mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-11 03:09:47 +00:00
Merge branch 'master' into test_resolve_conflicts
This commit is contained in:
MalumaDev
@@ -157,8 +157,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
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# sort by reverse by likelihood and normal for alpha, and format tag text as requested
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unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
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for weight, tag in unsorted_tags_in_theshold:
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# note: tag_outformat will still have a colon if include_ranks is True
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tag_outformat = tag.replace(':', ' ')
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tag_outformat = tag
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if use_spaces:
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tag_outformat = tag_outformat.replace('_', ' ')
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if use_escape:
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@@ -216,8 +216,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
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if theta_func1:
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for key in tqdm.tqdm(theta_1.keys()):
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if 'model' in key:
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t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
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theta_1[key] = theta_func1(theta_1[key], t2)
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if key in theta_2:
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t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
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theta_1[key] = theta_func1(theta_1[key], t2)
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else:
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theta_1[key] = torch.zeros_like(theta_1[key])
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del theta_2, teritary_model
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for key in tqdm.tqdm(theta_0.keys()):
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@@ -419,11 +419,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
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subseed_strength=p.subseed_strength)
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if state.interrupted or state.skipped:
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# if we are interrupted, sample returns just noise
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# use the image collected previously in sampler loop
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samples_ddim = shared.state.current_latent
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samples_ddim = samples_ddim.to(devices.dtype_vae)
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x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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@@ -96,6 +96,7 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
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class ScriptRunner:
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def __init__(self):
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self.scripts = []
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self.titles = []
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def setup_ui(self, is_img2img):
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for script_class, path in scripts_data:
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@@ -107,9 +108,10 @@ class ScriptRunner:
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self.scripts.append(script)
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titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
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self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
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dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
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dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
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dropdown.save_to_config = True
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inputs = [dropdown]
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for script in self.scripts:
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@@ -139,6 +141,15 @@ class ScriptRunner:
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return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
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def init_field(title):
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if title == 'None':
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return
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script_index = self.titles.index(title)
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script = self.scripts[script_index]
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for i in range(script.args_from, script.args_to):
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inputs[i].visible = True
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dropdown.init_field = init_field
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dropdown.change(
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fn=select_script,
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inputs=[dropdown],
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@@ -296,10 +296,16 @@ def xformers_attnblock_forward(self, x):
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try:
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h_ = x
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h_ = self.norm(h_)
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q1 = self.q(h_).contiguous()
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k1 = self.k(h_).contiguous()
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v = self.v(h_).contiguous()
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out = xformers.ops.memory_efficient_attention(q1, k1, v)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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b, c, h, w = q.shape
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q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
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q = q.contiguous()
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k = k.contiguous()
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v = v.contiguous()
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out = xformers.ops.memory_efficient_attention(q, k, v)
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out = rearrange(out, 'b (h w) c -> b c h w', h=h)
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out = self.proj_out(out)
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return x + out
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except NotImplementedError:
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@@ -98,25 +98,8 @@ def store_latent(decoded):
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shared.state.current_image = sample_to_image(decoded)
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def extended_tdqm(sequence, *args, desc=None, **kwargs):
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state.sampling_steps = len(sequence)
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state.sampling_step = 0
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seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
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for x in seq:
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if state.interrupted or state.skipped:
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break
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yield x
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state.sampling_step += 1
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shared.total_tqdm.update()
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ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
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ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
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class InterruptedException(BaseException):
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pass
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class VanillaStableDiffusionSampler:
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@@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler:
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self.init_latent = None
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self.sampler_noises = None
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self.step = 0
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self.stop_at = None
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self.eta = None
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self.default_eta = 0.0
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self.config = None
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self.last_latent = None
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def number_of_needed_noises(self, p):
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return 0
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def launch_sampling(self, steps, func):
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state.sampling_steps = steps
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state.sampling_step = 0
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try:
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return func()
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except InterruptedException:
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return self.last_latent
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def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
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if state.interrupted or state.skipped:
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raise InterruptedException
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if self.stop_at is not None and self.step > self.stop_at:
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raise InterruptedException
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conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
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unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
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@@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler:
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res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
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if self.mask is not None:
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store_latent(self.init_latent * self.mask + self.nmask * res[1])
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self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
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else:
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store_latent(res[1])
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self.last_latent = res[1]
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store_latent(self.last_latent)
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self.step += 1
