From fd66199769ebe0851d2ff33fdc7b191421822454 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 6 Sep 2022 19:33:51 +0300
Subject: added preview option

---
 modules/processing.py  |  5 +++++
 modules/sd_samplers.py |  9 ++++++--
 modules/shared.py      |  4 ++++
 modules/ui.py          | 59 +++++++++++++++++++++++++++++++++++++++++++-------
 4 files changed, 67 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index e8923a7a..e615ffdc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -176,6 +176,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
+            if state.interrupted:
+
+                # if we are interruped, sample returns just noise
+                # use the image collected previously in sampler loop
+                samples_ddim = shared.state.current_latent
 
             x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 896e8b3f..ff7e686e 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -42,6 +42,8 @@ def p_sample_ddim_hook(sampler_wrapper, x_dec, cond, ts, *args, **kwargs):
         img_orig = sampler_wrapper.sampler.model.q_sample(sampler_wrapper.init_latent, ts)
         x_dec = img_orig * sampler_wrapper.mask + sampler_wrapper.nmask * x_dec
 
+    state.current_latent = x_dec
+
     return sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
 
 
@@ -141,6 +143,9 @@ class KDiffusionSampler:
         self.func = getattr(k_diffusion.sampling, self.funcname)
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
 
+    def callback_state(self, d):
+        state.current_latent = d["denoised"]
+
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
         t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
         sigmas = self.model_wrap.get_sigmas(p.steps)
@@ -157,7 +162,7 @@ class KDiffusionSampler:
         if hasattr(k_diffusion.sampling, 'trange'):
             k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
 
-        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
+        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
 
     def sample(self, p, x, conditioning, unconditional_conditioning):
         sigmas = self.model_wrap.get_sigmas(p.steps)
@@ -166,6 +171,6 @@ class KDiffusionSampler:
         if hasattr(k_diffusion.sampling, 'trange'):
             k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
 
-        samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
+        samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
         return samples_ddim
 
diff --git a/modules/shared.py b/modules/shared.py
index d57aba37..e9c88e31 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -39,6 +39,7 @@ gpu = torch.device("cuda")
 device = gpu if torch.cuda.is_available() else cpu
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 
+
 class State:
     interrupted = False
     job = ""
@@ -46,6 +47,8 @@ class State:
     job_count = 0
     sampling_step = 0
     sampling_steps = 0
+    current_latent = None
+    current_image = None
 
     def interrupt(self):
         self.interrupted = True
@@ -99,6 +102,7 @@ class Options:
         "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
         "upscale_at_full_resolution_padding": OptionInfo(16, "Inpainting at full resolution: padding, in pixels, for the masked region.", gr.Slider, {"minimum": 0, "maximum": 128, "step": 4}),
         "show_progressbar": OptionInfo(True, "Show progressbar"),
+        "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
     }
 
     def __init__(self):
diff --git a/modules/ui.py b/modules/ui.py
index 1df74070..8e7a3ee4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -9,6 +9,8 @@ import sys
 import time
 import traceback
 
+import numpy as np
+import torch
 from PIL import Image
 
 import gradio as gr
@@ -119,6 +121,9 @@ def wrap_gradio_call(func):
             print("Arguments:", args, kwargs, file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
+            shared.state.job = ""
+            shared.state.job_count = 0
+
             res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
         elapsed = time.perf_counter() - t
@@ -134,11 +139,9 @@ def wrap_gradio_call(func):
 
 
 def check_progress_call():
-    if not opts.show_progressbar:
-        return ""
 
     if shared.state.job_count == 0:
-        return ""
+        return "", gr_show(False), gr_show(False)
 
     progress = 0
 
@@ -149,9 +152,29 @@ def check_progress_call():
 
     progress = min(progress, 1)
 
-    progressbar = f"""<div class='progressDiv'><div class='progress' style="width:{progress * 100}%">{str(int(progress*100))+"%" if progress > 0.01 else ""}</div></div>"""
+    progressbar = ""
+    if opts.show_progressbar:
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="width:{progress * 100}%">{str(int(progress*100))+"%" if progress > 0.01 else ""}</div></div>"""
+
+    image = gr_show(False)
+    preview_visibility = gr_show(False)
+
+    if opts.show_progress_every_n_steps > 0:
+        if (shared.state.sampling_step-1) % opts.show_progress_every_n_steps == 0 and shared.state.current_latent is not None:
+            x_sample = shared.sd_model.decode_first_stage(shared.state.current_latent[0:1].type(shared.sd_model.dtype))[0]
+            x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+            x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+            x_sample = x_sample.astype(np.uint8)
+            shared.state.current_image = Image.fromarray(x_sample)
 
-    return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>"
+        image = shared.state.current_image
+
+        if image is None or progress >= 1:
+            image = gr.update(value=None)
+        else:
+            preview_visibility = gr_show(True)
+
+    return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
 
 
 def roll_artist(prompt):
@@ -204,6 +227,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
 
             with gr.Column(variant='panel'):
                 with gr.Group():
+                    txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery')
 
 
@@ -251,8 +275,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
 
             check_progress.click(
                 fn=check_progress_call,
+                show_progress=False,
                 inputs=[],
-                outputs=[progressbar],
+                outputs=[progressbar, txt2img_preview, txt2img_preview],
             )
 
 
@@ -337,13 +362,16 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
 
             with gr.Column(variant='panel'):
                 with gr.Group():
+                    img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
                     img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery')
 
                 with gr.Group():
                     with gr.Row():
-                        interrupt = gr.Button('Interrupt')
                         save = gr.Button('Save')
+                        img2img_send_to_img2img = gr.Button('Send to img2img')
+                        img2img_send_to_inpaint = gr.Button('Send to inpaint')
                         img2img_send_to_extras = gr.Button('Send to extras')
+                        interrupt = gr.Button('Interrupt')
 
                 progressbar = gr.HTML(elem_id="progressbar")
 
@@ -426,8 +454,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
 
             check_progress.click(
                 fn=check_progress_call,
+                show_progress=False,
                 inputs=[],
-                outputs=[progressbar],
+                outputs=[progressbar, img2img_preview, img2img_preview],
             )
 
             interrupt.click(
@@ -463,6 +492,20 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                 outputs=[init_img_with_mask],
             )
 
+            img2img_send_to_img2img.click(
+                fn=lambda x: image_from_url_text(x),
+                _js="extract_image_from_gallery",
+                inputs=[img2img_gallery],
+                outputs=[init_img],
+            )
+
+            img2img_send_to_inpaint.click(
+                fn=lambda x: image_from_url_text(x),
+                _js="extract_image_from_gallery",
+                inputs=[img2img_gallery],
+                outputs=[init_img_with_mask],
+            )
+
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-- 
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