Merge branch 'master' into test_resolve_conflicts
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MalumaDev
GitHub
commit
97ceaa23d0
6 changed files with 30 additions and 26 deletions
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@ -104,6 +104,7 @@ def prepare_enviroment():
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args = shlex.split(commandline_args)
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args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
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args, reinstall_xformers = extract_arg(args, '--reinstall-xformers')
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xformers = '--xformers' in args
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deepdanbooru = '--deepdanbooru' in args
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ngrok = '--ngrok' in args
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@ -128,9 +129,9 @@ def prepare_enviroment():
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if not is_installed("clip"):
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run_pip(f"install {clip_package}", "clip")
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if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
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if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"):
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if platform.system() == "Windows":
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run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
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run_pip("install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
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elif platform.system() == "Linux":
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run_pip("install xformers", "xformers")
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@ -272,15 +272,17 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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pbar.set_description(f"loss: {losses.mean():.7f}")
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mean_loss = losses.mean()
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if torch.isnan(mean_loss):
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raise RuntimeError("Loss diverged.")
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pbar.set_description(f"loss: {mean_loss:.7f}")
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if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
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last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
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hypernetwork.save(last_saved_file)
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textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
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"loss": f"{losses.mean():.7f}",
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"loss": f"{mean_loss:.7f}",
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"learn_rate": scheduler.learn_rate
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})
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@ -328,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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shared.state.textinfo = f"""
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<p>
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Loss: {losses.mean():.7f}<br/>
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Loss: {mean_loss:.7f}<br/>
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Step: {hypernetwork.step}<br/>
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Last prompt: {html.escape(entries[0].cond_text)}<br/>
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Last saved embedding: {html.escape(last_saved_file)}<br/>
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@ -29,8 +29,8 @@ def apply_optimizations():
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ldm.modules.diffusionmodules.model.nonlinearity = silu
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if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (
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6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
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if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
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print("Applying xformers cross attention optimization.")
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ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
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ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
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@ -88,9 +88,9 @@ class EmbeddingDatabase:
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data = []
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if filename.upper().endswith('.PNG'):
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if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
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embed_image = Image.open(path)
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if 'sd-ti-embedding' in embed_image.text:
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if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
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data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
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name = data.get('name', name)
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else:
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@ -242,6 +242,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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last_saved_file = "<none>"
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last_saved_image = "<none>"
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embedding_yet_to_be_embedded = False
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ititial_step = embedding.step or 0
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if ititial_step > steps:
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@ -283,6 +284,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
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last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
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embedding.save(last_saved_file)
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embedding_yet_to_be_embedded = True
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write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
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"loss": f"{losses.mean():.7f}",
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@ -320,7 +322,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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shared.state.current_image = image
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if save_image_with_stored_embedding and os.path.exists(last_saved_file):
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if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
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last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
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@ -329,15 +331,22 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
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info.add_text("sd-ti-embedding", embedding_to_b64(data))
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title = "<{}>".format(data.get('name', '???'))
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try:
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vectorSize = list(data['string_to_param'].values())[0].shape[0]
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except Exception as e:
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vectorSize = '?'
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checkpoint = sd_models.select_checkpoint()
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footer_left = checkpoint.model_name
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footer_mid = '[{}]'.format(checkpoint.hash)
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footer_right = '{}'.format(embedding.step)
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footer_right = '{}v {}s'.format(vectorSize, embedding.step)
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captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
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captioned_image = insert_image_data_embed(captioned_image, data)
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captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
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embedding_yet_to_be_embedded = False
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image.save(last_saved_image)
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@ -158,10 +158,7 @@ def save_files(js_data, images, do_make_zip, index):
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writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
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for image_index, filedata in enumerate(images, start_index):
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if filedata.startswith("data:image/png;base64,"):
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filedata = filedata[len("data:image/png;base64,"):]
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image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
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image = image_from_url_text(filedata)
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is_grid = image_index < p.index_of_first_image
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i = 0 if is_grid else (image_index - p.index_of_first_image)
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@ -638,7 +635,7 @@ def create_ui(wrap_gradio_gpu_call):
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txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
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txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
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with gr.Group():
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with gr.Column():
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with gr.Row():
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save = gr.Button('Save')
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send_to_img2img = gr.Button('Send to img2img')
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@ -862,7 +859,7 @@ def create_ui(wrap_gradio_gpu_call):
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img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
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img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
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with gr.Group():
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with gr.Column():
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with gr.Row():
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save = gr.Button('Save')
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img2img_send_to_img2img = gr.Button('Send to img2img')
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@ -237,13 +237,6 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s
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margin: 0;
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}
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.gr-panel div.flex-col div.justify-between div{
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position: absolute;
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top: -0.1em;
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right: 1em;
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padding: 0 0.5em;
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}
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#settings .gr-panel div.flex-col div.justify-between div{
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position: relative;
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z-index: 200;
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@ -316,6 +309,8 @@ input[type="range"]{
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height: 100%;
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overflow: auto;
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background-color: rgba(20, 20, 20, 0.95);
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user-select: none;
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-webkit-user-select: none;
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}
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.modalControls {
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