Upload multi_frame_render.py

scripts/multi_frame_render.py

@@ -0,0 +1,312 @@
+# Beta V0.72
+import numpy as np
+from tqdm import trange
+from PIL import Image, ImageSequence, ImageDraw
+import math
+
+import modules.scripts as scripts
+import gradio as gr
+
+from modules import processing, shared, sd_samplers, images
+from modules.processing import Processed
+from modules.sd_samplers import samplers
+from modules.shared import opts, cmd_opts, state
+from modules import deepbooru
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "(Beta) Multi-frame Video rendering - V0.72"
+
+    def show(self, is_img2img):
+        return is_img2img
+
+    def ui(self, is_img2img):
+        first_denoise = gr.Slider(
+            minimum=0,
+            maximum=1,
+            step=0.05,
+            label="Initial Denoise Strength",
+            value=1,
+            elem_id=self.elem_id("first_denoise"),
+        )
+        append_interrogation = gr.Dropdown(
+            label="Append interrogated prompt at each iteration",
+            choices=["None", "CLIP", "DeepBooru"],
+            value="None",
+        )
+        third_frame_image = gr.Dropdown(
+            label="Third Frame Image",
+            choices=["None", "FirstGen", "GuideImg", "Historical"],
+            value="None",
+        )
+        # reference_imgs = gr.UploadButton(label="Upload Guide Frames", file_types = ['.png','.jpg','.jpeg'], live=True, file_count = "multiple")
+        reference_imgs = gr.File(
+            file_count="multiple",
+            file_types=[".png", ".jpg", ".jpeg"],
+            label="Upload Guide Frames",
+            show_label=True,
+            live=True,
+        )
+        color_correction_enabled = gr.Checkbox(
+            label="Enable Color Correction",
+            value=False,
+            elem_id=self.elem_id("color_correction_enabled"),
+        )
+        unfreeze_seed = gr.Checkbox(
+            label="Unfreeze Seed", value=False, elem_id=self.elem_id("unfreeze_seed")
+        )
+        loopback_source = gr.Dropdown(
+            label="Loopback Source",
+            choices=["PreviousFrame", "InputFrame", "FirstGen"],
+            value="PreviousFrame",
+        )
+
+        return [
+            append_interrogation,
+            reference_imgs,
+            first_denoise,
+            third_frame_image,
+            color_correction_enabled,
+            unfreeze_seed,
+            loopback_source,
+        ]
+
+    def run(
+        self,
+        p,
+        append_interrogation,
+        reference_imgs,
+        first_denoise,
+        third_frame_image,
+        color_correction_enabled,
+        unfreeze_seed,
+        loopback_source,
+    ):
+        freeze_seed = not unfreeze_seed
+
+        loops = len(reference_imgs)
+
+        processing.fix_seed(p)
+        batch_count = p.n_iter
+
+        p.batch_size = 1
+        p.n_iter = 1
+
+        output_images, info = None, None
+        initial_seed = None
+        initial_info = None
+
+        initial_width = p.width
+        initial_img = p.init_images[0]
+
+        grids = []
+        all_images = []
+        original_init_image = p.init_images
+        original_prompt = p.prompt
+        original_denoise = p.denoising_strength
+        state.job_count = loops * batch_count
+
+        initial_color_corrections = [
+            processing.setup_color_correction(p.init_images[0])
+        ]
+
+        for n in range(batch_count):
+            history = []
+            frames = []
+            third_image = None
+            third_image_index = 0
+            frame_color_correction = None
+
+            # Reset to original init image at the start of each batch
+            p.init_images = original_init_image
+            p.width = initial_width
+
+            for i in range(loops):
+                p.n_iter = 1
+                p.batch_size = 1
+                p.do_not_save_grid = True
+                p.control_net_input_image = (
+                    Image.open(reference_imgs[i].name)
+                    .convert("RGB")
+                    .resize((initial_width, p.height), Image.ANTIALIAS)
+                )
+
+                if i > 0:
+                    loopback_image = p.init_images[0]
+                    if loopback_source == "InputFrame":
+                        loopback_image = p.control_net_input_image
+                    elif loopback_source == "FirstGen":
+                        loopback_image = history[0]
+
+                    if third_frame_image != "None" and i > 1:
+                        p.width = initial_width * 3
+                        img = Image.new("RGB", (initial_width * 3, p.height))
+                        img.paste(p.init_images[0], (0, 0))
+                        # img.paste(p.init_images[0], (initial_width, 0))
+                        img.paste(loopback_image, (initial_width, 0))
+                        img.paste(third_image, (initial_width * 2, 0))
+                        p.init_images = [img]
+                        if color_correction_enabled:
+                            p.color_corrections = [
+                                processing.setup_color_correction(img)
+                            ]
+
+                        msk = Image.new("RGB", (initial_width * 3, p.height))
+                        msk.paste(
+                            Image.open(reference_imgs[i - 1].name)
+                            .convert("RGB")
+                            .resize((initial_width, p.height), Image.ANTIALIAS),
+                            (0, 0),
+                        )
+                        msk.paste(p.control_net_input_image, (initial_width, 0))
+                        msk.paste(
+                            Image.open(reference_imgs[third_image_index].name)
+                            .convert("RGB")
+                            .resize((initial_width, p.height), Image.ANTIALIAS),
+                            (initial_width * 2, 0),
+                        )
+                        p.control_net_input_image = msk
+
+                        latent_mask = Image.new(
+                            "RGB", (initial_width * 3, p.height), "black"
