Running on CPU 🥶 This demo does not work on CPU.
' model = Model() def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed def get_stage2_index(evt: gr.SelectData) -> int: return evt.index def check_if_stage2_selected(index: int) -> None: if index == -1: raise gr.Error( 'You need to select the image you would like to upscale from the Stage 1 results by clicking.' ) hf_api = HfApi(token=HF_TOKEN) if UPLOAD_REPO_ID: hf_api.create_repo(repo_id=UPLOAD_REPO_ID, private=True, repo_type='dataset', exist_ok=True) def get_param_file_hash_name(param_filepath: str) -> str: if not UPLOAD_REPO_ID: return '' with open(param_filepath, 'rb') as f: md5 = hashlib.md5(f.read()).hexdigest() utcnow = datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M-%S-%f') return f'{utcnow}-{md5}' def upload_stage1_result(stage1_param_path: str, stage1_result_path: str, save_name: str) -> None: if not UPLOAD_REPO_ID: return try: hf_api.upload_file(path_or_fileobj=stage1_param_path, path_in_repo=f'stage1_params/{save_name}.json', repo_id=UPLOAD_REPO_ID, repo_type='dataset') hf_api.upload_file(path_or_fileobj=stage1_result_path, path_in_repo=f'stage1_results/{save_name}.pth', repo_id=UPLOAD_REPO_ID, repo_type='dataset') except Exception as e: print(e) def upload_stage2_info(stage1_param_file_hash_name: str, stage2_output_path: str, selected_index_for_upscale: int, seed_2: int, guidance_scale_2: float, custom_timesteps_2: str, num_inference_steps_2: int) -> None: if not UPLOAD_REPO_ID: return if not stage1_param_file_hash_name: raise ValueError stage2_params = { 'stage1_param_file_hash_name': stage1_param_file_hash_name, 'selected_index_for_upscale': selected_index_for_upscale, 'seed_2': seed_2, 'guidance_scale_2': guidance_scale_2, 'custom_timesteps_2': custom_timesteps_2, 'num_inference_steps_2': num_inference_steps_2, } with tempfile.NamedTemporaryFile(mode='w', delete=False) as param_file: param_file.write(json.dumps(stage2_params)) stage2_param_file_hash_name = get_param_file_hash_name(param_file.name) save_name = f'{stage1_param_file_hash_name}_{stage2_param_file_hash_name}' try: hf_api.upload_file(path_or_fileobj=param_file.name, path_in_repo=f'stage2_params/{save_name}.json', repo_id=UPLOAD_REPO_ID, repo_type='dataset') if UPLOAD_RESULT_IMAGE: hf_api.upload_file(path_or_fileobj=stage2_output_path, path_in_repo=f'stage2_results/{save_name}.png', repo_id=UPLOAD_REPO_ID, repo_type='dataset') except Exception as e: print(e) def upload_stage2_3_info(stage1_param_file_hash_name: str, stage2_3_output_path: str, selected_index_for_upscale: int, seed_2: int, guidance_scale_2: float, custom_timesteps_2: str, num_inference_steps_2: int, prompt: str, negative_prompt: str, seed_3: int, guidance_scale_3: float, num_inference_steps_3: int) -> None: if not UPLOAD_REPO_ID: return if not stage1_param_file_hash_name: raise ValueError stage2_3_params = { 'stage1_param_file_hash_name': stage1_param_file_hash_name, 'selected_index_for_upscale': selected_index_for_upscale, 'seed_2': seed_2, 'guidance_scale_2': guidance_scale_2, 'custom_timesteps_2': custom_timesteps_2, 'num_inference_steps_2': num_inference_steps_2, 'prompt': prompt, 'negative_prompt': negative_prompt, 'seed_3': seed_3, 'guidance_scale_3': guidance_scale_3, 'num_inference_steps_3': num_inference_steps_3, } with tempfile.NamedTemporaryFile(mode='w', delete=False) as param_file: param_file.write(json.dumps(stage2_3_params)) stage2_3_param_file_hash_name = get_param_file_hash_name(param_file.name) save_name = f'{stage1_param_file_hash_name}_{stage2_3_param_file_hash_name}' try: hf_api.upload_file(path_or_fileobj=param_file.name, path_in_repo=f'stage2_3_params/{save_name}.json', repo_id=UPLOAD_REPO_ID, repo_type='dataset') if UPLOAD_RESULT_IMAGE: hf_api.upload_file( path_or_fileobj=stage2_3_output_path, path_in_repo=f'stage2_3_results/{save_name}.png', repo_id=UPLOAD_REPO_ID, repo_type='dataset') except Exception as e: print(e) def update_upscale_button(selected_index: int) -> tuple[dict, dict]: if selected_index == -1: return gr.update(interactive=False), gr.update(interactive=False) else: return gr.update(interactive=True), gr.update(interactive=True) def _update_result_view(show_gallery: bool) -> tuple[dict, dict]: return gr.update(visible=show_gallery), gr.update(visible=not show_gallery) def show_gallery_view() -> tuple[dict, dict]: return _update_result_view(True) def show_upscaled_view() -> tuple[dict, dict]: return _update_result_view(False) examples = [ 'high quality dslr photo, a photo product of a lemon inspired by natural and organic materials, wooden accents, intricately decorated with glowing vines of led lights, inspired by baroque luxury', 'Aerial photo of a beach, the words "what if?" written in the sand.', 'A photo of a red cube on top of a blue cube. a photo of a red cube with text "blue" on it is sitting on top of a blue cube with text "red" on it. photo realism', 'a photo of a violet baseball cap with yellow text: "DISdeep floyd". 50mm lens, photo realism, cine lens. violet baseball cap says "deep floyd". reflections, render. yellow stitch text "deep floyd"', 'ultra close-up color photo portrait of rainbow owl with deer horns in the woods', 'product image of a crochet Cthulhu the great old one emerging from a spacetime wormhole made of wool.', 'a little green budgie parrot driving small red toy car in new york street, photo', 'origami dancer in white paper, 3d render, ultra-detailed, on white background, studio shot.', 'glowing mushrooms in a natural environment with smoke in the frame', 'a bowl full of few adorable golden doodle puppies, the doodles dusted in powdered sugar and look delicious, bokeh, cannon. professional macro photo, super detailed. cute sweet golden doodle confectionery, baking puppies in powdered sugar in the bowl', 'a yellow ipe tree in the cerrado, the hills are dry and the weather is hot. the ipe tree shows all its beauty among the dry trees. cinematic film still of a movie, realism, 4k, 8mm, grainy, panavision', ] with gr.Blocks(css='style.css') as demo: gr.Markdown(TITLE) gr.Markdown(DESCRIPTION) with gr.Box(): with gr.Row(elem_id='prompt-container').style(equal_height=True): with gr.Column(): prompt = gr.Text( label='Prompt', show_label=False, max_lines=1, placeholder='Enter your prompt', elem_id='prompt-text-input', ).style(container=False) negative_prompt = gr.Text( label='Negative prompt', show_label=False, max_lines=1, placeholder='Enter a negative prompt', elem_id='negative-prompt-text-input', ).style(container=False) generate_button = gr.Button('Generate').style(full_width=False) with gr.Column() as gallery_view: gallery = gr.Gallery(label='Stage 1 results', show_label=False, elem_id='gallery').style( columns=GALLERY_COLUMN_NUM, object_fit='contain') gr.Markdown('Pick your favorite generation to upscale.') with gr.Row(): upscale_to_256_button = gr.Button( 'Upscale to 256px', visible=SHOW_UPSCALE_TO_256_BUTTON or DISABLE_SD_X4_UPSCALER, interactive=False) upscale_button = gr.Button('Upscale', interactive=False, visible=not DISABLE_SD_X4_UPSCALER) with gr.Column(visible=False) as upscale_view: result = gr.Image(label='Result', show_label=False, type='filepath', interactive=False, elem_id='upscaled-image').style(height=640) back_to_selection_button = gr.Button('Back to selection') with gr.Accordion('Advanced options', open=False, visible=SHOW_ADVANCED_OPTIONS): with gr.Tabs(): with gr.Tab(label='Generation'): seed_1 = gr.Slider(label='Seed', minimum=0, maximum=MAX_SEED, step=1, value=0) randomize_seed_1 = gr.Checkbox(label='Randomize seed', value=True) guidance_scale_1 = gr.Slider(label='Guidance scale', minimum=1, maximum=20, step=0.1, value=7.0) custom_timesteps_1 = gr.Dropdown( label='Custom timesteps 1', choices=[ 'none', 'fast27', 'smart27', 'smart50', 'smart100', 'smart185', ], value=DEFAULT_CUSTOM_TIMESTEPS_1, visible=SHOW_CUSTOM_TIMESTEPS_1) num_inference_steps_1 = gr.Slider( label='Number of inference steps', minimum=1, maximum=MAX_NUM_STEPS, step=1, value=100, visible=SHOW_NUM_STEPS_1) num_images = gr.Slider(label='Number of images', minimum=1, maximum=MAX_NUM_IMAGES, step=1, value=DEFAULT_NUM_IMAGES, visible=SHOW_NUM_IMAGES) with gr.Tab(label='Super-resolution 1'): seed_2 = gr.Slider(label='Seed', minimum=0, maximum=MAX_SEED, step=1, value=0) randomize_seed_2 = gr.Checkbox(label='Randomize seed', value=True) guidance_scale_2 = gr.Slider(label='Guidance scale', minimum=1, maximum=20, step=0.1, value=4.0) custom_timesteps_2 = gr.Dropdown( label='Custom timesteps 2', choices=[ 'none', 'fast27', 'smart27', 'smart50', 'smart100', 'smart185', ], value=DEFAULT_CUSTOM_TIMESTEPS_2, visible=SHOW_CUSTOM_TIMESTEPS_2) num_inference_steps_2 = gr.Slider( label='Number of inference steps', minimum=1, maximum=MAX_NUM_STEPS, step=1, value=50, visible=SHOW_NUM_STEPS_2) with gr.Tab(label='Super-resolution 2'): seed_3 = gr.Slider(label='Seed', minimum=0, maximum=MAX_SEED, step=1, value=0) randomize_seed_3 = gr.Checkbox(label='Randomize seed', value=True) guidance_scale_3 = gr.Slider(label='Guidance scale', minimum=1, maximum=20, step=0.1, value=9.0) num_inference_steps_3 = gr.Slider( label='Number of inference steps', minimum=1, maximum=MAX_NUM_STEPS, step=1, value=DEFAULT_NUM_STEPS_3, visible=SHOW_NUM_STEPS_3) gr.Examples(examples=examples, inputs=prompt, examples_per_page=4) with gr.Box(visible=DEBUG): with gr.Row(): with gr.Accordion(label='Hidden params'): stage1_param_path = gr.Text(label='Stage 1 param path') stage1_result_path = gr.Text(label='Stage 1 result path') stage1_param_file_hash_name = gr.Text( label='Stage 1 param file hash name') selected_index_for_stage2 = gr.Number( label='Selected index for Stage 2', value=-1, precision=0) gr.Markdown(DISCLAIMER) gr.HTML(FOOTER) stage1_inputs = [ prompt, negative_prompt, seed_1, num_images, guidance_scale_1, custom_timesteps_1, num_inference_steps_1, ] stage1_outputs = [ gallery, stage1_param_path, stage1_result_path, ] prompt.submit( fn=randomize_seed_fn, inputs=[seed_1, randomize_seed_1], outputs=seed_1, queue=False, ).then( fn=lambda: -1, outputs=selected_index_for_stage2, queue=False, ).then( fn=show_gallery_view, outputs=[ gallery_view, upscale_view, ], queue=False, ).then( fn=update_upscale_button, inputs=selected_index_for_stage2, outputs=[ upscale_button, upscale_to_256_button, ], queue=False, ).then( fn=model.run_stage1, inputs=stage1_inputs, outputs=stage1_outputs, ).success( fn=get_param_file_hash_name, inputs=stage1_param_path, outputs=stage1_param_file_hash_name, queue=False, ).then( fn=upload_stage1_result, inputs=[ stage1_param_path, stage1_result_path, stage1_param_file_hash_name, ], queue=False, ) negative_prompt.submit( fn=randomize_seed_fn, inputs=[seed_1, randomize_seed_1], outputs=seed_1, queue=False, ).then( fn=lambda: -1, outputs=selected_index_for_stage2, queue=False, ).then( fn=show_gallery_view, outputs=[ gallery_view, upscale_view, ], queue=False, ).then( fn=update_upscale_button, inputs=selected_index_for_stage2, outputs=[ upscale_button, upscale_to_256_button, ], queue=False, ).then( fn=model.run_stage1, inputs=stage1_inputs, outputs=stage1_outputs, ).success( fn=get_param_file_hash_name, inputs=stage1_param_path, outputs=stage1_param_file_hash_name, queue=False, ).then( fn=upload_stage1_result, inputs=[ stage1_param_path, stage1_result_path, stage1_param_file_hash_name, ], queue=False, ) generate_button.click( fn=randomize_seed_fn, inputs=[seed_1, randomize_seed_1], outputs=seed_1, queue=False, ).then( fn=lambda: -1, outputs=selected_index_for_stage2, queue=False, ).then( fn=show_gallery_view, outputs=[ gallery_view, upscale_view, ], queue=False, ).then( fn=update_upscale_button, inputs=selected_index_for_stage2, outputs=[ upscale_button, upscale_to_256_button, ], queue=False, ).then( fn=model.run_stage1, inputs=stage1_inputs, outputs=stage1_outputs, api_name='generate64', ).success( fn=get_param_file_hash_name, inputs=stage1_param_path, outputs=stage1_param_file_hash_name, queue=False, ).then( fn=upload_stage1_result, inputs=[ stage1_param_path, stage1_result_path, stage1_param_file_hash_name, ], queue=False, ) gallery.select( fn=get_stage2_index, outputs=selected_index_for_stage2, queue=False, ) selected_index_for_stage2.change( fn=update_upscale_button, inputs=selected_index_for_stage2, outputs=[ upscale_button, upscale_to_256_button, ], queue=False, ) stage2_inputs = [ stage1_result_path, selected_index_for_stage2, seed_2, guidance_scale_2, custom_timesteps_2, num_inference_steps_2, ] upscale_to_256_button.click( fn=check_if_stage2_selected, inputs=selected_index_for_stage2, queue=False, ).then( fn=randomize_seed_fn, inputs=[seed_2, randomize_seed_2], outputs=seed_2, queue=False, ).then( fn=show_upscaled_view, outputs=[ gallery_view, upscale_view, ], queue=False, ).then( fn=model.run_stage2, inputs=stage2_inputs, outputs=result, api_name='upscale256', ).success( fn=upload_stage2_info, inputs=[ stage1_param_file_hash_name, result, selected_index_for_stage2, seed_2, guidance_scale_2, custom_timesteps_2, num_inference_steps_2, ], queue=False, ) stage2_3_inputs = [ stage1_result_path, selected_index_for_stage2, seed_2, guidance_scale_2, custom_timesteps_2, num_inference_steps_2, prompt, negative_prompt, seed_3, guidance_scale_3, num_inference_steps_3, ] upscale_button.click( fn=check_if_stage2_selected, inputs=selected_index_for_stage2, queue=False, ).then( fn=randomize_seed_fn, inputs=[seed_2, randomize_seed_2], outputs=seed_2, queue=False, ).then( fn=randomize_seed_fn, inputs=[seed_3, randomize_seed_3], outputs=seed_3, queue=False, ).then( fn=show_upscaled_view, outputs=[ gallery_view, upscale_view, ], queue=False, ).then( fn=model.run_stage2_3, inputs=stage2_3_inputs, outputs=result, api_name='upscale1024', ).success( fn=upload_stage2_3_info, inputs=[ stage1_param_file_hash_name, result, selected_index_for_stage2, seed_2, guidance_scale_2, custom_timesteps_2, num_inference_steps_2, prompt, negative_prompt, seed_3, guidance_scale_3, num_inference_steps_3, ], queue=False, ) back_to_selection_button.click( fn=show_gallery_view, outputs=[ gallery_view, upscale_view, ], queue=False, ) demo.queue(api_open=False, max_size=MAX_QUEUE_SIZE).launch(debug=DEBUG)