add option to read generation params for learning previews from txt2img
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AUTOMATIC
parent
bb295f5478
commit
c344ba3b32
3 changed files with 51 additions and 15 deletions
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@ -180,7 +180,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
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return self.to_out(out)
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def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
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def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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assert hypernetwork_name, 'hypernetwork not selected'
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path = shared.hypernetworks.get(hypernetwork_name, None)
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@ -265,20 +265,31 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
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if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
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last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
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preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
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optimizer.zero_grad()
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shared.sd_model.cond_stage_model.to(devices.device)
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shared.sd_model.first_stage_model.to(devices.device)
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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prompt=preview_text,
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steps=20,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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if preview_from_txt2img:
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = entry.cond_text
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p.steps = 20
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preview_text = p.prompt
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images)>0 else None
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@ -172,7 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
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return fn
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def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
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def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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assert embedding_name, 'embedding not selected'
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shared.state.textinfo = "Initializing textual inversion training..."
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@ -259,18 +259,29 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
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if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
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last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
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preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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prompt=preview_text,
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steps=20,
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height=training_height,
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width=training_width,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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if preview_from_txt2img:
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_index = preview_sampler_index
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = entry.cond_text
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p.steps = 20
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p.width = training_width
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p.height = training_height
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preview_text = p.prompt
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processed = processing.process_images(p)
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image = processed.images[0]
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@ -711,6 +711,18 @@ def create_ui(wrap_gradio_gpu_call):
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(firstphase_width, "First pass size-1"),
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(firstphase_height, "First pass size-2"),
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]
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txt2img_preview_params = [
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txt2img_prompt,
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txt2img_negative_prompt,
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steps,
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sampler_index,
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cfg_scale,
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seed,
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width,
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height,
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]
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token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
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with gr.Blocks(analytics_enabled=False) as img2img_interface:
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@ -1162,7 +1174,7 @@ def create_ui(wrap_gradio_gpu_call):
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create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
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save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
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save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
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preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
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preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
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with gr.Row():
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interrupt_training = gr.Button(value="Interrupt")
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@ -1240,7 +1252,8 @@ def create_ui(wrap_gradio_gpu_call):
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save_embedding_every,
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template_file,
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save_image_with_stored_embedding,
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preview_image_prompt,
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preview_from_txt2img,
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*txt2img_preview_params,
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],
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outputs=[
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ti_output,
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@ -1260,7 +1273,8 @@ def create_ui(wrap_gradio_gpu_call):
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create_image_every,
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save_embedding_every,
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template_file,
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preview_image_prompt,
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preview_from_txt2img,
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*txt2img_preview_params,
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],
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outputs=[
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ti_output,
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