theluyuan/DiffSynth-Studio
1
0
Fork 0
mirror of https://github.com/modelscope/DiffSynth-Studio.git synced 2026-04-24 06:46:13 +00:00
Code Issues Packages Projects Releases Wiki Activity

sd and sdxl training

Browse Source
This commit is contained in:
mi804
2026-04-24 10:19:58 +08:00
parent a8a0f082bb
commit 5cdab9ed01
17 changed files with 543 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

44
diffsynth/pipelines/stable_diffusion_xl.py
View File

@@ -87,9 +87,7 @@ class StableDiffusionXLPipeline(BasePipeline):
def __call__(
self,
prompt: str,
prompt_2: str = None,
negative_prompt: str = "",
negative_prompt_2: str = None,
cfg_scale: float = 5.0,
height: int = 1024,
width: int = 1024,
@@ -103,8 +101,6 @@ class StableDiffusionXLPipeline(BasePipeline):
target_size: tuple = None,
progress_bar_cmd=tqdm,
):
prompt_2 = prompt_2 or prompt
negative_prompt_2 = negative_prompt_2 or negative_prompt
original_size = original_size or (height, width)
target_size = target_size or (height, width)
@@ -116,11 +112,9 @@ class StableDiffusionXLPipeline(BasePipeline):
# 2. Three-dict input preparation
inputs_posi = {
"prompt": prompt,
"prompt_2": prompt_2,
}
inputs_nega = {
"prompt": negative_prompt,
"prompt_2": negative_prompt_2,
}
inputs_shared = {
"cfg_scale": cfg_scale,
@@ -221,8 +215,8 @@ class SDXLUnit_PromptEmbedder(PipelineUnit):
def __init__(self):
super().__init__(
seperate_cfg=True,
input_params_posi={"prompt": "prompt", "prompt_2": "prompt_2"},
input_params_nega={"prompt": "prompt", "prompt_2": "prompt_2"},
input_params_posi={"prompt": "prompt"},
input_params_nega={"prompt": "prompt"},
output_params=("prompt_embeds", "pooled_prompt_embeds"),
onload_model_names=("text_encoder", "text_encoder_2")
)
@@ -231,10 +225,9 @@ class SDXLUnit_PromptEmbedder(PipelineUnit):
self,
pipe: StableDiffusionXLPipeline,
prompt: str,
prompt_2: str,
device: torch.device,
) -> tuple:
"""Encode prompt using both text encoders.
"""Encode prompt using both text encoders (same prompt for both).
Returns (prompt_embeds, pooled_prompt_embeds):
- prompt_embeds: concat(encoder1_output, encoder2_output) -> (B, 77, 2048)
@@ -254,7 +247,7 @@ class SDXLUnit_PromptEmbedder(PipelineUnit):
# Text Encoder 2 (CLIP-bigG, 1280-dim) — uses penultimate hidden states + pooled
text_input_ids_2 = pipe.tokenizer_2(
prompt_2,
prompt,
padding="max_length",
max_length=pipe.tokenizer_2.model_max_length,
truncation=True,
@@ -270,9 +263,9 @@ class SDXLUnit_PromptEmbedder(PipelineUnit):
return prompt_embeds, pooled_prompt_embeds
def process(self, pipe: StableDiffusionXLPipeline, prompt, prompt_2):
def process(self, pipe: StableDiffusionXLPipeline, prompt):
pipe.load_models_to_device(self.onload_model_names)
prompt_embeds, pooled_prompt_embeds = self.encode_prompt(pipe, prompt, prompt_2, pipe.device)
prompt_embeds, pooled_prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
return {"prompt_embeds": prompt_embeds, "pooled_prompt_embeds": pooled_prompt_embeds}
@@ -294,14 +287,27 @@ class SDXLUnit_NoiseInitializer(PipelineUnit):
class SDXLUnit_InputImageEmbedder(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("noise",),
output_params=("latents",),
input_params=("input_image", "noise"),
output_params=("latents", "input_latents"),
onload_model_names=("vae",),
)
def process(self, pipe: StableDiffusionXLPipeline, noise):
# For Text-to-Image, latents = noise (scaled by scheduler)
latents = noise * pipe.scheduler.init_noise_sigma
return {"latents": latents}
def process(self, pipe: StableDiffusionXLPipeline, input_image, noise):
if input_image is None:
return {"latents": noise * pipe.scheduler.init_noise_sigma, "input_latents": None}
if pipe.scheduler.training:
pipe.load_models_to_device(self.onload_model_names)
input_tensor = pipe.preprocess_image(input_image)
input_latents = pipe.vae.encode(input_tensor).sample()
latents = noise * pipe.scheduler.init_noise_sigma
return {"latents": latents, "input_latents": input_latents}
else:
# Inference mode: VAE encode input image, add noise for initial latent
pipe.load_models_to_device(self.onload_model_names)
input_tensor = pipe.preprocess_image(input_image)
input_latents = pipe.vae.encode(input_tensor).sample()
latents = pipe.scheduler.add_noise(input_latents, noise, pipe.scheduler.timesteps[0])
return {"latents": latents}
def model_fn_stable_diffusion_xl(