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state.sampling_step = self.step
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shared.total_tqdm.update()
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return res
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def initialize(self, p):
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@@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler:
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self.init_latent = x
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self.step = 0
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samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
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samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
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return samples
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@@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler:
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# existing code fails with certain step counts, like 9
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try:
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samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
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samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
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except Exception:
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samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
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samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
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return samples_ddim
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@@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module):
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self.step = 0
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def forward(self, x, sigma, uncond, cond, cond_scale):
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if state.interrupted or state.skipped:
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raise InterruptedException
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conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
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uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
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@@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module):
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return denoised
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def extended_trange(sampler, count, *args, **kwargs):
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state.sampling_steps = count
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state.sampling_step = 0
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seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
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for x in seq:
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if state.interrupted or state.skipped:
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break
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if sampler.stop_at is not None and x > sampler.stop_at:
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break
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yield x
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state.sampling_step += 1
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shared.total_tqdm.update()
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class TorchHijack:
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def __init__(self, kdiff_sampler):
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self.kdiff_sampler = kdiff_sampler
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@@ -314,9 +304,28 @@ class KDiffusionSampler:
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self.eta = None
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self.default_eta = 1.0
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self.config = None
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self.last_latent = None
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def callback_state(self, d):
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store_latent(d["denoised"])
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step = d['i']
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latent = d["denoised"]
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store_latent(latent)
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self.last_latent = latent
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if self.stop_at is not None and step > self.stop_at:
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raise InterruptedException
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state.sampling_step = step
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shared.total_tqdm.update()
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def launch_sampling(self, steps, func):
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state.sampling_steps = steps
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state.sampling_step = 0
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try:
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return func()
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except InterruptedException:
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return self.last_latent
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def number_of_needed_noises(self, p):
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return p.steps
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@@ -339,9 +348,6 @@ class KDiffusionSampler:
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self.sampler_noise_index = 0
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self.eta = p.eta or opts.eta_ancestral
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
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if self.sampler_noises is not None:
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k_diffusion.sampling.torch = TorchHijack(self)
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@@ -383,8 +389,9 @@ class KDiffusionSampler:
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self.model_wrap_cfg.init_latent = x
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return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
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|
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return samples
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
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steps = steps or p.steps
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@@ -406,6 +413,8 @@ class KDiffusionSampler:
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||||
extra_params_kwargs['n'] = steps
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else:
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extra_params_kwargs['sigmas'] = sigmas
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samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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|
||||
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
||||
|
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return samples
|
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|
||||
|
||||
@@ -73,13 +73,13 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload
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parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
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parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
|
||||
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
|
||||
parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
|
||||
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
|
||||
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
|
||||
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
|
||||
parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
|
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parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
|
||||
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
restricted_opts = [
|
||||
"samples_filename_pattern",
|
||||
@@ -308,6 +308,7 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
|
||||
"disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
|
||||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
|
||||
@@ -45,7 +45,7 @@ class StyleDatabase:
|
||||
if not os.path.exists(path):
|
||||
return
|
||||
|
||||
with open(path, "r", encoding="utf8", newline='') as file:
|
||||
with open(path, "r", encoding="utf-8-sig", newline='') as file:
|
||||
reader = csv.DictReader(file)
|
||||
for row in reader:
|
||||
# Support loading old CSV format with "name, text"-columns
|
||||
@@ -79,7 +79,7 @@ class StyleDatabase:
|
||||
def save_styles(self, path: str) -> None:
|
||||
# Write to temporary file first, so we don't nuke the file if something goes wrong
|
||||
fd, temp_path = tempfile.mkstemp(".csv")
|
||||
with os.fdopen(fd, "w", encoding="utf8", newline='') as file:
|
||||
with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
|
||||
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
|
||||
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
|
||||
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
|
||||
|
||||
@@ -547,6 +547,10 @@ def apply_setting(key, value):
|
||||
if value is None:
|
||||
return gr.update()
|
||||
|
||||
# dont allow model to be swapped when model hash exists in prompt
|
||||
if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
|
||||
return gr.update()
|
||||
|
||||
if key == "sd_model_checkpoint":
|
||||
ckpt_info = sd_models.get_closet_checkpoint_match(value)
|
||||
|
||||
@@ -1809,7 +1813,7 @@ Requested path was: {f}
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
def loadsave(path, x):
|
||||
def apply_field(obj, field, condition=None):
|
||||
def apply_field(obj, field, condition=None, init_field=None):
|
||||
key = path + "/" + field
|
||||
|
||||
if getattr(obj,'custom_script_source',None) is not None:
|
||||
@@ -1825,6 +1829,8 @@ Requested path was: {f}
|
||||
print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
|
||||
else:
|
||||
setattr(obj, field, saved_value)
|
||||
if init_field is not None:
|
||||
init_field(saved_value)
|
||||
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
|
||||
apply_field(x, 'visible')
|
||||
@@ -1850,7 +1856,8 @@ Requested path was: {f}
|
||||
# Since there are many dropdowns that shouldn't be saved,
|
||||
# we only mark dropdowns that should be saved.
|
||||
if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
|
||||
apply_field(x, 'value', lambda val: val in x.choices)
|
||||
apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
|
||||
apply_field(x, 'visible')
|
||||
|
||||
visit(txt2img_interface, loadsave, "txt2img")
|
||||
visit(img2img_interface, loadsave, "img2img")
|
||||
@@ -1872,6 +1879,9 @@ for filename in sorted(os.listdir(jsdir)):
|
||||
with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
|
||||
javascript += f"\n<script>{jsfile.read()}</script>"
|
||||
|
||||
if cmd_opts.theme is not None:
|
||||
javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
|
||||
|
||||
javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
|
||||
|
||||
if 'gradio_routes_templates_response' not in globals():
|
||||
|
||||
Reference in New Issue
Block a user