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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-04 11:12:35 +00:00
getting SD2.1 to run on SDXL repo
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@@ -53,6 +53,28 @@ k_diffusion_scheduler = {
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}
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def catenate_conds(conds):
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if not isinstance(conds[0], dict):
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return torch.cat(conds)
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return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
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def subscript_cond(cond, a, b):
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if not isinstance(cond, dict):
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return cond[a:b]
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return {key: vec[a:b] for key, vec in cond.items()}
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def pad_cond(tensor, repeats, empty):
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if not isinstance(tensor, dict):
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return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
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tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
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return tensor
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class CFGDenoiser(torch.nn.Module):
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"""
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Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
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@@ -105,10 +127,13 @@ class CFGDenoiser(torch.nn.Module):
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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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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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make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
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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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@@ -140,28 +165,28 @@ class CFGDenoiser(torch.nn.Module):
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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 = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
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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 = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
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uncond = pad_cond(uncond, num_repeats, empty)
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self.padded_cond_uncond = True
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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 = torch.cat([tensor, uncond, uncond])
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cond_in = catenate_conds([tensor, uncond, uncond])
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elif skip_uncond:
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cond_in = tensor
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else:
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cond_in = torch.cat([tensor, uncond])
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cond_in = catenate_conds([tensor, uncond])
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if shared.batch_cond_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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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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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[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(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.batch_cond_uncond else batch_size
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@@ -170,14 +195,14 @@ class CFGDenoiser(torch.nn.Module):
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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 = [tensor[a:b]]
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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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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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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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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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