirements-local.txt

@@ -0,0 +1,18 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch==1.12.1+cu113
+torchvision==0.13.1+cu113
+diffusers==0.9.0
+accelerate==0.12.0
+OmegaConf
+wget
+pytorch_lightning
+huggingface_hub
+ftfy
+transformers
+pyfiglet
+triton==2.0.0.dev20220701
+bitsandbytes
+python-slugify
+requests
+tensorboard
+pip install git+https://github.com/facebookresearch/xformers@7e4c02c#egg=xformers

requirements.txt

@@ -0,0 +1,18 @@
+--extra-index-url https://download.pytorch.org/whl/cu113
+torch==1.12.1+cu113
+torchvision==0.13.1+cu113
+git+https://github.com/huggingface/diffusers.git
+accelerate==0.12.0
+OmegaConf
+wget
+pytorch_lightning
+huggingface_hub
+ftfy
+transformers
+pyfiglet
+triton==2.0.0.dev20220701
+bitsandbytes
+python-slugify
+requests
+tensorboard
+https://github.com/apolinario/xformers/releases/download/0.0.2/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl