initial v0.1 - in progress
vahid K. nejad
parent
987c849fb9
commit
c16f7bca7e
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@ -1,24 +1,78 @@
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import gradio as gr
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from types import SimpleNamespace
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import sys
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import os
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from modules import script_callbacks
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from webui import wrap_gradio_gpu_call
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basedirs = [os.getcwd()]
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import time
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basedir = os.getcwd()
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sys.path.extend(basedir + '/extensions/infinite-zoom-sd-webui/')
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import numpy as np
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import gradio as gr
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from PIL import Image
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inpaint_model_list = [
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"stabilityai/stable-diffusion-2-inpainting",
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"runwayml/stable-diffusion-inpainting",
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"parlance/dreamlike-diffusion-1.0-inpainting",
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"ghunkins/stable-diffusion-liberty-inpainting",
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"ImNoOne/f222-inpainting-diffusers"
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]
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from iz_helpers.image import shrink_and_paste_on_blank
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from iz_helpers.video import write_video
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from webui import wrap_gradio_gpu_call
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from modules import script_callbacks
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import modules.shared as shared
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import modules.scripts as scripts
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from modules.processing import process_images, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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from modules.ui import create_output_panel, plaintext_to_html, wrap_gradio_call
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output_path = basedir + '/extensions/infinite-zoom-sd-webui/out'
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default_prompt = "A psychedelic jungle with trees that have glowing, fractal-like patterns, Simon stalenhag poster 1920s style, street level view, hyper futuristic, 8k resolution, hyper realistic"
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default_negative_prompt = "frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur"
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def zoom(
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model_id,
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def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, width, height):
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processetd = None
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p = StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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outpath_samples=output_path,
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outpath_grids=output_path,
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prompt=prompt,
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negative_prompt=negative_prompt,
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# seed=-1,
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sampler_name=sampler,
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n_iter=1,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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)
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# script_runner = scripts.scripts_img2img
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# p.scripts = script_runner
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# shared.state.begin()
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processed = process_images(p)
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# shared.state.end()
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return processed
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def renderImg2Img(prompt, negative_prompt, sampler, steps, cfg_scale, width, height, init_image, mask_image):
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processetd = None
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p = StableDiffusionProcessingImg2Img(
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sd_model=shared.sd_model,
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outpath_samples=output_path,
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outpath_grids=output_path,
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prompt=prompt,
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negative_prompt=negative_prompt,
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# seed=-1,
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sampler_name=sampler,
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n_iter=1,
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steps=steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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init_images=[init_image],
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mask=mask_image
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)
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# script_runner = scripts.scripts_txt2img
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# p.scripts = script_runner
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# shared.state.begin()
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processed = process_images(p)
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# shared.state.end()
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return processed
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def create_zoom(
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prompts_array,
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negative_prompt,
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num_outpainting_steps,
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@ -26,7 +80,108 @@ def zoom(
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num_inference_steps,
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custom_init_image
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):
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pass
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prompts = {}
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for x in prompts_array:
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try:
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key = int(x[0])
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value = str(x[1])
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prompts[key] = value
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except ValueError:
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pass
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assert len(prompts_array) > 0, "prompts is empty"
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width = 512
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height = 512
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current_image = Image.new(mode="RGBA", size=(height, width))
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255-mask_image).convert("RGB")
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current_image = current_image.convert("RGB")
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if (custom_init_image):
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current_image = custom_init_image.resize(
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(width, height), resample=Image.LANCZOS)
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else:
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processed = renderTxt2Img(prompts[min(k for k in prompts.keys() if k >= 0)],
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negative_prompt, "Euler a", num_inference_steps, guidance_scale, width, height)
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current_image = processed.images[0]
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mask_width = 128
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num_interpol_frames = 30
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all_frames = []
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all_frames.append(current_image)
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for i in range(num_outpainting_steps):
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# print('Outpaint step: ' + str(i+1) +
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# ' / ' + str(num_outpainting_steps))
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prev_image_fix = current_image
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prev_image = shrink_and_paste_on_blank(current_image, mask_width)
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current_image = prev_image
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# create mask (black image with white mask_width width edges)
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255-mask_image).convert("RGB")
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# inpainting step
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current_image = current_image.convert("RGB")
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# images = pipe(prompt=prompts[max(k for k in prompts.keys() if k <= i)],
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# negative_prompt=negative_prompt,
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# image=current_image,
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# guidance_scale=guidance_scale,
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# height=height,
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# width=width,
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# # generator = g_cuda.manual_seed(seed),
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# mask_image=mask_image,
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# num_inference_steps=num_inference_steps)[0]
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# current_image = images[0]
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processed = renderImg2Img(prompts[max(k for k in prompts.keys() if k <= i)], negative_prompt, "Euler a", num_inference_steps, guidance_scale, width, height, current_image, mask_image)
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current_image = processed.images[0]
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current_image.paste(prev_image, mask=prev_image)
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# interpolation steps bewteen 2 inpainted images (=sequential zoom and crop)
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for j in range(num_interpol_frames - 1):
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interpol_image = current_image
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interpol_width = round(
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(1 - (1-2*mask_width/height)**(1-(j+1)/num_interpol_frames))*height/2
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)
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interpol_image = interpol_image.crop((interpol_width,
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interpol_width,
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width - interpol_width,
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height - interpol_width))
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interpol_image = interpol_image.resize((height, width))
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# paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
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interpol_width2 = round(
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(1 - (height-2*mask_width) / (height-2*interpol_width)) / 2*height
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)
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prev_image_fix_crop = shrink_and_paste_on_blank(
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prev_image_fix, interpol_width2)
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interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
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all_frames.append(interpol_image)
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all_frames.append(current_image)
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video_file_name = "infinite_zoom_" + str(time.time())
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fps = 30
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save_path = output_path + video_file_name + ".mp4"
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start_frame_dupe_amount = 15
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last_frame_dupe_amount = 15
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write_video(save_path, all_frames, fps, False,
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start_frame_dupe_amount, last_frame_dupe_amount)
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## to debug
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# img = custom_init_image.resize(
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# (width, height), resample=Image.LANCZOS)
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# img = shrink_and_paste_on_blank(img, 128)
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# mask_image = np.array(img)[:, :, 3]
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# mask_image = Image.fromarray(255-mask_image).convert("RGB")
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# processed = renderImg2Img(prompts[min(k for k in prompts.keys(
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# ) if k >= 0)], negative_prompt, "Euler a", num_inference_steps, guidance_scale, width, height, img, mask_image)
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## to debug
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return save_path , processed.images, processed.js(), plaintext_to_html(processed.info), plaintext_to_html("")
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def on_ui_tabs():
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@ -34,13 +189,12 @@ def on_ui_tabs():
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gr.HTML(
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"""
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<p style='text-align: center'>
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Text to Video - Infinite zoom effect
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Text to Video - Infinite zoom effect
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</p>
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"""
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)
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# with gr.Blocks():
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with gr.Row():
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with gr.Column():
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with gr.Column(scale=1, variant='panel'):
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outpaint_prompts = gr.Dataframe(
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type="array",
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headers=["outpaint steps", "prompt"],
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@ -65,12 +219,6 @@ def on_ui_tabs():
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label='Total Outpaint Steps'
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)
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with gr.Accordion("Advanced Options", open=False):
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model_id = gr.Dropdown(
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choices=inpaint_model_list,
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value=inpaint_model_list[0],
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label='Pre-trained Model ID'
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)
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guidance_scale = gr.Slider(
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minimum=0.1,
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maximum=15,
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@ -90,25 +238,30 @@ def on_ui_tabs():
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type="pil", label="custom initial image")
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generate_btn = gr.Button(value='Generate video')
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with gr.Column():
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with gr.Column(scale=1, variant='compact'):
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output_video = gr.Video(label='Output', format="mp4").style(
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width=512, height=512)
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generate_btn.click(
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fn=wrap_gradio_gpu_call(zoom, extra_outputs=[None, '', '']),
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inputs=[
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model_id,
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outpaint_prompts,
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outpaint_negative_prompt,
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outpaint_steps,
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guidance_scale,
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sampling_step,
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init_image
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],
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outputs=[
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output_video,
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],
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)
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width=512, height=512, interactive=False)
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# output_video = gr.Image(label="output", interactive=False)
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out_image, generation_info, html_info, html_log = create_output_panel(
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"infinit-zoom", output_path)
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generate_btn.click(
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fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, '', '']),
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inputs=[
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outpaint_prompts,
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outpaint_negative_prompt,
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outpaint_steps,
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guidance_scale,
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sampling_step,
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init_image
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],
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outputs=[
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output_video,
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out_image,
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generation_info,
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html_info,
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html_log
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],
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)
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return [(infinite_zoom_interface, "Infinite Zoom", "iz_interface")]
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