+                        )
+                        latent_draw = ImageDraw.Draw(latent_mask)
+                        latent_draw.rectangle(
+                            (initial_width, 0, initial_width * 2, p.height),
+                            fill="white",
+                        )
+                        p.image_mask = latent_mask
+                        p.denoising_strength = original_denoise
+                    else:
+                        p.width = initial_width * 2
+                        img = Image.new("RGB", (initial_width * 2, p.height))
+                        img.paste(p.init_images[0], (0, 0))
+                        # img.paste(p.init_images[0], (initial_width, 0))
+                        img.paste(loopback_image, (initial_width, 0))
+                        p.init_images = [img]
+                        if color_correction_enabled:
+                            p.color_corrections = [
+                                processing.setup_color_correction(img)
+                            ]
+
+                        msk = Image.new("RGB", (initial_width * 2, p.height))
+                        msk.paste(
+                            Image.open(reference_imgs[i - 1].name)
+                            .convert("RGB")
+                            .resize((initial_width, p.height), Image.ANTIALIAS),
+                            (0, 0),
+                        )
+                        msk.paste(p.control_net_input_image, (initial_width, 0))
+                        p.control_net_input_image = msk
+                        frames.append(msk)
+
+                        # latent_mask = Image.new("RGB", (initial_width*2, p.height), "white")
+                        # latent_draw = ImageDraw.Draw(latent_mask)
+                        # latent_draw.rectangle((0,0,initial_width,p.height), fill="black")
+                        latent_mask = Image.new(
+                            "RGB", (initial_width * 2, p.height), "black"
+                        )
+                        latent_draw = ImageDraw.Draw(latent_mask)
+                        latent_draw.rectangle(
+                            (initial_width, 0, initial_width * 2, p.height),
+                            fill="white",
+                        )
+
+                        # p.latent_mask = latent_mask
+                        p.image_mask = latent_mask
+                        p.denoising_strength = original_denoise
+                else:
+                    latent_mask = Image.new("RGB", (initial_width, p.height), "white")
+                    # p.latent_mask = latent_mask
+                    p.image_mask = latent_mask
+                    p.denoising_strength = first_denoise
+                    p.control_net_input_image = p.control_net_input_image.resize(
+                        (initial_width, p.height)
+                    )
+                    frames.append(p.control_net_input_image)
+
+                if append_interrogation != "None":
+                    p.prompt = original_prompt + ", " if original_prompt != "" else ""
+                    if append_interrogation == "CLIP":
+                        p.prompt += shared.interrogator.interrogate(p.init_images[0])
+                    elif append_interrogation == "DeepBooru":
+                        p.prompt += deepbooru.model.tag(p.init_images[0])
+
+                state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
+
+                processed = processing.process_images(p)
+
+                if initial_seed is None:
+                    initial_seed = processed.seed
+                    initial_info = processed.info
+
+                init_img = processed.images[0]
+                if i > 0:
+                    init_img = init_img.crop(
+                        (initial_width, 0, initial_width * 2, p.height)
+                    )
+
+                if third_frame_image != "None":
+                    if third_frame_image == "FirstGen" and i == 0:
+                        third_image = init_img
+                        third_image_index = 0
+                    elif third_frame_image == "GuideImg" and i == 0:
+                        third_image = original_init_image[0]
+                        third_image_index = 0
+                    elif third_frame_image == "Historical":
+                        third_image = processed.images[0].crop(
+                            (0, 0, initial_width, p.height)
+                        )
+                        third_image_index = i - 1
+
+                p.init_images = [init_img]
+                if freeze_seed:
+                    p.seed = processed.seed
+                else:
+                    p.seed = processed.seed + 1
+
+                history.append(init_img)
+                if opts.samples_save:
+                    images.save_image(
+                        init_img,
+                        p.outpath_samples,
+                        "Frame",
+                        p.seed,
+                        p.prompt,
+                        opts.grid_format,
+                        info=info,
+                        short_filename=not opts.grid_extended_filename,
+                        grid=True,
+                        p=p,
+                    )
+
+                frames.append(processed.images[0])
+
+            grid = images.image_grid(history, rows=1)
+            if opts.grid_save:
+                images.save_image(
+                    grid,
+                    p.outpath_grids,
+                    "grid",
+                    initial_seed,
+                    p.prompt,
+                    opts.grid_format,
+                    info=info,
+                    short_filename=not opts.grid_extended_filename,
+                    grid=True,
+                    p=p,
+                )
+
+            grids.append(grid)
+            # all_images += history + frames
+            all_images += history
+
+            p.seed = p.seed + 1
+
+        if opts.return_grid:
+            all_images = grids + all_images
+
+        processed = Processed(p, all_images, initial_seed, initial_info)
+
+        return processed