Upload multi_frame_render.py

script/multi_frame_render.py

@@ -0,0 +1,201 @@
+# 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") 
+        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