diff --git a/webui.py b/webui.py
index bb53e5fff..5b990a5f9 100644
--- a/webui.py
+++ b/webui.py
@@ -8,12 +8,12 @@ from omegaconf import OmegaConf
 from PIL import Image
 from itertools import islice
 from einops import rearrange, repeat
-from torchvision.utils import make_grid
 from torch import autocast
 from contextlib import contextmanager, nullcontext
 import mimetypes
 import random
 import math
+import csv
 
 import k_diffusion as K
 from ldm.util import instantiate_from_config
@@ -28,6 +28,8 @@ mimetypes.add_type('application/javascript', '.js')
 opt_C = 4
 opt_f = 8
 
+invalid_filename_chars = '<>:"/\|?*'
+
 parser = argparse.ArgumentParser()
 parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None)
 parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",)
@@ -127,13 +129,14 @@ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp
 model = model.half().to(device)
 
 
-def image_grid(imgs, batch_size):
+def image_grid(imgs, batch_size, round_down=False):
     if opt.n_rows > 0:
         rows = opt.n_rows
     elif opt.n_rows == 0:
         rows = batch_size
     else:
-        rows = round(math.sqrt(len(imgs)))
+        rows = math.sqrt(len(imgs))
+        rows = int(rows) if round_down else round(rows)
 
     cols = math.ceil(len(imgs) / rows)
 
@@ -146,7 +149,7 @@ def image_grid(imgs, batch_size):
     return grid
 
 
-def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
+def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int):
     torch.cuda.empty_cache()
 
     outpath = opt.outdir or "outputs/txt2img-samples"
@@ -155,6 +158,7 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, ddi
         seed = random.randrange(4294967294)
 
     seed = int(seed)
+    keep_same_seed = False
 
     is_PLMS = sampler_name == 'PLMS'
     is_DDIM = sampler_name == 'DDIM'
@@ -177,59 +181,99 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, ddi
     batch_size = n_samples
 
     assert prompt is not None
-    data = [batch_size * [prompt]]
+    prompts = batch_size * [prompt]
 
     sample_path = os.path.join(outpath, "samples")
     os.makedirs(sample_path, exist_ok=True)
     base_count = len(os.listdir(sample_path))
     grid_count = len(os.listdir(outpath)) - 1
 
+    prompt_matrix_prompts = []
+    comment = ""
+    if prompt_matrix:
+        keep_same_seed = True
+        comment = "Image prompts:\n\n"
+
+        items = prompt.split("|")
+        combination_count = 2 ** (len(items)-1)
+        for combination_num in range(combination_count):
+            current = items[0]
+            label = 'A'
+
+            for n, text in enumerate(items[1:]):
+                if combination_num & (2**n) > 0:
+                    current += ("" if text.strip().startswith(",") else ", ") + text
+                    label += chr(ord('B') + n)
+
+            comment += " - " + label + "\n"
+
+            prompt_matrix_prompts.append(current)
+        n_iter = math.ceil(len(prompt_matrix_prompts) / batch_size)
+
+        comment += "\nwhere:\n"
+        for n, text in enumerate(items):
+            comment += "  " + chr(ord('A') + n) + " = " + items[n] + "\n"
+
     precision_scope = autocast if opt.precision == "autocast" else nullcontext
     output_images = []
     with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
         for n in range(n_iter):
-            for batch_index, prompts in enumerate(data):
-                uc = None
-                if cfg_scale != 1.0:
-                    uc = model.get_learned_conditioning(batch_size * [""])
-                if isinstance(prompts, tuple):
-                    prompts = list(prompts)
-                c = model.get_learned_conditioning(prompts)
-                shape = [opt_C, height // opt_f, width // opt_f]
+            if prompt_matrix:
+                prompts = prompt_matrix_prompts[n*batch_size:(n+1)*batch_size]
 
-                current_seed = seed + n * len(data) + batch_index
+            uc = None
+            if cfg_scale != 1.0:
+                uc = model.get_learned_conditioning(len(prompts) * [""])
+            if isinstance(prompts, tuple):
+                prompts = list(prompts)
+            c = model.get_learned_conditioning(prompts)
+            shape = [opt_C, height // opt_f, width // opt_f]
+
+            batch_seed = seed if keep_same_seed else seed + n * len(prompts)
+
+            # we manually generate all input noises because each one should have a specific seed
+            xs = []
+            for i in range(len(prompts)):
+                current_seed = seed if keep_same_seed else batch_seed + i
                 torch.manual_seed(current_seed)
+                xs.append(torch.randn(shape, device=device))
+            x = torch.stack(xs)
 
