diff options
| -rw-r--r-- | modules/devices.py | 4 | ||||
| -rw-r--r-- | modules/sd_hijack.py | 2 | ||||
| -rw-r--r-- | modules/shared.py | 6 | ||||
| -rw-r--r-- | v2-inference.yaml | 67 |
4 files changed, 75 insertions, 4 deletions
diff --git a/modules/devices.py b/modules/devices.py index 397b4b95..f8cffae1 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -38,8 +38,8 @@ def get_optimal_device(): if torch.cuda.is_available(): return torch.device(get_cuda_device_string()) - # if has_mps(): - # return torch.device("mps") + if has_mps(): + return torch.device("mps") return cpu diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index eb679ef9..9b5890e7 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -29,7 +29,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At # new memory efficient cross attention blocks do not support hypernets and we already
# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
-# ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
# silence new console spam from SD2
ldm.modules.attention.print = lambda *args: None
diff --git a/modules/shared.py b/modules/shared.py index 7d82f4ee..522c56c1 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -110,7 +110,11 @@ restricted_opts = { from omegaconf import OmegaConf
config = OmegaConf.load(f"{cmd_opts.config}")
# XLMR-Large
-text_model_name = config.model.params.cond_stage_config.params.name
+try:
+ text_model_name = config.model.params.cond_stage_config.params.name
+
+except :
+ text_model_name = "stable_diffusion"
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
diff --git a/v2-inference.yaml b/v2-inference.yaml new file mode 100644 index 00000000..0eb25395 --- /dev/null +++ b/v2-inference.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate"
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