From 345028099d893f8a66726cfd13627d8cc1bcc724 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Sep 2022 12:08:45 +0300
Subject: split codebase into multiple files; to anyone this affects
 negatively: sorry

---
 modules/img2img.py | 133 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 133 insertions(+)
 create mode 100644 modules/img2img.py

(limited to 'modules/img2img.py')

diff --git a/modules/img2img.py b/modules/img2img.py
new file mode 100644
index 00000000..f2817ba8
--- /dev/null
+++ b/modules/img2img.py
@@ -0,0 +1,133 @@
+import math
+from PIL import Image
+
+from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
+from modules.shared import opts, state
+import modules.shared as shared
+import modules.processing as processing
+from modules.ui import plaintext_to_html
+import modules.images as images
+
+def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
+    is_inpaint = mode == 1
+    is_loopback = mode == 2
+    is_upscale = mode == 3
+
+    if is_inpaint:
+        image = init_img_with_mask['image']
+        mask = init_img_with_mask['mask']
+    else:
+        image = init_img
+        mask = None
+
+    assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
+
+    p = StableDiffusionProcessingImg2Img(
+        sd_model=shared.sd_model,
+        outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
+        outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
+        prompt=prompt,
+        seed=seed,
+        sampler_index=sampler_index,
+        batch_size=batch_size,
+        n_iter=n_iter,
+        steps=steps,
+        cfg_scale=cfg_scale,
+        width=width,
+        height=height,
+        prompt_matrix=prompt_matrix,
+        use_GFPGAN=use_GFPGAN,
+        init_images=[image],
+        mask=mask,
+        mask_blur=mask_blur,
+        inpainting_fill=inpainting_fill,
+        resize_mode=resize_mode,
+        denoising_strength=denoising_strength,
+        inpaint_full_res=inpaint_full_res,
+        extra_generation_params={"Denoising Strength": denoising_strength}
+    )
+
+    if is_loopback:
+        output_images, info = None, None
+        history = []
+        initial_seed = None
+        initial_info = None
+
+        for i in range(n_iter):
+            p.n_iter = 1
+            p.batch_size = 1
+            p.do_not_save_grid = True
+
+            state.job = f"Batch {i + 1} out of {n_iter}"
+            processed = process_images(p)
+
+            if initial_seed is None:
+                initial_seed = processed.seed
+                initial_info = processed.info
+
+            p.init_images = [processed.images[0]]
+            p.seed = processed.seed + 1
+            p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
+            history.append(processed.images[0])
+
+        grid = images.image_grid(history, batch_size, rows=1)
+
+        images.save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
+
+        processed = Processed(p, history, initial_seed, initial_info)
+
+    elif is_upscale:
+        initial_seed = None
+        initial_info = None
+
+        upscaler = shared.sd_upscalers.get(upscaler_name, next(iter(shared.sd_upscalers.values())))
+        img = upscaler(init_img)
+
+        processing.torch_gc()
+
+        grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap)
+
+        p.n_iter = 1
+        p.do_not_save_grid = True
+        p.do_not_save_samples = True
+
+        work = []
+        work_results = []
+
+        for y, h, row in grid.tiles:
+            for tiledata in row:
+                work.append(tiledata[2])
+
+        batch_count = math.ceil(len(work) / p.batch_size)
+        print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches.")
+
+        for i in range(batch_count):
+            p.init_images = work[i*p.batch_size:(i+1)*p.batch_size]
+
+            state.job = f"Batch {i + 1} out of {batch_count}"
+            processed = process_images(p)
+
+            if initial_seed is None:
+                initial_seed = processed.seed
+                initial_info = processed.info
+
+            p.seed = processed.seed + 1
+            work_results += processed.images
+
+        image_index = 0
+        for y, h, row in grid.tiles:
+            for tiledata in row:
+                tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
+                image_index += 1
+
+        combined_image = images.combine_grid(grid)
+
+        if opts.samples_save:
+            images.save_image(combined_image, p.outpath_samples, "", initial_seed, prompt, opts.grid_format, info=initial_info)
+
+        processed = Processed(p, [combined_image], initial_seed, initial_info)
+
+    else:
+        processed = process_images(p)
+
+    return processed.images, processed.js(), plaintext_to_html(processed.info)
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