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https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-03-12 00:19:50 +00:00
better sampler
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lllyasviel
@@ -6,7 +6,7 @@ import modules.shared as shared
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from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
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from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
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from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
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from ldm_patched.modules import model_management
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from modules_forge import forge_sampler
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def catenate_conds(conds):
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@@ -76,41 +76,7 @@ class CFGDenoiser(torch.nn.Module):
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for cond_index, weight in conds:
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denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
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if "sampler_cfg_function" in model_options or "sampler_post_cfg_function" in model_options:
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cond_scale = float(cond_scale)
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model = self.inner_model.inner_model.forge_objects.unet.model
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x = x_in[-uncond.shape[0]:]
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uncond_pred = denoised_uncond
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cond_pred = ((denoised - uncond_pred) / cond_scale) + uncond_pred
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timestep = timestep[-uncond.shape[0]:]
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from modules_forge.forge_util import cond_from_a1111_to_patched_ldm
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if "sampler_cfg_function" in model_options:
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args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale,
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"timestep": timestep, "input": x, "sigma": timestep,
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"cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model,
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"model_options": model_options}
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cfg_result = x - model_options["sampler_cfg_function"](args)
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else:
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cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
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# sanity_check = torch.allclose(cfg_result, denoised)
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for fn in model_options.get("sampler_post_cfg_function", []):
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args = {"denoised": cfg_result,
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"cond": cond_from_a1111_to_patched_ldm(cond),
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"uncond": cond_from_a1111_to_patched_ldm(uncond),
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"model": model,
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"uncond_denoised": uncond_pred,
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"cond_denoised": cond_pred,
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"sigma": timestep,
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"model_options": model_options,
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"input": x}
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cfg_result = fn(args)
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else:
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cfg_result = denoised
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return cfg_result
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return denoised
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def combine_denoised_for_edit_model(self, x_out, cond_scale):
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out_cond, out_img_cond, out_uncond = x_out.chunk(3)
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@@ -161,138 +127,11 @@ class CFGDenoiser(torch.nn.Module):
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if self.mask_before_denoising and self.mask is not None:
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x = apply_blend(x)
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batch_size = len(conds_list)
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repeats = [len(conds_list[i]) for i in range(batch_size)]
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if shared.sd_model.model.conditioning_key == "crossattn-adm":
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image_uncond = torch.zeros_like(image_cond)
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make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
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else:
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image_uncond = image_cond
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if isinstance(uncond, dict):
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make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
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else:
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make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
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if not is_edit_model:
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x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
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sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
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image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
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else:
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x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
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sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
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image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
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denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond, self)
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denoiser_params = CFGDenoiserParams(x, image_cond, sigma, state.sampling_step, state.sampling_steps, tensor, uncond, self)
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cfg_denoiser_callback(denoiser_params)
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x_in = denoiser_params.x
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image_cond_in = denoiser_params.image_cond
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sigma_in = denoiser_params.sigma
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tensor = denoiser_params.text_cond
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uncond = denoiser_params.text_uncond
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skip_uncond = False
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# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
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if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
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skip_uncond = True
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x_in = x_in[:-batch_size]
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sigma_in = sigma_in[:-batch_size]
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self.padded_cond_uncond = False
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if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
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empty = shared.sd_model.cond_stage_model_empty_prompt
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num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
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if num_repeats < 0:
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tensor = pad_cond(tensor, -num_repeats, empty)
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self.padded_cond_uncond = True
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elif num_repeats > 0:
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uncond = pad_cond(uncond, num_repeats, empty)
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self.padded_cond_uncond = True
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unet_input_dtype = torch.float16 if model_management.should_use_fp16() else torch.float32
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x_input_dtype = x_in.dtype
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x_in = x_in.to(unet_input_dtype)
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sigma_in = sigma_in.to(unet_input_dtype)
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image_cond_in = image_cond_in.to(unet_input_dtype)
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tensor = tensor.to(unet_input_dtype)
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uncond = uncond.to(unet_input_dtype)
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self.inner_model.inner_model.current_sigmas = sigma_in
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if tensor.shape[1] == uncond.shape[1] or skip_uncond:
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if is_edit_model:
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cond_in = catenate_conds([tensor, uncond, uncond])
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cond_or_uncond = [0] * int(tensor.shape[0]) + [1] * int(uncond.shape[0]) + [1] * int(uncond.shape[0])
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elif skip_uncond:
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cond_in = tensor
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cond_or_uncond = [0] * int(tensor.shape[0])
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else:
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cond_in = catenate_conds([tensor, uncond])
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cond_or_uncond = [0] * int(tensor.shape[0]) + [1] * int(uncond.shape[0])
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if shared.opts.batch_cond_uncond:
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self.inner_model.inner_model.cond_or_uncond = cond_or_uncond
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x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
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else:
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x_out = torch.zeros_like(x_in)
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for batch_offset in range(0, x_out.shape[0], batch_size):
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a = batch_offset
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b = a + batch_size
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self.inner_model.inner_model.cond_or_uncond = cond_or_uncond[a:b]
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
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else:
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x_out = torch.zeros_like(x_in)
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batch_size = batch_size*2 if shared.opts.batch_cond_uncond else batch_size
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for batch_offset in range(0, tensor.shape[0], batch_size):
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a = batch_offset
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b = min(a + batch_size, tensor.shape[0])
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if not is_edit_model:
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c_crossattn = subscript_cond(tensor, a, b)
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else:
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c_crossattn = torch.cat([tensor[a:b]], uncond)
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self.inner_model.inner_model.cond_or_uncond = [0] * int(sigma_in[a:b].shape[0])
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
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if not skip_uncond:
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self.inner_model.inner_model.cond_or_uncond = [1] * int(sigma_in[-uncond.shape[0]:].shape[0])
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
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denoised_image_indexes = [x[0][0] for x in conds_list]
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if skip_uncond:
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fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
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x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
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x_out = x_out.to(x_input_dtype)
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denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
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cfg_denoised_callback(denoised_params)
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devices.test_for_nans(x_out, "unet")
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if is_edit_model:
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denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
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elif skip_uncond:
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denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0, sigma_in, x_in, tensor)
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else:
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denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale, sigma_in, x_in, tensor)
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# Blend in the original latents (after)
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if not self.mask_before_denoising and self.mask is not None:
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denoised = apply_blend(denoised)
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self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
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if opts.live_preview_content == "Prompt":
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preview = self.sampler.last_latent
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elif opts.live_preview_content == "Negative prompt":
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preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
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else:
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preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
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denoised = forge_sampler.forge_sample(self, denoiser_params=denoiser_params, cond_scale=cond_scale)
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preview = self.sampler.last_latent = denoised
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sd_samplers_common.store_latent(preview)
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after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
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@@ -240,30 +240,8 @@ def load_model_for_a1111(timer, checkpoint_info=None, state_dict=None):
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sd_model.get_first_stage_encoding = lambda x: x
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sd_model.decode_first_stage = patched_decode_first_stage
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sd_model.encode_first_stage = patched_encode_first_stage
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sd_model.current_controlnet_signals = {
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'input': [],
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'middle': [],
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'output': []
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}
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sd_model.current_controlnet_required_memory = 0
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original_forward = sd_model.model.diffusion_model.forward
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def forge_unet_forward(*args, **kwargs):
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current_transformer_options = kwargs.get('transformer_options', {})
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current_transformer_options.update(dict(cond_or_uncond=sd_model.cond_or_uncond, sigmas=sd_model.current_sigmas))
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current_transformer_options.update(sd_model.forge_objects.unet.model_options.get('transformer_options', {}))
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kwargs.update(dict(
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control=sd_model.current_controlnet_signals,
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transformer_options=current_transformer_options
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))
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return original_forward(*args, **kwargs)
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sd_model.model.diffusion_model.forward = forge_unet_forward
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sd_model.clip = sd_model.cond_stage_model
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sd_model.current_controlnet_required_memory = 0
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timer.record("forge finalize")
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sd_model.current_lora_hash = str([])
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49
modules_forge/forge_sampler.py
Normal file
49
modules_forge/forge_sampler.py
Normal file
@@ -0,0 +1,49 @@
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import torch
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from ldm_patched.modules.conds import CONDRegular, CONDCrossAttn
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from ldm_patched.modules.samplers import sampling_function
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def cond_from_a1111_to_patched_ldm(cond):
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if isinstance(cond, torch.Tensor):
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result = dict(
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cross_attn=cond,
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model_conds=dict(
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c_crossattn=CONDCrossAttn(cond),
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)
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)
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return [result, ]
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cross_attn = cond['crossattn']
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pooled_output = cond['vector']
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result = dict(
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cross_attn=cross_attn,
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pooled_output=pooled_output,
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model_conds=dict(
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c_crossattn=CONDCrossAttn(cross_attn),
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y=CONDRegular(pooled_output)
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)
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)
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return [result, ]
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def forge_sample(self, denoiser_params, cond_scale):
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model = self.inner_model.inner_model.forge_objects.unet.model
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x = denoiser_params.x
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timestep = denoiser_params.sigma
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uncond = cond_from_a1111_to_patched_ldm(denoiser_params.text_uncond)
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cond = cond_from_a1111_to_patched_ldm(denoiser_params.text_cond)
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model_options = self.inner_model.inner_model.forge_objects.unet.model_options
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seed = self.p.seeds[0]
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image_cond_in = denoiser_params.image_cond
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if isinstance(image_cond_in, torch.Tensor):
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if image_cond_in.shape[0] == x.shape[0] \
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and image_cond_in.shape[2] == x.shape[2] \
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and image_cond_in.shape[3] == x.shape[3]:
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uncond[0]['model_conds']['c_concat'] = CONDRegular(image_cond_in)
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cond[0]['model_conds']['c_concat'] = CONDRegular(image_cond_in)
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denoised = sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options, seed)
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return denoised
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@@ -5,8 +5,6 @@ import time
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import random
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import string
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from ldm_patched.modules.conds import CONDRegular, CONDCrossAttn
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def generate_random_filename(extension=".txt"):
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timestamp = time.strftime("%Y%m%d-%H%M%S")
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@@ -15,31 +13,6 @@ def generate_random_filename(extension=".txt"):
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return filename
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def cond_from_a1111_to_patched_ldm(cond):
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if isinstance(cond, torch.Tensor):
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result = dict(
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cross_attn=cond,
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model_conds=dict(
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c_crossattn=CONDCrossAttn(cond),
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)
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)
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return [result, ]
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cross_attn = cond['crossattn']
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pooled_output = cond['vector']
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result = dict(
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cross_attn=cross_attn,
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pooled_output=pooled_output,
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model_conds=dict(
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c_crossattn=CONDCrossAttn(cross_attn),
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y=CONDRegular(pooled_output)
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)
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)
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return [result, ]
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@torch.no_grad()
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@torch.inference_mode()
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def pytorch_to_numpy(x):
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