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add an option (on by default) to disable T5

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revert t5xxl back to fp16
This commit is contained in:
AUTOMATIC1111 2024-06-16 21:57:17 +03:00
parent d4b814aed6
commit 34b4443cc3
2 changed files with 20 additions and 6 deletions
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22
modules/models/sd3/sd3_model.py
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@ -29,7 +29,7 @@ CLIPL_CONFIG = {
"num_hidden_layers": 12, "num_hidden_layers": 12,
} }
T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp8_e4m3fn.safetensors" T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
T5_CONFIG = { T5_CONFIG = {
"d_ff": 10240, "d_ff": 10240,
"d_model": 4096, "d_model": 4096,
@ -63,7 +63,11 @@ class SD3Cond(torch.nn.Module):
with torch.no_grad(): with torch.no_grad():
self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype) self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype)
self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG) self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
if shared.opts.sd3_enable_t5:
self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
else:
self.t5xxl = None
self.weights_loaded = False self.weights_loaded = False
@ -74,7 +78,12 @@ class SD3Cond(torch.nn.Module):
tokens = self.tokenizer.tokenize_with_weights(prompt) tokens = self.tokenizer.tokenize_with_weights(prompt)
l_out, l_pooled = self.clip_l.encode_token_weights(tokens["l"]) l_out, l_pooled = self.clip_l.encode_token_weights(tokens["l"])
g_out, g_pooled = self.clip_g.encode_token_weights(tokens["g"]) g_out, g_pooled = self.clip_g.encode_token_weights(tokens["g"])
t5_out, t5_pooled = self.t5xxl.encode_token_weights(tokens["t5xxl"])
if self.t5xxl and shared.opts.sd3_enable_t5:
t5_out, t5_pooled = self.t5xxl.encode_token_weights(tokens["t5xxl"])
else:
t5_out = torch.zeros(l_out.shape[0:2] + (4096,), dtype=l_out.dtype, device=l_out.device)
lg_out = torch.cat([l_out, g_out], dim=-1) lg_out = torch.cat([l_out, g_out], dim=-1)
lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
lgt_out = torch.cat([lg_out, t5_out], dim=-2) lgt_out = torch.cat([lg_out, t5_out], dim=-2)
@ -101,9 +110,10 @@ class SD3Cond(torch.nn.Module):
with safetensors.safe_open(clip_l_file, framework="pt") as file: with safetensors.safe_open(clip_l_file, framework="pt") as file:
self.clip_l.transformer.load_state_dict(SafetensorsMapping(file), strict=False) self.clip_l.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp8_e4m3fn.safetensors") if self.t5xxl:
with safetensors.safe_open(t5_file, framework="pt") as file: t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp16.safetensors")
self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False) with safetensors.safe_open(t5_file, framework="pt") as file:
self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
self.weights_loaded = True self.weights_loaded = True

4
modules/shared_options.py
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@ -191,6 +191,10 @@ options_templates.update(options_section(('sdxl', "Stable Diffusion XL", "sd"),
"sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"), "sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
})) }))
options_templates.update(options_section(('sd3', "Stable Diffusion 3", "sd"), {
"sd3_enable_t5": OptionInfo(False, "Enable T5").info("load T5 text encoder; increases VRAM use by a lot, potentially improving quality of generation; requires model reload to apply"),
}))
options_templates.update(options_section(('vae', "VAE", "sd"), { options_templates.update(options_section(('vae', "VAE", "sd"), {
"sd_vae_explanation": OptionHTML(""" "sd_vae_explanation": OptionHTML("""
<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr> <abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>