-                if is_Kdif:
-                    sigmas = model_wrap.get_sigmas(ddim_steps)
-                    x = torch.randn([n_samples, *shape], device=device) * sigmas[0]  # for GPU draw
-                    model_wrap_cfg = CFGDenoiser(model_wrap)
-                    samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}, disable=False)
+            if is_Kdif:
+                sigmas = model_wrap.get_sigmas(ddim_steps)
+                x = x * sigmas[0]
+                model_wrap_cfg = CFGDenoiser(model_wrap)
+                samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}, disable=False)
 
-                elif sampler is not None:
-                    samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=None)
+            elif sampler is not None:
+                samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=len(prompts), shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=x)
 
-                x_samples_ddim = model.decode_first_stage(samples_ddim)
-                x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+            x_samples_ddim = model.decode_first_stage(samples_ddim)
+            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-                if not opt.skip_save or not opt.skip_grid:
-                    for x_sample in x_samples_ddim:
-                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                        x_sample = x_sample.astype(np.uint8)
+            if not opt.skip_save or not opt.skip_grid:
+                for i, x_sample in enumerate(x_samples_ddim):
+                    x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                    x_sample = x_sample.astype(np.uint8)
+
+                    if use_GFPGAN and GFPGAN is not None:
+                        cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
+                        x_sample = restored_img
+
+                    image = Image.fromarray(x_sample)
+                    filename = f"{base_count:05}-{seed if keep_same_seed else batch_seed + i}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"
+
+                    image.save(os.path.join(sample_path, filename))
+
+                    output_images.append(image)
+                    base_count += 1
 
-                        if use_GFPGAN and GFPGAN is not None:
-                            cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
-                            x_sample = restored_img
 
-                        image = Image.fromarray(x_sample)
 
-                        image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
-                        output_images.append(image)
-                        base_count += 1
 
         if not opt.skip_grid:
             # additionally, save as grid
-            grid = image_grid(output_images, batch_size)
+            grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
             grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
             grid_count += 1
 
@@ -242,8 +286,49 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, ddi
 Steps: {ddim_steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}
     """.strip()
 
+    if len(comment) > 0:
+        info += "\n\n" + comment
+
     return output_images, seed, info
 
+class Flagging(gr.FlaggingCallback):
+
+    def setup(self, components, flagging_dir: str):
+        pass
+
+    def flag(self, flag_data, flag_option=None, flag_index=None, username=None) -> int:
+        os.makedirs("log/images", exist_ok=True)
+
+        # those must match the "dream" function
+        prompt, ddim_steps, sampler_name, use_GFPGAN, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, images, seed, comment = flag_data
+
+        filenames = []
+
+        with open("log/log.csv", "a", encoding="utf8", newline='') as file:
+            import time
+            import base64
+
+            at_start = file.tell() == 0
+            writer = csv.writer(file)
+            if at_start:
+                writer.writerow(["prompt", "seed", "width", "height", "cfgs", "steps", "filename"])
+
+            filename_base = str(int(time.time() * 1000))
+            for i, filedata in enumerate(images):
+                filename = "log/images/"+filename_base + ("" if len(images) == 1 else "-"+str(i+1)) + ".png"
+
+                if filedata.startswith("data:image/png;base64,"):
+                    filedata = filedata[len("data:image/png;base64,"):]
+
+                with open(filename, "wb") as imgfile:
+                    imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
+
+                filenames.append(filename)
+
+            writer.writerow([prompt, seed, width, height, cfg_scale, ddim_steps, filenames[0]])
+
+        print("Logged:", filenames[0])
+
 
 dream_interface = gr.Interface(
     dream,
@@ -252,10 +337,11 @@ dream_interface = gr.Interface(
         gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
         gr.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"),
         gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
+        gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
         gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
-        gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1),
-        gr.Slider(minimum=1, maximum=4, step=1, label='Samples per iteration', value=1),
-        gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0),
+        gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
+        gr.Slider(minimum=1, maximum=4, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
+        gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly should the image follow the prompt)', value=7.0),
         gr.Number(label='Seed', value=-1),
         gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
         gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
@@ -267,7 +353,7 @@ dream_interface = gr.Interface(
     ],
     title="Stable Diffusion Text-to-Image K",
     description="Generate images from text with Stable Diffusion (using K-LMS)",
-    allow_flagging="never"
+    flagging_callback=Flagging()
 )
 
 
@@ -346,8 +432,8 @@ def translation(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, ddim_e
                             x_sample = restored_img
 
                         image = Image.fromarray(x_sample)
+                        image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.png"))
 
-                        image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png"))
                         output_images.append(image)
                         base_count += 1